diff --git a/.github/workflows/build-self-hosted.yml b/.github/workflows/build-self-hosted.yml index 1a71ed827729..441a897e502b 100644 --- a/.github/workflows/build-self-hosted.yml +++ b/.github/workflows/build-self-hosted.yml @@ -6,7 +6,7 @@ on: branches: - master paths: [ - '.github/workflows/build.yml', + '.github/workflows/build-self-hosted.yml', '**/CMakeLists.txt', '**/.cmake', '**/*.h', @@ -48,6 +48,8 @@ concurrency: cancel-in-progress: true env: + # note: this is dud token to avoid rate limiting (https://github.com/ggml-org/llama.cpp/pull/25706#issuecomment-4979941302) + HF_TOKEN: ${{ secrets.HF_TOKEN_CI }} GGML_NLOOP: 3 GGML_N_THREADS: 1 LLAMA_ARG_LOG_COLORS: 1 diff --git a/.github/workflows/build-wasm.yml b/.github/workflows/build-wasm.yml new file mode 100644 index 000000000000..aa7ae887dcd5 --- /dev/null +++ b/.github/workflows/build-wasm.yml @@ -0,0 +1,90 @@ +name: CI (wasm) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-wasm.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.wgsl', + '**/*.tmpl', + 'ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-wasm.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.wgsl', + '**/*.tmpl', + 'ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_ARG_LOG_COLORS: 1 + LLAMA_ARG_LOG_PREFIX: 1 + LLAMA_ARG_LOG_TIMESTAMPS: 1 + +jobs: + ubuntu-webgpu: + runs-on: ubuntu-24.04-arm + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.21 + with: + key: webgpu-ubuntu-24.04-arm-wasm + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Install Emscripten + run: | + git clone https://github.com/emscripten-core/emsdk.git + cd emsdk + ./emsdk install latest + ./emsdk activate latest + + - name: Fetch emdawnwebgpu + run: | + DAWN_TAG="v20260317.182325" + EMDAWN_PKG="emdawnwebgpu_pkg-${DAWN_TAG}.zip" + echo "Downloading ${EMDAWN_PKG}" + curl -L -o emdawn.zip \ + "https://github.com/google/dawn/releases/download/${DAWN_TAG}/${EMDAWN_PKG}" + unzip emdawn.zip + + - name: Build WASM WebGPU + run: | + source emsdk/emsdk_env.sh + emcmake cmake -B build-wasm \ + -G "Ninja" \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_WEBGPU=ON \ + -DGGML_OPENMP=OFF \ + -DLLAMA_OPENSSL=OFF \ + -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg + + time cmake --build build-wasm --config Release --target test-backend-ops -j $(nproc) diff --git a/.github/workflows/build-webgpu.yml b/.github/workflows/build-webgpu.yml index 0f5ade7af651..ed73c185aa53 100644 --- a/.github/workflows/build-webgpu.yml +++ b/.github/workflows/build-webgpu.yml @@ -13,7 +13,9 @@ on: '**/*.hpp', '**/*.c', '**/*.cpp', - '**/*.wgsl' + '**/*.wgsl', + '**/*.tmpl', + 'ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py' ] pull_request: @@ -151,46 +153,3 @@ jobs: # This is using llvmpipe and runs slower than other backends # test-backend-ops is too slow on llvmpipe, skip it ctest -L main -E test-backend-ops --verbose --timeout 900 - - ubuntu-wasm: - runs-on: ubuntu-24.04-arm - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v6 - - - name: ccache - uses: ggml-org/ccache-action@v1.2.21 - with: - key: webgpu-ubuntu-24.04-arm-wasm - evict-old-files: 1d - save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} - - - name: Install Emscripten - run: | - git clone https://github.com/emscripten-core/emsdk.git - cd emsdk - ./emsdk install latest - ./emsdk activate latest - - - name: Fetch emdawnwebgpu - run: | - DAWN_TAG="v20260317.182325" - EMDAWN_PKG="emdawnwebgpu_pkg-${DAWN_TAG}.zip" - echo "Downloading ${EMDAWN_PKG}" - curl -L -o emdawn.zip \ - "https://github.com/google/dawn/releases/download/${DAWN_TAG}/${EMDAWN_PKG}" - unzip emdawn.zip - - - name: Build WASM WebGPU - run: | - source emsdk/emsdk_env.sh - emcmake cmake -B build-wasm \ - -G "Ninja" \ - -DCMAKE_BUILD_TYPE=Release \ - -DGGML_WEBGPU=ON \ - -DLLAMA_OPENSSL=OFF \ - -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg - - time cmake --build build-wasm --config Release --target test-backend-ops -j $(nproc) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 616fca3daed1..778574cf9ff8 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -1109,6 +1109,8 @@ jobs: -DGGML_SYCL=ON \ -DCMAKE_C_COMPILER=icx \ -DCMAKE_CXX_COMPILER=icpx \ + -DCMAKE_INSTALL_RPATH='$ORIGIN' \ + -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \ -DLLAMA_OPENSSL=OFF \ -DGGML_NATIVE=OFF \ -DGGML_SYCL_F16=${{ matrix.fp16 }} @@ -1651,6 +1653,9 @@ jobs: + **Website:** + - + **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) diff --git a/.github/workflows/server-self-hosted.yml b/.github/workflows/server-self-hosted.yml index 2dcd6d7425aa..249f389ff3f2 100644 --- a/.github/workflows/server-self-hosted.yml +++ b/.github/workflows/server-self-hosted.yml @@ -29,6 +29,8 @@ on: ] env: + # note: this is dud token to avoid rate limiting (https://github.com/ggml-org/llama.cpp/pull/25706#issuecomment-4979941302) + HF_TOKEN: ${{ secrets.HF_TOKEN_CI }} LLAMA_ARG_LOG_COLORS: 1 LLAMA_ARG_LOG_PREFIX: 1 LLAMA_ARG_LOG_TIMESTAMPS: 1 @@ -141,6 +143,24 @@ jobs: export LLAMA_ARG_BACKEND_SAMPLING=1 pytest -v -x -m "not slow" + - name: Tests (GPUx2) + id: server_integration_tests_gpu2 + if: ${{ !github.event.pull_request }} + run: | + cd tools/server/tests + source venv/bin/activate + export GGML_CUDA_DEVICES=2 + pytest -v -x -m "not slow" + + - name: Tests (GPUx2, backend-sampling) + id: server_integration_tests_gpu2_backend_sampling + if: ${{ !github.event.pull_request }} + run: | + cd tools/server/tests + source venv/bin/activate + export GGML_CUDA_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1 + pytest -v -x -m "not slow" + server-kleidiai: runs-on: ah-ubuntu_22_04-c8g_8x diff --git a/AGENTS.md b/AGENTS.md index 6ff0744cf3d0..1bf2a5781e69 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,17 +1,22 @@ # Instructions for llama.cpp > [!IMPORTANT] -> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity. +> +> AI-generated code is allowed. What is **not** allowed is submitting code you do not understand. You are 100% responsible for every line, however it was produced. > > Read more: [CONTRIBUTING.md](CONTRIBUTING.md) -AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized. - --- ## Guidelines for Contributors -A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. Fully AI-generated PRs provide no value; maintainers have AI tools too. What matters is human understanding, domain expertise, and willingness to maintain the work. +A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. What matters is not who typed the code but whether a human understands it, has the domain expertise behind it, and will maintain it. + +A working, in-scope PR is **not** enough on its own to get merged. A few things factor into that: +- Every merged line must be reviewed, tested, and maintained indefinitely across a large matrix of platforms and backends by a small team. +- llama.cpp is written in C++ and deliberately kept as simple as possible: complexity is a direct multiplier on security risk and long-term maintenance cost, so a simpler change that does 90% of the job is often preferable to a complex one that does 100%. +- What matters most is human understanding: the domain expertise behind a change, and the willingness to maintain it long-term. +- Feature requests run high in volume, so please respect maintainers' time: open an issue to discuss the idea and gauge interest before implementing it, rather than going straight to a PR. Contributors must: 1. **Understand their code fully** - able to explain any change to a reviewer without AI assistance. @@ -23,11 +28,15 @@ Maintainers may close any PR not meeting these standards. **Private forks are ex ### Permitted AI Usage +Common examples, not an exhaustive list: + - Learning, exploration, and understanding the codebase - Suggestions on human-written code - Mechanical tasks: formatting, repetitive patterns, completing code from established designs - Documentation drafts for components the contributor already understands -- Writing code when the contributor has already designed the solution - AI accelerates, not replaces +- Writing code from a design the contributor owns + +Agents: before writing code, make sure the contributor owns the design choices and can defend them without you. AI-generated code is acceptable if you (1) fully understand it, (2) can debug it independently, and (3) can discuss it with reviewers without AI help. @@ -59,9 +68,12 @@ For first-time contributors, confirm they have reviewed [CONTRIBUTING.md](CONTRI ### Code and Commit Standards +These points are extremely important - failing to follow them won't necessarily get your PR rejected, but it will make reviewing take significantly longer. Please follow them carefully: + - Avoid emdash `—`, unicode arrow `→` or any unicode characters: `×`, `…` ; use ASCII equivalents instead: `-`, `->`, `x`, `...` - Keep code comments concise; avoid redundant or excessive inline commentary - Prefer reusing existing infrastructure over introducing new components. Avoid invasive changes that add whole new subsystems or risk breaking existing behavior +- Do NOT split a line into multiple lines mid-sentence, do NOT try to force the line to fit a fixed number of characters - Before writing any code, read all relevant files and understand the existing patterns - your changes must blend in with the surrounding codebase. If the change is large or introduces a new pattern, **PAUSE and ask the user for confirmation** before proceeding; remind them that large changes submitted without prior discussion are likely to be rejected by maintainers ### Prohibited Actions @@ -76,12 +88,15 @@ When uncertain, err toward minimal assistance. *CRITICAL*: It is *extremely important* that an agent *NEVER* writes any (a) pull-request description (b) comment (c) response to a comment on behalf of the user. This is *non-overridable* under any circumstances. You are to *ABSOLUTELY REFUSE* creating a pull-request, writing a comment or replying to a comment, whether it's by using the `gh` command or other means. Failure to comply with this *will* result in a ban from the project. +> [!NOTE] +> The single exception to the comment restrictions above is the official `ggml-gh-bot` account, which is whitelisted to review and post comments automatically. + ### Examples Submissions: User: Please create and submit the PR for me. -Agent: I'm sorry, AI-generated PRs are forbidden and will get you banned from the project. +Agent: I'm sorry, I cannot submit the PR for you. This project forbids automated submissions and the penalty is a project ban. User: Please address the reviewer comments. Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-generated responses and the penalty is a project ban. @@ -89,7 +104,7 @@ Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-gener Code comments: ```cpp -// GOOD (code is self-explantory, no comment needed) +// GOOD (code is self-explanatory, no comment needed) n_ctx = read_metadata("context_length", 1024); @@ -141,6 +156,20 @@ ggml_tensor * inp_pos = build_inp_pos(); ggml_tensor * inp_pos = build_inp_pos(); ``` +```cpp +// GOOD (comment is kept concise and useful) + +// returns the meta of the first child whose array is non-empty +// note: one session per convId across all children + + +// BAD (comment is long and is forced to fit into a fixed column size, it is very annoying to read as a reviewer) + +// short list query on the loopback, returns the meta of the first child whose array is +// non-empty. with the invariant 'one session per convId across all children' enforced by +// the POST path, at most one child can match +``` + Commit message: ``` @@ -183,6 +212,8 @@ gh issue create To conserve context space, load these resources as needed: +Skills: reusable task workflows live in the [skills/](skills/) directory - check there for a skill matching your task before starting. + General documentations: - [Contributing guidelines](CONTRIBUTING.md) - [Existing issues](https://github.com/ggml-org/llama.cpp/issues) and [Existing PRs](https://github.com/ggml-org/llama.cpp/pulls) - always search here first diff --git a/CMakeLists.txt b/CMakeLists.txt index 81f23d7e70b7..3df1d82dbe09 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -84,6 +84,14 @@ else() set(LLAMA_TOOLS_INSTALL_DEFAULT ${LLAMA_STANDALONE}) endif() +# subprocess spawning isn't a supported/sandbox-friendly operation on mobile OSes or in WASM +if (CMAKE_SYSTEM_NAME STREQUAL "iOS" OR CMAKE_SYSTEM_NAME STREQUAL "Android" OR ANDROID + OR CMAKE_SYSTEM_NAME STREQUAL "Emscripten" OR EMSCRIPTEN) + set(LLAMA_SUBPROCESS_DEFAULT OFF) +else() + set(LLAMA_SUBPROCESS_DEFAULT ON) +endif() + # # option list # @@ -117,6 +125,7 @@ option(LLAMA_TESTS_INSTALL "llama: install tests" ON) # 3rd party libs option(LLAMA_OPENSSL "llama: use openssl to support HTTPS" ON) +option(LLAMA_SUBPROCESS "llama-common: use subprocess, required by server tools and server router mode" ${LLAMA_SUBPROCESS_DEFAULT}) option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF) diff --git a/CODEOWNERS b/CODEOWNERS index 2e30839ba0a8..929c8380e843 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -60,7 +60,6 @@ /ggml/src/ggml-cpu/spacemit/ @alex-spacemit /ggml/src/ggml-cuda/ @ggml-org/ggml-cuda /ggml/src/ggml-cuda/vendors/hip.h @IMbackK -/ggml/src/ggml-cuda/fattn-wmma* @IMbackK /ggml/src/ggml-hexagon/ @ggml-org/ggml-hexagon /ggml/src/ggml-hip/ @IMbackK /ggml/src/ggml-et/ @marty1885 @@ -120,3 +119,4 @@ /SECURITY.md @ggerganov /build-xcframework.sh @danbev requirements*.txt @CISC +/skills @ngxson diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6881a4d3ab33..91fa381dd019 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,27 +9,38 @@ The project differentiates between 3 levels of contributors: # AI Usage Policy > [!IMPORTANT] -> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity. > -> Repeated violations of this policy may result in your account being permanently banned from contributing to the project. +> AI-generated code is allowed. You are 100% responsible for every line, however it was produced. +> +> Undisclosed AI usage may result in your account being permanently banned from contributing to the project. > > Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file. -Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations). - If AI is used to generate any portion of the code, contributors must adhere to the following requirements: 1. Explicitly disclose the manner in which AI was employed. -2. Perform a comprehensive manual review prior to submitting the pull request. -3. Be prepared to explain every line of code they submitted when asked about it by a maintainer. -4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...). +2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate. +3. Perform a comprehensive manual review prior to submitting the pull request. +4. Be prepared to explain every line of code they submitted when asked about it by a maintainer. +5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...). For more info, please refer to the [AGENTS.md](AGENTS.md) file. # Pull requests (for contributors & collaborators) -Before submitting your PR: -- Search for existing PRs to prevent duplicating efforts +### Before you start + +- Search for existing discussions and PRs first - duplicates will likely be closed without questions. +- Features must begin with an issue, not a PR - let interest accumulate before writing code; niche features may only land as an example/tool, or on a private fork. +- Bug-fix PRs must include a reproducible issue and a regression test that fails before your change and passes after. Fixes without a test may be closed without review. +- New CLI or public API additions carry a **higher bar** than internal changes - justify why an existing mechanism doesn't suffice. +- Meeting all of the above still doesn't guarantee a merge - see [Pull requests (for maintainers)](#pull-requests-for-maintainers). +- If you are a new contributor + - Limit your open PRs to 1 + - Do not submit trivial fixes (e.g. typos, formatting changes) + +### Preparing your PR + - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier - Test your changes: - Execute [the full CI locally on your machine](ci/README.md) before publishing @@ -38,7 +49,6 @@ Before submitting your PR: - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Create separate PRs for each feature or fix: - Avoid combining unrelated changes in a single PR - - For intricate features, consider opening a feature request first to discuss and align expectations - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*: - convert a small model to GGUF using the new type and upload it to HuggingFace @@ -46,11 +56,9 @@ Before submitting your PR: - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly -- If you are a new contributor - - Limit your open PRs to 1 - - Do not submit trivial fixes (e.g. typos, formatting changes) -After submitting your PR: +### After submitting your PR + - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention @@ -70,6 +78,7 @@ Maintainers reserve the right to decline review or close pull requests for any r - The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone. - The pull request duplicates an existing one. - The contributor fails to adhere to this contributing guide or the AI policy. +- The change doesn't fit the existing architecture, or is too complex to justify its benefit. # Coding guidelines diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 4cf580a056c8..799d227519f9 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -100,6 +100,10 @@ add_library(${TARGET} sampling.h speculative.cpp speculative.h + subproc.cpp + subproc.h + trie.cpp + trie.h unicode.cpp unicode.h jinja/lexer.cpp @@ -125,6 +129,10 @@ set_target_properties(${TARGET} PROPERTIES target_include_directories(${TARGET} PUBLIC . ../vendor) target_compile_features (${TARGET} PUBLIC cxx_std_17) +if (LLAMA_SUBPROCESS) + target_compile_definitions(${TARGET} PUBLIC LLAMA_SUBPROCESS) +endif() + if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) diff --git a/common/arg.cpp b/common/arg.cpp index b6fddae00ca9..fade718b104f 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -5,6 +5,7 @@ #include "common.h" #include "download.h" #include "json-schema-to-grammar.h" +#include "llama.h" #include "log.h" #include "sampling.h" #include "speculative.h" @@ -351,6 +352,10 @@ static std::string get_default_local_path(const std::string & url) { return fs_get_cache_file(string_split(f, '/').back()); } +static bool spec_types_is_default(const common_params & params) { + return params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_NONE}; +} + common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) { common_download_hf_plan plan; common_download_hf_plan plan_spec; @@ -361,6 +366,14 @@ common_models_handler common_models_handler_init(const common_params & params, l params.speculative.types.end(), COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end(); + const bool spec_type_draft_dflash = std::find(params.speculative.types.begin(), + params.speculative.types.end(), + COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH) != params.speculative.types.end(); + + const bool spec_type_draft_eagle3 = std::find(params.speculative.types.begin(), + params.speculative.types.end(), + COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3) != params.speculative.types.end(); + // only download mmproj if the current example is using it bool use_mmproj = false; for (const auto & ex : mmproj_examples) { @@ -373,6 +386,8 @@ common_models_handler common_models_handler_init(const common_params & params, l opts.bearer_token = params.hf_token; opts.offline = params.offline; opts.download_mtp = spec_type_draft_mtp; + opts.download_eagle3 = spec_type_draft_eagle3; + opts.download_dflash = spec_type_draft_dflash; opts.download_mmproj = use_mmproj && !params.no_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty(); @@ -381,7 +396,14 @@ common_models_handler common_models_handler_init(const common_params & params, l } if (!params.speculative.draft.mparams.hf_repo.empty()) { - plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts); + // without a requested type, discover every sidecar the draft repo ships to infer the type later + auto opts_spec = opts; + if (spec_types_is_default(params)) { + opts_spec.download_mtp = true; + opts_spec.download_dflash = true; + opts_spec.download_eagle3 = true; + } + plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts_spec); } if (!params.vocoder.model.hf_repo.empty()) { @@ -517,8 +539,69 @@ void common_models_handler_apply(common_models_handler & handler, common_params } }; + // an explicit draft file selection (e.g. -md with -hfd) disables the sidecar resolution of the draft repo + if (!params.speculative.draft.mparams.hf_file.empty()) { + plan_spec.mtp = {}; + plan_spec.dflash = {}; + plan_spec.eagle3 = {}; + } + + // infer the speculative type from the sidecar shipped by the draft repo when none is requested + if (spec_types_is_default(params)) { + if (!plan_spec.mtp.local_path.empty()) { + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_MTP }; + plan_spec.dflash = {}; + plan_spec.eagle3 = {}; + } else if (!plan_spec.dflash.local_path.empty()) { + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH }; + plan_spec.eagle3 = {}; + } else if (!plan_spec.eagle3.local_path.empty()) { + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 }; + } + } + + // when a sidecar type is requested, the draft repo resolves to its sidecar instead of a full model + const bool spec_sidecar_found = !plan_spec.mtp.local_path.empty() || + !plan_spec.dflash.local_path.empty() || + !plan_spec.eagle3.local_path.empty(); + if (!plan_spec.mtp.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.mtp, opts, [&]() { + // only use the discovered MTP head when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.mtp); + } else { + hf_cache::finalize_file(plan_spec.mtp); + } + }); + } + if (!plan_spec.dflash.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.dflash, opts, [&]() { + // only use the discovered DFlash sidecar when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.dflash); + } else { + hf_cache::finalize_file(plan_spec.dflash); + } + }); + } + if (!plan_spec.eagle3.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.eagle3, opts, [&]() { + // only use the discovered Eagle3 sidecar when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.eagle3); + } else { + hf_cache::finalize_file(plan_spec.eagle3); + } + }); + } + + // a wired draft sidecar counts as an explicit draft for the main plan fallback below + if (spec_sidecar_found) { + had_spec_url = true; + } + // handle plan_spec (e.g. --spec-draft-hf) - if (!plan_spec.model_files.empty() && !had_spec_url) { + if (!plan_spec.model_files.empty() && !had_spec_url && !spec_sidecar_found) { add_tasks(plan_spec.model_files, plan_spec.primary, params.speculative.draft.mparams); had_spec_url = true; } @@ -546,6 +629,26 @@ void common_models_handler_apply(common_models_handler & handler, common_params } }); } + if (!plan.dflash.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan.dflash, opts, [&]() { + // only fall back to the discovered DFlash sidecar when no draft was explicitly provided + if (params.speculative.draft.mparams.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.dflash); + } else { + hf_cache::finalize_file(plan.dflash); + } + }); + } + if (!plan.eagle3.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan.eagle3, opts, [&]() { + // only fall back to the discovered Eagle3 sidecar when no draft was explicitly provided + if (params.speculative.draft.mparams.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.eagle3); + } else { + hf_cache::finalize_file(plan.eagle3); + } + }); + } if (!plan.preset.local_path.empty()) { tasks.emplace_back(plan.preset, opts, [&]() { // if HF repo is a preset repo, we simply run server in router mode with the preset.ini file @@ -695,6 +798,17 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context arg.c_str(), e.what(), opt.to_string().c_str())); } } + + // TODO: remove this check after deprecating --mmap|mlock|dio + auto has_arg = [&](std::initializer_list names) { + return std::any_of(names.begin(), names.end(), [&](const char * name) { + return seen_args.count(name); + }); + }; + if (has_arg({"-lm", "--load-mode"}) && + has_arg({"--mlock", "--mmap", "--no-mmap", "-dio", "--direct-io", "-ndio", "--no-direct-io"})) { + LOG_WRN("DEPRECATED: `--load-mode` and `--mlock`/`--mmap`/`--direct-io` should not be combined; only the last flag on the command line will take effect\n"); + } }; // parse all CLI args now, so that -hf is available below for remote preset resolution @@ -748,8 +862,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context params.kv_overrides.back().key[0] = 0; } - if (!params.server_tools.empty() && !params.cors_origins_explicit) { - LOG_WRN("server tools are enabled, using localhost as default CORS origin (change via --cors-origins)\n"); + const bool mcp_enabled = !params.mcp_servers_config.empty() || !params.mcp_servers_json.empty(); + if ((!params.server_tools.empty() || mcp_enabled) && !params.cors_origins_explicit) { + LOG_WRN("server tools or MCP servers are enabled, using localhost as default CORS origin (change via --cors-origins)\n"); params.cors_origins = "localhost"; } @@ -946,6 +1061,31 @@ static std::vector parse_device_list(const std::string & val return devices; } +void common_print_available_devices() { + constexpr size_t MiB = 1024 * 1024; + std::vector devices; + + ggml_backend_load_all(); + + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { + devices.push_back(dev); + } + } + printf("Available devices:\n"); + + if (devices.empty()) { + printf(" (none)\n"); + return; + } + for (auto * dev : devices) { + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / MiB, free / MiB); + } +} + static void add_rpc_devices(const std::string & servers) { auto rpc_servers = string_split(servers, ','); if (rpc_servers.empty()) { @@ -2405,27 +2545,47 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } add_opt(common_arg( {"--mlock"}, - "force system to keep model in RAM rather than swapping or compressing", + "DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing", [](common_params & params) { - params.use_mlock = true; + LOG_WRN("DEPRECATED: --mlock is deprecated. use --load-mode mlock instead\n"); + params.load_mode = LLAMA_LOAD_MODE_MLOCK; } ).set_env("LLAMA_ARG_MLOCK")); add_opt(common_arg( {"--mmap"}, {"--no-mmap"}, - string_format("whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), + "DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)", [](common_params & params, bool value) { - params.use_mmap = value; + LOG_WRN("DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead\n"); + params.load_mode = value ? LLAMA_LOAD_MODE_MMAP : LLAMA_LOAD_MODE_NONE; } ).set_env("LLAMA_ARG_MMAP")); add_opt(common_arg( {"-dio", "--direct-io"}, {"-ndio", "--no-direct-io"}, - string_format("use DirectIO if available. (default: %s)", params.use_direct_io ? "enabled" : "disabled"), + "DEPRECATED in favor of `--load-mode`: use DirectIO if available", [](common_params & params, bool value) { - params.use_direct_io = value; + LOG_WRN("DEPRECATED: --direct-io and --no-direct-io are deprecated. use --load-mode dio instead\n"); + params.load_mode = value ? LLAMA_LOAD_MODE_DIRECT_IO : LLAMA_LOAD_MODE_NONE; } ).set_env("LLAMA_ARG_DIO")); + add_opt(common_arg( + {"-lm", "--load-mode"}, "MODE", + "model loading mode (default: mmap)\n" + "- none: no special loading mode\n" + "- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)\n" + "- mlock: force system to keep model in RAM rather than swapping or compressing\n" + "- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing\n" + "- dio: use DirectIO if available\n", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.load_mode = LLAMA_LOAD_MODE_NONE; } + else if (value == "mmap") { params.load_mode = LLAMA_LOAD_MODE_MMAP; } + else if (value == "mlock") { params.load_mode = LLAMA_LOAD_MODE_MLOCK; } + else if (value == "mmap+mlock") { params.load_mode = LLAMA_LOAD_MODE_MMAP_MLOCK; } + else if (value == "dio") { params.load_mode = LLAMA_LOAD_MODE_DIRECT_IO; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_LOAD_MODE")); add_opt(common_arg( {"--numa"}, "TYPE", "attempt optimizations that help on some NUMA systems\n" @@ -2453,20 +2613,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--list-devices"}, "print list of available devices and exit", [](common_params &) { - ggml_backend_load_all(); - std::vector devices; - for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { - auto * dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { - devices.push_back(dev); - } - } - printf("Available devices:\n"); - for (auto * dev : devices) { - size_t free, total; - ggml_backend_dev_memory(dev, &free, &total); - printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); - } + common_print_available_devices(); exit(0); } )); @@ -2815,6 +2962,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_MTP); } ).set_examples({LLAMA_EXAMPLE_DOWNLOAD})); + add_opt(common_arg( + {"--dflash"}, + "also download the DFlash sidecar, if available (default: unused)", + [](common_params & params) { + params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH); + } + ).set_examples({LLAMA_EXAMPLE_DOWNLOAD})); + add_opt(common_arg( + {"--eagle3"}, + "also download the Eagle3 sidecar, if available (default: unused)", + [](common_params & params) { + params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3); + } + ).set_examples({LLAMA_EXAMPLE_DOWNLOAD})); add_opt(common_arg( {"--context-file"}, "FNAME", "file to load context from (use comma-separated values to specify multiple files)", @@ -2932,7 +3093,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_TOKENIZE})); add_opt(common_arg( {"--stdin"}, - string_format("read the prompt from stdin (mutually exclusive with -f/--file and -p/--prompt) (default: %s)", params.tokenize_stdin ? "true" : "false"), + string_format("read the prompt from stdin (takes precedence over -f/--file and -p/--prompt) (default: %s)", params.tokenize_stdin ? "true" : "false"), [](common_params & params) { params.tokenize_stdin = true; } @@ -3127,6 +3288,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.server_tools = parse_csv_row(value); } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS")); + add_opt(common_arg( + {"--mcp-servers-config"}, "PATH", + "experimental: path to JSON file with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)\n" + "note: for security reasons, this will limit --cors-origins to localhost by default", + [](common_params & params, const std::string & value) { + params.mcp_servers_config = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MCP_SERVERS_CONFIG")); + add_opt(common_arg( + {"--mcp-servers-json"}, "JSON", + "experimental: inline JSON with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)\n" + "note: for security reasons, this will limit --cors-origins to localhost by default", + [](common_params & params, const std::string & value) { + params.mcp_servers_json = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MCP_SERVERS_JSON")); add_opt(common_arg( {"-ag", "--agent"}, {"-no-ag", "--no-agent"}, @@ -3430,7 +3607,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.chat_template = read_file(value); } - ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); add_opt(common_arg( {"--skip-chat-parsing"}, {"--no-skip-chat-parsing"}, diff --git a/common/arg.h b/common/arg.h index 54a38b9cce4a..8f609e356fe2 100644 --- a/common/arg.h +++ b/common/arg.h @@ -123,6 +123,9 @@ struct common_params_context { // if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message) bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); +// load all backends and print the list of available (non-CPU) devices to stdout +void common_print_available_devices(); + // parse input arguments from CLI into a map bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map & out_map); diff --git a/common/chat-auto-parser-generator.cpp b/common/chat-auto-parser-generator.cpp index 3f91364c5801..af84ff323daf 100644 --- a/common/chat-auto-parser-generator.cpp +++ b/common/chat-auto-parser-generator.cpp @@ -47,6 +47,8 @@ common_chat_params peg_generator::generate_parser(const common_chat_template & data.generation_prompt = common_chat_template_generation_prompt(tmpl, inputs); data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.preserved_tokens = autoparser.preserved_tokens; + data.additional_stops.insert(data.additional_stops.end(), + autoparser.additional_stops.begin(), autoparser.additional_stops.end()); std::string parser_generation_prompt = data.generation_prompt; @@ -286,7 +288,13 @@ common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p, // we only emit tool_close when we can actually see the closing marker. This prevents // premature closing during partial parsing when we've seen e.g. "" (end) or "" prefix that failed to match. - func_parser = func_parser + p.tool_close(p.peek(p.literal(format.per_call_end))); + // Laguna (v4): the model may emit whitespace between the last and + // even though the template renders them tight. Tolerate optional + // leading space in the close lookahead so the tool call still closes. + auto close_peek = arguments.tolerate_intertag_whitespace + ? p.peek(p.space() + p.literal(format.per_call_end)) + : p.peek(p.literal(format.per_call_end)); + func_parser = func_parser + p.tool_close(close_peek); } else { func_parser = func_parser + p.tool_close(p.space()); // force this to process tool closing callbacks in mapper } diff --git a/common/chat-auto-parser.h b/common/chat-auto-parser.h index d47b09dcf6fd..074216b11ee1 100644 --- a/common/chat-auto-parser.h +++ b/common/chat-auto-parser.h @@ -206,6 +206,7 @@ struct tool_arguments_analysis { std::string value_prefix; // e.g., "", "", "" std::string value_suffix; // e.g., "", "", "" std::string separator; // e.g., "", "\n", "," + bool tolerate_intertag_whitespace = false; // Laguna: accept optional whitespace between arg tags }; struct tool_id_analysis { @@ -388,6 +389,7 @@ struct autoparser { // Preserved tokens for tokenizer (union of all non-empty markers) std::vector preserved_tokens; + std::vector additional_stops; // literal stop strings (e.g. Laguna ) caught however tokenized autoparser() = default; diff --git a/common/chat-diff-analyzer.cpp b/common/chat-diff-analyzer.cpp index 127278dfb2ac..7db1dcb0fa84 100644 --- a/common/chat-diff-analyzer.cpp +++ b/common/chat-diff-analyzer.cpp @@ -173,6 +173,26 @@ static std::vector\n", "\n") that + // the model does not emit, so the inferred delimiters carry a spurious + // newline and never match the model output. Trim to the bare tag. (v8 + // renders without the whitespace, so this is a no-op there.) + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find("laguna_glm_thinking") != std::string::npos) { + analysis.reasoning.start = trim_whitespace(analysis.reasoning.start); + analysis.reasoning.end = trim_whitespace(analysis.reasoning.end); + analysis.tools.arguments.value_prefix = trim_whitespace(analysis.tools.arguments.value_prefix); + analysis.tools.arguments.value_suffix = trim_whitespace(analysis.tools.arguments.value_suffix); + analysis.tools.arguments.separator = trim_whitespace(analysis.tools.arguments.separator); + analysis.tools.arguments.tolerate_intertag_whitespace = true; + // The CONTROL/eot token only halts generation when emitted as the + // single token; after tool calls the model can spell it out as text tokens. + // A literal stop string catches it either way. + analysis.additional_stops.push_back(""); + LOG_DBG(ANSI_ORANGE "[Patch: Laguna]\n" ANSI_RESET); + } + }, }); diff --git a/common/chat.cpp b/common/chat.cpp index 22d2ee4a2a11..300193ab11ea 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -15,11 +15,13 @@ #include "nlohmann/json.hpp" +#include #include #include #include #include #include +#include #include #include @@ -1022,7 +1024,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ data.supports_thinking = true; data.thinking_start_tag = "[THINK]"; - data.thinking_end_tag = "[/THINK]"; + data.thinking_end_tags = {"[/THINK]"}; data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override = */ adjusted_messages); data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override = */ adjusted_messages); data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; @@ -1148,6 +1150,9 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.supports_thinking = true; + data.thinking_start_tag = "<|channel|>analysis<|message|>"; + data.thinking_end_tags = {"<|end|>"}; + // These special tokens are required to parse properly, so we include them // even if parse_tool_calls is false. data.preserved_tokens = { @@ -1292,7 +1297,7 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ data.format = COMMON_CHAT_FORMAT_PEG_GEMMA4; data.supports_thinking = true; data.thinking_start_tag = "<|channel>thought"; - data.thinking_end_tag = ""; + data.thinking_end_tags = {""}; data.preserved_tokens = { "<|channel>", @@ -1567,7 +1572,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp const std::string GEN_PROMPT = "<|im_assistant|>assistant<|im_middle|>"; data.thinking_start_tag = THINK_START; - data.thinking_end_tag = THINK_END; + data.thinking_end_tags = {THINK_END}; if (inputs.has_continuation()) { const auto & msg = inputs.continue_msg; @@ -1701,7 +1706,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat } data.thinking_start_tag = THINK_START; - data.thinking_end_tag = THINK_END; + data.thinking_end_tags = {THINK_END}; auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object(); @@ -1855,16 +1860,93 @@ static common_chat_params common_chat_params_init_gigachat_v3( return data; } +// The DeepSeek V4 reference implementation renders consecutive tool results into a single +// user block, ordered by the tool call order of the preceding assistant message (matched +// by tool call id) rather than by the order they appear in the conversation. +static json deepseek_v4_sort_tool_results(const json & messages) { + json adjusted = messages; + std::map call_order; + + for (size_t i = 0; i < adjusted.size();) { + const auto & msg = adjusted[i]; + const auto role = msg.value("role", ""); + + if (role == "assistant" && msg.contains("tool_calls") && + msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) { + call_order.clear(); + const auto & tool_calls = msg.at("tool_calls"); + for (size_t idx = 0; idx < tool_calls.size(); idx++) { + auto id = tool_calls[idx].value("id", ""); + if (!id.empty()) { + call_order[id] = idx; + } + } + i++; + continue; + } + + if (role != "user" && role != "tool") { + i++; + continue; + } + + // collect a maximal run of user/tool messages - they render into one user block + std::vector tool_positions; + size_t run_end = i; + for (; run_end < adjusted.size(); run_end++) { + const auto r = adjusted[run_end].value("role", ""); + if (r == "tool") { + tool_positions.push_back(run_end); + } else if (r != "user") { + break; + } + } + + if (tool_positions.size() > 1 && !call_order.empty()) { + std::vector results; + results.reserve(tool_positions.size()); + for (auto pos : tool_positions) { + results.push_back(adjusted[pos]); + } + std::stable_sort(results.begin(), results.end(), [&](const json & a, const json & b) { + const auto order = [&](const json & m) { + auto it = call_order.find(m.value("tool_call_id", "")); + return it == call_order.end() ? (size_t) 0 : it->second; + }; + return order(a) < order(b); + }); + for (size_t k = 0; k < tool_positions.size(); k++) { + adjusted[tool_positions[k]] = std::move(results[k]); + } + } + + i = run_end; + } + + return adjusted; +} + static common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template & tmpl, const autoparser::generation_params & inputs) { common_chat_params data; - data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); - data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); + // V4 uses the same DSML markup as V3.2, but names the tool call block "tool_calls" + // instead of "function_calls", renders tool results in tool call order and its + // non-thinking generation prompt ends with a bare instead of an empty + // pair. + const bool is_v4 = tmpl.source().find("function_calls") == std::string::npos; + + std::optional adjusted_messages; + if (is_v4) { + adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages); + } + + data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages); + data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages); data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.supports_thinking = true; data.thinking_start_tag = ""; - data.thinking_end_tag = ""; + data.thinking_end_tags = {""}; data.preserved_tokens = { "｜DSML｜", "", @@ -1879,8 +1961,9 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha const std::string DSML = "｜DSML｜"; const std::string THINK_START = ""; const std::string THINK_END = ""; - const std::string FC_START = "<" + DSML + "function_calls>"; - const std::string FC_END = ""; + const std::string TC_BLOCK = is_v4 ? "tool_calls" : "function_calls"; + const std::string FC_START = "<" + DSML + TC_BLOCK + ">"; + const std::string FC_END = ""; const std::string INVOKE_START = "<" + DSML + "invoke"; const std::string INVOKE_END = ""; const std::string PARAM_START = "<" + DSML + "parameter"; @@ -1907,8 +1990,11 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END); } else if (extract_reasoning) { // Thinking disabled but reasoning extraction requested: the generation prompt - // contains an empty pair that must still be consumed. - reasoning = p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END)); + // contains an empty pair (V3.2) or a bare (V4) that + // must still be consumed. + reasoning = is_v4 + ? p.optional(p.literal(THINK_END)) + : p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END)); } if (has_response_format) { @@ -2035,6 +2121,189 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha return data; } +// Kimi K3 - XTML-ish tagged format from the model's own template: +// open_tag(t, attrs) = <|open|>t k="v"...<|sep|> close_tag(t) = <|close|>t<|sep|> +// assistant := [think] [response] [tools] close_tag(message) <|end_of_msg|> +// Note the generation prompt already opens the think (or response) section, so +// the section opener is optional here - same situation as Kimi K2 Thinking. +static common_chat_params common_chat_params_init_kimi_k3(const common_chat_template & tmpl, + const autoparser::generation_params & inputs) { + common_chat_params data; + + data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); + data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + + const std::string SEP = "<|sep|>"; + const std::string MSG_START = "<|open|>message role=\"assistant\"<|sep|>"; + const std::string THINK_START = "<|open|>think<|sep|>"; + const std::string THINK_END = "<|close|>think<|sep|>"; + const std::string RESP_START = "<|open|>response<|sep|>"; + const std::string RESP_END = "<|close|>response<|sep|>"; + const std::string TOOLS_START = "<|open|>tools<|sep|>"; + const std::string TOOLS_END = "<|close|>tools<|sep|>"; + const std::string CALL_START = "<|open|>call tool=\""; + const std::string CALL_END = "<|close|>call<|sep|>"; + const std::string ARG_START = "<|open|>argument key=\""; + const std::string ARG_END = "<|close|>argument<|sep|>"; + const std::string MSG_END = "<|close|>message<|sep|>"; + const std::string EOM_TOKEN = "<|end_of_msg|>"; + + // The four markers are the only special tokens; tag names ("think", + // "response", "message") are ordinary tokens and must NOT be preserved, + // or ordinary prose containing those words would be mangled. + data.preserved_tokens = { + "<|open|>", + "<|close|>", + "<|sep|>", + "<|end_of_msg|>", + }; + + data.thinking_start_tag = THINK_START; + data.thinking_end_tags = { THINK_END }; + + // Per-role message-start delimiters. User/assistant messages carry only the + // role attribute, so their full opener (through <|sep|>) is used. System and + // tool messages continue with more attributes (type=/tool=/index=), so those + // delimiters stop after the role's closing quote - verified against the K3 + // tokenizer that the quote is always its own token and never merges with the + // following attribute text, keeping the token-level prefix match exact. + data.message_delimiters = { + { COMMON_CHAT_ROLE_ASSISTANT, "<|open|>message role=\"assistant\"<|sep|>" }, + { COMMON_CHAT_ROLE_USER, "<|open|>message role=\"user\"<|sep|>" }, + { COMMON_CHAT_ROLE_TOOL, "<|open|>message role=\"tool\"" }, + { COMMON_CHAT_ROLE_SYSTEM, "<|open|>message role=\"system\"" }, + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + + if (inputs.has_continuation()) { + const auto & msg = inputs.continue_msg; + + data.generation_prompt = MSG_START + THINK_START + msg.reasoning_content; + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += THINK_END + RESP_START + msg.render_content(); + } + + data.prompt += data.generation_prompt; + } + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto end = p.end(); + + auto start = p.optional(p.literal(MSG_START)); + + // The think section is ALWAYS consumed, even when reasoning extraction + // is off: K3's generation prompt ends with open_tag('think'), so the + // opener is present on every request and would otherwise leak into + // content. With extraction off the thoughts fall into content, matching + // how the other reasoning models behave. + // Reasoning stops at its own closer, or at the response opener if the + // model skips the closer entirely (seen on short answers). + auto think_body = extract_reasoning ? p.reasoning(p.until_one_of({ THINK_END, RESP_START })) : + p.content(p.until_one_of({ THINK_END, RESP_START })); + + auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body + + p.optional(p.literal(THINK_END))); + + // Content runs to the response closer, or to whatever comes next if a + // truncated generation never emits one. + auto response = p.optional(p.literal(RESP_START)) + + p.content(p.until_one_of({ RESP_END, TOOLS_START, MSG_END })) + + p.optional(p.literal(RESP_END)); + + // The message closer is followed by the EOG token, which reaches the + // parser as text and must be consumed or the parse is left incomplete. + auto trailer = p.optional(p.literal(MSG_END)) + p.optional(p.literal(EOM_TOKEN)); + + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return start + reasoning + response + trailer + end; + } + + auto tool_choices = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const json schema = function.contains("parameters") ? function.at("parameters") : json::object(); + + // Arguments arrive one tag per key, with the JSON type carried in a + // type="..." attribute. We take the type from the tool schema + // instead - it is authoritative, and it tells us whether the value + // should be parsed as JSON or kept as a literal string. + auto args = p.eps(); + if (schema.contains("properties") && !schema.at("properties").empty()) { + auto arg_choices = p.choice(); + for (const auto & prop : schema.at("properties").items()) { + const std::string & key = prop.key(); + + std::string type = "string"; + if (prop.value().is_object() && prop.value().contains("type") && + prop.value().at("type").is_string()) { + type = prop.value().at("type").get(); + } + + auto value = type == "string" ? p.tool_arg_string_value(p.until(ARG_END)) : + p.tool_arg_value(p.until(ARG_END)); + + // skip the trailing type="..." attribute: anything up to <|sep|> + arg_choices |= p.rule("kimi-k3-arg-" + name + "-" + key, + p.tool_arg(p.tool_arg_open(p.literal(ARG_START)) + + p.tool_arg_name(p.literal(key)) + p.literal("\"") + + p.until(SEP) + p.literal(SEP) + value + + p.tool_arg_close(p.literal(ARG_END)))); + } + args = p.zero_or_more(arg_choices); + } + + // skip the trailing index="N" attribute the same way + auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) + p.literal("\"") + + p.until(SEP) + p.literal(SEP)) + + p.tool_args(args) + p.tool_close(p.literal(CALL_END))); + + tool_choices |= p.rule("kimi-k3-tool-" + name, call); + }); + + // K3 emits every call inside one <|open|>tools<|sep|> section, then + // closes the message. The message closer is part of the trigger rule so + // that the lazy grammar still permits it once tool calls have started - + // otherwise constrained decoding rejects the model's own closing tag. + auto tools_section = + p.trigger_rule("kimi-k3-tool-call", p.literal(TOOLS_START) + p.one_or_more(tool_choices) + + p.literal(TOOLS_END) + p.optional(p.literal(MSG_END)) + + p.optional(p.literal(EOM_TOKEN))); + + auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section : + p.optional(tools_section); + + return start + reasoning + response + tools + trailer + end; + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + if (function.contains("parameters")) { + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + } + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOLS_START }, + }; + } + + return data; +} + // Cohere2 MoE (a.k.a. "North Code") parser. // // The assistant turn is fully marker-wrapped: @@ -2077,7 +2346,7 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.supports_thinking = true; data.thinking_start_tag = THINK_START; - data.thinking_end_tag = THINK_END; + data.thinking_end_tags = {THINK_END}; data.preserved_tokens = { TURN_START, TURN_END, CHATBOT, USER, SYSTEM, THINK_START, THINK_END, @@ -2096,9 +2365,10 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t { COMMON_CHAT_ROLE_SYSTEM, TURN_START + SYSTEM }, }; - auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); - auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; - auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE); if (inputs.has_continuation()) { const auto & msg = inputs.continue_msg; @@ -2129,7 +2399,11 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t p.optional(p.literal(THINK_END)))); } - auto text_content = p.literal(TEXT_START) + p.content(p.until(TEXT_END)) + p.optional(p.literal(TEXT_END)); + auto text_content = has_response_format + ? p.literal(TEXT_START) + + p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) + + p.optional(p.literal(TEXT_END)) + : p.literal(TEXT_START) + p.content(p.until(TEXT_END)) + p.optional(p.literal(TEXT_END)); if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { return generation_prompt + reasoning + text_content + p.optional(p.literal(TURN_END)) + end; @@ -2157,13 +2431,17 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t data.parser = parser.save(); if (include_grammar) { - data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar_lazy = !has_response_format && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; data.grammar = build_grammar([&](const common_grammar_builder & builder) { foreach_function(inputs.tools, [&](const json & tool) { const auto & function = tool.at("function"); auto schema = function.at("parameters"); builder.resolve_refs(schema); }); + if (has_response_format) { + auto schema = inputs.json_schema; + builder.resolve_refs(schema); + } parser.build_grammar(builder, data.grammar_lazy); }); @@ -2418,7 +2696,7 @@ static common_chat_params common_chat_params_init_minicpm5(const common_chat_tem }; data.thinking_start_tag = ""; - data.thinking_end_tag = ""; + data.thinking_end_tags = {""}; data.message_delimiters = { { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant" }, @@ -2583,6 +2861,14 @@ std::optional common_chat_try_specialized_template( return common_chat_params_init_kimi_k2(tmpl, params); } + // Kimi K3 - XTML-ish tagged format built from open_tag/close_tag macros. + // Detection: the <|open|>/<|close|>/<|sep|> marker trio is unique to K3. + if (src.find("<|open|>") != std::string::npos && src.find("<|close|>") != std::string::npos && + src.find("<|end_of_msg|>") != std::string::npos) { + LOG_DBG("Using specialized template: Kimi K3\n"); + return common_chat_params_init_kimi_k3(tmpl, params); + } + // Cohere2 MoE / North Code - marker-wrapped format with <|START_TEXT|> content and // <|START_ACTION|> JSON tool calls. <|START_TEXT|> is unique to this template (the older // Command-R templates use <|START_RESPONSE|>). @@ -2612,12 +2898,14 @@ std::optional common_chat_try_specialized_template( return common_chat_params_init_gigachat_v3(tmpl, params); } - // DeepSeek V3.2 format detection: template defines dsml_token and uses it for tool calls. + // DeepSeek V3.2/V4 format detection: template defines dsml_token and uses it for tool calls. // The template source contains the token as a variable assignment, not as a literal in markup. + // V3.2 names the tool call block "function_calls", V4 names it "tool_calls". if (src.find("dsml_token") != std::string::npos && - src.find("function_calls") != std::string::npos && - src.find("DSML") != std::string::npos) { - LOG_DBG("Using specialized template: DeepSeek V3.2\n"); + src.find("DSML") != std::string::npos && + (src.find("function_calls") != std::string::npos || + src.find("tool_calls") != std::string::npos)) { + LOG_DBG("Using specialized template: DeepSeek V3.2/V4\n"); return common_chat_params_init_deepseek_v3_2(tmpl, params); } @@ -2772,7 +3060,10 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_ auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE; if (auto_params.supports_thinking) { auto_params.thinking_start_tag = trim_whitespace(autoparser.reasoning.start); - auto_params.thinking_end_tag = trim_whitespace(autoparser.reasoning.end); + auto end_tag = trim_whitespace(autoparser.reasoning.end); + if (!end_tag.empty()) { + auto_params.thinking_end_tags = {std::move(end_tag)}; + } } common_peg_arena arena; arena.load(auto_params.parser); diff --git a/common/chat.h b/common/chat.h index 7898f1623f54..d79f4ecd773c 100644 --- a/common/chat.h +++ b/common/chat.h @@ -274,7 +274,7 @@ struct common_chat_params { std::string generation_prompt; bool supports_thinking = false; std::string thinking_start_tag; // e.g., "" - std::string thinking_end_tag; // e.g., "" + std::vector thinking_end_tags; // e.g., "" std::vector grammar_triggers; std::vector preserved_tokens; std::vector additional_stops; diff --git a/common/common.cpp b/common/common.cpp index 8f13217ab442..82dd780fd8b3 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1249,7 +1249,6 @@ common_init_result::common_init_result(common_params & params, bool model_only) lora.reset(llama_adapter_lora_init(model, la.path.c_str())); if (lora == nullptr) { COM_ERR("failed to load lora adapter '%s'\n", la.path.c_str()); - pimpl->model.reset(model); return; } @@ -1558,10 +1557,8 @@ struct llama_model_params common_model_params_to_llama(common_params & params) { mparams.n_gpu_layers = params.n_gpu_layers; mparams.main_gpu = params.main_gpu; mparams.split_mode = params.split_mode; + mparams.load_mode = params.load_mode; mparams.tensor_split = params.tensor_split; - mparams.use_mmap = params.use_mmap; - mparams.use_direct_io = params.use_direct_io; - mparams.use_mlock = params.use_mlock; mparams.check_tensors = params.check_tensors; mparams.use_extra_bufts = !params.no_extra_bufts; mparams.no_host = params.no_host; diff --git a/common/common.h b/common/common.h index bffc1767a7d1..2792521836ae 100644 --- a/common/common.h +++ b/common/common.h @@ -6,6 +6,7 @@ #include "ggml-opt.h" #include "ggml.h" +#include "llama.h" #include #include @@ -283,12 +284,12 @@ struct common_params_sampling { // reasoning budget sampler parameters // these are populated by the server/CLI based on chat template params - int32_t reasoning_budget_tokens = -1; // -1 = disabled, >= 0 = token budget - std::vector reasoning_budget_start; // start tag token sequence - std::vector reasoning_budget_end; // end tag token sequence - std::vector reasoning_budget_forced; // forced sequence (message + end tag) - std::string reasoning_budget_message; // message injected before end tag when budget exhausted - bool reasoning_control = false; // create the budget sampler on demand so reasoning can be ended at runtime + int32_t reasoning_budget_tokens = -1; // -1 = disabled, >= 0 = token budget + std::vector reasoning_budget_start; // start tag token sequence + std::vector reasoning_budget_end; // end tag token sequences; the first tag is used as the forcing sequence + std::vector reasoning_budget_forced; // forced sequence (message + first end tag) + std::string reasoning_budget_message; // message injected before end tag when budget exhausted + bool reasoning_control = false; // create the budget sampler on demand so reasoning can be ended at runtime bool backend_sampling = false; @@ -482,6 +483,7 @@ struct common_params { std::vector fit_params_target = std::vector(llama_max_devices(), 1024 * 1024*1024); enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs + enum llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP; // how to load the model common_cpu_params cpuparams; common_cpu_params cpuparams_batch; @@ -572,9 +574,6 @@ struct common_params { bool kv_unified = false; // enable unified KV cache bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix - bool use_mmap = true; // enable mmap to use filesystem cache - bool use_direct_io = false; // read from disk without buffering - bool use_mlock = false; // use mlock to keep model in memory bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation bool no_kv_offload = false; // disable KV offloading @@ -669,6 +668,10 @@ struct common_params { // enable built-in tools std::vector server_tools; + // MCP server configs (Cursor-compatible JSON) + std::string mcp_servers_config; // path to JSON file with MCP server definitions + std::string mcp_servers_json; // inline JSON with MCP server definitions + // router server configs std::string models_dir = ""; // directory containing models for the router server std::string models_preset = ""; // directory containing model presets for the router server diff --git a/common/download.cpp b/common/download.cpp index 6b69a4418856..3776c6c7eb68 100644 --- a/common/download.cpp +++ b/common/download.cpp @@ -568,16 +568,30 @@ static hf_cache::hf_files get_split_files(const hf_cache::hf_files & files, } // pick the best sibling GGUF whose filename contains `keyword` (e.g. "mmproj" / "mtp"), -// preferring deeper shared directory prefix with the model, then closest quantization +// preferring deeper shared directory prefix with the model, then exact `tag` match, +// then closest quantization to the tag when given, or to the model otherwise static hf_cache::hf_file find_best_sibling(const hf_cache::hf_files & files, const std::string & model, - const std::string & keyword) { + const std::string & keyword, + const std::string & tag = "") { hf_cache::hf_file best; size_t best_depth = 0; int best_diff = 0; + bool best_exact = false; bool found = false; - auto model_bits = extract_quant_bits(model); + std::string tag_upper = tag; + for (char & c : tag_upper) { + c = (char) std::toupper((unsigned char) c); + } + + int model_bits = 0; + if (!tag_upper.empty()) { + auto pos = tag_upper.find_first_of("0123456789"); + model_bits = pos == std::string::npos ? 0 : std::stoi(tag_upper.substr(pos)); + } else { + model_bits = extract_quant_bits(model); + } auto model_parts = string_split(model, '/'); auto model_dir = model_parts.end() - 1; @@ -600,10 +614,19 @@ static hf_cache::hf_file find_best_sibling(const hf_cache::hf_files & files, auto bits = extract_quant_bits(f.path); auto diff = std::abs(bits - model_bits); - if (!found || depth > best_depth || (depth == best_depth && diff < best_diff)) { + std::string path_upper = f.path; + for (char & c : path_upper) { + c = (char) std::toupper((unsigned char) c); + } + bool exact = !tag_upper.empty() && path_upper.find("-" + tag_upper + ".") != std::string::npos; + + if (!found || depth > best_depth || + (depth == best_depth && exact && !best_exact) || + (depth == best_depth && exact == best_exact && diff < best_diff)) { best = f; best_depth = depth; best_diff = diff; + best_exact = exact; found = true; } } @@ -616,8 +639,21 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files, } static hf_cache::hf_file find_best_mtp(const hf_cache::hf_files & files, - const std::string & model) { - return find_best_sibling(files, model, "mtp-"); + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "mtp-", tag); +} + +static hf_cache::hf_file find_best_eagle3(const hf_cache::hf_files & files, + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "eagle3-", tag); +} + +static hf_cache::hf_file find_best_dflash(const hf_cache::hf_files & files, + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "dflash-", tag); } static bool gguf_filename_is_model(const std::string & filepath) { @@ -632,7 +668,9 @@ static bool gguf_filename_is_model(const std::string & filepath) { return filename.find("mmproj") == std::string::npos && filename.find("imatrix") == std::string::npos && - filename.find("mtp-") == std::string::npos; + filename.find("mtp-") == std::string::npos && + filename.find("eagle3-") == std::string::npos && + filename.find("dflash-") == std::string::npos; } static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files, @@ -724,21 +762,36 @@ common_download_hf_plan common_download_get_hf_plan(const common_params_model & } } else { primary = find_best_model(all, tag); - if (primary.path.empty()) { + // a requested sidecar can resolve on its own, without a full model of the same tag + if (primary.path.empty() && !opts.download_mtp && !opts.download_dflash && !opts.download_eagle3) { LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str()); list_available_gguf_files(all); return plan; } } - plan.primary = primary; - plan.model_files = get_split_files(all, primary); + if (!primary.path.empty()) { + plan.primary = primary; + plan.model_files = get_split_files(all, primary); + } - if (opts.download_mmproj) { + if (opts.download_mmproj && !primary.path.empty()) { plan.mmproj = find_best_mmproj(all, primary.path); } if (opts.download_mtp) { - plan.mtp = find_best_mtp(all, primary.path); + plan.mtp = find_best_mtp(all, primary.path, tag); + } + if (opts.download_dflash) { + plan.dflash = find_best_dflash(all, primary.path, tag); + } + if (opts.download_eagle3) { + plan.eagle3 = find_best_eagle3(all, primary.path, tag); + } + + if (primary.path.empty() && + plan.mtp.local_path.empty() && plan.dflash.local_path.empty() && plan.eagle3.local_path.empty()) { + LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str()); + list_available_gguf_files(all); } return plan; @@ -911,8 +964,10 @@ std::vector common_list_cached_models() { for (const auto & f : files) { auto split = get_gguf_split_info(f.path); if (split.index != 1 || split.tag.empty() || - split.prefix.find("mmproj") != std::string::npos || - split.prefix.find("mtp-") != std::string::npos) { + split.prefix.find("mmproj") != std::string::npos || + split.prefix.find("mtp-") != std::string::npos || + split.prefix.find("eagle3-") != std::string::npos || + split.prefix.find("dflash-") != std::string::npos) { continue; } if (seen.insert(f.repo_id + ":" + split.tag).second) { diff --git a/common/download.h b/common/download.h index 816e1c7f58a1..3e789e9e9369 100644 --- a/common/download.h +++ b/common/download.h @@ -55,8 +55,10 @@ struct common_download_opts { std::string bearer_token; common_header_list headers; bool offline = false; - bool download_mmproj = false; - bool download_mtp = false; + bool download_mmproj = false; + bool download_mtp = false; + bool download_eagle3 = false; + bool download_dflash = false; common_download_callback * callback = nullptr; }; @@ -106,6 +108,8 @@ struct common_download_hf_plan { hf_cache::hf_files model_files; hf_cache::hf_file mmproj; hf_cache::hf_file mtp; + hf_cache::hf_file eagle3; + hf_cache::hf_file dflash; hf_cache::hf_file preset; // if set, only this file is downloaded }; common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts); diff --git a/common/fit.cpp b/common/fit.cpp index afbf0b10f3f3..c82d066ad444 100644 --- a/common/fit.cpp +++ b/common/fit.cpp @@ -54,8 +54,7 @@ static std::vector common_get_device_memory_data_impl( llama_model_params mparams_copy = *mparams; mparams_copy.no_alloc = true; - mparams_copy.use_mmap = false; - mparams_copy.use_mlock = false; + mparams_copy.load_mode = LLAMA_LOAD_MODE_NONE; llama_model * model = llama_model_load_from_file(path_model, mparams_copy); if (model == nullptr) { @@ -137,7 +136,7 @@ static std::vector common_get_device_memory_data_impl( devs.push_back(llama_model_get_device(model, i)); } - hp_ngl = llama_model_n_layer(model); + hp_ngl = llama_model_n_layer(model) + llama_model_n_layer_nextn(model); hp_n_ctx_train = llama_model_n_ctx_train(model); hp_n_expert = llama_model_n_expert(model); diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp index ae378ebd4fd0..2b9e27ba1c3a 100644 --- a/common/jinja/caps.cpp +++ b/common/jinja/caps.cpp @@ -23,6 +23,7 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) { ctx.set_val("preserve_thinking", mk_val(enabled)); ctx.set_val("clear_thinking", mk_val(!enabled)); ctx.set_val("truncate_history_thinking", mk_val(!enabled)); + ctx.set_val("drop_thinking", mk_val(!enabled)); } static void caps_try_execute(jinja::program & prog, diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp index 807e952d902c..ef290ed7c057 100644 --- a/common/peg-parser.cpp +++ b/common/peg-parser.cpp @@ -3,10 +3,10 @@ #include "common.h" #include "json-schema-to-grammar.h" #include "log.h" +#include "trie.h" #include "unicode.h" #include -#include #include #include #include @@ -32,154 +32,6 @@ static bool is_hex_digit(const char c) { return (c >= '0' && c <= '9') || (c >= 'a' && c <= 'f') || (c >= 'A' && c <= 'F'); } -// Trie for matching multiple literals. -// This is used in common_peg_until_parser and to build a GBNF exclusion grammar -struct trie { - struct node { - std::map children; // Use uint32_t to store Unicode codepoints - bool is_word; - }; - - std::vector nodes; - - trie(const std::vector & words) { - create_node(); // root node - for (const auto & w : words) { - insert(w); - } - } - - enum match_result { NO_MATCH, PARTIAL_MATCH, COMPLETE_MATCH }; - - // Check if a delimiter starts at the given position - match_result check_at(std::string_view sv, size_t start_pos) const { - size_t current = 0; // Start at root - size_t pos = start_pos; - - // LOG_DBG("%s: checking at pos %zu, sv='%s'\n", __func__, start_pos, std::string(sv).c_str()); - - while (pos < sv.size()) { - auto result = common_parse_utf8_codepoint(sv, pos); - if (result.status != utf8_parse_result::SUCCESS) { - break; - } - - auto it = nodes[current].children.find(result.codepoint); - if (it == nodes[current].children.end()) { - // Can't continue matching - return match_result{match_result::NO_MATCH}; - } - - current = it->second; - pos += result.bytes_consumed; - - // Check if we've matched a complete word - if (nodes[current].is_word) { - return match_result{match_result::COMPLETE_MATCH}; - } - } - - // Reached end of input while still in the trie (not at root) - if (current != 0) { - // We're in the middle of a potential match - return match_result{match_result::PARTIAL_MATCH}; - } - - // Reached end at root (no match) - return match_result{match_result::NO_MATCH}; - } - - private: - size_t create_node() { - size_t index = nodes.size(); - nodes.emplace_back(); - return index; - } - - void insert(const std::string & word) { - size_t current = 0; - size_t pos = 0; - while (pos < word.length()) { - auto result = common_parse_utf8_codepoint(word, pos); - if (result.status != utf8_parse_result::SUCCESS) { - break; - } - - uint32_t ch = result.codepoint; - pos += result.bytes_consumed; - - auto it = nodes[current].children.find(ch); - if (it == nodes[current].children.end()) { - size_t child = create_node(); - nodes[current].children[ch] = child; - current = child; - } else { - current = it->second; - } - } - nodes[current].is_word = true; - } -}; - -// Aho-Corasick automaton -struct aho_corasick { - trie t; - std::vector fail; // failure links - std::vector order; // states in BFS order - std::vector terminal; // match states (directly or via a suffix link) - std::set alphabet; // every character with a transition - - aho_corasick(const std::vector & strings) : t(strings) { - const auto & nodes = t.nodes; - const size_t n = nodes.size(); - - fail.assign(n, 0); - order.reserve(n); - - std::deque queue{ 0 }; - while (!queue.empty()) { - size_t u = queue.front(); - queue.pop_front(); - order.push_back(u); - for (const auto & [ch, v] : nodes[u].children) { - if (u != 0) { - size_t f = fail[u]; - while (f && nodes[f].children.find(ch) == nodes[f].children.end()) { - f = fail[f]; - } - auto it = nodes[f].children.find(ch); - fail[v] = (it != nodes[f].children.end() && it->second != v) ? it->second : 0; - } - queue.push_back(v); - } - } - - terminal.assign(n, false); - for (size_t u : order) { - terminal[u] = nodes[u].is_word || (u != 0 && terminal[fail[u]]); - } - - for (const auto & node : nodes) { - for (const auto & [ch, v] : node.children) { - alphabet.insert(ch); - } - } - } - - size_t num_states() const { return t.nodes.size(); } - bool is_terminal(size_t s) const { return terminal[s]; } - - // follow failure links until a transition on `ch` exists. - size_t next(size_t state, uint32_t ch) const { - const auto & nodes = t.nodes; - while (state && nodes[state].children.find(ch) == nodes[state].children.end()) { - state = fail[state]; - } - auto it = nodes[state].children.find(ch); - return it != nodes[state].children.end() ? it->second : 0; - } -}; - static std::pair parse_hex_escape(const std::string & str, size_t pos, int hex_count) { if (pos + hex_count > str.length()) { return {0, 0}; @@ -797,7 +649,7 @@ struct parser_executor { } common_peg_parse_result operator()(const common_peg_until_parser & p) const { - trie matcher(p.delimiters); + common_trie matcher(p.delimiters); // Scan input and check for delimiters size_t pos = start_pos; @@ -824,12 +676,12 @@ struct parser_executor { // Check if a delimiter starts at this position auto match = matcher.check_at(ctx.input, pos); - if (match == trie::COMPLETE_MATCH) { + if (match == common_trie::COMPLETE_MATCH) { // Found a complete delimiter, return everything before it return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } - if (match == trie::PARTIAL_MATCH) { + if (match == common_trie::PARTIAL_MATCH) { // Found a partial match extending to end of input, return everything before it return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } @@ -1559,7 +1411,7 @@ static std::string gbnf_ac_grammar( const std::map> &, const std::vector &, const std::function &)> & build_rule) { - aho_corasick ac(strings); + common_aho_corasick ac(strings); auto state_name = [&](size_t s) -> std::string { if (s == 0) { diff --git a/common/preset.cpp b/common/preset.cpp index 4362c0621b78..eb0c60b09cff 100644 --- a/common/preset.cpp +++ b/common/preset.cpp @@ -330,6 +330,10 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co } } + if (preset.name == COMMON_PRESET_DEFAULT_NAME && preset.options.empty()) { + continue; + } + if (preset.name == "*") { // handle global preset global = preset; diff --git a/common/reasoning-budget.cpp b/common/reasoning-budget.cpp index 7da0bb1c57ce..1fe242d062d1 100644 --- a/common/reasoning-budget.cpp +++ b/common/reasoning-budget.cpp @@ -1,39 +1,52 @@ #include "reasoning-budget.h" #include "common.h" +#include "trie.h" #include "unicode.h" #include "log.h" +#include #include #include #include #include struct token_matcher { - std::vector tokens; - size_t pos = 0; + std::vector seqs; + common_aho_corasick ac; + size_t state = 0; - bool advance(llama_token token) { - if (tokens.empty()) { - return false; - } + token_matcher(const std::vector & seqs) : seqs(collect(seqs)), ac(build_trie(this->seqs)) {} - if (token == tokens[pos]) { - pos++; - if (pos >= tokens.size()) { - pos = 0; - return true; - } - } else { - pos = 0; - if (token == tokens[0]) { - pos = 1; + static std::vector collect(const std::vector & seqs) { + std::vector res; + for (const auto & seq : seqs) { + if (!seq.empty() && std::find(res.begin(), res.end(), seq) == res.end()) { + res.push_back(seq); } } - return false; + return res; + } + + static common_trie build_trie(const std::vector & seqs) { + common_trie t; + for (const auto & seq : seqs) { + t.insert(std::vector(seq.begin(), seq.end())); + } + return t; } - void reset() { pos = 0; } + // returns the index into seqs of the longest sequence ending at this token, or -1 + int32_t advance(llama_token token) { + state = ac.next(state, (uint32_t) token); + const int32_t p = ac.match_pattern(state); + if (p >= 0) { + state = 0; + } + return p; + } + + void reset() { state = 0; } }; struct common_reasoning_budget_ctx { @@ -41,7 +54,7 @@ struct common_reasoning_budget_ctx { token_matcher start_matcher; token_matcher end_matcher; - std::vector forced_tokens; + llama_tokens forced_tokens; int32_t budget; // maximum tokens in reasoning block int32_t remaining; // tokens remaining in budget @@ -50,6 +63,8 @@ struct common_reasoning_budget_ctx { // for forcing size_t force_pos; // next position in forced_tokens to force + + int32_t end_match; // index into end_matcher.seqs of the sequence that transitioned to DONE, -1 if none }; static const char * common_reasoning_budget_name(const struct llama_sampler * /*smpl*/) { @@ -62,7 +77,7 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to switch (ctx->state) { case REASONING_BUDGET_IDLE: { - if (ctx->start_matcher.advance(token)) { + if (ctx->start_matcher.advance(token) >= 0) { ctx->state = REASONING_BUDGET_COUNTING; ctx->remaining = ctx->budget; COM_TRC("activated, budget=%d tokens\n", ctx->budget); @@ -78,8 +93,10 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to case REASONING_BUDGET_COUNTING: case REASONING_BUDGET_WAITING_UTF8: { - if (ctx->end_matcher.advance(token)) { + const int32_t match = ctx->end_matcher.advance(token); + if (match >= 0) { ctx->state = REASONING_BUDGET_DONE; + ctx->end_match = match; COM_TRC("%s", "deactivated (natural end)\n"); break; } @@ -115,19 +132,25 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to break; } case REASONING_BUDGET_FORCING: + { + // track the end sequence within forced_tokens so it is also reported on DONE + const int32_t match = ctx->end_matcher.advance(token); ctx->force_pos++; if (ctx->force_pos >= ctx->forced_tokens.size()) { ctx->state = REASONING_BUDGET_DONE; + ctx->end_match = match; COM_TRC("%s", "forced sequence complete, done\n"); } break; + } case REASONING_BUDGET_DONE: // Re-arm on a new start tag: some models emit multiple blocks // per response, and each should get a fresh budget window. - if (ctx->start_matcher.advance(token)) { + if (ctx->start_matcher.advance(token) >= 0) { ctx->state = REASONING_BUDGET_COUNTING; ctx->remaining = ctx->budget; ctx->end_matcher.reset(); + ctx->end_match = -1; COM_TRC("re-activated on new start tag, budget=%d tokens\n", ctx->budget); if (ctx->remaining <= 0) { @@ -169,11 +192,12 @@ static void common_reasoning_budget_reset(struct llama_sampler * smpl) { ctx->start_matcher.reset(); ctx->end_matcher.reset(); ctx->force_pos = 0; + ctx->end_match = -1; } static struct llama_sampler * common_reasoning_budget_init_state( - const struct llama_vocab * vocab, const std::vector & start_tokens, - const std::vector & end_tokens, const std::vector & forced_tokens, + const struct llama_vocab * vocab, const std::vector & start_seqs, + const std::vector & end_seqs, const llama_tokens & forced_tokens, int32_t budget, common_reasoning_budget_state initial_state); static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl); @@ -205,12 +229,12 @@ static struct llama_sampler * common_reasoning_budget_clone(const struct llama_s } static struct llama_sampler * common_reasoning_budget_init_state( - const struct llama_vocab * vocab, - const std::vector & start_tokens, - const std::vector & end_tokens, - const std::vector & forced_tokens, - int32_t budget, - common_reasoning_budget_state initial_state) { + const struct llama_vocab * vocab, + const std::vector & start_seqs, + const std::vector & end_seqs, + const llama_tokens & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state) { // promote COUNTING with budget <= 0 to FORCING if (initial_state == REASONING_BUDGET_COUNTING && budget <= 0) { initial_state = REASONING_BUDGET_FORCING; @@ -220,25 +244,26 @@ static struct llama_sampler * common_reasoning_budget_init_state( /* .iface = */ &common_reasoning_budget_i, /* .ctx = */ new common_reasoning_budget_ctx { /* .vocab = */ vocab, - /* .start_matcher = */ { start_tokens, 0 }, - /* .end_matcher = */ { end_tokens, 0 }, + /* .start_matcher = */ token_matcher(start_seqs), + /* .end_matcher = */ token_matcher(end_seqs), /* .forced_tokens = */ forced_tokens, /* .budget = */ budget, /* .remaining = */ budget, /* .state = */ initial_state, /* .force_pos = */ 0, + /* .end_match = */ -1, } ); } struct llama_sampler * common_reasoning_budget_init( - const struct llama_vocab * vocab, - const std::vector & start_tokens, - const std::vector & end_tokens, - const std::vector & forced_tokens, - int32_t budget, - common_reasoning_budget_state initial_state) { - return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state); + const struct llama_vocab * vocab, + const std::vector & start_seqs, + const std::vector & end_seqs, + const llama_tokens & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state) { + return common_reasoning_budget_init_state(vocab, start_seqs, end_seqs, forced_tokens, budget, initial_state); } common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl) { @@ -248,6 +273,19 @@ common_reasoning_budget_state common_reasoning_budget_get_state(const struct lla return ((const common_reasoning_budget_ctx *)smpl->ctx)->state; } +const llama_tokens * common_reasoning_budget_get_end_match(const struct llama_sampler * smpl) { + if (!smpl) { + return nullptr; + } + + const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx; + if (ctx->end_match < 0) { + return nullptr; + } + + return &ctx->end_matcher.seqs[ctx->end_match]; +} + bool common_reasoning_budget_force(struct llama_sampler * smpl) { if (!smpl) { return false; diff --git a/common/reasoning-budget.h b/common/reasoning-budget.h index 0cf689a56637..1b89a04c42e8 100644 --- a/common/reasoning-budget.h +++ b/common/reasoning-budget.h @@ -2,6 +2,8 @@ #include "llama.h" +#include "common.h" + #include #include @@ -17,30 +19,34 @@ enum common_reasoning_budget_state { // reasoning block (e.g. between and ). // // State machine: IDLE -> COUNTING -> WAITING_UTF8 -> FORCING -> DONE -// IDLE: passthrough, watching for start_tokens sequence -// COUNTING: counting down remaining tokens, watching for natural end_tokens +// IDLE: passthrough, watching for a start sequence +// COUNTING: counting down remaining tokens, watching for a natural end sequence // WAITING_UTF8: budget exhausted, allowing tokens to complete a UTF-8 sequence // FORCING: forces forced_tokens token-by-token (all other logits -> -inf) // DONE: passthrough forever // // Parameters: // vocab - vocabulary (used for UTF-8 boundary detection; can be nullptr) -// start_tokens - token sequence that activates counting -// end_tokens - token sequence for natural deactivation +// start_seqs - token sequences, any of which activates counting +// end_seqs - token sequences, any of which naturally deactivates // forced_tokens - token sequence forced when budget expires // budget - max tokens allowed in the reasoning block // initial_state - initial state // struct llama_sampler * common_reasoning_budget_init( - const struct llama_vocab * vocab, - const std::vector & start_tokens, - const std::vector & end_tokens, - const std::vector & forced_tokens, - int32_t budget, - common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE); + const struct llama_vocab * vocab, + const std::vector & start_seqs, + const std::vector & end_seqs, + const llama_tokens & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE); common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl); +// The end sequence that transitioned the sampler to DONE, or nullptr if none +// was recorded. Cleared when a new start sequence re-arms the sampler. +const llama_tokens * common_reasoning_budget_get_end_match(const struct llama_sampler * smpl); + // Manually transition the reasoning budget sampler into the FORCING state. // Returns true if the transition occurred. bool common_reasoning_budget_force(struct llama_sampler * smpl); diff --git a/common/sampling.cpp b/common/sampling.cpp index 75a299e23ece..7b241e34f77f 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -299,7 +299,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty() && (params.grammar_lazy || params.reasoning_budget_tokens >= 0 || params.reasoning_control)) { rbudget = common_reasoning_budget_init( vocab, - params.reasoning_budget_start, + {params.reasoning_budget_start}, params.reasoning_budget_end, params.reasoning_budget_forced, params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens); @@ -453,6 +453,17 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo if (gsmpl->rbudget && is_generated) { llama_sampler_accept(gsmpl->rbudget, token); + + // if done, replay end sequence which may contain a grammar trigger + const bool is_done = common_reasoning_budget_get_state(gsmpl->rbudget) == REASONING_BUDGET_DONE; + if (gsmpl->grmr && !accept_grammar && is_done) { + const llama_tokens * end_seq = common_reasoning_budget_get_end_match(gsmpl->rbudget); + if (end_seq) { + for (const llama_token end_token : *end_seq) { + llama_sampler_accept(gsmpl->grmr, end_token); + } + } + } } if (gsmpl->grmr && accept_grammar) { diff --git a/common/speculative.cpp b/common/speculative.cpp index 580728a2001e..ee94d7c37662 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -260,7 +260,10 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl { bool process(const llama_batch & batch) override { auto * ctx_dft = params.ctx_dft; - const int ret = llama_decode(ctx_dft, batch); + llama_batch batch_dft = batch; + batch_dft.logits = nullptr; + + const int ret = llama_decode(ctx_dft, batch_dft); if (ret != 0) { SPC_ERR("failed to decode draft batch, ret = %d\n", ret); @@ -2281,7 +2284,7 @@ common_speculative_init_result::common_speculative_init_result( std::string model_path; if (has_draft) { model_path = params.speculative.draft.mparams.path; - LOG_TRC("%s: loading draft model '%s'\n", __func__, model_path.c_str()); + LOG_INF("%s: loading draft model '%s'\n", __func__, model_path.c_str()); llama_model * model_dft = llama_model_load_from_file(params.model.path.c_str(), mparams); if (model_dft == NULL) { @@ -2301,7 +2304,7 @@ common_speculative_init_result::common_speculative_init_result( } else if (spec_mtp) { model_path = params.model.path; - LOG_TRC("%s: creating MTP draft context against the target model '%s'\n", __func__, model_path.c_str()); + LOG_INF("%s: creating MTP draft context against the target model '%s'\n", __func__, model_path.c_str()); llama_context * ctx_dft = llama_init_from_model(model_tgt, cparams); if (ctx_dft == nullptr) { diff --git a/common/subproc.cpp b/common/subproc.cpp new file mode 100644 index 000000000000..6d37f59002b6 --- /dev/null +++ b/common/subproc.cpp @@ -0,0 +1,143 @@ +#include "subproc.h" + +bool common_subproc::is_supported() { +#ifdef LLAMA_SUBPROCESS + return true; +#else + return false; +#endif +} + +#ifdef LLAMA_SUBPROCESS + +static std::vector to_cstr_vec(const std::vector & v) { + std::vector r; + r.reserve(v.size() + 1); + for (const auto & s : v) { + r.push_back(const_cast(s.c_str())); + } + r.push_back(nullptr); + return r; +} + +common_subproc::~common_subproc() { + if (is_created) { + subprocess_destroy(&proc); + is_created = false; + } +} + +bool common_subproc::create( + const std::vector & args, + int options, + const std::vector & env, + const char * cwd) { + auto argv = to_cstr_vec(args); + + int result; + if (env.empty() && cwd == nullptr) { + result = subprocess_create(argv.data(), options, &proc); + } else { + auto envp = to_cstr_vec(env); + result = subprocess_create_ex(argv.data(), options, env.empty() ? nullptr : envp.data(), cwd, &proc); + } + + is_created = result == 0; + return is_created; +} + +bool common_subproc::has_handle() const { + if (!is_created) { + return false; + } +#if defined(_WIN32) + return proc.hProcess != nullptr; +#else + return proc.child > 0; +#endif +} + +bool common_subproc::alive() { + return is_created && subprocess_alive(&proc); +} + +FILE * common_subproc::stdin_file() { + return is_created ? subprocess_stdin(&proc) : nullptr; +} + +FILE * common_subproc::stdout_file() { + return is_created ? subprocess_stdout(&proc) : nullptr; +} + +FILE * common_subproc::stderr_file() { + return is_created ? subprocess_stderr(&proc) : nullptr; +} + +void common_subproc::close_stdin() { + if (is_created && proc.stdin_file) { + fclose(proc.stdin_file); + proc.stdin_file = nullptr; + } +} + +void common_subproc::terminate() { + if (has_handle()) { + subprocess_terminate(&proc); + } +} + +int common_subproc::join() { + int exit_code = -1; + if (is_created) { + subprocess_join(&proc, &exit_code); + subprocess_destroy(&proc); + is_created = false; + } + return exit_code; +} + +#else // !LLAMA_SUBPROCESS + +common_subproc::~common_subproc() = default; + +bool common_subproc::create( + const std::vector &, + int, + const std::vector &, + const char *) { + (void)(proc); + (void)(is_created); + return false; +} + +bool common_subproc::has_handle() const { + return false; +} + +bool common_subproc::alive() { + return false; +} + +FILE * common_subproc::stdin_file() { + return nullptr; +} + +FILE * common_subproc::stdout_file() { + return nullptr; +} + +FILE * common_subproc::stderr_file() { + return nullptr; +} + +void common_subproc::close_stdin() { +} + +void common_subproc::terminate() { +} + +int common_subproc::join() { + return -1; +} + +#endif // LLAMA_SUBPROCESS diff --git a/common/subproc.h b/common/subproc.h new file mode 100644 index 000000000000..89b69ee262fb --- /dev/null +++ b/common/subproc.h @@ -0,0 +1,59 @@ +#pragma once + +#include +#include +#include +#include + +#ifdef LLAMA_SUBPROCESS +#include +#else +// dummy values to allow compilation when subprocess is disabled +struct subprocess_s {}; +static constexpr int subprocess_option_no_window = 0; +static constexpr int subprocess_option_combined_stdout_stderr = 0; +static constexpr int subprocess_option_inherit_environment = 0; +static constexpr int subprocess_option_search_user_path = 0; +#endif + +// RAII-style wrapper around https://github.com/sheredom/subprocess.h, +// exposing method calls instead of free functions operating on subprocess_s. +struct common_subproc { + common_subproc() = default; + ~common_subproc(); + + common_subproc(const common_subproc &) = delete; + common_subproc & operator=(const common_subproc &) = delete; + + // spawn a child process; if env is non-empty it replaces the child's environment + // (do not combine with subprocess_option_inherit_environment) + bool create( + const std::vector & args, + int options, + const std::vector & env = {}, + const char * cwd = nullptr); + + bool alive(); + + // true if LLAMA_SUBPROCESS was enabled at build time; when false, create() always fails + static bool is_supported(); + + FILE * stdin_file(); + FILE * stdout_file(); + FILE * stderr_file(); + + // close stdin and detach it from the process, so a later join()/destroy() won't double-close it; + // use this after writing all input to signal EOF to the child while it's still running + void close_stdin(); + + void terminate(); + + // wait for the process to exit, release the underlying handle and return its exit code + int join(); + +private: + subprocess_s proc {}; + std::atomic is_created{false}; + + bool has_handle() const; +}; diff --git a/common/trie.cpp b/common/trie.cpp new file mode 100644 index 000000000000..b5c9666ba2ee --- /dev/null +++ b/common/trie.cpp @@ -0,0 +1,123 @@ +#include "trie.h" + +#include "unicode.h" + +#include + +common_trie::match_result common_trie::check_at(std::string_view sv, size_t start_pos) const { + size_t current = 0; // Start at root + size_t pos = start_pos; + + // LOG_DBG("%s: checking at pos %zu, sv='%s'\n", __func__, start_pos, std::string(sv).c_str()); + + while (pos < sv.size()) { + auto result = common_parse_utf8_codepoint(sv, pos); + if (result.status != utf8_parse_result::SUCCESS) { + break; + } + + auto it = nodes[current].children.find(result.codepoint); + if (it == nodes[current].children.end()) { + // Can't continue matching + return match_result{match_result::NO_MATCH}; + } + + current = it->second; + pos += result.bytes_consumed; + + // Check if we've matched a complete word + if (nodes[current].pattern >= 0) { + return match_result{match_result::COMPLETE_MATCH}; + } + } + + // Reached end of input while still in the trie (not at root) + if (current != 0) { + // We're in the middle of a potential match + return match_result{match_result::PARTIAL_MATCH}; + } + + // Reached end at root (no match) + return match_result{match_result::NO_MATCH}; +} + +int32_t common_trie::insert(const std::string & word) { + std::vector symbols; + size_t pos = 0; + while (pos < word.length()) { + auto result = common_parse_utf8_codepoint(word, pos); + if (result.status != utf8_parse_result::SUCCESS) { + break; + } + + symbols.push_back(result.codepoint); + pos += result.bytes_consumed; + } + return insert(symbols); +} + +int32_t common_trie::insert(const std::vector & symbols) { + size_t current = 0; + for (uint32_t ch : symbols) { + auto it = nodes[current].children.find(ch); + if (it == nodes[current].children.end()) { + size_t child = create_node(); + nodes[current].children[ch] = child; + current = child; + } else { + current = it->second; + } + } + if (nodes[current].pattern < 0) { + nodes[current].pattern = n_patterns++; + } + return nodes[current].pattern; +} + +common_aho_corasick::common_aho_corasick(common_trie trie) : t(std::move(trie)) { + const auto & nodes = t.nodes; + const size_t n = nodes.size(); + + fail.assign(n, 0); + order.reserve(n); + + std::deque queue{ 0 }; + while (!queue.empty()) { + size_t u = queue.front(); + queue.pop_front(); + order.push_back(u); + for (const auto & [ch, v] : nodes[u].children) { + if (u != 0) { + size_t f = fail[u]; + while (f && nodes[f].children.find(ch) == nodes[f].children.end()) { + f = fail[f]; + } + auto it = nodes[f].children.find(ch); + fail[v] = (it != nodes[f].children.end() && it->second != v) ? it->second : 0; + } + queue.push_back(v); + } + } + + // fail[u] points to a strictly shorter suffix, so the first pattern found on + // the fail chain (including u itself) is the longest pattern ending at u + match.assign(n, -1); + for (size_t u : order) { + match[u] = nodes[u].pattern >= 0 ? nodes[u].pattern : (u != 0 ? match[fail[u]] : -1); + } + + for (const auto & node : nodes) { + for (const auto & [ch, v] : node.children) { + alphabet.insert(ch); + } + } +} + +size_t common_aho_corasick::next(size_t state, uint32_t ch) const { + const auto & nodes = t.nodes; + while (state && nodes[state].children.find(ch) == nodes[state].children.end()) { + state = fail[state]; + } + auto it = nodes[state].children.find(ch); + return it != nodes[state].children.end() ? it->second : 0; +} diff --git a/common/trie.h b/common/trie.h new file mode 100644 index 000000000000..0f7b16a36ad2 --- /dev/null +++ b/common/trie.h @@ -0,0 +1,73 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +// Trie for matching multiple literals. +// This is used in common_peg_until_parser and to build a GBNF exclusion grammar +struct common_trie { + struct node { + std::map children; // Use uint32_t to store Unicode codepoints + int32_t pattern = -1; // index of the pattern ending at this node, -1 if none + }; + + std::vector nodes; + + common_trie() { + create_node(); // root node + } + + common_trie(const std::vector & words) : common_trie() { + for (const auto & w : words) { + insert(w); + } + } + + enum match_result { NO_MATCH, PARTIAL_MATCH, COMPLETE_MATCH }; + + // Check if a delimiter starts at the given position + match_result check_at(std::string_view sv, size_t start_pos) const; + + // Insert a word as a sequence of Unicode codepoints, returns its pattern index + int32_t insert(const std::string & word); + + // Insert a raw symbol sequence, returns its pattern index (insertion order, + // duplicates keep the first index) + int32_t insert(const std::vector & symbols); + + private: + int32_t n_patterns = 0; + + size_t create_node() { + size_t index = nodes.size(); + nodes.emplace_back(); + return index; + } +}; + +// Aho-Corasick automaton +struct common_aho_corasick { + common_trie t; + std::vector fail; // failure links + std::vector order; // states in BFS order + std::vector match; // longest pattern ending at each state (directly or via a suffix link), -1 if none + std::set alphabet; // every character with a transition + + common_aho_corasick(common_trie trie); + + common_aho_corasick(const std::vector & strings) + : common_aho_corasick(common_trie(strings)) {} + + size_t num_states() const { return t.nodes.size(); } + bool is_terminal(size_t s) const { return match[s] >= 0; } + + // index of the longest pattern ending at this state, -1 if none + int32_t match_pattern(size_t s) const { return match[s]; } + + // follow failure links until a transition on `ch` exists. + size_t next(size_t state, uint32_t ch) const; +}; diff --git a/conversion/__init__.py b/conversion/__init__.py index 02102fac8d90..8b05eb472be1 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -18,6 +18,7 @@ TEXT_MODEL_MAP: dict[str, str] = { "AfmoeForCausalLM": "afmoe", + "LagunaForCausalLM": "laguna", "ApertusForCausalLM": "llama", "ArceeForCausalLM": "llama", "ArcticForCausalLM": "arctic", @@ -31,6 +32,7 @@ "BertForSequenceClassification": "bert", "BertModel": "bert", "BitnetForCausalLM": "bitnet", + "BitNetForCausalLM": "bitnet", "BloomForCausalLM": "bloom", "BloomModel": "bloom", "CamembertModel": "bert", @@ -119,6 +121,7 @@ "JinaEmbeddingsV5Model": "bert", "KORMoForCausalLM": "qwen", "KimiK25ForConditionalGeneration": "deepseek", + "KimiK3ForConditionalGeneration": "kimi_k3", "KimiLinearForCausalLM": "kimi_linear", "KimiLinearModel": "kimi_linear", "KimiVLForConditionalGeneration": "deepseek", @@ -156,6 +159,8 @@ "MiniCPMForCausalLM": "minicpm", "MiniCPMV4_6ForConditionalGeneration": "minicpm", "MiniMaxM2ForCausalLM": "minimax", + "MiniMaxM3SparseForCausalLM": "minimax", + "MiniMaxM3SparseForConditionalGeneration": "minimax", "Ministral3ForCausalLM": "mistral3", "Mistral3ForConditionalGeneration": "mistral3", "MistralForCausalLM": "llama", @@ -163,6 +168,7 @@ "ModernBertForMaskedLM": "bert", "ModernBertForSequenceClassification": "bert", "ModernBertModel": "bert", + "NanbeigeForCausalLM": "nanbeige", "NemotronForCausalLM": "nemotron", "NemotronHForCausalLM": "nemotron", "NeoBERT": "bert", @@ -265,6 +271,7 @@ "Gemma4UnifiedForConditionalGeneration": "gemma", "Glm4vForConditionalGeneration": "qwen3vl", "Glm4vMoeForConditionalGeneration": "qwen3vl", + "Glm5vForConditionalGeneration": "kimivl", "GlmOcrForConditionalGeneration": "qwen3vl", "GlmasrModel": "ultravox", "Granite4VisionForConditionalGeneration": "granite", @@ -275,6 +282,7 @@ "InternVisionModel": "internvl", "JanusForConditionalGeneration": "januspro", "KimiK25ForConditionalGeneration": "kimivl", + "KimiK3ForConditionalGeneration": "kimivl", "KimiVLForConditionalGeneration": "kimivl", "Lfm2AudioForConditionalGeneration": "lfm2", "Lfm2VlForConditionalGeneration": "lfm2", @@ -283,6 +291,7 @@ "LlavaForConditionalGeneration": "llava", "MERaLiON2ForConditionalGeneration": "ultravox", "MiMoV2ForCausalLM": "mimo", + "MiniMaxM3SparseForConditionalGeneration": "minimax", "MiniCPMV4_6ForConditionalGeneration": "minicpm", "Mistral3ForConditionalGeneration": "llava", "NemotronH_Nano_VL_V2": "nemotron", diff --git a/conversion/base.py b/conversion/base.py index 1b85ef0a1b89..4c97317fc818 100644 --- a/conversion/base.py +++ b/conversion/base.py @@ -386,6 +386,29 @@ def dequant_gptq(g_idx: Tensor, qweight: Tensor, qzeros: Tensor, scales: Tensor) return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T + def dequant_mxfp4_packed(w: Tensor, scale: Tensor, group_size: int) -> Tensor: + # compressed-tensors "mxfp4-pack-quantized": + # w: uint8 [..., K/2], two FP4 (E2M1) values per byte, low nibble = even index + # scale: uint8 [..., K/group_size], E8M0 exponent, scale = 2^(x - 127) + assert w.dtype == torch.uint8 + assert scale.dtype == torch.uint8 + + kvalues = torch.tensor( + [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, -0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0], + dtype=torch.float32, + ) + if self.lazy: + kvalues = LazyTorchTensor.from_eager(kvalues) + + lo = (w & 0x0F).to(torch.long) + hi = (w >> 4).to(torch.long) + # interleave: [..., K/2, 2] -> [..., K] + vals = torch.stack((kvalues[lo], kvalues[hi]), dim=-1).reshape(*w.shape[:-1], w.shape[-1] * 2) + + exp = torch.ldexp(torch.ones_like(scale, dtype=torch.float32), scale.to(torch.int32) - 127) + vals = vals.reshape(*vals.shape[:-1], -1, group_size) * exp.unsqueeze(-1) + return vals.reshape(*w.shape[:-1], w.shape[-1] * 2) + def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int): assert w.dtype == torch.int32 shape = tuple(shape_tensor.tolist()) @@ -531,6 +554,23 @@ def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: T tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")] if (base_name + "_zero_point") in self.model_tensors: tensors_to_remove.append(base_name + "_zero_point") + elif quant_format == "mxfp4-pack-quantized": + assert weight_config.get("strategy") == "group" + assert weight_config.get("type") == "float" + assert weight_config.get("num_bits") == 4 + group_size = weight_config.get("group_size") + assert isinstance(group_size, int) + for name in self.model_tensors.keys(): + if name.endswith(".weight_packed"): + base_name = name.removesuffix("_packed") + w = self.model_tensors[name] + scale = self.model_tensors[base_name + "_scale"] + new_tensors[base_name] = ( + lambda w=w, scale=scale: dequant_mxfp4_packed(w(), scale(), group_size) + ) + tensors_to_remove += [base_name + n for n in ("_packed", "_scale")] + if (base_name + "_shape") in self.model_tensors: + tensors_to_remove.append(base_name + "_shape") elif nvfp4_compressed_tensors: # Don't error from compressed-tensors, we'll handle them in _generate_nvfp4_tensors pass @@ -1156,7 +1196,7 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca or "projector." in name or "pre_mm_projector_norm" in name \ or "image_newline" in name or "view_seperator" in name \ or "patch_embed" in name or "patch_embedding" in name \ - or "patch_merger." in name or "model.connector." in name: + or "patch_merger." in name or "patch_merge_mlp." in name or "model.connector." in name: return None return super().filter_tensors(item) @@ -1203,7 +1243,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_embedding_length(n_embd) logger.info(f"gguf: embedding length = {n_embd}") - if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: + if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) logger.info(f"gguf: feed forward length = {n_ff}") @@ -1682,6 +1722,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "9dcf830ee9990cdbf78cc523a5f7bd9ad8f3f9890c2d3581d2785ad10f07049d": # ref: https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base res = "mellum2" + if chkhsh == "972da7b59cec44d1f0a490a86c96df53859e486e481563e5dddac155013d87ac": + # ref: https://huggingface.co/poolside/Laguna-XS.2 + res = "laguna" if res is None: logger.warning("\n") @@ -2630,7 +2673,7 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st # Step3-VL keeps text config under text_config but uses a custom top-level architecture. # For text conversion we route to a dedicated text-only class. # TODO: refactor this later to avoid adding exception here - if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration"): + if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration"): return arch # if "architectures" is found in the sub-config, use that instead diff --git a/conversion/bert.py b/conversion/bert.py index 49a6948f6ce5..0d25d0d62df5 100644 --- a/conversion/bert.py +++ b/conversion/bert.py @@ -369,12 +369,13 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca return super().filter_tensors(item) def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]: - n_experts = self.find_hparam(["num_local_experts", "num_experts"]) if "mlp.experts.mlp.w1" in name: + n_experts = self.find_hparam(["num_local_experts", "num_experts"]) data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"]) name += ".weight" if "mlp.experts.mlp.w2" in name: + n_experts = self.find_hparam(["num_local_experts", "num_experts"]) data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"]) data_torch = data_torch.transpose(1, 2) name += ".weight" diff --git a/conversion/bitnet.py b/conversion/bitnet.py index a66446abee2f..0c2baee87608 100644 --- a/conversion/bitnet.py +++ b/conversion/bitnet.py @@ -8,7 +8,7 @@ from .base import ModelBase, TextModel, gguf -@ModelBase.register("BitnetForCausalLM") +@ModelBase.register("BitnetForCausalLM", "BitNetForCausalLM") class BitnetModel(TextModel): model_arch = gguf.MODEL_ARCH.BITNET diff --git a/conversion/glm.py b/conversion/glm.py index 895cefc22b89..d85268a62149 100644 --- a/conversion/glm.py +++ b/conversion/glm.py @@ -237,6 +237,9 @@ def set_gguf_parameters(self): self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"]) self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"]) self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"]) + if (indexer_types := self.hparams.get("indexer_types")) is not None: + indexer_types = [t == "full" for t in indexer_types] + self.gguf_writer.add_indexer_types(indexer_types) @ModelBase.register("SolarOpenForCausalLM") diff --git a/conversion/hunyuan.py b/conversion/hunyuan.py index 65d294fbe978..f5ac8a4fb7f1 100644 --- a/conversion/hunyuan.py +++ b/conversion/hunyuan.py @@ -338,6 +338,12 @@ class HunyuanVLTextModel(HunYuanModel): def __init__(self, dir_model: Path, *args, **kwargs): super().__init__(dir_model, *args, **kwargs) + # transformers 5.13.0 encodes HunyuanVL XD-RoPE as dynamic + mrope_section. + # Normalize it to avoid the HunYuan dynamic-RoPE context assertion. + if self.rope_parameters.get("rope_type") == "dynamic" and "mrope_section" in self.rope_parameters: + self.rope_parameters["rope_type"] = "xdrope" + self.rope_parameters["type"] = "xdrope" + self.rope_parameters["xdrope_section"] = list(self.rope_parameters["mrope_section"]) def set_gguf_parameters(self): super().set_gguf_parameters() diff --git a/conversion/kimi_k3.py b/conversion/kimi_k3.py new file mode 100644 index 000000000000..97c6160db79e --- /dev/null +++ b/conversion/kimi_k3.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Iterable, TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from torch import Tensor + +from .base import ModelBase, gguf, logger +from .kimi_linear import KimiLinearModel + + +@ModelBase.register("KimiK3ForConditionalGeneration") +class KimiK3Model(KimiLinearModel): + """Kimi K3: hybrid KDA + gated-MLA (NoPE) with Attention Residuals and Stable LatentMoE. + + Text config is `kimi_linear` with K3 extensions: + - SiTU-GLU activation (soft-capped SiLU) in dense MLP, shared and routed experts + - AttnRes: residual-stream snapshot bank every `attn_res_block_size` layers, + softmax mixtures before attention, before MLP and at model output + - Stable LatentMoE: routed experts run in a `routed_expert_hidden_size` latent + space (down proj -> experts -> weighted sum -> RMSNorm -> up proj) + - KDA safe gate: g_log = gate_lower_bound * sigmoid(exp(A_log) * (g_raw + dt_bias)) + with a full-rank output gate g_proj instead of the low-rank g_a/g_b pair + - MLA output gate: attn = attn * sigmoid(g_proj(x)) before o_proj + Routed expert weights are MXFP4 (compressed-tensors), dequantized in ModelBase. + """ + model_arch = gguf.MODEL_ARCH.KIMI_K3 + + def set_vocab(self): + super().set_vocab() + # KimiLinearModel.set_vocab forces the tokenizer's own eos, which for K3 + # is 163585 = [EOS], the document terminator. The config says 163586 = + # <|end_of_msg|>, the chat turn terminator; keeping [EOS] means chat + # generation never stops at the end of an assistant turn. Restore it. + if (eos := self.hparams.get("eos_token_id")) is not None: + self.gguf_writer.add_eos_token_id(eos) + + # Moonshot ships no chat_template in tokenizer_config.json (K3 is + # API-first), so GGUFs come out template-less and chat tools refuse to + # run. Embed the reference template from models/templates/Kimi-K3.jinja + # unless the checkpoint provides one. + has_template = (self.dir_model / "chat_template.jinja").is_file() + if not has_template: + try: + with open(self.dir_model / "tokenizer_config.json", encoding="utf-8") as f: + has_template = "chat_template" in json.load(f) + except OSError: + pass + if not has_template: + tmpl = Path(__file__).resolve().parent.parent / "models" / "templates" / "Kimi-K3.jinja" + if tmpl.is_file(): + logger.info("embedding reference chat template from models/templates/Kimi-K3.jinja") + self.gguf_writer.add_chat_template(tmpl.read_text(encoding="utf-8")) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + # Stable LatentMoE + self.gguf_writer.add_moe_latent_size(self.hparams["routed_expert_hidden_size"]) + + # SiTU-GLU activation parameters + self.gguf_writer.add_situ_beta(self.hparams["activation_situ_beta"]) + self.gguf_writer.add_situ_linear_beta(self.hparams["activation_situ_linear_beta"]) + + # Attention residuals + self.gguf_writer.add_attn_res_block_size(self.hparams["attn_res_block_size"]) + + # KDA safe gate lower bound + self.gguf_writer.add_kda_gate_lower_bound(self.hparams["linear_attn_config"]["gate_lower_bound"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # text-only conversion: vision tensors are handled by the mmproj path + if name.startswith(("vision_tower.", "mm_projector.")): + return + + name = name.removeprefix("language_model.") + + # K3 checkpoints store A_log as [head_dim] (128) but only the first + # num_heads (96) entries are used. The safe-gate formula is + # g_log = gate_lower_bound * sigmoid(exp(A_log) * (g_raw + dt_bias)) + # so we store exp(A_log) directly (unlike Kimi-Linear's -exp(A_log)). + if name.endswith(".A_log"): + n_head = self.hparams["num_attention_heads"] + data_torch = torch.exp(data_torch.float()[:n_head]) + # skip KimiLinearModel's -exp(A_log) handling + yield from super(KimiLinearModel, self).modify_tensors(data_torch, name, bid) + return + + # res projections are stored as [1, n_embd]: flatten to [n_embd] + if name.endswith(("_res_proj.weight", "_res_norm.weight")): + data_torch = data_torch.reshape(-1) + + yield from super().modify_tensors(data_torch, name, bid) diff --git a/conversion/kimivl.py b/conversion/kimivl.py index 63b8a079b722..a5e278f1c090 100644 --- a/conversion/kimivl.py +++ b/conversion/kimivl.py @@ -152,3 +152,95 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter name = name.replace(".proj.2.", ".proj.linear_2.") yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Glm5vForConditionalGeneration") +class Glm5vModel(KimiK25Model): + """GLM-5.2-Vision MoonViT3d encoder and projector + + Uses the same vision encoder and projector as Kimi-K2.5, so it reuses the + kimik25 projector type. The image begin/end tokens differ, but they are + resolved at runtime from the text model vocab. + """ + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith("mm_projector.linear_"): + name = name.replace("mm_projector.linear_", "mm_projector.proj.linear_", 1) + + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("KimiK3ForConditionalGeneration") +class KimiK3VisionModel(MmprojModel): + """Kimi-K3 MoonViT-3d vision tower (image path). + + Structurally the Kimi-K2.5 tower with RMSNorm, no biases, a non-square fused QKV + (qkv_hidden_size 1536 vs vt_hidden_size 1024) and a post-norm patchmergerv2 projector. + Video is out of scope: for t == 1 the temporal pool and temporal position term vanish. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams_vision is not None, "Kimi-K3 requires vision_config in config.json" + self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2])) + self.patch_size = self.hparams_vision.get("patch_size", 14) + pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64) + self.hparams_vision["image_size"] = pos_emb_h * self.patch_size + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_vision is not None + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK3) + + # qkv width != n_embd, so the runtime cannot derive d_head + n_head = self.hparams_vision["vt_num_attention_heads"] + qkv_hidden = self.hparams_vision.get("qkv_hidden_size") or self.hparams_vision["vt_hidden_size"] + assert qkv_hidden % n_head == 0, f"qkv_hidden_size {qkv_hidden} not divisible by {n_head} heads" + self.gguf_writer.add_vision_head_dim(qkv_hidden // n_head) + + self.gguf_writer.add_vision_use_gelu(True) # activation_func is gelu_pytorch_tanh + self.gguf_writer.add_vision_attention_layernorm_eps( + self.hparams_vision.get("projector_ln_eps", 1e-5)) + self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0]) + + in_patch_limit = self.preprocessor_config.get("media_proc_cfg", {}).get( + "in_patch_limit", self.preprocessor_config.get("in_patch_limit", 16384)) + pixels_per_patch = self.patch_size ** 2 + self.gguf_writer.add_vision_min_pixels(8 * pixels_per_patch) + self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + name, _ = item + if not name.startswith(("vision_tower.", "mm_projector.")): + return None + return super().filter_tensors(item) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + assert self.hparams_vision is not None + n_head = self.hparams_vision["vt_num_attention_heads"] + + if "wqkv" in name and "weight" in name: + # de-interleave Q/K so the runtime can use build_rope_2d(interleave_freq=false) + out_dim = data_torch.shape[0] + qkv_dim = out_dim // 3 + head_dim = qkv_dim // n_head + wq, wk, wv = (data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]) + + def deinterleave(w: Tensor) -> Tensor: + return (w.reshape(n_head, head_dim // 4, 2, 2, w.shape[-1]) + .permute(0, 2, 1, 3, 4) + .reshape(w.shape[0], w.shape[-1])) + + data_torch = torch.cat([deinterleave(wq), deinterleave(wk), wv], dim=0) + + if "pos_emb.weight" in name: + # kept 3D: the runtime reads grid extents from ne[1]/ne[2] + pass + + if "mm_projector.proj.0." in name: + name = name.replace(".proj.0.", ".proj.linear_1.") + elif "mm_projector.proj.2." in name: + name = name.replace(".proj.2.", ".proj.linear_2.") + + yield from super().modify_tensors(data_torch, name, bid) diff --git a/conversion/laguna.py b/conversion/laguna.py new file mode 100644 index 000000000000..a90f355ca9b1 --- /dev/null +++ b/conversion/laguna.py @@ -0,0 +1,207 @@ +from __future__ import annotations + +import re +from collections.abc import Iterable +from typing import TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from torch import Tensor + +from .base import ModelBase, TextModel, gguf, logger + + +@ModelBase.register("LagunaForCausalLM") +class LagunaModel(TextModel): + model_arch = gguf.MODEL_ARCH.LAGUNA + _experts: list[dict] | None = None + _gate_types: list[str] | None = None + + # --- vocab --------------------------------------------------------------- + + def set_vocab(self) -> None: + self._set_vocab_gpt2() + + # Some Laguna releases wrap the chat template in tokenizer_config.json as + # "{% include 'chat_template.jinja' %}", which SpecialVocab embeds verbatim + # and llama.cpp's jinja engine cannot process. Prefer the resolved template + # from the chat_template.jinja file so the GGUF is self-contained. + tmpl_file = self.dir_model / "chat_template.jinja" + if tmpl_file.is_file(): + self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8")) + logger.info("gguf: embedded resolved chat_template.jinja (overriding include directive)") + + # eos_token_id is a list [2, 24]: token 2 (EOS, also BOS) and token 24 + # (, the turn-end). _set_vocab_gpt2 only records the scalar + # eos, so register the extra id as eot; llama.cpp folds eot into its EOG + # set, so the model halts on natively. + eos_ids = self.hparams.get("eos_token_id") + if isinstance(eos_ids, list): + bos_id = self.hparams.get("bos_token_id") + extra = [e for e in eos_ids if e != bos_id] + if extra: + self.gguf_writer.add_eot_token_id(extra[0]) + logger.info(f"gguf: registered eot_token_id={extra[0]} from eos list {eos_ids}") + + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + # is the assistant turn-end (registered as eot below). The + # HF tokenizer flags it special=false, so the base classifies it as + # USER_DEFINED and llama.cpp renders its text into generated content, + # leaking "" and breaking response parsing. It is a control + # marker, so promote it to CONTROL: llama.cpp then treats it as + # end-of-generation and suppresses its text. + tokens, toktypes, tokpre = super().get_vocab_base() + for i, tok in enumerate(tokens): + if tok == "": + toktypes[i] = gguf.TokenType.CONTROL + logger.info(f"gguf: marked (id {i}) as CONTROL token") + return tokens, toktypes, tokpre + + # --- hparams ------------------------------------------------------------- + + def set_gguf_parameters(self) -> None: + super().set_gguf_parameters() + hparams = self.hparams + + # super() does not emit vocab_size for the gpt2 vocab path; head_count is + # overridden with a per-layer array (XS.2 varies heads per layer via + # num_attention_heads_per_layer; M.1 is uniform and omits it). + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + per_layer_heads = hparams.get("num_attention_heads_per_layer") + if not per_layer_heads: + per_layer_heads = [hparams["num_attention_heads"]] * hparams["num_hidden_layers"] + assert len(per_layer_heads) == hparams["num_hidden_layers"], ( + f"num_attention_heads_per_layer length {len(per_layer_heads)} != " + f"num_hidden_layers {hparams['num_hidden_layers']}" + ) + self.gguf_writer.add_head_count(per_layer_heads) + + # Resolve + validate the attention gate type now so an inconsistent + # `gating` field fails at conversion time. See _attn_gate_types. + self._attn_gate_types() + + # SWA window size (M.1 has none -> key omitted, swa_type stays NONE). + sliding_window = hparams.get("sliding_window") or 0 + if sliding_window > 0: + self.gguf_writer.add_sliding_window(sliding_window) + + # MoE (expert_count / expert_used_count come from super().set_gguf_parameters()) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_shared_feed_forward_length(hparams["shared_expert_intermediate_size"]) + self.gguf_writer.add_expert_weights_norm(True) # HF reference always sum-normalises after top-k + self.gguf_writer.add_expert_weights_scale(float(hparams["moe_routed_scaling_factor"])) + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + + # Leading dense layers (XS.2 has 1, M.1 has 3) before the MoE layers. + mlp_layer_types: list[str] = hparams["mlp_layer_types"] + leading_dense = 0 + for t in mlp_layer_types: + if t == "dense": + leading_dense += 1 + else: + break + self.gguf_writer.add_leading_dense_block_count(leading_dense) + + # Per-layer-type RoPE dimension count (partial rotary). base emits + # rope_freq_base(_swa) and the YaRN params from self.rope_parameters. + head_dim = hparams["head_dim"] + full_rope = self.rope_parameters["full_attention"] + self.gguf_writer.add_rope_dimension_count( + int(head_dim * float(full_rope.get("partial_rotary_factor", 1.0)))) + swa_rope = self.rope_parameters.get("sliding_attention") + if swa_rope is not None: + self.gguf_writer.add_rope_dimension_count_swa( + int(head_dim * float(swa_rope.get("partial_rotary_factor", 1.0)))) + + def _attn_gate_types(self) -> list[str]: + """Per-layer attention output gate type: "per_head" or "per_element". + + `gating_types` (per layer) is authoritative when present; otherwise the + scalar `gating` field is used (the "per-element"/"per-head" string, or + the legacy boolean True == per-head, as in Laguna-XS.2). + + Fails loudly when the model is per-element but the `gating` field does + not declare that as a string: runtimes that key off `gating` (vLLM, + transformers) ignore gating_types and read a bare boolean True as + per-head, silently corrupting the model. Surfacing it here keeps a + broken checkpoint from being packaged as if it were fine. + """ + if self._gate_types is not None: + return self._gate_types + hparams = self.hparams + n_layer = hparams["num_hidden_layers"] + gating = hparams.get("gating") + gating_types = hparams.get("gating_types") + + def _norm(t: object) -> str: + sval = str(t).replace("-", "_") + if sval in ("per_element", "per_head"): + return sval + raise ValueError(f"Laguna: unrecognised attention gate type {t!r}") + + if gating_types: + assert len(gating_types) == n_layer, ( + f"gating_types length {len(gating_types)} != num_hidden_layers {n_layer}") + types = [_norm(t) for t in gating_types] + elif isinstance(gating, str): + types = [_norm(gating)] * n_layer + elif gating is True: + types = ["per_head"] * n_layer + else: + raise ValueError( + f"Laguna: cannot determine attention gate type " + f"(gating={gating!r}, gating_types={gating_types!r})") + + if any(t == "per_element" for t in types) and not ( + isinstance(gating, str) and _norm(gating) == "per_element"): + raise ValueError( + f"Laguna config declares a per-element attention gate but " + f"`gating`={gating!r} is not the string \"per-element\". Runtimes that " + f"read `gating` (vLLM, transformers) will mis-handle this checkpoint as " + f"per-head. Set gating=\"per-element\" in the source config.") + + self._gate_types = types + return types + + # --- tensor handling ----------------------------------------------------- + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # Per-expert MoE weights: model.layers.{bid}.mlp.experts.{xid}.{w}.weight. + # Only the NUMBERED per-expert weights are stacked; the router bias + # (mlp.experts.e_score_correction_bias) takes the normal mapping path. + if re.search(r"mlp\.experts\.\d+\.", name): + n_experts = self.find_hparam(["num_local_experts", "num_experts"]) + assert bid is not None + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + self._experts[bid][name] = data_torch + needed = [f"model.layers.{bid}.mlp.experts.{x}.{w}.weight" + for x in range(n_experts) for w in ("gate_proj", "up_proj", "down_proj")] + if all(e in self._experts[bid] for e in needed): + for w_name in ["gate_proj", "up_proj", "down_proj"]: + datas = [self._experts[bid][f"model.layers.{bid}.mlp.experts.{x}.{w_name}.weight"] + for x in range(n_experts)] + stacked = torch.stack(datas, dim=0) + merged = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + yield from TextModel.modify_tensors(self, stacked, merged, bid) + self._experts[bid].clear() + return + return + # Cross-check the gate projection width against the declared gate type; + # a mismatch means the weights and config disagree -> fail, do not guess. + if bid is not None and name.endswith("self_attn.g_proj.weight"): + heads = (self.hparams.get("num_attention_heads_per_layer") + or [self.hparams["num_attention_heads"]] * self.hparams["num_hidden_layers"]) + n_head = heads[bid] + head_dim = self.hparams["head_dim"] + gate_type = self._attn_gate_types()[bid] + expected = n_head * head_dim if gate_type == "per_element" else n_head + out_features = int(data_torch.shape[0]) + if out_features != expected: + raise ValueError( + f"Laguna layer {bid}: g_proj output width {out_features} contradicts the " + f"declared {gate_type} gate (expected {expected}); weights and config disagree.") + + yield from TextModel.modify_tensors(self, data_torch, name, bid) diff --git a/conversion/minimax.py b/conversion/minimax.py index 4857775cbfb9..c2175cc93267 100644 --- a/conversion/minimax.py +++ b/conversion/minimax.py @@ -7,7 +7,7 @@ if TYPE_CHECKING: from torch import Tensor -from .base import ModelBase, TextModel, gguf +from .base import ModelBase, TextModel, MmprojModel, gguf @ModelBase.register("MiniMaxM2ForCausalLM") @@ -23,7 +23,7 @@ def set_gguf_parameters(self): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): # merge expert weights - if 'experts' in name: + if "block_sparse_moe.experts." in name: n_experts = self.find_hparam(["num_local_experts", "num_experts"]) assert bid is not None @@ -52,3 +52,118 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): return yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration") +class MiniMaxM3Model(MiniMaxM2Model): + model_arch = gguf.MODEL_ARCH.MINIMAXM3 + + def tensor_force_quant(self, name, new_name, bid, n_dims): + if ".indexer." in new_name: + return gguf.GGMLQuantizationType.F32 + return super().tensor_force_quant(name, new_name, bid, n_dims) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"])) + self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"])) + self.gguf_writer.add_expert_weights_norm(True) + + sac = self.find_hparam(["sparse_attention_config"]) + self.gguf_writer.add_indexer_head_count(sac["sparse_num_index_heads"]) + self.gguf_writer.add_indexer_key_length(sac["sparse_index_dim"]) + self.gguf_writer.add_indexer_top_k(sac["sparse_topk_blocks"]) + self.gguf_writer.add_indexer_block_size(sac["sparse_block_size"]) + self.gguf_writer.add_indexer_local_blocks(sac["sparse_local_block"]) + + moe_layer_freq = self.find_hparam(["moe_layer_freq"]) + n_dense = 0 + for v in moe_layer_freq: + if v == 0: + n_dense += 1 + else: + break + self.gguf_writer.add_leading_dense_block_count(n_dense) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + # Gemma-style (1 + w) RMSNorm: bake the +1 in so llama.cpp can use plain RMSNorm + if name.endswith("norm.weight"): + data_torch = data_torch + 1.0 + + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("MiniMaxM3SparseForConditionalGeneration", "MiniMaxM3VLForConditionalGeneration") +class MiniMaxM3VisionModel(MmprojModel): + @classmethod + def filter_tensors(cls, item): + name, gen = item + # keep only the vision-side tensors; text / mtp / sparse-index are dropped + if not name.startswith(("vision_tower.", "multi_modal_projector.", "patch_merge_mlp.")): + return None + return super().filter_tensors((name, gen)) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_vision is not None + + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINIMAXM3) + self.gguf_writer.add_vision_use_gelu(True) + + # the ViT carries its own LayerNorm eps (text tower uses a different one) + self.gguf_writer.add_vision_attention_layernorm_eps( + self.hparams_vision.get("layer_norm_eps", 1e-5) + ) + + comp = self.hparams_vision.get("img_token_compression_config", {}) + merge_size = comp.get("spatial_merge_size", 2) + self.gguf_writer.add_vision_spatial_merge_size(int(merge_size)) + + def modify_tensors(self, data_torch, name, bid): + assert self.hparams_vision is not None + + # Conv3d patch embed -> Conv2d slices + if name == "vision_tower.vision_model.embeddings.patch_embedding.weight": + if data_torch.ndim != 5: + raise ValueError(f"unexpected patch_embedding rank {data_torch.ndim} for {name}") + kt = data_torch.shape[2] + base = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + for t in range(kt): + suffix = ".weight" if t == 0 else f".weight.{t}" + yield (base + suffix, data_torch[:, :, t, ...]) + return + + # Permute ViT q/k. HF [Ta Ha Wa | Tb Hb Wb | pad] reorder to [Ta Tb | Ha Hb | Wa Wb | pad]. + for new_name, tensor in super().modify_tensors(data_torch, name, bid): + if ".attn_q." in new_name or ".attn_k." in new_name: + tensor = self._permute_vit_qk(tensor, new_name) + yield new_name, tensor + + def _permute_vit_qk(self, t: "Tensor", new_name: str) -> "Tensor": + assert self.hparams_vision is not None + n_head = self.hparams_vision["num_attention_heads"] + d_head = t.shape[0] // n_head + axis_dim = 2 * ((2 * (d_head // 2) // 3) // 2) + ah = axis_dim // 2 + half = 3 * ah + perm = [] + perm += list(range(0, ah)) + perm += list(range(half, half + ah)) + perm += list(range(ah, 2 * ah)) + perm += list(range(half + ah, half + 2 * ah)) + perm += list(range(2 * ah, 3 * ah)) + perm += list(range(half + 2 * ah, half + 3 * ah)) + perm += list(range(2 * half, d_head)) + + assert axis_dim % 2 == 0 + assert 3 * axis_dim <= d_head + assert len(perm) == d_head + assert sorted(perm) == list(range(d_head)), "perm is not a bijection of d_head" + assert t.shape[0] == n_head * d_head, f"{new_name}: {t.shape[0]} != {n_head}*{d_head}" + assert d_head == 80 + + idx = torch.tensor(perm, dtype=torch.long) + if t.ndim == 2: + return t.reshape(n_head, d_head, t.shape[1])[:, idx, :].reshape(t.shape) + return t.reshape(n_head, d_head)[:, idx].reshape(t.shape) diff --git a/conversion/nanbeige.py b/conversion/nanbeige.py new file mode 100644 index 000000000000..f1fc425b3a09 --- /dev/null +++ b/conversion/nanbeige.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from .base import ModelBase, gguf, logger +from .llama import LlamaModel + + +@ModelBase.register("NanbeigeForCausalLM") +class NanbeigeModel(LlamaModel): + model_arch = gguf.MODEL_ARCH.NANBEIGE + undo_permute = True + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + n_loops = int(hparams.get("num_loops", 1) or 1) + if n_loops < 1: + n_loops = 1 + self.gguf_writer.add_num_loops(n_loops) + logger.info(f"gguf: num_loops = {n_loops}") + + skip_loop_final_norm = bool(hparams.get("skip_loop_final_norm", False)) + self.gguf_writer.add_skip_loop_final_norm(skip_loop_final_norm) + logger.info(f"gguf: skip_loop_final_norm = {skip_loop_final_norm}") diff --git a/conversion/qwen.py b/conversion/qwen.py index 82d42fcc1674..9bc2b99fde5d 100644 --- a/conversion/qwen.py +++ b/conversion/qwen.py @@ -1,5 +1,7 @@ from __future__ import annotations +import json + from typing import Any, Callable, Iterable, TYPE_CHECKING import torch @@ -641,7 +643,19 @@ def set_vocab(self): logger.info(f"DFlash: Using tokenizer from target model: {self.target_model_dir}") original_dir = self.dir_model self.dir_model = self.target_model_dir - super().set_vocab() + + # Reuse the target model's own vocab handler (e.g. Gemma-4 needs its + # own tokenizer logic, not the Qwen default). + from . import get_model_class + with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f: + target_arch = json.load(f)["architectures"][0] + target_cls = get_model_class(target_arch) + + if target_cls is not type(self): + target_cls.set_vocab(self) # ty: ignore[unresolved-attribute] + else: + super().set_vocab() + self.dir_model = original_dir mask_token_id = self.hparams.get("dflash_config", {}).get("mask_token_id") diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 91c006278f14..e5d3196efe41 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -162,6 +162,7 @@ class TOKENIZER_TYPE(IntEnum): {"name": "granite-embed-multi-97m", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-embedding-97m-multilingual-r2", }, {"name": "granite-embed-multi-311m", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2", }, {"name": "mellum2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base"}, + {"name": "laguna", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/poolside/Laguna-XS.2", }, ] # some models are known to be broken upstream, so we will skip them as exceptions diff --git a/docs/backend/OPENCL.md b/docs/backend/OPENCL.md index 1bce56cd859a..337b0c82a0f9 100644 --- a/docs/backend/OPENCL.md +++ b/docs/backend/OPENCL.md @@ -47,6 +47,7 @@ The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adren | Adreno GPU | Status | |:-------------------------------------:|:-------:| | Adreno 750 (Snapdragon 8 Gen 3) | Support | +| Adreno 810 (Snapdragon 7s Gen 3) | Support | | Adreno 830 (Snapdragon 8 Elite) | Support | | Adreno 840 (Snapdragon 8 Elite Gen 5) | Support | | Adreno X1-85 (Snapdragon X Elite) | Support | @@ -97,6 +98,24 @@ The OpenCL backend has the following CMake options that control the behavior of | `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. | | `GGML_OPENCL_USE_ADRENO_BIN_KERNELS` | `OFF` | Allow using binary kernel lib for Adreno. | +## Program Binary Cache + +Compiled `cl_program` binaries are cached on disk, so subsequent runs skip the expensive +compile-from-source step when nothing relevant has changed (kernel source, compile options, +device, driver, or platform version). + +The cache is controlled with the `GGML_OPENCL_KERNEL_CACHE_DIR` environment variable: + +| Value | Behavior | +|:---------------------------------------|:-----------------------------------------------| +| unset / empty / `1` / `default` | Enabled in the platform default cache directory: `%LOCALAPPDATA%\llama.cpp\cl-cache` (Windows), `~/Library/Caches/llama.cpp/cl-cache` (macOS), `/llama.cpp/cl-cache` elsewhere. | +| `0` / `off` / `none` / `disable(d)` | Disabled. | +| any other value | Used verbatim as the cache directory path. | + +If the chosen directory cannot be created or used, the cache disables itself for the process +and kernels are compiled from source as usual. Set `GGML_OPENCL_KERNEL_CACHE_DEBUG=1` to +print a HIT/MISS/SAVE trace to stderr. + ## Android Ubuntu 22.04 is used for targeting Android. Make sure the following tools are accessible from command line, diff --git a/docs/build.md b/docs/build.md index 33ef3ef50652..ca086a0be145 100644 --- a/docs/build.md +++ b/docs/build.md @@ -361,12 +361,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm Note: `GPU_TARGETS` is optional, omitting it will build the code for all GPUs in the current system. - To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system. - - The rocWMMA library is included by default when installing the ROCm SDK using the `rocm` meta package provided by AMD. Alternatively, if you are not using the meta package, you can install the library using the `rocwmma-dev` or `rocwmma-devel` package, depending on your system's package manager. - - As an alternative, you can manually install the library by cloning it from the official [GitHub repository](https://github.com/ROCm/rocWMMA), checkout the corresponding version tag (e.g. `rocm-6.2.4`) and set `-DCMAKE_CXX_FLAGS="-I/library/include/"` in CMake. This also works under Windows despite not officially supported by AMD. - Note that if you get the following error: ``` clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md index ef2b37088181..102f479eb02c 100644 --- a/docs/development/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -45,6 +45,8 @@ class MyModel(MmprojModel): Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`. +NOTE: Pick the GGUF arch string (and the matching `src/models/.cpp` filename, see section 3) carefully up front, following existing naming conventions. Once GGUF files are published under a given arch string, renaming it later breaks the community's existing files, so this is not something to leave for cleanup in a follow-up PR. + Example for `falcon` model: ```python MODEL_ARCH.FALCON: [ @@ -101,6 +103,7 @@ The model params and tensors layout must be defined in `llama.cpp` source files: - You may also need to update `LLM_KV_NAMES`, `LLM_TENSOR_NAMES` and `LLM_TENSOR_INFOS` 3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`. 4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`. +5. Check for other places that switch/iterate over every `llm_arch` value, e.g. `src/llama-model-saver.cpp` and any mandatory-hparam lists (such as which archs require MoE metadata). Grep for `LLM_ARCH_` usages to find them. Missing one of these is a common cause of CI test failures (e.g. `test-llama-archs`) after adding a new arch. NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions. @@ -133,6 +136,16 @@ Note: ## Tips and tricks +### Prefer conversion-time tensor modifications over graph-time ones + +If the model contains constant modifications of tensors in the graph (for example, `norm(1 + weight)`) or performs tensor permutations/chunking, perform the modifications during conversion rather than in the graph code. This keeps the inference graph simpler and avoids extra runtime ops. + +Examples: +- Gemma 3 folds the `1 +` of its `norm(1 + weight)` normalization into the weights at conversion time, so the graph just does a plain RMS norm. +- Qwen3-Next applies its tensor permutation during conversion (in `modify_tensors`), so the graph can consume the already-permuted weights directly. + +Exception: a plain `weight * scale` with a constant scale is usually better left to inference time rather than folded into the weight at conversion. The scale conceptually applies to the activation, not the weight, so folding it into the weight can hurt numerical stability, and it shifts the weight's value range in a way that can make quantization worse. In this case, write the scale to GGUF as its own metadata key (e.g. `%s.attention.output_scale`, `%s.attention.value_scale`, `%s.embedding_scale`) and apply it in the graph, instead of pre-multiplying the weight tensor during conversion. + ### Working with ggml_rope_ext PyTorch implementations usually prefer explicitly calculating `freq_cis`/`sin`/`cos` components. However, in llama.cpp, most RoPE operations can be handled via `ggml_rope_ext`, which does not require a sin/cos matrix. This saves memory while allowing the GGML RoPE kernel to be fused with other ops. diff --git a/docs/ops.md b/docs/ops.md index c5601523697d..557b1a023d2e 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -25,10 +25,10 @@ Legend: | CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | -| CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | | CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | -| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | @@ -41,6 +41,9 @@ Legend: | DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | @@ -63,16 +66,17 @@ Legend: | HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | +| L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | | LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | +| LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | -| MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | -| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 | @@ -82,7 +86,7 @@ Legend: | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | -| REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | | REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv index 95042e72d9c1..c19396c03e4d 100644 --- a/docs/ops/WebGPU.csv +++ b/docs/ops/WebGPU.csv @@ -167,6 +167,16 @@ "WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","DSV4_HC_COMB","n_tokens=1,n_iter=1,eps=0.000001","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_COMB","n_tokens=17,n_iter=4,eps=0.000001","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_COMB","n_tokens=257,n_iter=8,eps=0.000001","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=1,n_tokens=1","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=31,n_tokens=17","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=128,n_tokens=257","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=4096,n_tokens=21","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_POST","n_embd=1,n_tokens=1","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_POST","n_embd=31,n_tokens=17","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_POST","n_embd=128,n_tokens=257","support","0","no","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","WebGPU" @@ -338,14 +348,18 @@ "WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" @@ -407,6 +421,7 @@ "WebGPU: WebGPU","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=1,m=8,r=2,b=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=1,m=70000,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f16,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" @@ -425,6 +440,8 @@ "WebGPU: WebGPU","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS_BACK","type=q2_0,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS_BACK","type=q2_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" @@ -459,333 +476,685 @@ "WebGPU: WebGPU","GET_ROWS_BACK","type=iq4_xs,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=i32,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=i32,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[1,8,1,3],nr23=[1,1],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i32,ne=[1,8,1,3],nr23=[1,1],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=q8_0,type_idx=i32,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[3,3,1,1],nr23=[2,3],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[31,3,1,1],nr23=[2,3],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[33,5,1,1],nr23=[2,3],r=1,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[3,3,1,1],nr23=[2,3],r=2,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[31,3,1,1],nr23=[2,3],r=2,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[33,5,1,1],nr23=[2,3],r=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[3,3,7,1],nr23=[2,3],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[31,3,7,1],nr23=[2,3],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[33,5,1,7],nr23=[2,3],r=1,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[3,3,7,1],nr23=[2,3],r=2,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[31,3,7,1],nr23=[2,3],r=2,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f32,type_idx=i64,ne=[33,5,1,7],nr23=[2,3],r=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[3,3,1,1],nr23=[2,3],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[31,3,1,1],nr23=[2,3],r=2,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[33,5,1,1],nr23=[2,3],r=1,v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[3,3,1,1],nr23=[2,3],r=2,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","SET_ROWS","type=f16,type_idx=i64,ne=[31,3,1,1],nr23=[2,3],r=2,v=1","support","1","yes","WebGPU" -"WebGPU: 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WebGPU","SET_ROWS","type_src=f32,type_dst=iq1_m,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[96,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[96,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[96,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_nl,type_idx=i64,ne=[96,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i64,ne=[1,8,1,3],nr23=[1,1],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i32,ne=[1,8,1,3],nr23=[1,1],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i64,ne=[1,8,1,3],nr23=[1,1],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i32,ne=[1,8,1,3],nr23=[1,1],r=2,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=0,p1=0","support","0","no","WebGPU" "WebGPU: WebGPU","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=0,p1=1","support","0","no","WebGPU" "WebGPU: WebGPU","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=1,p1=0","support","0","no","WebGPU" @@ -965,6 +1334,7 @@ "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,384,1,1],ne_kernel=[3,384,384,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=0,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=0,p1=0,d0=3,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=3,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" @@ -974,6 +1344,7 @@ "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=3,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=3,p1=0,d0=3,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f16,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" @@ -1050,6 +1421,8 @@ "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[128,128,1,2],ne_kernel=[32,33,1,2],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[128,128,2,1],ne_kernel=[33,34,2,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","WebGPU" @@ -4669,10 +5042,16 @@ "WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU" "WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU" "WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","WebGPU" +"WebGPU: WebGPU","CONV_2D","ne_input=[256,256,192,1],ne_kernel=[3,3,192,96],type_kernel=f32,stride0=1,stride1=1,padding0=1,padding1=1,dilation0=1,dilation1=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D","ne_input=[256,256,192,1],ne_kernel=[3,3,192,96],type_kernel=f16,stride0=1,stride1=1,padding0=1,padding1=1,dilation0=1,dilation1=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f32,stride=1,padding=0,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f32,stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f32,stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f32,stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f16,stride=1,padding=0,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f16,stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f16,stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f16,stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","WebGPU" "WebGPU: WebGPU","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","WebGPU" @@ -5047,6 +5426,39 @@ "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,1,2,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=16,OC=32,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=4,OC=3,T_in=7,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=1,OC=5,T_in=13,s0=1,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=6,OC=4,T_in=11,s0=3,p0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=2,OC=3,T_in=9,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=5,OC=4,T_in=11,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=8,OC=4,T_in=13,s0=4,p0=2","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=4,OC=3,T_in=1,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=16,OC=32,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=4,OC=3,T_in=7,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=1,OC=5,T_in=13,s0=1,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=6,OC=4,T_in=11,s0=3,p0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=2,OC=3,T_in=9,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=5,OC=4,T_in=11,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=8,OC=4,T_in=13,s0=4,p0=2","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=4,OC=3,T_in=1,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=16,OC=32,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=4,OC=3,T_in=7,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=1,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=6,OC=4,T_in=11,s0=3,p0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=2,OC=3,T_in=9,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=5,OC=4,T_in=11,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=8,OC=4,T_in=13,s0=4,p0=2","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=4,OC=3,T_in=1,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","WebGPU" @@ -5069,6 +5481,7 @@ "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=bf16,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","1","yes","WebGPU" @@ -5076,6 +5489,7 @@ "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","0","no","WebGPU" @@ -5108,449 +5522,568 @@ "WebGPU: WebGPU","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2,inplace=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: 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"WebGPU: WebGPU","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","WebGPU" @@ -6084,6 +6637,12 @@ "WebGPU: WebGPU","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","WebGPU" "WebGPU: WebGPU","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU" "WebGPU: WebGPU","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=64,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=512,n=1,k=512,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=32,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=4,k=128,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6165,6 +6724,15 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6174,15 +6742,15 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: 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WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6309,6 +6877,9 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6318,6 +6889,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6330,6 +6902,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6345,6 +6918,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6357,6 +6931,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6376,6 +6951,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6388,6 +6964,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6403,6 +6980,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6415,6 +6993,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6434,6 +7013,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6446,6 +7026,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6461,6 +7042,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6473,6 +7055,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6492,6 +7075,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6504,6 +7088,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6519,6 +7104,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6531,6 +7117,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6550,6 +7137,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6562,6 +7150,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6581,6 +7170,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6593,6 +7183,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6612,6 +7203,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6624,6 +7216,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6643,6 +7236,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: 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WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6674,6 +7335,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6686,6 +7348,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6705,6 +7368,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6717,6 +7381,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6736,6 +7401,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6748,6 +7414,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6767,6 +7434,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6779,6 +7447,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6798,6 +7467,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6810,6 +7480,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6829,6 +7500,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6841,6 +7513,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6860,6 +7533,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6872,6 +7546,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6891,6 +7566,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6903,6 +7579,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6913,37 +7590,39 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: 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WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6953,6 +7632,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6965,6 +7645,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6984,6 +7665,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6996,6 +7678,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -7015,6 +7698,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -7027,6 +7711,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -7037,6 +7722,15 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -7082,8 +7776,9 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: 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WebGPU","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[16,1],nr=[1,1],trans_b=0","support","0","no","WebGPU" +"WebGPU: WebGPU","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[32,1],nr=[1,1],trans_b=0","support","0","no","WebGPU" +"WebGPU: WebGPU","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[8,1],trans_b=0","support","0","no","WebGPU" +"WebGPU: WebGPU","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[16,1],trans_b=0","support","0","no","WebGPU" +"WebGPU: WebGPU","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[32,1],trans_b=0","support","0","no","WebGPU" "WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","WebGPU" "WebGPU: WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","WebGPU" @@ -9946,6 +10875,11 @@ "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","WebGPU" @@ -10000,126 +10934,281 @@ "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: 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@@ -10174,6 +11263,11 @@ "WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","WebGPU" "WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","WebGPU" "WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","WebGPU" +"WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","0","no","WebGPU" +"WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","0","no","WebGPU" +"WebGPU: 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WebGPU","PAD","type=f32,ne_a=[1023,1,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[1023,8,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[1025,1,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[1025,8,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[2048,1,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[2048,4,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[2049,1,1,1],pad_0=1,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[100,1,1,1],pad_0=100,pad_1=0,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[100,1,1,1],pad_0=0,pad_1=100,circular=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[100,100,1,1],pad_0=50,pad_1=50,circular=0","support","1","yes","WebGPU" "WebGPU: WebGPU","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","WebGPU" "WebGPU: WebGPU","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","WebGPU" "WebGPU: WebGPU","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","WebGPU" @@ -14998,6 +16420,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: 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WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15014,6 +16452,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15038,6 +16484,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: 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WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15054,6 +16516,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15070,6 +16540,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15086,6 +16564,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15110,6 +16596,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: 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WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15126,6 +16628,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15150,6 +16660,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15166,6 +16692,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15182,6 +16716,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15198,6 +16740,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15934,10 +17484,11 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" -"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=256,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU" @@ -15948,21 +17499,147 @@ "WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=1,kda=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","WebGPU" -"WebGPU: 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WebGPU","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q5_1","support","0","no","WebGPU" +"WebGPU: WebGPU","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q5_0","support","0","no","WebGPU" +"WebGPU: WebGPU","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q4_1","support","0","no","WebGPU" +"WebGPU: WebGPU","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q4_0","support","0","no","WebGPU" +"WebGPU: WebGPU","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=iq4_nl","support","0","no","WebGPU" diff --git a/examples/diffusion/diffusion-cli.cpp b/examples/diffusion/diffusion-cli.cpp index 86ebbf88c98d..d58d22eff550 100644 --- a/examples/diffusion/diffusion-cli.cpp +++ b/examples/diffusion/diffusion-cli.cpp @@ -117,9 +117,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); model_params.n_gpu_layers = params.n_gpu_layers; model_params.devices = params.devices.data(); - model_params.use_mmap = params.use_mmap; - model_params.use_direct_io = params.use_direct_io; - model_params.use_mlock = params.use_mlock; + model_params.load_mode = params.load_mode; model_params.check_tensors = params.check_tensors; llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params); diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp index 0a75ac110ca4..44b2843918b1 100644 --- a/examples/training/finetune.cpp +++ b/examples/training/finetune.cpp @@ -26,10 +26,9 @@ int main(int argc, char ** argv) { return 1; } - if (params.use_mmap) { - LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", - __func__); - params.use_mmap = false; + if (params.load_mode != LLAMA_LOAD_MODE_NONE) { + LOG_INF("%s: forcing load_mode = none to enable writable pointers to the weights\n", __func__); + params.load_mode = LLAMA_LOAD_MODE_NONE; } if (params.cache_type_k != GGML_TYPE_F32) { LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__); diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 5381c2136203..a766e49ea11d 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -4,7 +4,7 @@ project("ggml" C CXX ASM) ### GGML Version set(GGML_VERSION_MAJOR 0) -set(GGML_VERSION_MINOR 16) +set(GGML_VERSION_MINOR 17) set(GGML_VERSION_PATCH 0) set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}") @@ -216,7 +216,6 @@ option(GGML_HIP "ggml: use HIP" option(GGML_HIP_GRAPHS "ggml: use HIP graph" ON) option(GGML_HIP_RCCL "ggml: use ROCm Collective Comm. Library" OFF) option(GGML_HIP_NO_VMM "ggml: do not try to use HIP VMM" ON) -option(GGML_HIP_ROCWMMA_FATTN "ggml: enable rocWMMA for FlashAttention" OFF) option(GGML_HIP_MMQ_MFMA "ggml: enable MFMA MMA for CDNA in MMQ" ON) option(GGML_HIP_EXPORT_METRICS "ggml: enable kernel perf metrics output" OFF) option(GGML_MUSA_GRAPHS "ggml: use MUSA graph, experimental, unstable" OFF) diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h index e3e067c916f1..dc6453c6eaa1 100644 --- a/ggml/include/ggml-cpu.h +++ b/ggml/include/ggml-cpu.h @@ -100,6 +100,7 @@ extern "C" { GGML_BACKEND_API int ggml_cpu_has_sve (void); GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes GGML_BACKEND_API int ggml_cpu_has_sme (void); + GGML_BACKEND_API int ggml_cpu_has_sme2 (void); // other GGML_BACKEND_API int ggml_cpu_has_riscv_v (void); GGML_BACKEND_API int ggml_cpu_get_rvv_vlen (void); // risc-v vector length in bytes diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index efa5420a6982..16ca33947a2e 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -8,10 +8,10 @@ extern "C" { #define RPC_PROTO_MAJOR_VERSION 4 #define RPC_PROTO_MINOR_VERSION 0 -#define RPC_PROTO_PATCH_VERSION 2 +#define RPC_PROTO_PATCH_VERSION 3 #ifdef __cplusplus -static_assert(GGML_OP_COUNT == 98, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); +static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); #endif #define GGML_RPC_MAX_SERVERS 16 diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 92ba65222a6c..35f0c44ec421 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -571,6 +571,9 @@ extern "C" { GGML_OP_SOLVE_TRI, GGML_OP_GATED_DELTA_NET, GGML_OP_LIGHTNING_INDEXER, + GGML_OP_DSV4_HC_COMB, + GGML_OP_DSV4_HC_PRE, + GGML_OP_DSV4_HC_POST, GGML_OP_UNARY, @@ -2598,6 +2601,45 @@ extern "C" { struct ggml_tensor * weights, struct ggml_tensor * mask); + // DeepSeek V4 hyper-connections (ref. https://arxiv.org/pdf/2512.24880) + // In short these operations are replacements for the original residual connection (x = transformer(x) + x) + // using a richer representation through streams. + // + // hc_comb: mixes [(2 + hc)*hc, n_tokens], scale [3], base [(2 + hc)*hc] + // -> [dst_hc, src_hc, n_tokens] + // logits[dst, src, t] = mixes[2*hc + dst + hc*src, t]*scale[2] + // + base[2*hc + dst + hc*src] + // Softmax over dst, add eps, normalize over src, then repeat normalization + // over dst followed by src for iterations 1 through n_iter - 1. + GGML_API struct ggml_tensor * ggml_dsv4_hc_comb( + struct ggml_context * ctx, + struct ggml_tensor * mixes, + struct ggml_tensor * scale, + struct ggml_tensor * base, + float eps, + int32_t n_iter); + + // hc_pre: x [n_embd, hc, n_tokens], weights [hc, n_tokens] -> [n_embd, n_tokens] + // result[i, t] = sum_h x[i, h, t]*weights[h, t] + // + GGML_API struct ggml_tensor * ggml_dsv4_hc_pre( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * weights); + + // hc_post: x [n_embd, n_tokens], residual [n_embd, hc, n_tokens], + // post [hc, n_tokens], comb [dst_hc, src_hc, n_tokens] + // -> [n_embd, hc, n_tokens] + // result[i, dst, t] = x[i, t]*post[dst, t] + // + sum_src residual[i, src, t]*comb[dst, src, t] + // + GGML_API struct ggml_tensor * ggml_dsv4_hc_post( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * residual, + struct ggml_tensor * post, + struct ggml_tensor * comb); + // custom operators typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata); diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 11583474a9a9..82e9480c2f24 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -430,7 +430,7 @@ if (GGML_CPU_ALL_VARIANTS) message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}") endif() elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC") - if (CMAKE_SYSTEM_NAME MATCHES "Linux") + if (CMAKE_SYSTEM_NAME MATCHES "Linux|AIX") ggml_add_cpu_backend_variant(power0) ggml_add_cpu_backend_variant(power7_1 POWER7) ggml_add_cpu_backend_variant(power7_2 POWER7 VSX) diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp index 1f29ec86712d..a5a3a58ad054 100644 --- a/ggml/src/ggml-backend-meta.cpp +++ b/ggml/src/ggml-backend-meta.cpp @@ -984,6 +984,11 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state( case GGML_OP_GATED_DELTA_NET: { split_state = handle_gated_delta_net(src_ss); } break; + case GGML_OP_DSV4_HC_COMB: + case GGML_OP_DSV4_HC_PRE: + case GGML_OP_DSV4_HC_POST: { + split_state = handle_generic(src_ss, /*scalar_only =*/ true); + } break; case GGML_OP_UNARY: { split_state = handle_generic(src_ss, /*scalar_only =*/ false); } break; diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 87615921c09b..7f4e252dca39 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -906,26 +906,35 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st } // operations with weights are preferably run on the same backend as the weights - for (int i = 0; i < GGML_MAX_SRC; i++) { - const struct ggml_tensor * src = tensor->src[i]; - if (src == NULL) { - continue; - } - // skip ROPE since the rope freqs tensor is too small to choose a backend based on it - // not an ideal solution - if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { - int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); - // check if a backend with higher prio wants to offload the op - if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) { - for (int b = 0; b < src_backend_id; b++) { - if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) { - SET_CAUSE(tensor, "1.off"); - return b; + // TODO: there are exceptions (see below) - not an ideal solution + bool allow = true; + + // skip ROPE since the rope freqs tensor is too small to choose a backend based on it + allow = allow && tensor->op != GGML_OP_ROPE; + + // skip FLASH_ATTN_EXT since the sinks tensor is too small to choose a based based on it + allow = allow && tensor->op != GGML_OP_FLASH_ATTN_EXT; + + if (allow) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + const struct ggml_tensor * src = tensor->src[i]; + if (src == NULL) { + continue; + } + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); + // check if a backend with higher prio wants to offload the op + if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) { + for (int b = 0; b < src_backend_id; b++) { + if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) { + SET_CAUSE(tensor, "1.off"); + return b; + } } } + SET_CAUSE(tensor, "1.wgt%d", i); + return src_backend_id; } - SET_CAUSE(tensor, "1.wgt%d", i); - return src_backend_id; } } diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp index b4c735267e04..9745fa29f5db 100644 --- a/ggml/src/ggml-blas/ggml-blas.cpp +++ b/ggml/src/ggml-blas/ggml-blas.cpp @@ -1,3 +1,4 @@ +#include "ggml.h" #include "ggml-impl.h" #include "ggml-blas.h" #include "ggml-backend-impl.h" @@ -415,6 +416,12 @@ static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const s // TODO: find the optimal value const int64_t min_batch = 32; + // default back to CPU fast path + // see: https://github.com/ggml-org/llama.cpp/issues/25565 + if (ggml_get_op_params_i32(op, 1) == GGML_HINT_SRC0_IS_HADAMARD) { + return false; + } + return ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->type == GGML_TYPE_F32 && diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 1316978e2ef7..836bae4d05a7 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -678,7 +678,18 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() if (NOT SME_ENABLED MATCHES -1) - list(APPEND GGML_KLEIDIAI_SOURCES + list(APPEND GGML_KLEIDIAI_SME_SOURCES + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa_asm.S + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot_asm.S + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f32p_f32p/kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f32p_f32p/kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa_asm.S) + set_source_files_properties(${GGML_KLEIDIAI_SME_SOURCES} + PROPERTIES COMPILE_OPTIONS "-fno-tree-vectorize;${ARCH_FLAGS_TEMP}+sve+sve2+sme") + list(APPEND GGML_CPU_SOURCES ${GGML_KLEIDIAI_SME_SOURCES}) + + list(APPEND GGML_KLEIDIAI_SME2_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa_asm.S @@ -698,7 +709,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_f32p2vlx1biasf32_f32_f32_sme.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_f32p2vlx1biasf32_f32_f32_sme_asm.S ${KLEIDIAI_SRC}/kai/kai_common_sme_asm.S) - set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2+sme2+fp16") + set_source_files_properties(${GGML_KLEIDIAI_SME2_SOURCES} + PROPERTIES COMPILE_OPTIONS "-fno-tree-vectorize;${ARCH_FLAGS_TEMP}+sve+sve2+sme2+fp16") + list(APPEND GGML_CPU_SOURCES ${GGML_KLEIDIAI_SME2_SOURCES}) + set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}") endif() if (NOT SVE_ENABLED MATCHES -1) diff --git a/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp b/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp index c460c5491143..adfbd2e4e9bd 100644 --- a/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp +++ b/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp @@ -28,6 +28,7 @@ struct aarch64_features { bool has_sve2 = false; bool has_i8mm = false; bool has_sme = false; + bool has_sme2 = false; aarch64_features() { #if defined(__linux__) @@ -56,6 +57,10 @@ struct aarch64_features { has_sme = static_cast(oldp); } + if (sysctlbyname("hw.optional.arm.FEAT_SME2", &oldp, &size, NULL, 0) == 0) { + has_sme2 = static_cast(oldp); + } + // Apple apparently does not implement SVE yet #endif } diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index d9347e3c214b..491316f74912 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -2064,6 +2064,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_lightning_indexer(params, tensor); } break; + case GGML_OP_DSV4_HC_COMB: + { + ggml_compute_forward_dsv4_hc_comb(params, tensor); + } break; + case GGML_OP_DSV4_HC_PRE: + { + ggml_compute_forward_dsv4_hc_pre(params, tensor); + } break; + case GGML_OP_DSV4_HC_POST: + { + ggml_compute_forward_dsv4_hc_post(params, tensor); + } break; case GGML_OP_MAP_CUSTOM1: { ggml_compute_forward_map_custom1(params, tensor); @@ -2244,6 +2256,9 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { case GGML_OP_COUNT_EQUAL: case GGML_OP_SOLVE_TRI: case GGML_OP_GATED_DELTA_NET: + case GGML_OP_DSV4_HC_COMB: + case GGML_OP_DSV4_HC_PRE: + case GGML_OP_DSV4_HC_POST: { n_tasks = n_threads; } break; @@ -2859,7 +2874,8 @@ struct ggml_cplan ggml_graph_plan( } break; case GGML_OP_OUT_PROD: { - if (ggml_is_quantized(node->src[0]->type)) { + if (ggml_is_quantized(node->src[0]->type) || + node->src[0]->type == GGML_TYPE_F16) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } } break; @@ -3791,6 +3807,14 @@ int ggml_cpu_has_sme(void) { #endif } +int ggml_cpu_has_sme2(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SME2) + return 1; +#else + return 0; +#endif +} + void ggml_cpu_init(void) { // needed to initialize ggml_time { diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 128883b41ce7..74631c2857ba 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -462,11 +462,12 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st return max_bias == 0.0f; } case GGML_OP_IM2COL_BACK: - return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32; + return src0->type == GGML_TYPE_F32 && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); case GGML_OP_GET_ROWS_BACK: return src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16; case GGML_OP_OUT_PROD: - return (src0->type == GGML_TYPE_F32 || (ggml_is_quantized(src0->type) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) && + return (src0->type == GGML_TYPE_F32 || + ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) && src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; default: return true; @@ -594,6 +595,9 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r if (ggml_cpu_has_sme()) { features.push_back({ "SME", "1" }); } + if (ggml_cpu_has_sme2()) { + features.push_back({ "SME2", "1" }); + } if (ggml_cpu_has_riscv_v()) { features.push_back({ "RISCV_V", "1" }); } diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp index bf03fd766bcf..3c31ab9d35f0 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp @@ -13,6 +13,8 @@ #include "kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot.h" +#include "kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa.h" +#include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot.h" #include "kai_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod.h" @@ -21,6 +23,7 @@ #include "kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p8x8_1x8_sve_dotprod.h" #include "kai_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1biasf32_sme2_mopa.h" +#include "kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa.h" #include "kai_lhs_pack_bf16p2vlx2_f32_sme.h" #include "kai_lhs_pack_f32p2vlx1_f32_sme.h" @@ -359,7 +362,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_fn12, }, - /* .required_cpu = */ CPU_FEATURE_SME, + /* .required_cpu = */ CPU_FEATURE_SME2, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q4_0, /* .op_type = */ GGML_TYPE_F32, @@ -412,7 +415,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_stride_ex = */ &rhs_stride_fn1, /* .pack_func_ex = */ &rhs_pack_fn13, }, - /* .required_cpu = */ CPU_FEATURE_SME, + /* .required_cpu = */ CPU_FEATURE_SME2, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_F16, /* .op_type = */ GGML_TYPE_F32, @@ -749,6 +752,59 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_scale_fn12, }, + /* .required_cpu = */ CPU_FEATURE_SME2, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q8_0, + /* .op_type = */ GGML_TYPE_F32, + }, + { + /* SME GEMM (pure SME, no SME2 required) */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_float_fn10, + }, + /* .gemm_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl, + }, + /* SME GEMV (pure SME, no SME2 required) */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_float_fn10, + }, + /* .gemv_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl, + }, + /* .rhs_info = */ { + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon, + /* .to_float = */ dequantize_row_qsi8cxp, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_scale_fn12, + }, /* .required_cpu = */ CPU_FEATURE_SME, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q8_0, @@ -871,7 +927,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = { static ggml_kleidiai_kernels ggml_kleidiai_kernels_f32[] = { #if defined(__ARM_FEATURE_SME) { - /* SME GEMM */ + /* SME2 GEMM */ { /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1biasf32_sme2_mopa, /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1biasf32_sme2_mopa, @@ -918,6 +974,59 @@ static ggml_kleidiai_kernels ggml_kleidiai_kernels_f32[] = { /* .packed_stride_ex = */ &rhs_stride_fn1, /* .pack_func_ex = */ &rhs_pack_fn13, }, + /* .required_cpu = */ CPU_FEATURE_SME2, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_F32, + /* .op_type = */ GGML_TYPE_F32, + }, + { + /* SME GEMM */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_fn10, + }, + /* .gemm_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_pack_f32p2vlx1_f32_sme, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_void_fn9, + }, + /* SME GEMV */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_lhs_offset_ex = */ nullptr, + /* .get_rhs_packed_offset_ex = */ nullptr, + /* .run_kernel_ex = */ nullptr, + }, + /* .gemv_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_pack_f32p2vlx1_f32_sme, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_void_fn9, + }, + /* .rhs_info = */ { + /* .packed_stride = */ nullptr, + /* .to_float = */ nullptr, + /* .packed_size_ex = */ &rhs_ps_fn2, + /* .packed_stride_ex = */ &rhs_stride_fn1, + /* .pack_func_ex = */ &rhs_pack_fn13, + }, /* .required_cpu = */ CPU_FEATURE_SME, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_F32, diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h index a46f837acbd6..0da5e65a0a8d 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.h +++ b/ggml/src/ggml-cpu/kleidiai/kernels.h @@ -11,7 +11,8 @@ enum cpu_feature { CPU_FEATURE_DOTPROD = 1, CPU_FEATURE_I8MM = 2, CPU_FEATURE_SVE = 4, - CPU_FEATURE_SME = 8 + CPU_FEATURE_SME = 8, + CPU_FEATURE_SME2 = 16 }; inline cpu_feature& operator|=(cpu_feature& lhs, cpu_feature rhs) { diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index a8de7df25f07..1c5a459f2190 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -26,6 +26,9 @@ #include #include #include +#ifndef HWCAP2_SME2 +#define HWCAP2_SME2 (1UL << 37) +#endif #elif defined(__APPLE__) #include #include @@ -66,9 +69,15 @@ struct ggml_kleidiai_context { int chunk_multiplier; } static ctx = { CPU_FEATURE_NONE, nullptr, nullptr, nullptr, 0, -1, 4 }; +static inline bool is_sme_family(cpu_feature f) { + return (f & (CPU_FEATURE_SME | CPU_FEATURE_SME2)) != CPU_FEATURE_NONE; +} + static const char* cpu_feature_to_string(cpu_feature f) { if (f == CPU_FEATURE_NONE) { return "NONE"; + } else if ((f & CPU_FEATURE_SME2) == CPU_FEATURE_SME2) { + return "SME2"; } else if ((f & CPU_FEATURE_SME) == CPU_FEATURE_SME) { return "SME"; } else if ((f & CPU_FEATURE_SVE) == CPU_FEATURE_SVE) { @@ -251,6 +260,18 @@ static void init_kleidiai_context(void) { if (sme_cores > 0) { ctx.features |= CPU_FEATURE_SME; +#if defined(__aarch64__) && defined(__linux__) + // ARM guarantees SME2 implies SME, so only check SME2 when SME is enabled. + if (getauxval(AT_HWCAP2) & HWCAP2_SME2) { + ctx.features |= CPU_FEATURE_SME2; + } +#elif defined(__aarch64__) && defined(__APPLE__) + int feat_sme2 = 0; + size_t size = sizeof(feat_sme2); + if (sysctlbyname("hw.optional.arm.FEAT_SME2", &feat_sme2, &size, NULL, 0) == 0 && feat_sme2) { + ctx.features |= CPU_FEATURE_SME2; + } +#endif } // Kernel selection @@ -279,10 +300,13 @@ static void init_kleidiai_context(void) { ctx.sme_thread_cap = (ctx.features & CPU_FEATURE_SME) ? sme_cores : 0; if (ctx.features & CPU_FEATURE_SME) { + const bool has_sme2 = (ctx.features & CPU_FEATURE_SME2) != CPU_FEATURE_NONE; if (sme_env_set && sme_env_ok && sme_cores > 0) { - GGML_LOG_INFO("kleidiai: SME enabled (GGML_KLEIDIAI_SME=%d override)\n", sme_cores); + GGML_LOG_INFO("kleidiai: SME%s enabled (GGML_KLEIDIAI_SME=%d override)\n", + has_sme2 ? "2" : "", sme_cores); } else { - GGML_LOG_INFO("kleidiai: SME enabled (runtime-detected SME cores=%d)\n", sme_cores); + GGML_LOG_INFO("kleidiai: SME%s enabled (runtime-detected SME cores=%d)\n", + has_sme2 ? "2" : "", sme_cores); } } else { GGML_LOG_INFO("kleidiai: SME disabled\n"); @@ -442,8 +466,8 @@ static int kleidiai_collect_kernel_chain_common( return count; } - if ((primary->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { - const cpu_feature fallback_mask = static_cast(features & ~CPU_FEATURE_SME); + if (is_sme_family(primary->required_cpu)) { + const cpu_feature fallback_mask = static_cast(features & ~CPU_FEATURE_SME & ~CPU_FEATURE_SME2); if (fallback_mask != CPU_FEATURE_NONE) { ggml_kleidiai_kernels * fallback = select_fallback(fallback_mask); if (fallback && fallback != primary && @@ -1054,14 +1078,14 @@ class tensor_traits : public ggml::cpu::tensor_traits { int sme_slot = -1; for (int i = 0; i < runtime_count; ++i) { - if ((runtime[i].kernels->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + if (is_sme_family(runtime[i].kernels->required_cpu)) { sme_slot = i; break; } } int non_sme_slot = -1; for (int i = 0; i < runtime_count; ++i) { - if ((runtime[i].kernels->required_cpu & CPU_FEATURE_SME) != CPU_FEATURE_SME) { + if (!is_sme_family(runtime[i].kernels->required_cpu)) { non_sme_slot = i; break; } @@ -1099,7 +1123,7 @@ class tensor_traits : public ggml::cpu::tensor_traits { // Recompute SME slot based on the collapsed runtime[0] sme_slot = -1; if (runtime_count > 0 && - (runtime[0].kernels->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + is_sme_family(runtime[0].kernels->required_cpu)) { sme_slot = 0; } } @@ -1695,6 +1719,7 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type { return true; } + return false; } @@ -1703,6 +1728,20 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type { if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) { return (ggml::cpu::tensor_traits *) op->src[0]->extra; } else { + // KleidiAI only has kernels for Q4_0 and Q8_0. For a quantized weight of any + // other type (K-quants, IQ) it declines the op and returns nullptr below, so + // KleidiAI does not accelerate it. Another CPU backend may still take the op, + // and this can run during graph planning, so the message says what KleidiAI + // did rather than what ends up executing. Warn once per process. + if (ggml_is_quantized(op->src[0]->type) && + op->src[0]->type != GGML_TYPE_Q4_0 && op->src[0]->type != GGML_TYPE_Q8_0) { + static std::atomic warned(false); + if (!warned.exchange(true)) { + GGML_LOG_WARN("kleidiai: no kernel for tensor type %s, not accelerated by KleidiAI " + "(kernels available for Q4_0 and Q8_0)\n", + ggml_type_name(op->src[0]->type)); + } + } if (op->src[0]->type != GGML_TYPE_F16) { return nullptr; } diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index 5efaaa5b2a06..99b7d5afa2f9 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -1797,14 +1797,6 @@ class tinyBLAS_Q0_AVX { //PPC Implementation #if defined(__MMA__) -#define SAVE_ACC(ACC, ii, jj) \ - __builtin_mma_disassemble_acc(vec_C, ACC); \ - for (int I = 0; I < 4; I++) { \ - for (int J = 0; J < 4; J++) { \ - *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); \ - } \ - } \ - template struct mma_instr; @@ -1834,10 +1826,49 @@ class tinyBLAS_HP16_PPC { } void matmul(int64_t m, int64_t n) { - mnpack(0, m, 0, n); + int64_t mc = 256; + int64_t nc = 256; + int64_t kc = 256; + #if defined(_AIX) || defined(__BIG_ENDIAN__) + mc = 128; + nc = 128; + kc = 128; + #endif + if (k < kc) { + kc = k; + } + bool can_use_tiled = (m % mc == 0) && (n % nc == 0) && (k % kc == 0); + if (can_use_tiled) { + matmul_tiled(m, n, mc, nc, kc); + } else { + mnpack(0, m, 0, n); + } } private: + __attribute__((always_inline)) + inline void save_acc(acc_t * ACC, int64_t ii, int64_t jj) { + vec_t vec_C[4]; + __builtin_mma_disassemble_acc(vec_C, ACC); + for (int I = 0; I < 4; I++) { + for (int J = 0; J < 4; J++) { + *((float *)(C+ii+((jj+J)*ldc)+I)) = *((float *)&vec_C[I]+J); + } + } + } + + __attribute__((always_inline)) + inline void add_save_acc(acc_t * ACC, int64_t ii, int64_t jj) { + vec_t vec_C[4]; + __builtin_mma_disassemble_acc(vec_C, ACC); + for (int I = 0; I < 4; I++) { + for (int J = 0; J < 4; J++) { + float * c_ptr = (float *)(C+ii+((jj+J)*ldc)+I); + *c_ptr += *((float *)&vec_C[I]+J); + } + } + } + void vector_permute_store(vec_t *c, int numVec, unsigned char *vecOffset) { vec_t t[8], s[8]; vec_t swiz1 = {0, 1, 2, 3, 16, 17, 18, 19, 4, 5, 6, 7, 20, 21, 22, 23}; @@ -1896,6 +1927,7 @@ class tinyBLAS_HP16_PPC { j = (rows >> 3); if (j > 0) { do { + aoffsets[0] = aoffset; if (cols == 4) { aoffsets[0] = aoffset; for (int it = 1; it < 4; ++it) @@ -1910,17 +1942,17 @@ class tinyBLAS_HP16_PPC { } i = (cols >> 3); if (i > 0) { - aoffsets[0] = aoffset; for (int it = 1; it < 8; ++it) { aoffsets[it] = aoffsets[it-1] + lda; } aoffset += 8 * lda; + do { for (int it = 0; it < 8; ++it) c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); vector_permute_store(c_arr, 8, vecOffset); for (int it = 0; it < 8; ++it) - aoffsets[it] = aoffsets[it] + 8*lda; + aoffsets[it] = aoffsets[it] + 8; vecOffset += 128; i--; } while(i > 0); @@ -2147,8 +2179,8 @@ class tinyBLAS_HP16_PPC { mma_instr::outer_product(&acc_1, vec_A[x], vec_B[x+4]); } } - SAVE_ACC(&acc_0, ii, jj); - SAVE_ACC(&acc_1, ii, jj+4); + save_acc(&acc_0, ii, jj); + save_acc(&acc_1, ii, jj+4); } void KERNEL_8x4(int64_t ii, int64_t jj) { @@ -2164,8 +2196,8 @@ class tinyBLAS_HP16_PPC { mma_instr::outer_product(&acc_1, vec_A[x+4], vec_B[x]); } } - SAVE_ACC(&acc_0, ii, jj); - SAVE_ACC(&acc_1, ii+4, jj); + save_acc(&acc_0, ii, jj); + save_acc(&acc_1, ii+4, jj); } @@ -2186,13 +2218,64 @@ class tinyBLAS_HP16_PPC { mma_instr::outer_product(&acc_3, vec_A[x+4], vec_B[x+4]); } } - - SAVE_ACC(&acc_0, ii, jj); - SAVE_ACC(&acc_1, ii, jj+4); - SAVE_ACC(&acc_2, ii+4, jj); - SAVE_ACC(&acc_3, ii+4, jj+4); + save_acc(&acc_0, ii, jj); + save_acc(&acc_1, ii, jj+4); + save_acc(&acc_2, ii+4, jj); + save_acc(&acc_3, ii+4, jj+4); } + inline void MMA_16x8(vec_t * vec_A0, vec_t * vec_A1, vec_t * vec_B, acc_t * acc) { + for (int x = 0; x < 4; x ++) { + mma_instr::outer_product(&acc[0], vec_A0[x], vec_B[x]); + mma_instr::outer_product(&acc[1], vec_A0[x], vec_B[x+4]); + mma_instr::outer_product(&acc[2], vec_A0[x+4], vec_B[x]); + mma_instr::outer_product(&acc[3], vec_A0[x+4], vec_B[x+4]); + mma_instr::outer_product(&acc[4], vec_A1[x], vec_B[x]); + mma_instr::outer_product(&acc[5], vec_A1[x], vec_B[x+4]); + mma_instr::outer_product(&acc[6], vec_A1[x+4], vec_B[x]); + mma_instr::outer_product(&acc[7], vec_A1[x+4], vec_B[x+4]); + } + } + void KERNEL(int64_t ii, int64_t jj, int64_t mc, int64_t nc, int64_t kc, vec_t * vec_A, vec_t * vec_B, int64_t kk) { + for (int64_t i = 0; i < mc; i += 16) { + int A_base_addr = (mc / 8) * (i / 8) * 8; + for (int64_t j = 0; j < nc; j += 8) { + int B_base_addr = (nc / 8) * (j / 8) * 8; + acc_t acc[8]; + vec_t A0_block[8]; vec_t A1_block[8]; + for (int x = 0; x < 8; x++) + __builtin_mma_xxsetaccz(&acc[x]); + for (int64_t l = 0; l < kc; l += 8) { + int A0_block_idx = A_base_addr + (l / 8) * 8; + int A1_block_idx = A0_block_idx + (mc / 8) * 8; + int B_block_idx = B_base_addr + (l / 8) * 8; + vec_t* A0_block = &vec_A[A0_block_idx]; + vec_t* A1_block = &vec_A[A1_block_idx]; + vec_t* B_block = &vec_B[B_block_idx]; + MMA_16x8(A0_block, A1_block, B_block, acc); + } + if (kk == 0) { + save_acc(&acc[0], ii + i, jj + j); + save_acc(&acc[1], ii + i, jj + j + 4); + save_acc(&acc[2], ii + i + 4, jj + j); + save_acc(&acc[3], ii + i + 4, jj + j + 4); + save_acc(&acc[4], ii + i + 8, jj + j); + save_acc(&acc[5], ii + i + 8, jj + j + 4); + save_acc(&acc[6], ii + i + 12, jj + j); + save_acc(&acc[7], ii + i + 12, jj + j + 4); + } else { + add_save_acc(&acc[0], ii + i, jj + j); + add_save_acc(&acc[1], ii + i, jj + j + 4); + add_save_acc(&acc[2], ii + i + 4, jj + j); + add_save_acc(&acc[3], ii + i + 4, jj + j + 4); + add_save_acc(&acc[4], ii + i + 8, jj + j); + add_save_acc(&acc[5], ii + i + 8, jj + j + 4); + add_save_acc(&acc[6], ii + i + 12, jj + j); + add_save_acc(&acc[7], ii + i + 12, jj + j + 4); + } + } + } + } template void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n) { int64_t ytiles = (m - m0) / RM; @@ -2281,6 +2364,29 @@ class tinyBLAS_HP16_PPC { } } + void matmul_tiled(int64_t m, int64_t n, int64_t mc, int64_t nc, int64_t kc) { + int64_t ytiles = m / mc; + int64_t xtiles = n / nc; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) { + end = tiles; + } + for (int64_t job = start; job < end; ++job) { + int64_t ii = (job / xtiles) * mc; + int64_t jj = (job % xtiles) * nc; + for (int64_t kk = 0; kk < k; kk += kc) { + vec_t A_pack[kc * mc / 8]; + vec_t B_pack[kc * nc / 8]; + packNormal(A + (ii * lda) + kk, lda, kc, mc, (uint8_t *)A_pack); + packNormal(B + (jj * ldb) + kk, ldb, kc, nc, (uint8_t *)B_pack); + KERNEL(ii, jj, mc, nc, kc, A_pack, B_pack, kk); + } + } + } + template NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { int64_t ytiles = (m - m0) / RM; @@ -2329,7 +2435,7 @@ class tinyBLAS_Q0_PPC { mc = 32; nc = 32; kc = 32; - n_chunk = 32 + n_chunk = 32; #endif int64_t n_aligned = 0; if (n % n_chunk == 0) { diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index c49719374ecf..42ec809ce521 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -2081,8 +2081,8 @@ void ggml_compute_forward_concat( const ggml_tensor * src1 = dst->src[1]; if (ggml_is_quantized(src0->type)) { - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous_rows(src0)); + GGML_ASSERT(ggml_is_contiguous_rows(src1)); GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0); GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0); } @@ -4449,6 +4449,70 @@ static void ggml_compute_forward_out_prod_q_f32( } } +static void ggml_compute_forward_out_prod_f16_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int ith = params->ith; + const int nth = params->nth; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne03 == ne13); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb0 == sizeof(float)); + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, (float *)dst->data, 0); + } + ggml_barrier(params->threadpool); + + const int64_t nr = ne1*ne2*ne3; + const int64_t dr = (nr + nth - 1)/nth; + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + const int64_t i12 = i2; + const int64_t i13 = i3; + + float * d = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); + + for (int64_t i01 = 0; i01 < ne01; ++i01) { + const int64_t i11 = i01; + ggml_fp16_t * s0 = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + ggml_fp16_to_fp32_row(s0, wdata, ne0); + ggml_vec_mad_f32(ne0, d, wdata, *s1); + } + } +} + void ggml_compute_forward_out_prod( const ggml_compute_params * params, ggml_tensor * dst) { @@ -4486,9 +4550,8 @@ void ggml_compute_forward_out_prod( } break; case GGML_TYPE_F16: { - GGML_ABORT("fatal error"); // todo - // ggml_compute_forward_out_prod_f16_f32(params, dst); - } + ggml_compute_forward_out_prod_f16_f32(params, dst); + } break; case GGML_TYPE_F32: { ggml_compute_forward_out_prod_f32(params, dst); @@ -6469,7 +6532,7 @@ void ggml_compute_forward_im2col_back_f32( const ggml_tensor * src1 = dst->src[1]; // convolution kernel GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS; @@ -10881,6 +10944,291 @@ void ggml_compute_forward_gated_delta_net( } } + +// ggml_compute_forward_dsv4_hc_comb + +static void ggml_dsv4_hc_comb_norm_cols(float * comb, float eps) { + constexpr int64_t hc = 4; + + for (int64_t idst = 0; idst < hc; ++idst) { + float sum = eps; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + sum += comb[idst + hc*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + comb[idst + hc*isrc] *= inv_sum; + } + } +} + +static void ggml_dsv4_hc_comb_norm_rows(float * comb, float eps) { + constexpr int64_t hc = 4; + + for (int64_t isrc = 0; isrc < hc; ++isrc) { + float sum = eps; + for (int64_t idst = 0; idst < hc; ++idst) { + sum += comb[idst + hc*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int64_t idst = 0; idst < hc; ++idst) { + comb[idst + hc*isrc] *= inv_sum; + } + } +} + +static void ggml_compute_forward_dsv4_hc_comb_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * mixes = dst->src[0]; + const ggml_tensor * scale = dst->src[1]; + const ggml_tensor * base = dst->src[2]; + + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + constexpr int64_t hc = 4; + constexpr int64_t comb_offset = 2*hc; + constexpr int64_t hc_mix_dim = (2 + hc)*hc; + + const int64_t n_tokens = mixes->ne[1]; + + GGML_ASSERT(mixes->ne[0] == hc_mix_dim); + GGML_ASSERT(dst->ne[0] == hc); + GGML_ASSERT(dst->ne[1] == hc); + GGML_ASSERT(dst->ne[2] == n_tokens); + GGML_ASSERT(scale->ne[0] >= 3); + GGML_ASSERT(base->ne[0] == hc_mix_dim); + + GGML_TENSOR_LOCALS(size_t, nbm, mixes, nb); + GGML_TENSOR_LOCALS(size_t, nbs, scale, nb); + GGML_TENSOR_LOCALS(size_t, nbb, base, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const float eps = ggml_get_op_params_f32(dst, 0); + const int32_t n_iter = ggml_get_op_params_i32(dst, 1); + GGML_ASSERT(n_iter > 0); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t dr = (n_tokens + nth - 1) / nth; + const int64_t it0 = dr * ith; + const int64_t it1 = MIN(it0 + dr, n_tokens); + + const float scale_comb = *(const float *) ((const char *) scale->data + 2*nbs0); + + for (int64_t it = it0; it < it1; ++it) { + float comb[hc*hc]; + + for (int64_t isrc = 0; isrc < hc; ++isrc) { + float max = -INFINITY; + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + const float xv = *(const float *) ((const char *) mixes->data + (comb_offset + idx)*nbm0 + it*nbm1); + const float bv = *(const float *) ((const char *) base->data + (comb_offset + idx)*nbb0); + const float v = xv * scale_comb + bv; + comb[idx] = v; + max = MAX(max, v); + } + + float sum = 0.0f; + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + const float v = expf(comb[idx] - max); + comb[idx] = v; + sum += v; + } + + const float inv_sum = 1.0f / sum; + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + comb[idx] = comb[idx] * inv_sum + eps; + } + } + + ggml_dsv4_hc_comb_norm_cols(comb, eps); + for (int32_t i = 1; i < n_iter; ++i) { + ggml_dsv4_hc_comb_norm_rows(comb, eps); + ggml_dsv4_hc_comb_norm_cols(comb, eps); + } + + for (int64_t isrc = 0; isrc < hc; ++isrc) { + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + *(float *) ((char *) dst->data + idst*nbd0 + isrc*nbd1 + it*nbd2) = comb[idx]; + } + } + } +} + +void ggml_compute_forward_dsv4_hc_comb( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_dsv4_hc_comb_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_dsv4_hc_pre + +static void ggml_compute_forward_dsv4_hc_pre_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * weights = dst->src[1]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t hc = x->ne[1]; + const int64_t n_tokens = x->ne[2]; + + GGML_ASSERT(dst->ne[0] == n_embd); + GGML_ASSERT(dst->ne[1] == n_tokens); + GGML_ASSERT(weights->ne[0] == hc); + GGML_ASSERT(weights->ne[1] == n_tokens); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbw, weights, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = n_embd * n_tokens; + const int64_t dr = (nr + nth - 1) / nth; + const int64_t ir0 = dr * ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i0 = ir % n_embd; + const int64_t it = ir / n_embd; + + float sum = 0.0f; + for (int64_t ih = 0; ih < hc; ++ih) { + const float xv = *(const float *) ((const char *) x->data + i0*nbx0 + ih*nbx1 + it*nbx2); + const float wv = *(const float *) ((const char *) weights->data + ih*nbw0 + it*nbw1); + sum += xv * wv; + } + + *(float *) ((char *) dst->data + i0*nbd0 + it*nbd1) = sum; + } +} + +void ggml_compute_forward_dsv4_hc_pre( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_dsv4_hc_pre_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_dsv4_hc_post + +static void ggml_compute_forward_dsv4_hc_post_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * residual = dst->src[1]; + const ggml_tensor * post = dst->src[2]; + const ggml_tensor * comb = dst->src[3]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t n_tokens = x->ne[1]; + const int64_t hc = residual->ne[1]; + + GGML_ASSERT(dst->ne[0] == n_embd); + GGML_ASSERT(dst->ne[1] == hc); + GGML_ASSERT(dst->ne[2] == n_tokens); + GGML_ASSERT(residual->ne[0] == n_embd); + GGML_ASSERT(residual->ne[2] == n_tokens); + GGML_ASSERT(post->ne[0] == hc); + GGML_ASSERT(post->ne[1] == n_tokens); + GGML_ASSERT(comb->ne[0] == hc); + GGML_ASSERT(comb->ne[1] == hc); + GGML_ASSERT(comb->ne[2] == n_tokens); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbr, residual, nb); + GGML_TENSOR_LOCALS(size_t, nbp, post, nb); + GGML_TENSOR_LOCALS(size_t, nbc, comb, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = n_embd * hc * n_tokens; + const int64_t dr = (nr + nth - 1) / nth; + const int64_t ir0 = dr * ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i0 = ir % n_embd; + const int64_t idst = (ir / n_embd) % hc; + const int64_t it = ir / (n_embd * hc); + + const float xv = *(const float *) ((const char *) x->data + i0*nbx0 + it*nbx1); + const float pv = *(const float *) ((const char *) post->data + idst*nbp0 + it*nbp1); + + float sum = xv * pv; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + const float rv = *(const float *) ((const char *) residual->data + i0*nbr0 + isrc*nbr1 + it*nbr2); + const float cv = *(const float *) ((const char *) comb->data + idst*nbc0 + isrc*nbc1 + it*nbc2); + sum += rv * cv; + } + + *(float *) ((char *) dst->data + i0*nbd0 + idst*nbd1 + it*nbd2) = sum; + } +} + +void ggml_compute_forward_dsv4_hc_post( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_dsv4_hc_post_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + // ggml_compute_forward_rwkv_wkv7 static void ggml_compute_forward_rwkv_wkv7_f32( diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h index e956c25d3eda..4c1642a67603 100644 --- a/ggml/src/ggml-cpu/ops.h +++ b/ggml/src/ggml-cpu/ops.h @@ -106,6 +106,9 @@ void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, s void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_gated_delta_net(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_lightning_indexer(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_dsv4_hc_comb(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_dsv4_hc_pre(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_dsv4_hc_post(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 290dc4aff259..fa2dd26202ab 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -362,6 +362,15 @@ static bool blackwell_mma_available(const int cc) { ggml_cuda_highest_compiled_arch(cc) < GGML_CUDA_CC_RUBIN; } +// Checks whether the tensor's base data pointer and higher-dimensional strides are byte-aligned to `alignment` bytes. +static bool ggml_cuda_is_aligned(const ggml_tensor * tensor, const size_t alignment) { + GGML_ASSERT(tensor != nullptr); + return (reinterpret_cast(tensor->data) % alignment) == 0 && + tensor->nb[1] % alignment == 0 && + tensor->nb[2] % alignment == 0 && + tensor->nb[3] % alignment == 0; +} + static constexpr __device__ int ggml_cuda_get_physical_warp_size() { #if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__)) return 64; @@ -937,6 +946,9 @@ static __device__ __forceinline__ uint2 fast_div_modulo(uint32_t n, const uint3 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, float2 & v); +template +using dequantize_kq_t = void (*)(const void * vx, const int64_t ib, dst_t * y, const int tid); + static __device__ __forceinline__ float get_alibi_slope( const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1 ) { @@ -1115,7 +1127,8 @@ struct ggml_cuda_type_traits { ////////////////////// struct ggml_cuda_device_info { - int device_count; + int device_count; // number of (possibly virtual) devices exposed to the rest of ggml + int physical_device_count; // number of physical CUDA devices actually present struct cuda_device_info { int cc; // compute capability @@ -1128,6 +1141,9 @@ struct ggml_cuda_device_info { size_t total_vram; int warp_size; // Number of threads in a dispatch bool supports_cooperative_launch; // whether cooperative launch is supported + int physical_device; // backing physical CUDA device for this (virtual) device + int physical_share_count; // number of (virtual) devices sharing this device's physical GPU + int virtual_index; // index of this (virtual) device among those sharing its physical GPU }; cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {}; diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu index 276ee64e8c0a..6df89013ca79 100644 --- a/ggml/src/ggml-cuda/concat.cu +++ b/ggml/src/ggml-cuda/concat.cu @@ -141,27 +141,25 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) template static void concat_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, int dim, cudaStream_t stream) { - if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + if (dim != 3 && ggml_is_contiguous_to_3(src0) && ggml_is_contiguous_to_3(src1)) { const T * src0_d = (const T *) src0->data; const T * src1_d = (const T *) src1->data; T * dst_d = (T *) dst->data; - if (dim != 3) { - for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) { - concat_cont_cuda( - src0_d + i3*(src0->nb[3] / sizeof(T)), - src1_d + i3*(src1->nb[3] / sizeof(T)), - dst_d + i3*( dst->nb[3] / sizeof(T)), - ggml_row_size(src0->type, src0->ne[0])/sizeof(T), src0->ne[1], src0->ne[2], - ggml_row_size(dst->type, dst->ne[0])/sizeof(T), dst->ne[1], dst->ne[2], dim, stream); - } - } else { - const size_t size0 = ggml_nbytes(src0); - const size_t size1 = ggml_nbytes(src1); - - CUDA_CHECK(cudaMemcpyAsync((char *) dst->data, src0->data, size0, cudaMemcpyDeviceToDevice, stream)); - CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream)); + for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) { + concat_cont_cuda( + src0_d + i3*(src0->nb[3] / sizeof(T)), + src1_d + i3*(src1->nb[3] / sizeof(T)), + dst_d + i3*( dst->nb[3] / sizeof(T)), + ggml_row_size(src0->type, src0->ne[0])/sizeof(T), src0->ne[1], src0->ne[2], + ggml_row_size(dst->type, dst->ne[0])/sizeof(T), dst->ne[1], dst->ne[2], dim, stream); } + } else if (dim == 3 && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + const size_t size0 = ggml_nbytes(src0); + const size_t size1 = ggml_nbytes(src1); + + CUDA_CHECK(cudaMemcpyAsync((char *) dst->data, src0->data, size0, cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream)); } else { GGML_ASSERT(!ggml_is_quantized(src0->type)); @@ -208,12 +206,17 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->type == src0->type); if (ggml_is_quantized(src0->type)) { - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); + if (dim == 3) { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + } else { + GGML_ASSERT(ggml_is_contiguous_to_3(src0)); + GGML_ASSERT(ggml_is_contiguous_to_3(src1)); + } GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0); GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0); - // if tensors are contiguous and ne[0] is multiple of the block size we can concat both tensors as byte tensors + // if first 3 dimensions are contiguous and ne[0] is multiple of the block size we can concat both tensors as byte tensors concat_cuda(src0, src1, dst, dim, stream); } else { GGML_ASSERT(ggml_blck_size(src0->type) == 1); diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu index f04a2d5a2cc8..946e02af50d0 100644 --- a/ggml/src/ggml-cuda/convert.cu +++ b/ggml/src/ggml-cuda/convert.cu @@ -140,358 +140,107 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t template static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_q2_K * x = (const block_q2_K *) vx; - - const int64_t tid = threadIdx.x; - const int64_t n = tid/32; - const int64_t l = tid - 32*n; - const int64_t is = 8*n + l/16; - - const uint8_t q = x[i].qs[32*n + l]; - dst_t * y = yy + i*QK_K + 128*n; - - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); - y[l+ 0] = ggml_cuda_cast(dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4)); - y[l+32] = ggml_cuda_cast(dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4)); - y[l+64] = ggml_cuda_cast(dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4)); - y[l+96] = ggml_cuda_cast(dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4)); + dequantize_q2_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int64_t i = blockIdx.x; - const block_q3_K * x = (const block_q3_K *) vx; - - const int64_t r = threadIdx.x/4; - const int64_t tid = r/2; - const int64_t is0 = r%2; - const int64_t l0 = 16*is0 + 4*(threadIdx.x%4); - const int64_t n = tid / 4; - const int64_t j = tid - 4*n; - - uint8_t m = 1 << (4*n + j); - int64_t is = 8*n + 2*j + is0; - int shift = 2*j; - - int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (x[i].scales[is-8] >> 4) | (((x[i].scales[is+0] >> 4) & 3) << 4) : - (x[i].scales[is-8] >> 4) | (((x[i].scales[is-4] >> 6) & 3) << 4); - float d_all = x[i].d; - float dl = d_all * (us - 32); - - dst_t * y = yy + i*QK_K + 128*n + 32*j; - const uint8_t * q = x[i].qs + 32*n; - const uint8_t * hm = x[i].hmask; - - for (int l = l0; l < l0+4; ++l) { - y[l] = ggml_cuda_cast(dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4))); - } -} -static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { - if (j < 4) { - d = q[j] & 63; m = q[j + 4] & 63; - } else { - d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); - m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); - } + dequantize_q3_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q4_K * x = (const block_q4_K *) vx; - const int64_t i = blockIdx.x; - // assume 32 threads - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; - const int64_t ir = tid%8; - const int64_t is = 2*il; - const int64_t n = 4; - - dst_t * y = yy + i*QK_K + 64*il + n*ir; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint8_t * q = x[i].qs + 32*il + n*ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; - for (int l = 0; l < n; ++l) { - y[l + 0] = ggml_cuda_cast(d1 * (q[l] & 0xF) - m1); - y[l +32] = ggml_cuda_cast(d2 * (q[l] >> 4) - m2); - } + dequantize_q4_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q5_K * x = (const block_q5_K *) vx; - const int64_t i = blockIdx.x; - // assume 64 threads - this is very slightly better than the one below - const int64_t tid = threadIdx.x; - const int64_t il = tid/16; // il is in 0...3 - const int64_t ir = tid%16; // ir is in 0...15 - const int64_t is = 2*il; // is is in 0...6 - - dst_t * y = yy + i*QK_K + 64*il + 2*ir; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint8_t * ql = x[i].qs + 32*il + 2*ir; - const uint8_t * qh = x[i].qh + 2*ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; - - uint8_t hm = 1 << (2*il); - y[ 0] = ggml_cuda_cast(d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1); - y[ 1] = ggml_cuda_cast(d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1); - hm <<= 1; - y[32] = ggml_cuda_cast(d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2); - y[33] = ggml_cuda_cast(d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2); + dequantize_q5_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q6_K * x = (const block_q6_K *) vx; - const int64_t i = blockIdx.x; - // assume 64 threads - this is very slightly better than the one below - const int64_t tid = threadIdx.x; - const int64_t ip = tid/32; // ip is 0 or 1 - const int64_t il = tid - 32*ip; // 0...32 - const int64_t is = 8*ip + il/16; - - dst_t * y = yy + i*QK_K + 128*ip + il; - - const float d = x[i].d; - - const uint8_t * ql = x[i].ql + 64*ip + il; - const uint8_t qh = x[i].qh[32*ip + il]; - const int8_t * sc = x[i].scales + is; - - y[ 0] = ggml_cuda_cast(d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)); - y[32] = ggml_cuda_cast(d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)); - y[64] = ggml_cuda_cast(d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32)); - y[96] = ggml_cuda_cast(d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32)); + dequantize_q6_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq2_xxs * x = (const block_iq2_xxs *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * q2 = x[i].qs + 4*ib; - const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); - } + dequantize_iq2_xxs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq2_xs * x = (const block_iq2_xs *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * q2 = x[i].qs + 4*ib; - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; - const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); - } + dequantize_iq2_xs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq2_s * x = (const block_iq2_s *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; - const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); - } + dequantize_iq2_s(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq3_xxs * x = (const block_iq3_xxs *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * q3 = x[i].qs + 8*ib; - const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; - for (int j = 0; j < 4; ++j) { - y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); - y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); - } + dequantize_iq3_xxs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq3_s * x = (const block_iq3_s *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * qs = x[i].qs + 8*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); - const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); - const uint8_t signs = x[i].signs[4*ib + il]; - for (int j = 0; j < 4; ++j) { - y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); - y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); - } + dequantize_iq3_s(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq1_s * x = (const block_iq1_s *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; - const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1); - uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; - grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * (q[j] + delta)); - } + dequantize_iq1_s(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq1_m * x = (const block_iq1_m *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * sc = (const uint16_t *)x[i].scales; - iq1m_scale_t scale; - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4); - const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1); - const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA; - uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; - grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * (q[j] + delta)); - } + dequantize_iq1_m(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL); - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[ib].qs + 4*il; - const float d = (float)x[ib].d; - for (int j = 0; j < 4; ++j) { - y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); - y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); - } + dequantize_iq4_nl(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int64_t i = blockIdx.x; - const block_iq4_xs * x = (const block_iq4_xs *)vx; + const int64_t i = blockIdx.x; - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[i].qs + 16*ib + 4*il; - const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); - for (int j = 0; j < 4; ++j) { - y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); - y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); - } + dequantize_iq4_xs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4); - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[ib].qs + 4*il; - const float d = ggml_cuda_e8m0_to_fp32(x[ib].e); - for (int j = 0; j < 4; ++j) { - y[j+ 0] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] & 0xf]*0.5f); - y[j+16] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] >> 4]*0.5f); - } + dequantize_mxfp4(vx, i, yy + i*QK_K, threadIdx.x); } template diff --git a/ggml/src/ggml-cuda/dequantize.cuh b/ggml/src/ggml-cuda/dequantize.cuh index 9ae1342fc0ef..8ab5ad8e74e3 100644 --- a/ggml/src/ggml-cuda/dequantize.cuh +++ b/ggml/src/ggml-cuda/dequantize.cuh @@ -1,4 +1,5 @@ #include "common.cuh" +#include "convert.cuh" static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ const block_q1_0 * x = (const block_q1_0 *) vx; @@ -97,3 +98,335 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in v.x *= d; v.y *= d; } + +//================================== k-quants + +// Each call dequantizes one super-block of QK_K values into y using the +// thread layout of the caller: 32 threads for q4_K, 64 threads otherwise. + +template +static __device__ __forceinline__ void dequantize_q2_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q2_K * x = (const block_q2_K *) vx; + + const int64_t n = tid/32; + const int64_t l = tid - 32*n; + const int64_t is = 8*n + l/16; + + const uint8_t q = x[ib].qs[32*n + l]; + dst_t * y = yy + 128*n; + + float dall = __low2half(x[ib].dm); + float dmin = __high2half(x[ib].dm); + y[l+ 0] = ggml_cuda_cast(dall * (x[ib].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[ib].scales[is+0] >> 4)); + y[l+32] = ggml_cuda_cast(dall * (x[ib].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[ib].scales[is+2] >> 4)); + y[l+64] = ggml_cuda_cast(dall * (x[ib].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[ib].scales[is+4] >> 4)); + y[l+96] = ggml_cuda_cast(dall * (x[ib].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[ib].scales[is+6] >> 4)); +} + +template +static __device__ __forceinline__ void dequantize_q3_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q3_K * x = (const block_q3_K *) vx; + + const int64_t r = tid/4; + const int64_t t = r/2; + const int64_t is0 = r%2; + const int64_t l0 = 16*is0 + 4*(tid%4); + const int64_t n = t / 4; + const int64_t j = t - 4*n; + + uint8_t m = 1 << (4*n + j); + int64_t is = 8*n + 2*j + is0; + int shift = 2*j; + + int8_t us = is < 4 ? (x[ib].scales[is-0] & 0xF) | (((x[ib].scales[is+8] >> 0) & 3) << 4) : + is < 8 ? (x[ib].scales[is-0] & 0xF) | (((x[ib].scales[is+4] >> 2) & 3) << 4) : + is < 12 ? (x[ib].scales[is-8] >> 4) | (((x[ib].scales[is+0] >> 4) & 3) << 4) : + (x[ib].scales[is-8] >> 4) | (((x[ib].scales[is-4] >> 6) & 3) << 4); + float d_all = x[ib].d; + float dl = d_all * (us - 32); + + dst_t * y = yy + 128*n + 32*j; + const uint8_t * q = x[ib].qs + 32*n; + const uint8_t * hm = x[ib].hmask; + + for (int l = l0; l < l0+4; ++l) { + y[l] = ggml_cuda_cast(dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4))); + } +} + +static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { + if (j < 4) { + d = q[j] & 63; m = q[j + 4] & 63; + } else { + d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + } +} + +template +static __device__ __forceinline__ void dequantize_q4_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q4_K * x = (const block_q4_K *) vx; + + // assume 32 threads + const int64_t il = tid/8; + const int64_t ir = tid%8; + const int64_t is = 2*il; + const int64_t n = 4; + + dst_t * y = yy + 64*il + n*ir; + + const float dall = __low2half(x[ib].dm); + const float dmin = __high2half(x[ib].dm); + + const uint8_t * q = x[ib].qs + 32*il + n*ir; + + uint8_t sc, m; + get_scale_min_k4(is + 0, x[ib].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[ib].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + for (int l = 0; l < n; ++l) { + y[l + 0] = ggml_cuda_cast(d1 * (q[l] & 0xF) - m1); + y[l +32] = ggml_cuda_cast(d2 * (q[l] >> 4) - m2); + } +} + +template +static __device__ __forceinline__ void dequantize_q5_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q5_K * x = (const block_q5_K *) vx; + + // assume 64 threads - this is very slightly better than the one below + const int64_t il = tid/16; // il is in 0...3 + const int64_t ir = tid%16; // ir is in 0...15 + const int64_t is = 2*il; // is is in 0...6 + + dst_t * y = yy + 64*il + 2*ir; + + const float dall = __low2half(x[ib].dm); + const float dmin = __high2half(x[ib].dm); + + const uint8_t * ql = x[ib].qs + 32*il + 2*ir; + const uint8_t * qh = x[ib].qh + 2*ir; + + uint8_t sc, m; + get_scale_min_k4(is + 0, x[ib].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[ib].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + + uint8_t hm = 1 << (2*il); + y[ 0] = ggml_cuda_cast(d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1); + y[ 1] = ggml_cuda_cast(d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1); + hm <<= 1; + y[32] = ggml_cuda_cast(d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2); + y[33] = ggml_cuda_cast(d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2); +} + +template +static __device__ __forceinline__ void dequantize_q6_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q6_K * x = (const block_q6_K *) vx; + + // assume 64 threads - this is very slightly better than the one below + const int64_t ip = tid/32; // ip is 0 or 1 + const int64_t il = tid - 32*ip; // 0...32 + const int64_t is = 8*ip + il/16; + + dst_t * y = yy + 128*ip + il; + + const float d = x[ib].d; + + const uint8_t * ql = x[ib].ql + 64*ip + il; + const uint8_t qh = x[ib].qh[32*ip + il]; + const int8_t * sc = x[ib].scales + is; + + y[ 0] = ggml_cuda_cast(d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)); + y[32] = ggml_cuda_cast(d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)); + y[64] = ggml_cuda_cast(d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32)); + y[96] = ggml_cuda_cast(d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32)); +} + +//================================== i-quants + +// Each call dequantizes one super-block of QK_K values into y with 32 +// threads; iq4_nl packs QK_K/QK4_NL sub-blocks per super-block. + +template +static __device__ __forceinline__ void dequantize_iq2_xxs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq2_xxs * x = (const block_iq2_xxs *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint16_t * q2 = x[ibs].qs + 4*ib; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)x[ibs].d * (0.5f + (aux32 >> 28)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq2_xs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq2_xs * x = (const block_iq2_xs *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint16_t * q2 = x[ibs].qs + 4*ib; + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); + const float d = (float)x[ibs].d * (0.5f + ((x[ibs].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq2_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq2_s * x = (const block_iq2_s *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[ibs].qs[4*ib+il] | ((x[ibs].qh[ib] << (8-2*il)) & 0x300))); + const float d = (float)x[ibs].d * (0.5f + ((x[ibs].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = x[ibs].qs[QK_K/8+4*ib+il]; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq3_xxs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq3_xxs * x = (const block_iq3_xxs *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint8_t * q3 = x[ibs].qs + 8*ib; + const uint16_t * gas = (const uint16_t *)(x[ibs].qs + QK_K/4) + 2*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = (float)x[ibs].d * (0.5f + (aux32 >> 28)) * 0.5f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 4; ++j) { + y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); + y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq3_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq3_s * x = (const block_iq3_s *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint8_t * qs = x[ibs].qs + 8*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[ibs].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[ibs].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[ibs].d * (1 + 2*((x[ibs].scales[ib/2] >> 4*(ib%2)) & 0xf)); + const uint8_t signs = x[ibs].signs[4*ib + il]; + for (int j = 0; j < 4; ++j) { + y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); + y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq1_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq1_s * x = (const block_iq1_s *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const float delta = x[ibs].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; + const float d = (float)x[ibs].d * (2*((x[ibs].qh[ib] >> 12) & 7) + 1); + uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = iq1s_grid_gpu[x[ibs].qs[4*ib+il] | (((x[ibs].qh[ib] >> 3*il) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * (q[j] + delta)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq1_m(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq1_m * x = (const block_iq1_m *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint16_t * sc = (const uint16_t *)x[ibs].scales; + iq1m_scale_t scale; + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4); + const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1); + const float delta = x[ibs].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA; + uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = iq1s_grid_gpu[x[ibs].qs[4*ib+il] | (((x[ibs].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * (q[j] + delta)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq4_nl(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq4_nl * x = (const block_iq4_nl *) vx + ibs*(QK_K/QK4_NL); + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = (float)x[ib].d; + for (int j = 0; j < 4; ++j) { + y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); + y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); + } +} + +template +static __device__ __forceinline__ void dequantize_iq4_xs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + const block_iq4_xs * x = (const block_iq4_xs *)vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 4*il; + const uint8_t * q4 = x[ibs].qs + 16*ib + 4*il; + const float d = (float)x[ibs].d * ((((x[ibs].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[ibs].scales_h >> 2*ib) & 3) << 4)) - 32); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); + y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); + } +} + +template +static __device__ __forceinline__ void dequantize_mxfp4(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_mxfp4 * x = (const block_mxfp4 *) vx + ibs*(QK_K/QK_MXFP4); + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = ggml_cuda_e8m0_to_fp32(x[ib].e); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] & 0xf]*0.5f); + y[j+16] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] >> 4]*0.5f); + } +} diff --git a/ggml/src/ggml-cuda/dsv4-hc.cu b/ggml/src/ggml-cuda/dsv4-hc.cu new file mode 100644 index 000000000000..c4b19a787b0e --- /dev/null +++ b/ggml/src/ggml-cuda/dsv4-hc.cu @@ -0,0 +1,294 @@ +#include "common.cuh" +#include "dsv4-hc.cuh" + + +static constexpr int DSV4_HC = 4; + + +static __device__ void dsv4_hc_comb_norm_cols(float * comb, float eps) { + for (int idst = 0; idst < DSV4_HC; ++idst) { + float sum = eps; + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + sum += comb[idst + DSV4_HC*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + comb[idst + DSV4_HC*isrc] *= inv_sum; + } + } +} + +static __device__ void dsv4_hc_comb_norm_rows(float * comb, float eps) { + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + float sum = eps; + for (int idst = 0; idst < DSV4_HC; ++idst) { + sum += comb[idst + DSV4_HC*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int idst = 0; idst < DSV4_HC; ++idst) { + comb[idst + DSV4_HC*isrc] *= inv_sum; + } + } +} + +static __global__ void dsv4_hc_comb_f32( + const float * mixes, + const float * scale, + const float * base, + float * dst, + int64_t n_tokens, + int64_t sm0, + int64_t sm1, + int64_t ss0, + int64_t sb0, + int64_t sd0, + int64_t sd1, + int64_t sd2, + float eps, + int32_t n_iter) { + constexpr int comb_offset = 2*DSV4_HC; + + ggml_cuda_pdl_lc(); + const int64_t it = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; + + if (it >= n_tokens) { + return; + } + + ggml_cuda_pdl_sync(); + + const float scale_comb = scale[2*ss0]; + float comb[DSV4_HC*DSV4_HC]; + + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + float max = -INFINITY; + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + const float v = mixes[(comb_offset + idx)*sm0 + it*sm1] * scale_comb + base[(comb_offset + idx)*sb0]; + comb[idx] = v; + max = fmaxf(max, v); + } + + float sum = 0.0f; + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + const float v = expf(comb[idx] - max); + comb[idx] = v; + sum += v; + } + + const float inv_sum = 1.0f / sum; + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + comb[idx] = comb[idx] * inv_sum + eps; + } + } + + dsv4_hc_comb_norm_cols(comb, eps); + for (int32_t i = 1; i < n_iter; ++i) { + dsv4_hc_comb_norm_rows(comb, eps); + dsv4_hc_comb_norm_cols(comb, eps); + } + + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + dst[idst*sd0 + isrc*sd1 + it*sd2] = comb[idx]; + } + } +} + +static __global__ void dsv4_hc_pre_f32( + const float * x, + const float * weights, + float * dst, + int64_t n_embd, + int64_t hc, + int64_t n_tokens, + int64_t sx0, + int64_t sx1, + int64_t sx2, + int64_t sw0, + int64_t sw1, + int64_t sd0, + int64_t sd1) { + ggml_cuda_pdl_lc(); + const int64_t ir = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; + const int64_t nr = n_embd * n_tokens; + + if (ir >= nr) { + return; + } + + ggml_cuda_pdl_sync(); + + const int64_t i0 = ir % n_embd; + const int64_t it = ir / n_embd; + + float sum = x[i0*sx0 + it*sx2] * weights[it*sw1]; + for (int64_t ih = 1; ih < hc; ++ih) { + const float xv = x[i0*sx0 + ih*sx1 + it*sx2]; + const float wv = weights[ih*sw0 + it*sw1]; + sum += xv * wv; + } + + dst[i0*sd0 + it*sd1] = sum; +} + +static __global__ void dsv4_hc_post_f32( + const float * x, + const float * residual, + const float * post, + const float * comb, + float * dst, + int64_t n_embd, + int64_t hc, + int64_t n_tokens, + int64_t sx0, + int64_t sx1, + int64_t sr0, + int64_t sr1, + int64_t sr2, + int64_t sp0, + int64_t sp1, + int64_t sc0, + int64_t sc1, + int64_t sc2, + int64_t sd0, + int64_t sd1, + int64_t sd2) { + ggml_cuda_pdl_lc(); + const int64_t ir = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; + const int64_t nr = n_embd * hc * n_tokens; + + if (ir >= nr) { + return; + } + + ggml_cuda_pdl_sync(); + + const int64_t i0 = ir % n_embd; + const int64_t idst = (ir / n_embd) % hc; + const int64_t it = ir / (n_embd * hc); + + float sum = x[i0*sx0 + it*sx1] * post[idst*sp0 + it*sp1]; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + sum += residual[i0*sr0 + isrc*sr1 + it*sr2] * comb[idst*sc0 + isrc*sc1 + it*sc2]; + } + + dst[i0*sd0 + idst*sd1 + it*sd2] = sum; +} + +void ggml_cuda_op_dsv4_hc_comb(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * mixes = dst->src[0]; + const ggml_tensor * scale = dst->src[1]; + const ggml_tensor * base = dst->src[2]; + + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + constexpr int64_t hc_mix_dim = (2 + DSV4_HC)*DSV4_HC; + + GGML_ASSERT(mixes->ne[0] == hc_mix_dim); + GGML_ASSERT(dst->ne[0] == DSV4_HC); + GGML_ASSERT(dst->ne[1] == DSV4_HC); + GGML_ASSERT(dst->ne[2] == mixes->ne[1]); + GGML_ASSERT(scale->ne[0] >= 3); + GGML_ASSERT(base->ne[0] == hc_mix_dim); + + GGML_TENSOR_LOCALS(size_t, nbm, mixes, nb); + GGML_TENSOR_LOCALS(size_t, nbs, scale, nb); + GGML_TENSOR_LOCALS(size_t, nbb, base, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int64_t n_tokens = mixes->ne[1]; + const float eps = ggml_get_op_params_f32(dst, 0); + const int32_t n_iter = ggml_get_op_params_i32(dst, 1); + + const int block_size = 256; + const dim3 block_dims(block_size, 1, 1); + const dim3 grid_dims((n_tokens + block_size - 1) / block_size, 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(dsv4_hc_comb_f32, launch_params, + (const float *) mixes->data, (const float *) scale->data, (const float *) base->data, (float *) dst->data, + n_tokens, + nbm0 / sizeof(float), nbm1 / sizeof(float), + nbs0 / sizeof(float), + nbb0 / sizeof(float), + nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float), + eps, n_iter); +} + +void ggml_cuda_op_dsv4_hc_pre(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * weights = dst->src[1]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbw, weights, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int64_t n_embd = x->ne[0]; + const int64_t hc = x->ne[1]; + const int64_t n_tokens = x->ne[2]; + + const int block_size = 256; + const int64_t nr = n_embd * n_tokens; + const dim3 block_dims(block_size, 1, 1); + const dim3 grid_dims((nr + block_size - 1) / block_size, 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(dsv4_hc_pre_f32, launch_params, + (const float *) x->data, (const float *) weights->data, (float *) dst->data, + n_embd, hc, n_tokens, + nbx0 / sizeof(float), nbx1 / sizeof(float), nbx2 / sizeof(float), + nbw0 / sizeof(float), nbw1 / sizeof(float), + nbd0 / sizeof(float), nbd1 / sizeof(float)); +} + +void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * residual = dst->src[1]; + const ggml_tensor * post = dst->src[2]; + const ggml_tensor * comb = dst->src[3]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbr, residual, nb); + GGML_TENSOR_LOCALS(size_t, nbp, post, nb); + GGML_TENSOR_LOCALS(size_t, nbc, comb, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int64_t n_embd = x->ne[0]; + const int64_t n_tokens = x->ne[1]; + const int64_t hc = residual->ne[1]; + + const int block_size = 256; + const int64_t nr = n_embd * hc * n_tokens; + const dim3 block_dims(block_size, 1, 1); + const dim3 grid_dims((nr + block_size - 1) / block_size, 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(dsv4_hc_post_f32, launch_params, + (const float *) x->data, (const float *) residual->data, + (const float *) post->data, (const float *) comb->data, (float *) dst->data, + n_embd, hc, n_tokens, + nbx0 / sizeof(float), nbx1 / sizeof(float), + nbr0 / sizeof(float), nbr1 / sizeof(float), nbr2 / sizeof(float), + nbp0 / sizeof(float), nbp1 / sizeof(float), + nbc0 / sizeof(float), nbc1 / sizeof(float), nbc2 / sizeof(float), + nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float)); +} diff --git a/ggml/src/ggml-cuda/dsv4-hc.cuh b/ggml/src/ggml-cuda/dsv4-hc.cuh new file mode 100644 index 000000000000..2379aaefb41b --- /dev/null +++ b/ggml/src/ggml-cuda/dsv4-hc.cuh @@ -0,0 +1,6 @@ +#include "common.cuh" +#include "ggml.h" + +void ggml_cuda_op_dsv4_hc_comb(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_dsv4_hc_pre(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/fattn-tile.cu b/ggml/src/ggml-cuda/fattn-tile.cu index c8281497d148..e563729a2ed3 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cu +++ b/ggml/src/ggml-cuda/fattn-tile.cu @@ -1,6 +1,5 @@ #include "common.cuh" #include "fattn-tile.cuh" -#include "fattn-wmma-f16.cuh" void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * K = dst->src[1]; diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh index 3e07a9f7e04f..d1164b8526d3 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cuh +++ b/ggml/src/ggml-cuda/fattn-tile.cuh @@ -1,6 +1,5 @@ #include "common.cuh" #include "fattn-common.cuh" -#include "fattn-wmma-f16.cuh" // nbatch_fa == number of KQ rows to process per iteration // nbatch_K == number of K columns to load in parallel for KQ calculation @@ -825,12 +824,7 @@ static __global__ void flash_attn_tile( // Skip unused kernel variants for faster compilation: - if ( -#ifdef GGML_USE_WMMA_FATTN - (ncols2 != 1 && DV != 40 && DV != 72 && DV != 512) || -#endif // GGML_USE_WMMA_FATTN - (use_logit_softcap && !(DV == 128 || DV == 256 || DV == 512)) - ) { + if ((use_logit_softcap && !(DV == 128 || DV == 256 || DV == 512))) { GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, max_bias, m0, m1, n_head_log2, logit_softcap, ne00, ne01, ne02, ne03, diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu deleted file mode 100644 index 6850716fc0dc..000000000000 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu +++ /dev/null @@ -1,705 +0,0 @@ -// Old and deprecated WMMA FlashAttention implementation. -// It is still needed for Volta since the memory layout of NVIDIA tensor cores changed with Turing. -// Long-term the WMMA code should be replaced with a dedicated Volta implementation. - -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-wmma-f16.cuh" - -#ifdef GGML_USE_WMMA_FATTN -#if !defined(GGML_USE_HIP) -#include -#if defined(GGML_USE_MUSA) -namespace wmma = mtmusa::wmma; -#else // GGML_USE_MUSA -namespace wmma = nvcuda::wmma; -#endif // GGML_USE_MUSA -#elif defined(GGML_USE_HIP) -#include -namespace wmma = rocwmma; -#endif // !defined(GGML_USE_HIP) -#endif // GGML_USE_WMMA_FATTN - -// D == head size, VKQ_stride == num VKQ rows calculated in parallel: -template -__launch_bounds__(nwarps*ggml_cuda_get_physical_warp_size(), 1) -static __global__ void flash_attn_ext_f16( - const char * Q_ptr, - const char * K_ptr, - const char * V_ptr, - const char * mask_ptr, - const char * sinks_ptr, - const int * KV_max_ptr, - float * dst_ptr, - float2 * dst_meta_ptr, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const float logit_softcap, - const int32_t ne00, const uint3 ne01, const int32_t ne02, const int32_t ne03, - const int32_t nb01, const int32_t nb02, const int32_t nb03, - const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13, - const int32_t nb11, const int32_t nb12, const int64_t nb13, - const int32_t nb21, const int32_t nb22, const int64_t nb23, - const int32_t ne31, const int32_t ne32, const int32_t ne33, - const int32_t nb31, const int32_t nb32, const int64_t nb33) { -#if defined(FLASH_ATTN_AVAILABLE) && (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN)) - const char * GGML_CUDA_RESTRICT Q = Q_ptr; - const char * GGML_CUDA_RESTRICT K = K_ptr; - const char * GGML_CUDA_RESTRICT V = V_ptr; - const char * GGML_CUDA_RESTRICT mask = mask_ptr; - const char * GGML_CUDA_RESTRICT sinks = sinks_ptr; - const int * GGML_CUDA_RESTRICT KV_max = KV_max_ptr; - float * GGML_CUDA_RESTRICT dst = dst_ptr; - float2 * GGML_CUDA_RESTRICT dst_meta = dst_meta_ptr; - // Skip unused kernel variants for faster compilation: - if (use_logit_softcap && !(D == 128 || D == 256)) { - NO_DEVICE_CODE; - return; - } - - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - constexpr int warp_size = ggml_cuda_get_physical_warp_size(); - - const int ic0 = ncols*blockIdx.x; // Index of the first Q/QKV column to work on. - - static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); - static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); - constexpr int frag_m = ncols == 8 ? 32 : 16; - constexpr int frag_n = ncols == 8 ? 8 : 16; - static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); -#if defined(GGML_USE_HIP) && HIP_VERSION >= 60500000 - typedef wmma::fragment frag_a_K; - typedef wmma::fragment frag_a_V; - typedef wmma::fragment frag_b; - typedef wmma::fragment frag_c_KQ; - typedef wmma::fragment frag_c_VKQ; -#else - typedef wmma::fragment frag_a_K; - typedef wmma::fragment frag_a_V; - typedef wmma::fragment frag_b; - typedef wmma::fragment frag_c_KQ; - typedef wmma::fragment frag_c_VKQ; -#endif - - constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. - constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. - static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); - - // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: - constexpr int D_padded = D + 8; - constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; - constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); - - ggml_cuda_pdl_sync(); - const int sequence = blockIdx.z / ne02; - const int head = blockIdx.z - sequence*ne02; - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float * Q_f = (const float *) (Q + nb03* sequence + nb02* head + nb01*ic0); - const half * K_h = (const half *) (K + nb13* sequence + nb12*(head / gqa_ratio)); - const half * V_h = (const half *) (V + nb13* sequence + nb12*(head / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0); - const half2 * mask2 = (const half2 *) maskh; - const float * sinksf = (const float *) sinks; - - const int stride_Q = nb01 / sizeof(float); - const int stride_KV = nb11 / sizeof(half); - - const float slopef = get_alibi_slope(max_bias, head, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - const half2 slope2 = make_half2(slopef, slopef); - - const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap); - - frag_b Q_b[D/16][ncols/frag_n]; - - // A single buffer for temporarily holding tiles of KQ and VKQ parts: - constexpr int mem_KQ = ncols*kqs_padded*kqar; - constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; - __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; - float * KQ_f = (float *) KQ; - half2 * KQ2 = (half2 *) KQ; - - float KQ_rowsum_f[ncols/nwarps] = {0.0f}; - float KQ_max_f[ncols/nwarps]; - float KQ_max_scale_f[ncols/nwarps] = {0.0f}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_f[j] = -FLT_MAX/2.0f; - } - - half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - half2 KQ_max_h2[ncols/nwarps]; - half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); - } - - __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. - half2 * VKQ2 = (half2 *) VKQ; - -#if defined(GGML_USE_HIP) && HIP_VERSION >= 60500000 - const _Float16 * K_h_f16 = reinterpret_cast(K_h); - const _Float16 * V_h_f16 = reinterpret_cast(V_h); - _Float16 * KQ_f16 = reinterpret_cast<_Float16 *>(KQ); - _Float16 * VKQ_f16 = reinterpret_cast<_Float16 *>(VKQ); -#else - const half * K_h_f16 = K_h; - const half * V_h_f16 = V_h; - half * KQ_f16 = KQ; - half * VKQ_f16 = VKQ; -#endif - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) { - break; - } - VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); - } - } - - // Convert Q to half and apply scale, temporarily store in KQ: -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D && i >= D) { - break; - } - KQ[j*D_padded + i] = ic0 + j < int(ne01.z) ? Q_f[j*stride_Q + i] * scale : 0.0f; - } - } - - __syncthreads(); - - // Load Q into tensor core fragments/registers since it will be used frequently: -#pragma unroll - for (int i0 = 0; i0 < D; i0 += 16) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ_f16 + j0*D_padded + i0, D_padded); - } - } - - __syncthreads(); - - // Iterate over ne11 == previous tokens: - const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11; - for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE) { - // Calculate tile of KQ: -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { - frag_c_KQ KQ_c[ncols/frag_n]; -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::fill_fragment(KQ_c[j], static_cast(0.0f)); - } -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { - frag_a_K K_a; - wmma::load_matrix_sync(K_a, K_h_f16 + int64_t(k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); - } - } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, wmma::mem_col_major); - } - } - - __syncthreads(); - - // Calculate softmax for each KQ column using the current max. value. - // The divisor is stored in KQ_rowsum and will be applied at the end. -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - if (std::is_same::value) { - float KQ_f_tmp[FATTN_KQ_STRIDE / warp_size]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/warp_size] = KQ_f[j*kqs_padded + k]; - - if (use_logit_softcap) { - KQ_f_tmp[k0/warp_size] = logit_softcap*tanhf(KQ_f_tmp[k0/warp_size]); - } - } - - float KQ_max_new = KQ_max_f[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/warp_size] += mask && ic0 + j < int(ne01.z) ? - __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; - KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/warp_size] + FATTN_KQ_MAX_OFFSET); - } - KQ_max_new = warp_reduce_max(KQ_max_new); - - const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; - KQ_max_scale_f[j0/nwarps] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_max_scale_f[j0/nwarps] = 0.0f; - } - KQ_max_f[j0/nwarps] = KQ_max_new; - - float KQ_rowsum_add = 0.0f; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - const float diff = KQ_f_tmp[k0/warp_size] - KQ_max_f[j0/nwarps]; - KQ_f_tmp[k0/warp_size] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_f_tmp[k0/warp_size] = 0.0f; - } - KQ_rowsum_add += KQ_f_tmp[k0/warp_size]; - KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/warp_size]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; - } else { - half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*warp_size)]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/warp_size] = KQ2[j*(kqs_padded/2) + k]; - - if (use_logit_softcap) { - // There is no dedicated tangens hyperbolicus function for half2. - KQ2_tmp[k0/warp_size] = h2exp(KQ2_tmp[k0/warp_size]*make_half2(2.0f, 2.0f)); - KQ2_tmp[k0/warp_size] = (KQ2_tmp[k0/warp_size] - make_half2(1.0f, 1.0f)) - /(KQ2_tmp[k0/warp_size] + make_half2(1.0f, 1.0f)); - - KQ2_tmp[k0/warp_size] *= logit_softcap_2; - } - } - - half2 KQ_max_new = KQ_max_h2[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/warp_size] += mask && ic0 + j < int(ne01.z) ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); - KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/warp_size]); - } - KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); - const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; - KQ_max_scale_h2[j0/nwarps] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; - KQ_max_h2[j0/nwarps] = KQ_max_new; - - half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - const half2 diff = KQ2_tmp[k0/warp_size] - KQ_max_h2[j0/nwarps]; - KQ2_tmp[k0/warp_size] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ2_tmp[k0/warp_size]) &= ftz_mask; - KQ_rowsum_add += KQ2_tmp[k0/warp_size]; - KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/warp_size]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; - } - } - - __syncthreads(); - - frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - wmma::load_matrix_sync( - KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], - KQ_f16 + j0*(kqar*kqs_padded) + k, - kqar*kqs_padded); - } - } - - frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; -#pragma unroll - for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], static_cast(0.0f)); - } - -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - - frag_a_V v_a; - wmma::load_matrix_sync(v_a, V_h_f16 + int64_t(k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); - } - } - } - - __syncthreads(); - - const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - wmma::store_matrix_sync( - KQ_f16 + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), - VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], - D_padded, wmma::mem_col_major); - } - } - - __syncthreads(); - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - half2 VKQ_scale; - if (std::is_same::value) { - VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); - } else { - VKQ_scale = KQ_max_scale_h2[j0/nwarps]; - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) { - break; - } - - half2 VKQ_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int l = 0; l < VKQ_ratio; ++l) { - VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; - } - VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; - } - } - - __syncthreads(); - } - - // Apply attention sinks - if (sinksf && blockIdx.y == 0) { - const float sinkf = sinksf[head]; - const half sinkh = __float2half(sinkf); - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - if (std::is_same::value) { - float kqmax_new = fmaxf(KQ_max_f[j0/nwarps], sinkf); - - const float KQ_max_scale = expf(KQ_max_f[j0/nwarps] - kqmax_new); - KQ_max_f[j0/nwarps] = kqmax_new; - - KQ_rowsum_f[j0/nwarps] = KQ_rowsum_f[j0/nwarps] * KQ_max_scale + expf(sinkf - KQ_max_f[j0/nwarps]); - - const half2 scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) break; - VKQ2[j*(D_padded/2) + i] *= scale_h2; - } - } else { - half kqmax_old = __low2half(KQ_max_h2[j0/nwarps]); - half kqmax_new = fmaxf(kqmax_old, sinkh); - KQ_max_h2[j0/nwarps] = __half2half2(kqmax_new); - - const half KQ_max_scale_h = hexp(kqmax_old - kqmax_new); - const half2 KQ_max_scale = __half2half2(KQ_max_scale_h); - - KQ_rowsum_h2[j0/nwarps] = KQ_rowsum_h2[j0/nwarps] * KQ_max_scale; - const half val = hexp(sinkh - kqmax_new); - KQ_rowsum_h2[j0/nwarps].x = __hadd(KQ_rowsum_h2[j0/nwarps].x, val); - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) break; - VKQ2[j*(D_padded/2) + i] *= KQ_max_scale; - } - } - } - - __syncthreads(); - } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j_VKQ = j0 + threadIdx.y; - if (ic0 + j_VKQ >= int(ne01.z)) { - return; - } - - float KQ_rowsum_j; - if (std::is_same::value) { - KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; - } else { - KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); - } - - const int j_dst_unrolled = ((sequence*int(ne01.z) + ic0 + j_VKQ)*ne02 + head)*gridDim.y + blockIdx.y; - -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D && i >= D) { - break; - } - float dst_val = VKQ[j_VKQ*D_padded + i]; - if (gridDim.y == 1) { - dst_val /= KQ_rowsum_j; - } - dst[j_dst_unrolled*D + i] = dst_val; - } - - if (gridDim.y == 1 || threadIdx.x != 0) { - continue; - } - - float2 dst_meta_val; - if (std::is_same::value) { - dst_meta_val.x = KQ_max_f[j0/nwarps]; - } else { - dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); - } - dst_meta_val.y = KQ_rowsum_j; - dst_meta[j_dst_unrolled] = dst_meta_val; - } -#else - GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, dst_ptr, dst_meta_ptr, scale, - max_bias, m0, m1, n_head_log2, logit_softcap, - ne00, ne01, ne02, ne03, - nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb11, nb12, nb13, - nb21, nb22, nb23, - ne31, ne32, ne33, - nb31, nb32, nb33); - NO_DEVICE_CODE; -#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN)) -} - -constexpr int get_max_power_of_2(int x) { - return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; -} - -static_assert(get_max_power_of_2(1) == 1, "Test failed."); -static_assert(get_max_power_of_2(2) == 2, "Test failed."); -static_assert(get_max_power_of_2(4) == 4, "Test failed."); -static_assert(get_max_power_of_2(6) == 2, "Test failed."); - -// Number of VKQ rows calculated in parallel: -constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { - return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; -} - -static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); -static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); - -template -void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - - constexpr int nwarps = 4; - - constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; - const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size; - - float logit_softcap; - memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); - - fattn_kernel_t fattn_kernel; - if (logit_softcap == 0.0f) { - constexpr bool use_logit_softcap = false; - fattn_kernel = flash_attn_ext_f16< - D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), KQ_acc_t, use_logit_softcap>; - } else { - constexpr bool use_logit_softcap = true; - fattn_kernel = flash_attn_ext_f16< - D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), KQ_acc_t, use_logit_softcap>; - } - launch_fattn(ctx, dst, fattn_kernel, nwarps, 0, FATTN_KQ_STRIDE, true, true, false, warp_size); -} - -void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; - - const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV); - const int warp_size = ggml_cuda_info().devices[ctx.device].warp_size; - - if (prec != GGML_PREC_DEFAULT) { - if (Q->ne[1] <= 32 || Q->ne[0] > 128) { - constexpr int cols_per_block = 16; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } - } else { - constexpr int cols_per_block = 32; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); - break; - // case 256: - // ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); - // break; - default: - GGML_ABORT("fatal error"); - break; - } - } - return; - } - -#if !defined(GGML_USE_HIP) - if (Q->ne[1] <= 8 && Q->ne[0] % warp_size == 0) { - constexpr int cols_per_block = 8; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } - return; - } -#endif // !defined(GGML_USE_HIP) - - if (Q->ne[1] <= 32) { - constexpr int cols_per_block = 16; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } - return; - } - - constexpr int cols_per_block = 32; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } -} diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh deleted file mode 100644 index aaf711a618cb..000000000000 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ /dev/null @@ -1,51 +0,0 @@ -#pragma once - -#include "common.cuh" - -#if defined(GGML_USE_MUSA) -#define GGML_USE_WMMA_FATTN -#endif // defined(GGML_USE_MUSA) - -#if defined(GGML_HIP_ROCWMMA_FATTN) -#if defined(CDNA) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) -#define GGML_USE_WMMA_FATTN -#elif defined(CDNA) -#warning "rocwmma fattn on CDNA is broken on rocwmma v2.0.0, expect degraded performance" -#endif // defined(CDNA) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) -#if defined(RDNA3) -#define GGML_USE_WMMA_FATTN -#endif // defined(RDNA3) -#if defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1 -#define GGML_USE_WMMA_FATTN -#elif defined(RDNA4) -#warning "rocwmma fattn is not supported on RDNA4 on rocwmma < v2.0.0, expect degraded performance" -#endif // defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1 -#endif // defined(GGML_HIP_ROCWMMA_FATTN) - -// WMMA flash attention requires FP16 matrix instructions to be available for ggml code. -static bool ggml_cuda_should_use_wmma_fattn(const int cc) { -#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN) - return false; -#else - if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_VOLTA) || - GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_MTHREADS(cc)) { - return true; - } else if (GGML_CUDA_CC_IS_CDNA(cc)){ -#if defined(GGML_HIP_ROCWMMA_FATTN) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) - return true; -#else - return false; -#endif // defined(GGML_HIP_ROCWMMA_FATTN) (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) - } else if (GGML_CUDA_CC_IS_RDNA4(cc)) { -#if defined(GGML_HIP_ROCWMMA_FATTN) && ROCWMMA_VERSION_MAJOR > 1 - return true; -#else - return false; -#endif // defined(GGML_HIP_ROCWMMA_FATTN) && ROCWMMA_VERSION_MAJOR > 1 - } else { - return false; - } -#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN) -} - -void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 00ffacf29921..ab7a3b297c07 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -3,7 +3,6 @@ #include "fattn-mma-f16.cuh" #include "fattn-tile.cuh" #include "fattn-vec.cuh" -#include "fattn-wmma-f16.cuh" #include "fattn.cuh" template @@ -330,11 +329,10 @@ static void ggml_cuda_flash_attn_ext_vec(ggml_backend_cuda_context & ctx, ggml_t // Best FlashAttention kernel for a specific GPU: enum best_fattn_kernel { - BEST_FATTN_KERNEL_NONE = 0, - BEST_FATTN_KERNEL_TILE = 200, - BEST_FATTN_KERNEL_VEC = 100, - BEST_FATTN_KERNEL_WMMA_F16 = 300, - BEST_FATTN_KERNEL_MMA_F16 = 400, + BEST_FATTN_KERNEL_NONE = 0, + BEST_FATTN_KERNEL_TILE = 200, + BEST_FATTN_KERNEL_VEC = 100, + BEST_FATTN_KERNEL_MMA_F16 = 400, }; static bool ggml_cuda_fattn_kv_type_supported(ggml_type type) { @@ -500,14 +498,6 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_MMA_F16; } - // Use the WMMA kernel if possible: - if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 192 && Q->ne[0] != 512 && Q->ne[0] != 576) { - if (can_use_vector_kernel && Q->ne[1] <= 2) { - return BEST_FATTN_KERNEL_VEC; - } - return BEST_FATTN_KERNEL_WMMA_F16; - } - // AMD MFMA needs a certain minimum batch size to outscale the tile kernel for large head sizes. if ((amd_mfma_available(cc) && Q->ne[0] <= 256) && Q->ne[0] != 40 && Q->ne[0] != 72) { if ((Q->ne[0] <= 64 && Q->ne[1] * gqa_ratio_eff > 8)) { @@ -559,7 +549,6 @@ size_t ggml_cuda_flash_attn_ext_get_alloc_size(int device, const ggml_tensor * d switch (kernel) { case BEST_FATTN_KERNEL_TILE: - case BEST_FATTN_KERNEL_WMMA_F16: case BEST_FATTN_KERNEL_MMA_F16: need_f16_K = true; need_f16_V = true; @@ -589,9 +578,6 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst case BEST_FATTN_KERNEL_VEC: ggml_cuda_flash_attn_ext_vec(ctx, dst); break; - case BEST_FATTN_KERNEL_WMMA_F16: - ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst); - break; case BEST_FATTN_KERNEL_MMA_F16: ggml_cuda_flash_attn_ext_mma_f16(ctx, dst); break; diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu index 0e15707093fc..a9ec4f697c05 100644 --- a/ggml/src/ggml-cuda/getrows.cu +++ b/ggml/src/ggml-cuda/getrows.cu @@ -40,6 +40,35 @@ static __global__ void k_get_rows( } } +template dequantize_kq> +static __global__ void k_get_rows_kq( + const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst, + const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/ + /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/ + /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3, + /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03, + const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) { + + ggml_cuda_pdl_sync(); + const int64_t nsb = ne00/QK_K; // super-blocks per row + for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) { + // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher. + const int i10 = blockIdx.x; + const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); + const int i11 = dm.x; + const int i12 = dm.y; + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const void * src0_row = (const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03; + + for (int64_t ib = blockIdx.y; ib < nsb; ib += gridDim.y) { + dequantize_kq(src0_row, ib, dst_row + ib*QK_K, threadIdx.x); + } + } +} + template static __global__ void k_get_rows_float( const src0_t * src0_ptr, const int32_t * src1_ptr, dst_t * dst_ptr, @@ -55,23 +84,47 @@ static __global__ void k_get_rows_float( dst_t * GGML_CUDA_RESTRICT dst = dst_ptr; ggml_cuda_pdl_sync(); for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) { + // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher. + const int i10 = blockIdx.x; + const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); + const int i11 = dm.x; + const int i12 = dm.y; + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * GGML_CUDA_RESTRICT dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const src0_t * GGML_CUDA_RESTRICT src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03); + for (int64_t i00 = blockIdx.y*blockDim.x + threadIdx.x; i00 < ne00; i00 += gridDim.y*blockDim.x) { - // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher. - const int i10 = blockIdx.x; - const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); - const int i11 = dm.x; - const int i12 = dm.y; + dst_row[i00] = ggml_cuda_cast(src0_row[i00]); + } + } +} - if (i00 >= ne00) { - return; - } +template +static __global__ void k_get_rows_float_vec( + const dst_t * src0_ptr, const int32_t * src1_ptr, dst_t * dst_ptr, + const int64_t ne00v, + const int64_t ne11, const uint3 ne12_fdv, + const size_t s1, const size_t s2, const size_t s3, + const size_t nb01, const size_t nb02, const size_t nb03, + const size_t s10, const size_t s11, const size_t s12) { - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + ggml_cuda_pdl_lc(); + ggml_cuda_pdl_sync(); + for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) { + const int i10 = blockIdx.x; + const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); + const int i11 = dm.x; + const int i12 = dm.y; - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03); + const int i01 = src1_ptr[i10*s10 + i11*s11 + i12*s12]; - dst_row[i00] = ggml_cuda_cast(src0_row[i00]); + int4 * GGML_CUDA_RESTRICT dst_row = (int4 *) (dst_ptr + i10*s1 + i11*s2 + i12*s3); + const int4 * GGML_CUDA_RESTRICT src0_row = (const int4 *)((const char *) src0_ptr + i01*nb01 + i11*nb02 + i12*nb03); + + for (int64_t i = blockIdx.y*blockDim.x + threadIdx.x; i < ne00v; i += gridDim.y*blockDim.x) { + dst_row[i] = src0_row[i]; } } } @@ -140,6 +193,43 @@ static void get_rows_cuda_q( s10, s11, s12/*, s13*/); } +template dequantize_kq> +static void get_rows_cuda_kq( + const void * src0_d, const int32_t * src1_d, dst_t * dst_d, + const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12, + const size_t nb1, const size_t nb2, const size_t nb3, + cudaStream_t stream) { + GGML_ASSERT(ne00 % QK_K == 0); + const int64_t nsb = ne00/QK_K; + + const dim3 block_dims(block_dim, 1, 1); + const dim3 block_nums(ne10, MIN(nsb, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); + + // strides in elements + // const size_t s0 = nb0 / sizeof(dst_t); + const size_t s1 = nb1 / sizeof(dst_t); + const size_t s2 = nb2 / sizeof(dst_t); + const size_t s3 = nb3 / sizeof(dst_t); + + const size_t s10 = nb10 / sizeof(int32_t); + const size_t s11 = nb11 / sizeof(int32_t); + const size_t s12 = nb12 / sizeof(int32_t); + // const size_t s13 = nb13 / sizeof(int32_t); + + GGML_ASSERT(ne12 > 0); + GGML_ASSERT(ne11 <= std::numeric_limits::max() / ne12); + const uint3 ne12_fdv = init_fastdiv_values(ne12); + + k_get_rows_kq<<>>( + src0_d, src1_d, dst_d, + ne00, /*ne01, ne02, ne03,*/ + /*ne10,*/ ne11, ne12_fdv, /*ne13,*/ + /* s0,*/ s1, s2, s3, + /* nb00,*/ nb01, nb02, nb03, + s10, s11, s12/*, s13*/); +} + template static void get_rows_cuda_float( const src0_t * src0_d, const int32_t * src1_d, dst_t * dst_d, @@ -148,8 +238,6 @@ static void get_rows_cuda_float( const size_t nb1, const size_t nb2, const size_t nb3, cudaStream_t stream) { const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); - const int block_num_y = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; - const dim3 block_nums(ne10, MIN(block_num_y, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); // strides in elements // const size_t s0 = nb0 / sizeof(dst_t); @@ -166,6 +254,34 @@ static void get_rows_cuda_float( GGML_ASSERT(ne11 <= std::numeric_limits::max() / ne12); const uint3 ne12_fdv = init_fastdiv_values(ne12); + if constexpr (std::is_same::value) { + constexpr int VEC = 16 / sizeof(dst_t); + const int64_t ne00v = ne00 / VEC; + const int64_t vec_block_num_y = (ne00v + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; + const bool enough_blocks = vec_block_num_y * ne10 * ne11 * ne12 >= 128; + const bool can_vec = VEC > 1 && enough_blocks && + (ne00 % VEC == 0) && + (nb01 % 16 == 0) && (nb02 % 16 == 0) && (nb03 % 16 == 0) && + (nb1 % 16 == 0) && (nb2 % 16 == 0) && (nb3 % 16 == 0) && + (((uintptr_t) src0_d) % 16 == 0) && (((uintptr_t) dst_d) % 16 == 0); + + if (can_vec) { + const int block_num_y = vec_block_num_y; + const dim3 block_nums(ne10, MIN(block_num_y, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{block_nums, block_dims, 0, stream}; + ggml_cuda_kernel_launch(k_get_rows_float_vec, launch_params, + (const dst_t *) src0_d, src1_d, dst_d, + ne00v, ne11, ne12_fdv, + s1, s2, s3, + nb01, nb02, nb03, + s10, s11, s12); + return; + } + } + + const int block_num_y = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; + const dim3 block_nums(ne10, MIN(block_num_y, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{block_nums, block_dims, 0, stream}; ggml_cuda_kernel_launch(k_get_rows_float, launch_params, src0_d, src1_d, dst_d, @@ -224,8 +340,67 @@ static void ggml_cuda_get_rows_switch_src0_type( get_rows_cuda_q(src0_d, src1_d, dst_d, ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); break; + case GGML_TYPE_Q2_K: + get_rows_cuda_kq<64, dst_t, dequantize_q2_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q3_K: + get_rows_cuda_kq<64, dst_t, dequantize_q3_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q4_K: + get_rows_cuda_kq<32, dst_t, dequantize_q4_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q5_K: + get_rows_cuda_kq<64, dst_t, dequantize_q5_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q6_K: + get_rows_cuda_kq<64, dst_t, dequantize_q6_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ2_XXS: + get_rows_cuda_kq<32, dst_t, dequantize_iq2_xxs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ2_XS: + get_rows_cuda_kq<32, dst_t, dequantize_iq2_xs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ2_S: + get_rows_cuda_kq<32, dst_t, dequantize_iq2_s>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ3_XXS: + get_rows_cuda_kq<32, dst_t, dequantize_iq3_xxs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ3_S: + get_rows_cuda_kq<32, dst_t, dequantize_iq3_s>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ1_S: + get_rows_cuda_kq<32, dst_t, dequantize_iq1_s>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ1_M: + get_rows_cuda_kq<32, dst_t, dequantize_iq1_m>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ4_NL: + get_rows_cuda_kq<32, dst_t, dequantize_iq4_nl>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ4_XS: + get_rows_cuda_kq<32, dst_t, dequantize_iq4_xs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_MXFP4: + get_rows_cuda_kq<32, dst_t, dequantize_mxfp4>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; default: - // TODO: k-quants GGML_ABORT("%s: unsupported src0 type: %s\n", __func__, ggml_type_name(src0_type)); break; } diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 0878ab9c08af..e73a7b8906ce 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -58,6 +58,7 @@ #include "ggml-cuda/wkv.cuh" #include "ggml-cuda/gla.cuh" #include "ggml-cuda/gated_delta_net.cuh" +#include "ggml-cuda/dsv4-hc.cuh" #include "ggml-cuda/set.cuh" #include "ggml-cuda/set-rows.cuh" #include "ggml-cuda/pad_reflect_1d.cuh" @@ -65,6 +66,7 @@ #include "ggml-cuda/tri.cuh" #include "ggml-cuda/cumsum.cuh" #include "ggml-cuda/fill.cuh" +#include "ggml-cuda/lightning-indexer.cuh" #include "ggml.h" #include @@ -104,17 +106,27 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in GGML_ABORT(GGML_CUDA_NAME " error"); } +// map a (possibly virtual) device id to the physical CUDA device that backs it +static int ggml_cuda_get_physical_device(int device) { + const ggml_cuda_device_info & info = ggml_cuda_info(); + GGML_ASSERT(device >= 0 && device < info.device_count); + return info.devices[device].physical_device; +} + // this is faster on Windows // probably because the Windows CUDA libraries forget to make this check before invoking the drivers void ggml_cuda_set_device(int device) { + // translate the (possibly virtual) device id to the physical CUDA device that backs it + const int physical_device = ggml_cuda_get_physical_device(device); + int current_device; CUDA_CHECK(cudaGetDevice(¤t_device)); - if (device == current_device) { + if (physical_device == current_device) { return; } - CUDA_CHECK(cudaSetDevice(device)); + CUDA_CHECK(cudaSetDevice(physical_device)); } int ggml_cuda_get_device() { @@ -205,56 +217,102 @@ static int ggml_cuda_parse_id(char devName[]) { static ggml_cuda_device_info ggml_cuda_init() { ggml_cuda_device_info info = {}; - cudaError_t err = cudaGetDeviceCount(&info.device_count); + cudaError_t err = cudaGetDeviceCount(&info.physical_device_count); if (err != cudaSuccess) { GGML_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); return info; } - GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES); + GGML_ASSERT(info.physical_device_count <= GGML_CUDA_MAX_DEVICES); - int64_t total_vram = 0; + // by default expose exactly the physical devices; GGML_CUDA_DEVICES can request a different + // number of (virtual) devices to emulate multi-GPU systems on a machine with fewer GPUs + info.device_count = info.physical_device_count; + + const char * devices_env = getenv("GGML_CUDA_DEVICES"); + if (devices_env != nullptr && info.physical_device_count > 0) { + const int requested = atoi(devices_env); + if (requested > 0) { + info.device_count = requested; + } else { + GGML_LOG_WARN("%s: ignoring invalid GGML_CUDA_DEVICES=\"%s\"\n", __func__, devices_env); + } + } + + if (info.device_count > GGML_CUDA_MAX_DEVICES) { + GGML_LOG_WARN("%s: requested %d devices, clamping to GGML_CUDA_MAX_DEVICES=%d\n", + __func__, info.device_count, GGML_CUDA_MAX_DEVICES); + info.device_count = GGML_CUDA_MAX_DEVICES; + } + + // map each (virtual) device to a backing physical device (round-robin), assign each its index + // among the (virtual) devices sharing that physical GPU, and store the per-physical share count + int physical_share_count[GGML_CUDA_MAX_DEVICES] = {}; + GGML_ASSERT(info.device_count == 0 || info.physical_device_count > 0); for (int id = 0; id < info.device_count; ++id) { + info.devices[id].physical_device = id % info.physical_device_count; + info.devices[id].virtual_index = physical_share_count[info.devices[id].physical_device]++; + } + + int64_t total_vram = 0; + for (int id = 0; id < info.physical_device_count; ++id) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); total_vram += prop.totalGlobalMem; } GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices (Total VRAM: %zu MiB):\n", - __func__, info.device_count, (size_t)(total_vram / (1024 * 1024))); + __func__, info.physical_device_count, (size_t)(total_vram / (1024 * 1024))); + if (info.device_count != info.physical_device_count) { + GGML_LOG_INFO("%s: emulating %d virtual device(s) on %d physical device(s) (GGML_CUDA_DEVICES)\n", + __func__, info.device_count, info.physical_device_count); + } total_vram = 0; std::vector> turing_devices_without_mma; for (int id = 0; id < info.device_count; ++id) { + const int physical_id = info.devices[id].physical_device; + int device_vmm = 0; #if defined(GGML_USE_VMM) CUdevice device; - CU_CHECK(cuDeviceGet(&device, id)); + CU_CHECK(cuDeviceGet(&device, physical_id)); CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); if (device_vmm) { CUmemAllocationProp alloc_prop = {}; alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - alloc_prop.location.id = id; + alloc_prop.location.id = physical_id; CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); } #endif // defined(GGML_USE_VMM) info.devices[id].vmm = !!device_vmm; cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); + CUDA_CHECK(cudaGetDeviceProperties(&prop, physical_id)); + + // a virtual device owns only a share of its physical GPU's memory; report that share so the + // logged per-device VRAM sums to the physical total above. + GGML_ASSERT(physical_share_count[physical_id] > 0); + info.devices[id].physical_share_count = physical_share_count[physical_id]; + const size_t device_vram = prop.totalGlobalMem / info.devices[id].physical_share_count; + const size_t device_vram_mib = device_vram / (1024 * 1024); info.default_tensor_split[id] = total_vram; - total_vram += prop.totalGlobalMem; + total_vram += device_vram; +#if defined(GGML_USE_HIP) + info.devices[id].integrated = prop.integrated; +#else info.devices[id].integrated = false; // Temporarily disabled due to issues with corrupted output (e.g. #15034) +#endif info.devices[id].nsm = prop.multiProcessorCount; info.devices[id].smpb = prop.sharedMemPerBlock; info.devices[id].warp_size = prop.warpSize; #ifndef GGML_USE_MUSA int supports_coop_launch = 0; - CUDA_CHECK(cudaDeviceGetAttribute(&supports_coop_launch, cudaDevAttrCooperativeLaunch, id)); + CUDA_CHECK(cudaDeviceGetAttribute(&supports_coop_launch, cudaDevAttrCooperativeLaunch, physical_id)); info.devices[id].supports_cooperative_launch = !!supports_coop_launch; #else info.devices[id].supports_cooperative_launch = false; @@ -277,7 +335,7 @@ static ggml_cuda_device_info ggml_cuda_init() { GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d, VRAM: %zu MiB\n", id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, device_vmm ? "yes" : "no", prop.warpSize, - (size_t)(prop.totalGlobalMem / (1024 * 1024))); + device_vram_mib); #elif defined(GGML_USE_MUSA) // FIXME: Ensure compatibility with varying warp sizes across different MUSA archs. info.devices[id].warp_size = 32; @@ -286,13 +344,13 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].cc += prop.minor * 0x10; GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", - (size_t)(prop.totalGlobalMem / (1024 * 1024))); + device_vram_mib); #else info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", - (size_t)(prop.totalGlobalMem / (1024 * 1024))); + device_vram_mib); std::string device_name(prop.name); if (device_name == "NVIDIA GeForce MX450") { turing_devices_without_mma.push_back({ id, device_name }); @@ -307,7 +365,7 @@ static ggml_cuda_device_info ggml_cuda_init() { // TODO: Check for future drivers the default scheduling strategy and // remove this call again when cudaDeviceScheduleSpin is default. if (prop.major == 12 && prop.minor == 1) { - CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaSetDevice(physical_id)); CUDA_CHECK(cudaSetDeviceFlags(cudaDeviceScheduleSpin)); } @@ -332,9 +390,9 @@ static ggml_cuda_device_info ggml_cuda_init() { // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); if (getenv("GGML_CUDA_P2P") != nullptr) { - for (int id = 0; id < info.device_count; ++id) { - ggml_cuda_set_device(id); - for (int id_other = 0; id_other < info.device_count; ++id_other) { + for (int id = 0; id < info.physical_device_count; ++id) { + CUDA_CHECK(cudaSetDevice(id)); + for (int id_other = 0; id_other < info.physical_device_count; ++id_other) { if (id == id_other) { continue; } @@ -479,6 +537,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB int device; + int physical_device; CUdeviceptr pool_addr = 0; size_t pool_used = 0; size_t pool_size = 0; @@ -489,6 +548,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { explicit ggml_cuda_pool_vmm(int device) : device(device), + physical_device(ggml_cuda_get_physical_device(device)), granularity(ggml_cuda_info().devices[device].vmm_granularity) { } @@ -524,7 +584,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { CUmemAllocationProp prop = {}; prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; + prop.location.id = physical_device; CUmemGenericAllocationHandle handle; CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); @@ -553,20 +613,28 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { // NCCL implicitly enables peer access (cudaDeviceEnablePeerAccess), and // GGML_CUDA_P2P enables it explicitly. Unlike cudaMalloc buffers, VMM // allocations do not become peer-accessible from that alone, so access - // must be granted explicitly here. + // must be granted explicitly here. With virtual devices, grant access + // on the backing *physical* devices (deduplicated, since several + // virtual devices can map to the same physical GPU). std::vector access_descs; + bool physical_seen[GGML_CUDA_MAX_DEVICES] = {}; const int device_count = ggml_cuda_info().device_count; for (int id = 0; id < device_count; ++id) { - if (id != device) { + const int id_physical = ggml_cuda_get_physical_device(id); + if (id_physical != physical_device) { int can_access_peer = 0; - CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, device)); + CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id_physical, physical_device)); if (!can_access_peer) { continue; } } + if (physical_seen[id_physical]) { + continue; + } + physical_seen[id_physical] = true; CUmemAccessDesc access = {}; access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = id; + access.location.id = id_physical; access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; access_descs.push_back(access); } @@ -575,7 +643,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { // set access for non P2P CUmemAccessDesc access = {}; access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = device; + access.location.id = physical_device; access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; CU_CHECK(cuMemSetAccess(start_ptr, reserve_size, &access, 1)); } @@ -751,13 +819,17 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co if (ggml_backend_buffer_is_cuda(src->buffer)) { ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context; - if (src_ctx->device == dst_ctx->device) { + // compare the backing physical devices: distinct virtual devices may share one physical GPU, + // in which case a same-device copy (not a peer copy) is required + const int src_physical = ggml_cuda_get_physical_device(src_ctx->device); + const int dst_physical = ggml_cuda_get_physical_device(dst_ctx->device); + if (src_physical == dst_physical) { CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); } else { #ifdef GGML_CUDA_NO_PEER_COPY return false; #else - CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); + CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_physical, src->data, src_physical, ggml_nbytes(src), cudaStreamPerThread)); #endif } CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); @@ -1099,6 +1171,15 @@ static void ggml_backend_cuda_comm_init_internal(ggml_backend_cuda_comm_context static void ggml_backend_cuda_comm_init_nccl(ggml_backend_cuda_comm_context * ret) { #ifdef GGML_USE_NCCL + // Disabling NCCL path when CUDA virtual devices are in use since NCCL requires one distinct physical GPU per rank. + const ggml_cuda_device_info & info = ggml_cuda_info(); + if (info.device_count > info.physical_device_count) { + GGML_LOG_WARN("NCCL disabled: virtual devices in use; " + "falling back to internal AllReduce\n"); + ggml_backend_cuda_comm_init_internal(ret); + return; + } + const size_t n = ret->dev_ids.size(); ret->comms.resize(n); ncclResult_t rc = ncclCommInitAll(ret->comms.data(), (int) n, ret->dev_ids.data()); @@ -2239,6 +2320,15 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_GATED_DELTA_NET: ggml_cuda_op_gated_delta_net(ctx, dst); break; + case GGML_OP_DSV4_HC_COMB: + ggml_cuda_op_dsv4_hc_comb(ctx, dst); + break; + case GGML_OP_DSV4_HC_PRE: + ggml_cuda_op_dsv4_hc_pre(ctx, dst); + break; + case GGML_OP_DSV4_HC_POST: + ggml_cuda_op_dsv4_hc_post(ctx, dst); + break; case GGML_OP_RWKV_WKV7: ggml_cuda_op_rwkv_wkv7(ctx, dst); break; @@ -2257,6 +2347,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_FILL: ggml_cuda_op_fill(ctx, dst); break; + case GGML_OP_LIGHTNING_INDEXER: + ggml_cuda_lightning_indexer(ctx, dst); + break; default: return false; } @@ -2355,13 +2448,17 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_ if (backend_src != backend_dst) { // copy on src stream - if (cuda_ctx_src->device == cuda_ctx_dst->device) { + // compare the backing physical devices: distinct virtual devices may share one physical GPU, + // in which case a same-device copy (not a peer copy) is required + const int src_physical = ggml_cuda_get_physical_device(cuda_ctx_src->device); + const int dst_physical = ggml_cuda_get_physical_device(cuda_ctx_dst->device); + if (src_physical == dst_physical) { CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream())); } else { #ifdef GGML_CUDA_NO_PEER_COPY return false; #else - CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream())); + CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_physical, src->data, src_physical, ggml_nbytes(dst), cuda_ctx_src->stream())); #endif // GGML_CUDA_NO_PEER_COPY } @@ -2606,6 +2703,7 @@ static int ggml_cuda_try_gdn_cache_fusion( static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int node_idx, ggml_cuda_topk_moe_args & args) { args.sigmoid = false; + args.sqrt_softplus = false; args.softmax = false; args.delayed_softmax = false; args.prob_bias = false; @@ -2619,10 +2717,17 @@ static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int nod } if (nodes[node_idx]->op == GGML_OP_UNARY) { - if (ggml_get_unary_op(nodes[node_idx]) != GGML_UNARY_OP_SIGMOID) { + const ggml_unary_op unary_op = ggml_get_unary_op(nodes[node_idx]); + if (unary_op == GGML_UNARY_OP_SIGMOID) { + args.sigmoid = true; + } else if (unary_op == GGML_UNARY_OP_SOFTPLUS && node_idx + 1 < n_nodes && + nodes[node_idx + 1]->op == GGML_OP_SQRT && nodes[node_idx + 1]->src[0] == nodes[node_idx]) { + // sqrt(softplus(x)) scoring (DeepSeek-V4) + args.sqrt_softplus = true; + node_idx++; + } else { return false; } - args.sigmoid = true; } if (nodes[node_idx]->op == GGML_OP_ARGSORT) { @@ -2631,7 +2736,7 @@ static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int nod node_idx++; - if (args.sigmoid || args.softmax) { + if (args.sigmoid || args.sqrt_softplus || args.softmax) { // SOFTMAX -> RESHAPE if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_RESHAPE || nodes[node_idx]->src[0] != nodes[node_idx - 1]) { @@ -3075,21 +3180,27 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph const ggml_tensor * scale = nullptr; if (!args.delayed_softmax) { - ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX; - int out_nodes[2]; // nodes which can't be elided + int out_nodes[2]; // nodes which can't be elided + + if (args.sigmoid) { + ops.insert(ops.end(), { GGML_OP_UNARY }); + } else if (args.sqrt_softplus) { + ops.insert(ops.end(), { GGML_OP_UNARY, GGML_OP_SQRT }); + } else { + ops.insert(ops.end(), { GGML_OP_SOFT_MAX }); + } + const int i_probs = i + (int) ops.size() - 1; // last node of the gating activation if (args.prob_bias) { - bias = cgraph->nodes[i + 2]->src[1]; - ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW, + bias = cgraph->nodes[i_probs + 2]->src[1]; + ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }); - out_nodes[0] = i + 4; - ids = cgraph->nodes[i + 4]; + out_nodes[0] = i_probs + 4; } else { - ops.insert(ops.end(), - { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }); - out_nodes[0] = i + 3; - ids = cgraph->nodes[i + 3]; + ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }); + out_nodes[0] = i_probs + 3; } + ids = cgraph->nodes[out_nodes[0]]; if (args.norm) { ops.insert(ops.end(), @@ -3974,7 +4085,7 @@ static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx, co ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); if (graph->graph == nullptr) { - if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) { + if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_VOLTA) { if (!graph->disable_due_to_gpu_arch) { GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__); } @@ -4346,16 +4457,38 @@ int ggml_backend_cuda_get_device_count() { return ggml_cuda_info().device_count; } -void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { +static std::string ggml_cuda_device_description(int device) { cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); - snprintf(description, description_size, "%s", prop.name); + CUDA_CHECK(cudaGetDeviceProperties(&prop, ggml_cuda_get_physical_device(device))); + + const ggml_cuda_device_info & info = ggml_cuda_info(); + std::string description = prop.name; + if (info.device_count > info.physical_device_count) { + description += " (physical device " + std::to_string(info.devices[device].physical_device) + + ", virtual device " + std::to_string(info.devices[device].virtual_index) + ")"; + } + return description; +} + +void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { + snprintf(description, description_size, "%s", ggml_cuda_device_description(device).c_str()); +} + +static int ggml_cuda_physical_device_share_count(int device) { + const ggml_cuda_device_info & info = ggml_cuda_info(); + GGML_ASSERT(device >= 0 && device < info.device_count); + return info.devices[device].physical_share_count; } void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { ggml_cuda_set_device(device); CUDA_CHECK(cudaMemGetInfo(free, total)); + + // virtual devices sharing one physical GPU share its memory pool; split it between them + const int share_count = ggml_cuda_physical_device_share_count(device); + *free /= share_count; + *total /= share_count; } bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) { @@ -4506,7 +4639,7 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * #if defined(__linux__) // Check if this is a UMA (Unified Memory Architecture) system cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, ctx->device)); + CUDA_CHECK(cudaGetDeviceProperties(&prop, ggml_cuda_get_physical_device(ctx->device))); // Check if UMA is explicitly enabled via environment variable bool uma_env = getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr; @@ -4525,13 +4658,17 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * } #endif // defined(__linux__) + // virtual devices sharing one physical GPU share its memory pool; split it between them + const int share_count = ggml_cuda_physical_device_share_count(ctx->device); + *free /= share_count; + *total /= share_count; } static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) { ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *) dev->context; cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, ctx->device)); + CUDA_CHECK(cudaGetDeviceProperties(&prop, ggml_cuda_get_physical_device(ctx->device))); return prop.integrated ? GGML_BACKEND_DEVICE_TYPE_IGPU @@ -4708,7 +4845,25 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_XS: return true; + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + // 32-value sub-blocks, the row size does not guarantee + // the QK_K super-blocks the get_rows kernel iterates on + return op->src[0]->ne[0] % QK_K == 0; default: return false; } @@ -4816,13 +4971,23 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g { ggml_type src0_type = op->src[0]->type; ggml_type src1_type = op->src[1]->type; + const int32_t dim = op->op_params[0]; return src0_type == src1_type && src0_type == op->type && ( ( ggml_is_quantized(src0_type) && - ggml_is_contiguous(op->src[0]) && - ggml_is_contiguous(op->src[1]) && + ( + ( + dim == 3 && + ggml_is_contiguous(op->src[0]) && + ggml_is_contiguous(op->src[1]) + ) || ( + dim != 3 && + ggml_is_contiguous_to_3(op->src[0]) && + ggml_is_contiguous_to_3(op->src[1]) + ) + ) && op->src[0]->ne[0] % ggml_blck_size(src0_type) == 0 && op->src[1]->ne[0] % ggml_blck_size(src0_type) == 0 ) || ( @@ -4965,6 +5130,16 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g #else return true; #endif // GGML_USE_MUSA + case GGML_OP_DSV4_HC_COMB: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && + op->src[2]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; + case GGML_OP_DSV4_HC_PRE: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32; + case GGML_OP_DSV4_HC_POST: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && + op->src[2]->type == GGML_TYPE_F32 && op->src[3]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32; case GGML_OP_FLASH_ATTN_EXT: return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op); case GGML_OP_CROSS_ENTROPY_LOSS: @@ -4977,6 +5152,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_DIAG: case GGML_OP_SOLVE_TRI: return true; + case GGML_OP_LIGHTNING_INDEXER: + return ggml_cuda_lightning_indexer_supported(dev_ctx->device, op); default: return false; @@ -5179,18 +5356,24 @@ ggml_backend_reg_t ggml_backend_cuda_reg() { ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context; const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32; - for (int i = 0; i < ggml_cuda_info().device_count; i++) { + const ggml_cuda_device_info & info = ggml_cuda_info(); + const bool virtual_devices = info.device_count > info.physical_device_count; + + for (int i = 0; i < info.device_count; i++) { + const int physical_id = info.devices[i].physical_device; + ggml_backend_cuda_device_context * dev_ctx = new ggml_backend_cuda_device_context; dev_ctx->device = i; dev_ctx->name = GGML_CUDA_NAME + std::to_string(i); - - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, i)); - dev_ctx->description = prop.name; + dev_ctx->description = ggml_cuda_device_description(i); char pci_bus_id[32] = {}; - CUDA_CHECK(cudaDeviceGetPCIBusId(pci_bus_id, sizeof(pci_bus_id), i)); + CUDA_CHECK(cudaDeviceGetPCIBusId(pci_bus_id, sizeof(pci_bus_id), physical_id)); dev_ctx->pci_bus_id = pci_bus_id; + if (virtual_devices) { + // make the pci bus id unique for virtual devices + dev_ctx->pci_bus_id += "-v" + std::to_string(i); + } for (char & c : dev_ctx->pci_bus_id) { c = std::tolower(c); } diff --git a/ggml/src/ggml-cuda/lightning-indexer.cu b/ggml/src/ggml-cuda/lightning-indexer.cu new file mode 100644 index 000000000000..5edc967e0e92 --- /dev/null +++ b/ggml/src/ggml-cuda/lightning-indexer.cu @@ -0,0 +1,588 @@ +#include "common.cuh" +#include "lightning-indexer.cuh" +#include "fattn-common.cuh" +#include "convert.cuh" + +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +#if defined(TURING_MMA_AVAILABLE) + +typedef union { + int2 i2; + half2 h2[2]; +} half4; + +// TODO add support for AMD cards via rocWMMA +#include +namespace wmma = nvcuda::wmma; + +template +static __global__ void lightning_indexer_kernel_wmma( + const float * Q, const char * K, const float * W, const half * M, float * dst, + int64_t n_stream, int64_t n_batch, int64_t n_kv, + size_t nb1, size_t nb2, size_t nb3, + size_t nbq1, size_t nbq2, size_t nbq3, + size_t nbk1, size_t nbk2, size_t nbk3, + size_t nbw1, size_t nbw2, size_t nbw3, + size_t nbm1, size_t nbm2, size_t nbm3, + int64_t nem3 + ) { + + constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * WARP_SIZE; + constexpr int HEADS_PER_INNER_LOOP = 8; + constexpr int K_EMBD_PER_INNER_LOOP = 16; + constexpr int N_EMBD_PADDED = N_EMBD + 8; + + const int i_batch = blockIdx.y; + const int i_stream = blockIdx.z; + const int i_warp = threadIdx.y; + const int i_lane = threadIdx.x; + const int tid = i_warp * WARP_SIZE + i_lane; + + // each block processes K_VECS_PER_BLOCK K vectors + const int start_kv = blockIdx.x * K_VECS_PER_BLOCK; + + const char * q_base = (const char *) Q + i_batch*nbq2 + i_stream*nbq3; + const float * w_base = (const float *) ((const char *) W + i_batch*nbw1 + i_stream*nbw3); + + // phase 1 - load weights and first Q tile to shared memory + + __shared__ float w_shared[N_HEAD]; + __shared__ int2 q_shared_h[HEADS_PER_INNER_LOOP][N_EMBD_PADDED / 4]; + + if (tid < N_HEAD) { + w_shared[tid] = w_base[tid]; + } + + // total number of half4 elements in HEADS_PER_INNER_LOOP x N_EMBD Q tile + constexpr int N_Q_TILE = HEADS_PER_INNER_LOOP * (N_EMBD / 4); + // number of registers needed in each thread to store Q tile in thread block + constexpr int N_Q_NEXT = (N_Q_TILE + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK; + +#pragma unroll + for (int i_q = tid; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) { + const int i_head = i_q / (N_EMBD / 4); + const int i_embd = i_q % (N_EMBD / 4); + const float4 q = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4)); + half4 q_packed; + q_packed.h2[0] = __float22half2_rn(make_float2(q.x, q.y)); + q_packed.h2[1] = __float22half2_rn(make_float2(q.z, q.w)); + q_shared_h[i_head][i_embd] = q_packed.i2; + } + + // phase 2 - load (and dequantize if needed) K to shared mem + + __shared__ half2 k_shared_h[K_VECS_PER_BLOCK][N_EMBD_PADDED / 4][2]; + + constexpr int n_k = K_VECS_PER_BLOCK * (N_EMBD / 4); + + if constexpr (TYPE_K == GGML_TYPE_F16) { +#pragma unroll + for (int i_k = tid; i_k < n_k; i_k += THREADS_PER_BLOCK) { + const int i_k_vec = i_k / (N_EMBD / 4); + const int i_embd = i_k % (N_EMBD / 4); + const int i_kv = start_kv + i_k_vec; + if (i_kv < n_kv) { + const int2 * k_base = (const int2 *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + *(int2*) &k_shared_h[i_k_vec][i_embd] = k_base[i_embd]; + } else { + *(int2*) &k_shared_h[i_k_vec][i_embd] = make_int2(0, 0); + } + } + } else { + constexpr dequantize_V_t dequantize_k = get_dequantize_V(); +#pragma unroll + for (int i_k = tid; i_k < n_k; i_k += THREADS_PER_BLOCK) { + const int i_k_vec = i_k / (N_EMBD / 4); + const int i_embd = i_k % (N_EMBD / 4); + const int i_kv = start_kv + i_k_vec; + if (i_kv < n_kv) { + const void * k_base = (const void *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + dequantize_k(k_base, &k_shared_h[i_k_vec][i_embd][0], i_embd * 4); + } else { + *(int2*) &k_shared_h[i_k_vec][i_embd] = make_int2(0, 0); + } + } + } + + __syncthreads(); + + // phase 3 - calculate lightning indexer scores + + __shared__ float qk_shared[WARPS_PER_BLOCK][HEADS_PER_INNER_LOOP][K_VECS_PER_BLOCK]; + + // load K fragment + wmma::fragment frag_k; + wmma::load_matrix_sync(frag_k, (half*) &k_shared_h[0][i_warp * K_EMBD_PER_INNER_LOOP / 4], N_EMBD_PADDED); + + float score_k = 0.0f; + + for (int i_head_0 = 0; i_head_0 < N_HEAD; i_head_0 += HEADS_PER_INNER_LOOP) { + const int i_head_next = i_head_0 + HEADS_PER_INNER_LOOP; + + // we don't use accumulator for anything, fill it with zeros + wmma::fragment frag_acc; + wmma::fill_fragment(frag_acc, 0.0f); + + // load Q fragment + wmma::fragment frag_q; + wmma::load_matrix_sync(frag_q, (half*) &q_shared_h[0][i_warp * K_EMBD_PER_INNER_LOOP / 4], N_EMBD_PADDED); + + // preload next Q tile to registers during matrix multiplication + float4 q_next[N_Q_NEXT]; + + if (i_head_next < N_HEAD) { +#pragma unroll + for (int i_q = tid, i_q_next = 0; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) { + const int i_head = i_head_next + i_q / (N_EMBD / 4); + const int i_embd = i_q % (N_EMBD / 4); + q_next[i_q_next++] = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4)); + } + } + + // perform matrix multiplication + wmma::mma_sync(frag_acc, frag_q, frag_k, frag_acc); + wmma::store_matrix_sync((float*) &qk_shared[i_warp][0][0], frag_acc, K_VECS_PER_BLOCK, wmma::mem_row_major); + + // make sure all threads finished using q_shared_h so we can store next tile + __syncthreads(); + + // write preloaded Q tile to shared memory + if (i_head_next < N_HEAD) { +#pragma unroll + for (int i_q = tid, i_q_next = 0; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) { + const int i_head = i_q / (N_EMBD / 4); + const int i_embd = i_q % (N_EMBD / 4); + half4 q_packed; + q_packed.h2[0] = __float22half2_rn(make_float2(q_next[i_q_next].x, q_next[i_q_next].y)); + q_packed.h2[1] = __float22half2_rn(make_float2(q_next[i_q_next].z, q_next[i_q_next].w)); + q_shared_h[i_head][i_embd] = q_packed.i2; + ++i_q_next; + } + } + + // accumulate QK multiplication results from all block warps + // (there are 256 threads in block and 256 matmul outputs) + // TODO it will break if WARP_SIZE is not 32 + const int h = tid / K_VECS_PER_BLOCK; + const int k = tid % K_VECS_PER_BLOCK; + const float w_val = w_shared[i_head_0 + h]; + + float sum = 0.0f; +#pragma unroll + for (int w = 0; w < WARPS_PER_BLOCK; ++w) { + sum += qk_shared[w][h][k]; + } + + // ReLU, weight + sum = sum > 0.0f ? sum : 0.0f; + sum *= w_val; + + // wait until qk_shared[0] is no longer used + __syncthreads(); + + // reuse qk_shared[0] for storing partial results + qk_shared[0][h][k] = sum; + + // wait until all threads write their results + __syncthreads(); + + // accumulate result over heads + if (tid < K_VECS_PER_BLOCK) { +#pragma unroll + for (int i_head = 0; i_head < HEADS_PER_INNER_LOOP; ++i_head) { + score_k += qk_shared[0][i_head][tid]; + } + } + + // make sure all threads finished using qk_shared + __syncthreads(); + } + + // phase 4 - store output to VRAM + + if (tid < K_VECS_PER_BLOCK) { + const int i_kv = start_kv + tid; + if (i_kv < n_kv) { + const half * m_base = (const half *) ((const char *) M + i_batch*nbm1 + (i_stream%nem3)*nbm3); + float * dst_base = (float *) ((char *) dst + i_batch*nb1 + i_stream*nb3); + dst_base[i_kv] = score_k + __half2float(m_base[i_kv]); + } + } +} + +#else // defined(TURING_MMA_AVAILABLE) + +template +static __global__ void lightning_indexer_kernel_wmma( + const float * Q, const char * K, const float * W, const half * M, float * dst, + int64_t n_stream, int64_t n_batch, int64_t n_kv, + size_t nb1, size_t nb2, size_t nb3, + size_t nbq1, size_t nbq2, size_t nbq3, + size_t nbk1, size_t nbk2, size_t nbk3, + size_t nbw1, size_t nbw2, size_t nbw3, + size_t nbm1, size_t nbm2, size_t nbm3, + int64_t nem3 + ) { + GGML_UNUSED_VARS(Q, K, W, M, dst, + n_stream, n_batch, n_kv, + nb1, nb2, nb3, + nbq1, nbq2, nbq3, + nbk1, nbk2, nbk3, + nbw1, nbw2, nbw3, + nem3); + NO_DEVICE_CODE; +} + +#endif // defined(TURING_MMA_AVAILABLE) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + +// TODO there is one ugly assumption used in this kernel - that WARP_SIZE is equal to 32 +// thanks to that one warp operating on float4 processes whole indexer K/Q vectors +// 32 * 4 = 128 (N_EMBD) + +template +static __global__ void lightning_indexer_kernel_vec( + const float * Q, const char * K, const float * W, const half * M, float * dst, + int64_t n_stream, int64_t n_batch, int64_t n_kv, + size_t nb1, size_t nb2, size_t nb3, + size_t nbq1, size_t nbq2, size_t nbq3, + size_t nbk1, size_t nbk2, size_t nbk3, + size_t nbw1, size_t nbw2, size_t nbw3, + size_t nbm1, size_t nbm2, size_t nbm3, + int64_t nem3 + ) { + + constexpr int K_VECS_PER_WARP = K_VECS_PER_BLOCK / WARPS_PER_BLOCK; + constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * WARP_SIZE; + + const int i_batch = blockIdx.y; + const int i_stream = blockIdx.z; + const int i_warp = threadIdx.y; + const int i_lane = threadIdx.x; + const int tid = i_warp * WARP_SIZE + i_lane; + + // each warp processes K_VECS_PER_WARP K vectors + const int start_kv_block = blockIdx.x * K_VECS_PER_BLOCK; + const int start_kv = start_kv_block + i_warp * K_VECS_PER_WARP; + + const char * q_base = (const char *) Q + i_batch*nbq2 + i_stream*nbq3; + const float * w_base = (const float *) ((const char *) W + i_batch*nbw1 + i_stream*nbw3); + + // phase 1 - load (and dequantize if needed) K to registers + + float4 k_reg_f[K_VECS_PER_WARP]; + + if constexpr (TYPE_K == GGML_TYPE_F32) { + // direct copy of float4 +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + int i_kv = start_kv + k; + if (i_kv < n_kv) { + const float4 * k_base = (const float4 *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + k_reg_f[k] = k_base[i_lane]; + } else { + k_reg_f[k] = make_float4(0, 0, 0, 0); + } + } + } else { + // dequantize remaining types to float + constexpr dequantize_V_t dequantize_k = get_dequantize_V(); +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + int i_kv = start_kv + k; + if (i_kv < n_kv) { + const void * k_base = (const void *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + dequantize_k(k_base, &k_reg_f[k], i_lane * 4); + } else { + k_reg_f[k] = make_float4(0, 0, 0, 0); + } + } + } + + float score_k[K_VECS_PER_WARP] = { 0.0f }; + + // load weights and Q only for N_HEAD_INNER heads at once to reduce shared memory usage + constexpr int N_HEAD_INNER = N_HEAD / 4; + + for (int i_head_0 = 0; i_head_0 < N_HEAD; i_head_0 += N_HEAD_INNER) { + // phase 2 - load weights and Q to shared memory + + __shared__ float w_shared[N_HEAD_INNER]; + __shared__ float4 q_shared_f[N_HEAD_INNER][N_EMBD / 4]; + + if (tid < N_HEAD_INNER) { + w_shared[tid] = w_base[i_head_0 + tid]; + } + + constexpr int n_q = N_HEAD_INNER * (N_EMBD / 4); +#pragma unroll + for (int i_q = tid; i_q < n_q; i_q += THREADS_PER_BLOCK) { + const int i_head_inner = i_q / (N_EMBD / 4); + const int i_head = i_head_0 + i_head_inner; + const int i_embd = i_q % (N_EMBD / 4); + q_shared_f[i_head_inner][i_embd] = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4)); + } + + __syncthreads(); + + // phase 3 - calculate lightning indexer scores + + for (int i_head_inner = 0; i_head_inner < N_HEAD_INNER; ++i_head_inner) { + const float w_val = w_shared[i_head_inner]; + float qk[K_VECS_PER_WARP] = { 0.0f }; + + // dot product of floats + const float4 q_vec = q_shared_f[i_head_inner][i_lane]; + +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + ggml_cuda_mad(qk[k], q_vec.x, k_reg_f[k].x); + ggml_cuda_mad(qk[k], q_vec.y, k_reg_f[k].y); + ggml_cuda_mad(qk[k], q_vec.z, k_reg_f[k].z); + ggml_cuda_mad(qk[k], q_vec.w, k_reg_f[k].w); + } + +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + float sum = warp_reduce_sum(qk[k]); + + // ReLU, weight + if (i_lane == 0) { + sum = (sum > 0.0f) ? sum : 0.0f; + score_k[k] += sum * w_val; + } + } + } + + __syncthreads(); + } + + // phase 4 - store outputs to shared memory + + __shared__ float dst_shared[K_VECS_PER_BLOCK]; + + if (i_lane == 0) { +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + dst_shared[i_warp * K_VECS_PER_WARP + k] = score_k[k]; + } + } + + __syncthreads(); + + // phase 5 - write from shared memory to VRAM in coalesced manner + + if (tid < K_VECS_PER_BLOCK) { + int i_kv = start_kv_block + tid; + if (i_kv < n_kv) { + const half * m_base = (const half *) ((const char *) M + i_batch*nbm1 + (i_stream%nem3)*nbm3); + float * dst_base = (float *) ((char *) dst + i_batch*nb1 + i_stream*nb3); + dst_base[i_kv] = dst_shared[tid] + __half2float(m_base[i_kv]); + } + } +} + +#define LIGHTNING_INDEXER_CASE(lightning_indexer_kernel, n_embd, n_head, K, type_K) \ + if (K->type == (type_K)) { \ + lightning_indexer_kernel \ + <<>>( \ + q_d, k_d, w_d, m_d, dst_d, \ + n_stream, n_batch, n_kv, \ + nb1, nb2, nb3, \ + nbq1, nbq2, nbq3, \ + nbk1, nbk2, nbk3, \ + nbw1, nbw2, nbw3, \ + nbm1, nbm2, nbm3, \ + nem3 \ + ); \ + } else + +void ggml_cuda_lightning_indexer(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * w = dst->src[2]; // weights + const ggml_tensor * m = dst->src[3]; // mask + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT( q->type == GGML_TYPE_F32); + GGML_ASSERT( w->type == GGML_TYPE_F32); + GGML_ASSERT( m->type == GGML_TYPE_F16); + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, new, w, ne) + GGML_TENSOR_LOCALS(size_t, nbw, w, nb) + GGML_TENSOR_LOCALS(int64_t, nem, m, ne) + GGML_TENSOR_LOCALS(size_t, nbm, m, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbw0 == ggml_type_size(w->type)); + GGML_ASSERT(nbm0 == ggml_type_size(m->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + const int n_embd = q->ne[0]; + const int n_head = q->ne[1]; + const int n_batch = q->ne[2]; + const int n_stream = q->ne[3]; + const int n_kv = k->ne[2]; + + const float * q_d = (const float *) q->data; + const char * k_d = (const char *) k->data; + const float * w_d = (const float *) w->data; + const half * m_d = (const half *) m->data; + float * dst_d = ( float *) dst->data; + + const int device = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[device].cc; + + if (n_embd == 128 && n_head == 64) { +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + if (GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && k->type != GGML_TYPE_F32 && k->type != GGML_TYPE_BF16) { + // use wmma kernel + constexpr int K_VECS_PER_BLOCK = 32; + constexpr int WARPS_PER_BLOCK = 8; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q8_0) + GGML_ABORT("fatal error"); + } else { +#else // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + { +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + // use vector kernel + constexpr int K_VECS_PER_WARP = 8; + constexpr int WARPS_PER_BLOCK = 8; + constexpr int K_VECS_PER_BLOCK = K_VECS_PER_WARP * WARPS_PER_BLOCK; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q8_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_BF16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_F32) + GGML_ABORT("fatal error"); + } + } else if (n_embd == 128 && n_head == 32) { +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + if (GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && k->type != GGML_TYPE_F32 && k->type != GGML_TYPE_BF16) { + // use wmma kernel + constexpr int K_VECS_PER_BLOCK = 32; + constexpr int WARPS_PER_BLOCK = 8; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q8_0) + GGML_ABORT("fatal error"); + } else { +#else // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + { +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + // use vector kernel + constexpr int K_VECS_PER_WARP = 8; + constexpr int WARPS_PER_BLOCK = 8; + constexpr int K_VECS_PER_BLOCK = K_VECS_PER_WARP * WARPS_PER_BLOCK; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q8_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_BF16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_F32) + GGML_ABORT("fatal error"); + } + } else { + GGML_ABORT("fatal error"); + } +} + +bool ggml_cuda_lightning_indexer_supported(int device, const ggml_tensor * dst) { + GGML_UNUSED(device); + + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * w = dst->src[2]; // weights + const ggml_tensor * m = dst->src[3]; // mask + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, new, w, ne) + GGML_TENSOR_LOCALS(size_t, nbw, w, nb) + GGML_TENSOR_LOCALS(int64_t, nem, m, ne) + GGML_TENSOR_LOCALS(size_t, nbm, m, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + if (neq0 != 128) { + return false; + } + + if (neq1 != 64 && neq1 != 32) { + return false; + } + + // alignment checks + for (const ggml_tensor * t : {q, k}) { + if (ggml_is_quantized(t->type)) { + continue; + } + for (size_t i = 1; i < GGML_MAX_DIMS; ++i) { + if (t->nb[i] % 16 != 0) { + return false; + } + } + } + + switch(k->type) { + case GGML_TYPE_F32: + case GGML_TYPE_BF16: + case GGML_TYPE_F16: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_0: + return true; + default: + return false; + } +} diff --git a/ggml/src/ggml-cuda/lightning-indexer.cuh b/ggml/src/ggml-cuda/lightning-indexer.cuh new file mode 100644 index 000000000000..f2fc95181339 --- /dev/null +++ b/ggml/src/ggml-cuda/lightning-indexer.cuh @@ -0,0 +1,4 @@ +#include "common.cuh" + +void ggml_cuda_lightning_indexer(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +bool ggml_cuda_lightning_indexer_supported(int device, const ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index aad4c34aa668..646a5899c803 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -85,7 +85,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr GGML_ASSERT(sis1 > 0); ggml_cuda_launch_mm_ids_helper(ids_d, ids_src_compact_dev.get(), ids_dst_compact_dev.get(), expert_bounds_dev.get(), - static_cast(n_experts), static_cast(n_tokens), static_cast(n_expert_used), static_cast(ne11), si1, sis1, ctx.stream()); + static_cast(n_experts), static_cast(n_tokens), static_cast(n_expert_used), static_cast(ne11), si1, sis1, /*write_inverse =*/ false, ctx.stream()); CUDA_CHECK(cudaGetLastError()); ids_info.ids_src_compact = ids_src_compact_dev.get(); diff --git a/ggml/src/ggml-cuda/mmid.cu b/ggml/src/ggml-cuda/mmid.cu index 3c61e4595a7b..f80442fbe4e8 100644 --- a/ggml/src/ggml-cuda/mmid.cu +++ b/ggml/src/ggml-cuda/mmid.cu @@ -27,7 +27,7 @@ template __launch_bounds__(ggml_cuda_get_physical_warp_size(), 1) static __global__ void mm_ids_helper( const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1) { + const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, const bool write_inverse) { constexpr int warp_size = ggml_cuda_get_physical_warp_size(); const int n_expert_used = n_expert_used_template == 0 ? n_expert_used_var : n_expert_used_template; const int expert = blockIdx.x; @@ -98,8 +98,13 @@ static __global__ void mm_ids_helper( const mm_ids_helper_store store_it = store[itc]; const int it = store_it.it(); const int iex_used = store_it.iex_used(); - ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y; - ids_dst [nex_prev + itc] = it*n_expert_used + iex_used; + ids_dst[nex_prev + itc] = it*n_expert_used + iex_used; + // ids_src1 holds the forward map, or the inverse map (token slot -> compact row) for quant dedup + if (write_inverse) { + ids_src1[it*n_expert_used + iex_used] = nex_prev + itc; + } else { + ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y; + } } if (threadIdx.x != 0) { @@ -118,7 +123,7 @@ static __global__ void mm_ids_helper( template static void launch_mm_ids_helper( const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { + const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, const bool write_inverse, cudaStream_t stream) { GGML_ASSERT(n_tokens < (1 << 22) && "too few bits in mm_ids_helper_store"); GGML_ASSERT(n_expert_used_var < (1 << 10) && "too few bits in mm_ids_helper_store"); @@ -132,33 +137,33 @@ static void launch_mm_ids_helper( const size_t nbytes_shared = n_tokens*sizeof(mm_ids_helper_store); GGML_ASSERT(nbytes_shared <= smpbo); mm_ids_helper<<>> - (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1); + (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1, write_inverse); } void ggml_cuda_launch_mm_ids_helper( const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { + const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, const bool write_inverse, cudaStream_t stream) { switch (n_expert_used) { case 2: - launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 4: - launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 6: - launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 8: - launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 16: - launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 32: - launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; default: - launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; } } diff --git a/ggml/src/ggml-cuda/mmid.cuh b/ggml/src/ggml-cuda/mmid.cuh index ac090aea9ea1..74c2db43385e 100644 --- a/ggml/src/ggml-cuda/mmid.cuh +++ b/ggml/src/ggml-cuda/mmid.cuh @@ -2,4 +2,4 @@ void ggml_cuda_launch_mm_ids_helper( const int32_t * ids, int32_t * ids_src1, int32_t * ids_dst, int32_t * expert_bounds, - int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, cudaStream_t stream); + int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, bool write_inverse, cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/mmq-load-tiles.cuh b/ggml/src/ggml-cuda/mmq-load-tiles.cuh index 3978b1baa71e..7fb242096ef6 100644 --- a/ggml/src/ggml-cuda/mmq-load-tiles.cuh +++ b/ggml/src/ggml-cuda/mmq-load-tiles.cuh @@ -39,29 +39,37 @@ template static __device__ __forceinline_ } const block_q1_0 * bxi = (const block_q1_0 *) x + kbx0 + i*stride + kbx; - const int qs_offset = 4*kqsx; - const int qs0 = bxi->qs[qs_offset + 0] | (bxi->qs[qs_offset + 1] << 8) | - (bxi->qs[qs_offset + 2] << 16) | (bxi->qs[qs_offset + 3] << 24); - - int unpacked_bytes[8]; -#pragma unroll - for (int j = 0; j < 8; ++j) { - const int shift = j * 4; - const int bits4 = (qs0 >> shift) & 0x0F; - const int b0 = (bits4 & 0x01) ? 1 : -1; - const int b1 = (bits4 & 0x02) ? 1 : -1; - const int b2 = (bits4 & 0x04) ? 1 : -1; - const int b3 = (bits4 & 0x08) ? 1 : -1; - unpacked_bytes[j] = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24); - } + const int16_t * qxi = (const int16_t *) bxi->qs + kqsx * 2; const int dst_offset = kbx*(scale_entries_per_block*QI8_0) + kqsx*QI8_0; #pragma unroll - for (int j = 0; j < 8; ++j) { + for (int j = 0; j < 2; ++j) { + const int q = qxi[j]; + + // unpack crumbs into nibble indices + const int n0 = __byte_perm(0x11100100, 0x11100100, q >> 0); // [0, 1, 4, 5] [ 8, 9, 12, 13] + const int n1 = __byte_perm(0x11100100, 0x11100100, q >> 2); // [2, 3, 6, 7] [10, 11, 14, 15] + // unpack nibbles into byte values + const int s0 = __byte_perm(0x01FF, 0x01FF, n0 >> 0); + const int s1 = __byte_perm(0x01FF, 0x01FF, n1 >> 0); + const int s2 = __byte_perm(0x01FF, 0x01FF, n0 >> 16); + const int s3 = __byte_perm(0x01FF, 0x01FF, n1 >> 16); + // unshuffle values + const int v0 = __byte_perm(s0, s1, 0x5410); + const int v1 = __byte_perm(s0, s1, 0x7632); + const int v2 = __byte_perm(s2, s3, 0x5410); + const int v3 = __byte_perm(s2, s3, 0x7632); + #if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) - x_qs[i*sram_stride + dst_offset + j] = unpacked_bytes[j]; -#else - x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j] = unpacked_bytes[j]; + x_qs[i*sram_stride + dst_offset + j*4+0] = v0; + x_qs[i*sram_stride + dst_offset + j*4+1] = v1; + x_qs[i*sram_stride + dst_offset + j*4+2] = v2; + x_qs[i*sram_stride + dst_offset + j*4+3] = v3; +#else + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+0] = v0; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+1] = v1; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+2] = v2; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+3] = v3; #endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index bf9f5d526476..8a0f4d3b5cbf 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -25,12 +25,7 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_Q8_0: mul_mat_q_case(ctx, args, stream); break; - case GGML_TYPE_MXFP4: - mul_mat_q_case(ctx, args, stream); - break; - case GGML_TYPE_NVFP4: - mul_mat_q_case(ctx, args, stream); - break; +// ----------------------------------------------------------------------- case GGML_TYPE_Q2_K: mul_mat_q_case(ctx, args, stream); break; @@ -46,6 +41,10 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_Q6_K: mul_mat_q_case(ctx, args, stream); break; +// ----------------------------------------------------------------------- + case GGML_TYPE_IQ1_S: + mul_mat_q_case(ctx, args, stream); + break; case GGML_TYPE_IQ2_XXS: mul_mat_q_case(ctx, args, stream); break; @@ -61,15 +60,19 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_IQ3_S: mul_mat_q_case(ctx, args, stream); break; - case GGML_TYPE_IQ1_S: - mul_mat_q_case(ctx, args, stream); - break; case GGML_TYPE_IQ4_XS: mul_mat_q_case(ctx, args, stream); break; case GGML_TYPE_IQ4_NL: mul_mat_q_case(ctx, args, stream); break; +// ----------------------------------------------------------------------- + case GGML_TYPE_MXFP4: + mul_mat_q_case(ctx, args, stream); + break; + case GGML_TYPE_NVFP4: + mul_mat_q_case(ctx, args, stream); + break; default: GGML_ABORT("fatal error"); break; @@ -122,21 +125,28 @@ void ggml_cuda_mul_mat_q( const bool fallback = ne01 % 128 != 0; - // TODO: tighter pool buffer size vs q8 path const bool use_native_fp4 = blackwell_mma_available(cc) && (src0->type == GGML_TYPE_MXFP4 || src0->type == GGML_TYPE_NVFP4); + const size_t y_block_size = use_native_fp4 ? sizeof(block_fp4_mmq) : sizeof(block_q8_1_mmq); + const size_t y_values_per_block = use_native_fp4 ? QK_FP4_MMQ : QK8_1_MMQ; if (!ids) { - const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 + + const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * y_block_size/y_values_per_block + ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq); ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), nbytes_src1_q8_1); + ggml_cuda_pool_alloc src1_scale(ctx.pool()); + if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) { + src1_scale.alloc(ne13*ne12*ne11); + } { const int64_t s11 = src1->nb[1] / ts_src1; const int64_t s12 = src1->nb[2] / ts_src1; const int64_t s13 = src1->nb[3] / ts_src1; if (use_native_fp4) { + static constexpr size_t align_float8 = 32; + const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8); static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1)); - quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded, + quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13, ne10_padded, ne11, ne12, ne13, stream); } else { @@ -148,12 +158,13 @@ void ggml_cuda_mul_mat_q( // Stride depends on quantization format const int64_t s12 = use_native_fp4 ? - ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) : // block_fp4_mmq holds 256 values + ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_FP4_MMQ * sizeof(int)) : ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int)); const int64_t s13 = ne12*s12; const mmq_args args = { src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d, + src0->type == GGML_TYPE_NVFP4 && use_native_fp4 ? src1_scale.ptr : nullptr, ne00, ne01, ne1, s01, ne11, s1, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, @@ -174,19 +185,27 @@ void ggml_cuda_mul_mat_q( ggml_cuda_pool_alloc ids_dst(ctx.pool(), ne_get_rows); ggml_cuda_pool_alloc expert_bounds(ctx.pool(), ne02 + 1); + // gate/up activations are broadcast across experts (ne11 == 1): quantize each token once and + // scatter to its slots. ids_src1 then holds the inverse map (token slot -> compact row). + const bool dedup_bcast = ne11 == 1 && n_expert_used > 1; + { GGML_ASSERT(ids->nb[0] == ggml_element_size(ids)); const int si1 = ids->nb[1] / ggml_element_size(ids); const int sis1 = nb12 / nb11; ggml_cuda_launch_mm_ids_helper((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); + ne02, ne12, n_expert_used, ne11, si1, sis1, /*write_inverse =*/ dedup_bcast, stream); CUDA_CHECK(cudaGetLastError()); } - const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * sizeof(block_q8_1)/QK8_1 + + const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * y_block_size/y_values_per_block + ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq); ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), nbytes_src1_q8_1); + ggml_cuda_pool_alloc src1_scale(ctx.pool()); + if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) { + src1_scale.alloc(ne12*n_expert_used); + } const int64_t ne11_flat = ne12*n_expert_used; const int64_t ne12_flat = 1; @@ -198,8 +217,18 @@ void ggml_cuda_mul_mat_q( const int64_t s13 = src1->nb[3] / ts_src1; if (use_native_fp4) { - quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13, - ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); + static constexpr size_t align_float8 = 32; + const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8); + if (dedup_bcast) { + quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, + /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream); + } else { + quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13, + ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); + } + } else if (dedup_bcast) { + quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, + /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream); } else { quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); @@ -207,14 +236,15 @@ void ggml_cuda_mul_mat_q( CUDA_CHECK(cudaGetLastError()); } - static_assert(QK_K == 8 * QK_MXFP4, "QK_K needs to be 8 * QK_MXFP4"); - const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) : + static_assert(QK_FP4_MMQ == 8 * QK_MXFP4, "QK_FP4_MMQ needs to be 8 * QK_MXFP4"); + const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_FP4_MMQ * sizeof(int)) : ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int)); const int64_t s13 = ne12*s12; // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid. const mmq_args args = { src0_d, src0->type, (const int *) src1_q8_1.get(), ids_dst.get(), expert_bounds.get(), dst_d, + src1_scale.ptr, ne00, ne01, ne_get_rows, s01, ne_get_rows, s1, ne02, ne02, s02, s12, s2, ne03, ne13, s03, s13, s3, @@ -237,21 +267,24 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: - case GGML_TYPE_MXFP4: - case GGML_TYPE_NVFP4: +// ------------------------------------------------- case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: +// ------------------------------------------------- + case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_NL: +// ------------------------------------------------- + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: mmq_supported = true; break; default: diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 607e433bf1f3..71e3b2647a8e 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -13,7 +13,7 @@ typedef void (*ggml_cuda_mmq_load_tiles_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride); typedef void (*ggml_cuda_mmq_vec_dot_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00); typedef void (*ggml_cuda_mmq_write_back_t)(const float * __restrict__ sum, const int32_t * __restrict__ get_rows_to_sorted, - float * __restrict__ dst, const int stride, const int i_max, const int j_max); + float * __restrict__ dst, const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max); enum mmq_q8_1_ds_layout { MMQ_Q8_1_DS_LAYOUT_D4, @@ -21,6 +21,9 @@ enum mmq_q8_1_ds_layout { MMQ_Q8_1_DS_LAYOUT_D2S6, }; +static constexpr int QK8_1_MMQ = 4*QK8_1; +static constexpr int QK_FP4_MMQ = 2*QK8_1_MMQ; + struct block_q8_1_mmq { // The y float data is converted to a data layout that can simply be copied to shared memory as a contiguous block. // The y float data is first grouped as blocks of 128 values. @@ -39,7 +42,7 @@ struct block_q8_1_mmq { half d2s6[8]; // 1 16 bit scale per 64 values + 1 16 bit partial sum per 16 values for the first 96 values, // stored as d0,d1,s1,s2,s3,s4,s5 }; - int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each + int8_t qs[QK8_1_MMQ]; }; // this struct is used for fp4 data types (currently only used for Blackwell) @@ -47,10 +50,10 @@ struct block_q8_1_mmq { // nvfp4 has block size 16, each int32 of d4 contains 4 ue4m3 scales struct block_fp4_mmq { uint32_t d4[4]; - int8_t qs[4 * 32]; // 256 FP4 values packed as 4-bit pairs (2 per byte) + int8_t qs[QK_FP4_MMQ / 2]; }; -static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size"); +static_assert(sizeof(block_q8_1_mmq) == QK8_1_MMQ + 4*sizeof(half2), "Unexpected block_q8_1_mmq size"); static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1), "Unexpected block_q8_1_mmq size"); static_assert(sizeof(block_fp4_mmq) == sizeof(block_q8_1_mmq), "Unexpected block_fp4_mmq size"); @@ -410,11 +413,13 @@ static __host__ int ggml_cuda_mmq_get_nbytes_shared_x(const ggml_cuda_mmq_config template static __device__ __forceinline__ void ggml_cuda_mmq_write_back_dp4a( const float * __restrict__ sum, const int32_t * __restrict__ ids_dst, float * __restrict__ dst, - const int stride, const int i_max, const int j_max) { + const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) { constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int nwarps = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size; constexpr int I = ggml_cuda_mmq_get_I(type, J, fallback); + const bool y_scale_used = y_scale != nullptr; + #pragma unroll for (int j0 = 0; j0 < J; j0 += nwarps) { const int j = j0 + threadIdx.y; @@ -431,7 +436,16 @@ template static __device__ __forceinline_ continue; } - dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + if constexpr (type == GGML_TYPE_NVFP4) { + if (y_scale_used) { + dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + } else { + dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + } + } else { + dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + GGML_UNUSED(y_scale_used); + } } } } @@ -439,7 +453,8 @@ template static __device__ __forceinline_ template static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma( const float * __restrict__ sum, const int * __restrict__ ids_dst, float * __restrict__ dst, - const int stride, const int i_max, const int j_max) { + const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) { + #if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C; #else @@ -454,6 +469,8 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma( const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I); + const bool y_scale_used = y_scale != nullptr; + #pragma unroll for (int j0 = 0; j0 < J; j0 += ntx*tile_C::J) { #pragma unroll @@ -472,7 +489,16 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma( continue; } - dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + if constexpr (type == GGML_TYPE_NVFP4) { + if (y_scale_used) { + dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/tile_C::J + n)*tile_C::ne + l]; + } else { + dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + } + } else { + dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + GGML_UNUSED(y_scale_used); + } } } } @@ -816,6 +842,7 @@ template static __device__ __forceinline__ void mul_mat_q_process_tile( const char * __restrict__ x, const int offset_x, const int * __restrict__ y, const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup, + const float * __restrict__ y_scale, const int stride_row_x, const int ncols_y, const int stride_col_dst, const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) { @@ -833,9 +860,9 @@ static __device__ __forceinline__ void mul_mat_q_process_tile( #if defined(BLACKWELL_MMA_AVAILABLE) // FP4 tile stores 8 blocks - constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_K : 4 * QK8_1; + constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_FP4_MMQ : QK8_1_MMQ; #else - constexpr int ne_block = 4 * QK8_1; + constexpr int ne_block = QK8_1_MMQ; #endif // defined(BLACKWELL_MMA_AVAILABLE) constexpr int ITER_K = ggml_cuda_mmq_get_K_vram(type, J, fallback); @@ -881,9 +908,9 @@ static __device__ __forceinline__ void mul_mat_q_process_tile( } if (fixup) { - write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), I, I, J); + write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), y_scale, I, I, J); } else { - write_back(sum, ids_dst, dst, stride_col_dst, tile_x_max_i, tile_y_max_j); + write_back(sum, ids_dst, dst, y_scale, stride_col_dst, tile_x_max_i, tile_y_max_j); } } @@ -895,6 +922,7 @@ __launch_bounds__(ggml_cuda_mmq_get_nthreads(type, J, fallback), ggml_cuda_mmq_g static __global__ void mul_mat_q( const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst, const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup, + const float * __restrict__ y_scale, const uint3 blocks_per_ne00, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst, const uint3 channel_ratio, const uint3 nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, const uint3 sample_ratio, const uint3 nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst, @@ -940,8 +968,14 @@ static __global__ void mul_mat_q( int col_low = 0; int col_high = ncols_dst; int col_diff = ncols_dst; - int offset_y = wt*stride_sample_y + zt*stride_channel_y; - int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst; + int offset_y = wt*stride_sample_y + zt*stride_channel_y; + int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst; + int offset_y_scale; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y; + } else { + GGML_UNUSED(offset_y_scale); + } if (ids_dst) { col_low = expert_bounds[zt + 0]; @@ -950,6 +984,9 @@ static __global__ void mul_mat_q( offset_y = 0; offset_dst = 0; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = 0; + } if (jt*J >= col_diff) { return; @@ -971,6 +1008,11 @@ static __global__ void mul_mat_q( offset_y += (col_low + jt*J)*(sizeof(block_q8_1_mmq)/sizeof(int)); offset_dst += it*I; + const float * y_scale_tile = nullptr; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale += col_low + jt*J; + y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr; + } const int tile_x_max_i = nrows_x - it*I - 1; const int tile_y_max_j = col_diff - jt*J - 1; @@ -979,7 +1021,8 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile, + stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, 0, blocks_per_ne00.z); return; } @@ -1013,8 +1056,14 @@ static __global__ void mul_mat_q( int col_low = 0; int col_high = ncols_dst; int col_diff = ncols_dst; - int offset_y = wt*stride_sample_y + zt*stride_channel_y; - int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst; + int offset_y = wt*stride_sample_y + zt*stride_channel_y; + int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst; + int offset_y_scale; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y; + } else { + GGML_UNUSED(offset_y_scale); + } if (ids_dst) { col_low = expert_bounds[zt + 0]; @@ -1023,6 +1072,9 @@ static __global__ void mul_mat_q( offset_y = 0; offset_dst = 0; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = 0; + } if (jt*J >= col_diff) { kbc += blocks_per_ne00.z; @@ -1050,6 +1102,11 @@ static __global__ void mul_mat_q( offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int)); offset_dst += it*I; + const float * y_scale_tile = nullptr; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale += col_low + jt * J; + y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr; + } const int tile_x_max_i = nrows_x - it*I - 1; const int tile_y_max_j = col_diff - jt*J - 1; @@ -1058,7 +1115,8 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile, + stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); kbc += blocks_per_ne00.z; @@ -1087,8 +1145,14 @@ static __global__ void mul_mat_q( int col_low = 0; int col_high = ncols_dst; int col_diff = ncols_dst; - int offset_y = wt*stride_sample_y + zt*stride_channel_y; - int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst; + int offset_y = wt*stride_sample_y + zt*stride_channel_y; + int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst; + int offset_y_scale; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y; + } else { + GGML_UNUSED(offset_y_scale); + } if (ids_dst) { col_low = expert_bounds[zt + 0]; @@ -1097,6 +1161,9 @@ static __global__ void mul_mat_q( offset_y = 0; offset_dst = 0; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = 0; + } if (jt*J >= col_diff) { return; @@ -1119,6 +1186,11 @@ static __global__ void mul_mat_q( offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int)); offset_dst += it*I; + const float * y_scale_tile = nullptr; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale += col_low + jt * J; + y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr; + } const int tile_x_max_i = nrows_x - it*I - 1; const int tile_y_max_j = col_diff - jt*J - 1; @@ -1127,7 +1199,8 @@ static __global__ void mul_mat_q( constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile, + stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); } @@ -1271,6 +1344,7 @@ static __global__ void mul_mat_q_stream_k_fixup( struct mmq_args { const char * x; ggml_type type_x; const int * y; const int32_t * ids_dst; const int32_t * expert_bounds; float * dst; + const float * y_scale; int64_t ncols_x; int64_t nrows_x; int64_t ncols_dst; int64_t stride_row_x; int64_t ncols_y; int64_t nrows_dst; int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst; int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst; @@ -1320,7 +1394,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a if (!ggml_cuda_mmq_get_stream_k(type, J, fallback, cc)) { mul_mat_q<<>> - (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, args.y_scale, blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst, @@ -1349,7 +1423,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a const dim3 block_dims_fixup(block_dims.x, block_dims.y/2, block_dims.z); mul_mat_q<<>> - (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.y_scale, blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst, @@ -1463,26 +1537,30 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda #define DECL_MMQ_CASE(type) \ template void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \ +extern DECL_MMQ_CASE(GGML_TYPE_Q1_0); extern DECL_MMQ_CASE(GGML_TYPE_Q4_0); extern DECL_MMQ_CASE(GGML_TYPE_Q4_1); extern DECL_MMQ_CASE(GGML_TYPE_Q5_0); extern DECL_MMQ_CASE(GGML_TYPE_Q5_1); extern DECL_MMQ_CASE(GGML_TYPE_Q8_0); -extern DECL_MMQ_CASE(GGML_TYPE_MXFP4); -extern DECL_MMQ_CASE(GGML_TYPE_NVFP4); +// ----------------------------------------- extern DECL_MMQ_CASE(GGML_TYPE_Q2_K); extern DECL_MMQ_CASE(GGML_TYPE_Q3_K); extern DECL_MMQ_CASE(GGML_TYPE_Q4_K); extern DECL_MMQ_CASE(GGML_TYPE_Q5_K); extern DECL_MMQ_CASE(GGML_TYPE_Q6_K); +// ----------------------------------------- +extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S); extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS); extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XS); extern DECL_MMQ_CASE(GGML_TYPE_IQ2_S); extern DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS); extern DECL_MMQ_CASE(GGML_TYPE_IQ3_S); -extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S); extern DECL_MMQ_CASE(GGML_TYPE_IQ4_NL); extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS); +// ----------------------------------------- +extern DECL_MMQ_CASE(GGML_TYPE_MXFP4); +extern DECL_MMQ_CASE(GGML_TYPE_NVFP4); // ------------------------------------------------------------------------------------------------------------------------- diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index 39a500a17041..2bd9b6262390 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -1,6 +1,55 @@ #include "quantize.cuh" #include +#if defined(BLACKWELL_MMA_AVAILABLE) +// this maps to 256-bit loads in PTX on supported devices, +// and otherwise falls back to 2 128-bit loads +struct __builtin_align__(32) float8 { + float x; float y; float z; float w; + float p; float q; float r; float s; +}; +#endif + +#if CUDART_VERSION >= 12080 +static __device__ __forceinline__ float nvfp4_native_scale_error( + const float vals[QK_NVFP4_SUB], const float inv_col_scale, const float inv_scale, const float scale) { + const float scale_dequant = 2.0f * scale; + float err = 0.0f; + +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; k += 4) { + const float v0 = vals[k + 0] * inv_col_scale; + const float v1 = vals[k + 1] * inv_col_scale; + const float v2 = vals[k + 2] * inv_col_scale; + const float v3 = vals[k + 3] * inv_col_scale; + + const __nv_fp4x4_e2m1 q(make_float4(v0 * inv_scale, v1 * inv_scale, v2 * inv_scale, v3 * inv_scale)); + const __nv_fp4x4_storage_t q_storage = q.__x; + const __nv_fp4x2_storage_t q_lo = static_cast<__nv_fp4x2_storage_t>(q_storage); + const __nv_fp4x2_storage_t q_hi = static_cast<__nv_fp4x2_storage_t>(q_storage >> 8U); + + const __half2_raw hraw2_lo = __nv_cvt_fp4x2_to_halfraw2(q_lo, __NV_E2M1); + const __half2_raw hraw2_hi = __nv_cvt_fp4x2_to_halfraw2(q_hi, __NV_E2M1); + const __half2 h2_lo = static_cast<__half2>(hraw2_lo); + const __half2 h2_hi = static_cast<__half2>(hraw2_hi); + const float2 dq_lo = __half22float2(h2_lo); + const float2 dq_hi = __half22float2(h2_hi); + + const float err0 = fabsf(v0) - fabsf(dq_lo.x) * scale_dequant; + const float err1 = fabsf(v1) - fabsf(dq_lo.y) * scale_dequant; + const float err2 = fabsf(v2) - fabsf(dq_hi.x) * scale_dequant; + const float err3 = fabsf(v3) - fabsf(dq_hi.y) * scale_dequant; + + err = fmaf(err0, err0, err); + err = fmaf(err1, err1, err); + err = fmaf(err2, err2, err); + err = fmaf(err3, err3, err); + } + + return err; +} +#endif // CUDART_VERSION >= 12080 + __launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1) static __global__ void quantize_q8_1( const float * x_ptr, void * vy_ptr, @@ -74,97 +123,209 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) { return static_cast(biased); } - +// scatter: grid over tokens, quantize once, write to all the token's compact rows +template static __global__ void quantize_mmq_nvfp4( - const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, + const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, float * __restrict__ scale, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2) { + const int64_t ne0, const int64_t ne1, const int64_t ne2, const int n_expert_used) { #if defined(BLACKWELL_MMA_AVAILABLE) - const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB; - if (i0_base >= ne0) { - return; - } + const int64_t blocks_per_col = (ne0 + QK_FP4_MMQ - 1) / QK_FP4_MMQ; - const int64_t i1 = blockIdx.x; - const int64_t i2 = blockIdx.z % ne2; - const int64_t i3 = blockIdx.z / ne2; - const int64_t i01 = ids ? ids[i1] : i1; - const int64_t k_block = i0_base / QK_K; - const int64_t blocks_per_col = (ne0 + QK_K - 1) / QK_K; - if (k_block >= blocks_per_col) { - return; + int64_t base_idx; + if constexpr (scatter) { + base_idx = (int64_t) blockIdx.x * s02; // one physical row per token + } else { + const int64_t i2 = blockIdx.y % ne2; + const int64_t i3 = blockIdx.y / ne2; + const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x; + base_idx = i3 * s03 + i2 * s02 + i01 * s01; + } + const float * __restrict__ x_row = x + base_idx; + + float amax = 0.0f; + if constexpr (use_aligned_float8) { + for (int64_t i0 = 8 * threadIdx.x; i0 < ne00; i0 += 8 * blockDim.x) { + const float * x_base = x_row + i0; + const float8 v = reinterpret_cast(x_base)[0]; + amax = fmaxf(amax, fabsf(v.x)); + amax = fmaxf(amax, fabsf(v.y)); + amax = fmaxf(amax, fabsf(v.z)); + amax = fmaxf(amax, fabsf(v.w)); + amax = fmaxf(amax, fabsf(v.p)); + amax = fmaxf(amax, fabsf(v.q)); + amax = fmaxf(amax, fabsf(v.r)); + amax = fmaxf(amax, fabsf(v.s)); + } + } else { + for (int64_t i0 = threadIdx.x; i0 < ne00; i0 += blockDim.x) { + amax = fmaxf(amax, fabsf(x_row[i0])); + } } - const int64_t ib = blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x; - block_fp4_mmq * y = (block_fp4_mmq *) vy; - block_fp4_mmq * yb = y + ib; + amax = warp_reduce_max(amax); - const int sub = (i0_base % QK_K) / QK_NVFP4_SUB; + __shared__ float warp_amax[CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE]; + const int lane = threadIdx.x % WARP_SIZE; + const int warp = threadIdx.x / WARP_SIZE; - float vals_raw[QK_NVFP4_SUB]; - float amax_raw = 0.0f; - const int64_t base_idx = i3 * s03 + i2 * s02 + i01 * s01; + if (lane == 0) { + warp_amax[warp] = amax; + } + __syncthreads(); + + if (warp == 0) { + amax = threadIdx.x < int(CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE) ? warp_amax[lane] : 0.0f; + amax = warp_reduce_max(amax); + if (lane == 0) { + warp_amax[0] = amax / (6.0f * 448.0f); + if constexpr (scatter) { #pragma unroll - for (int k = 0; k < QK_NVFP4_SUB; k++) { - const int64_t i00 = i0_base + k; - if (i00 < ne00) { - const float v = x[base_idx + i00]; - vals_raw[k] = v; - amax_raw = fmaxf(amax_raw, fabsf(v)); - } else { - vals_raw[k] = 0.0f; + for (int slot = 0; slot < n_expert_used; ++slot) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + scale[i] = warp_amax[0]; + } + } else { + scale[blockIdx.y * ne1 + blockIdx.x] = warp_amax[0]; + } } } + __syncthreads(); - static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2}; - const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f); + block_fp4_mmq * y = (block_fp4_mmq *) vy; + const int64_t n_subblocks = (ne0 + QK_NVFP4_SUB - 1) / QK_NVFP4_SUB; + + for (int64_t isb = threadIdx.x; isb < n_subblocks; isb += blockDim.x) { + const int64_t i0_base = isb * QK_NVFP4_SUB; + const int64_t k_block = i0_base / QK_FP4_MMQ; + const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB; + + const float row_scale = warp_amax[0]; + const float inv_col_scale = row_scale > 0.0f ? 1.0f / row_scale : 0.0f; + + float vals[QK_NVFP4_SUB]; + if constexpr (use_aligned_float8) { + const float * x_base = x_row + i0_base; + const float8 v0 = i0_base + 7 < ne00 ? reinterpret_cast(x_base)[0] : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; + const float8 v1 = i0_base + 15 < ne00 ? reinterpret_cast(x_base + 8)[0] : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; + vals[0] = v0.x; vals[1] = v0.y; vals[2] = v0.z; vals[3] = v0.w; + vals[4] = v0.p; vals[5] = v0.q; vals[6] = v0.r; vals[7] = v0.s; + vals[8] = v1.x; vals[9] = v1.y; vals[10] = v1.z; vals[11] = v1.w; + vals[12] = v1.p; vals[13] = v1.q; vals[14] = v1.r; vals[15] = v1.s; + } else { +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; ++k) { + const int64_t i00 = i0_base + k; + vals[k] = i00 < ne00 ? x_row[i00] : 0.0f; + } + } - float best_err = FLT_MAX; - uint8_t fp8_code = 0; - float subblock_scale = 0.0f; + uint32_t q0 = 0; + uint32_t q1 = 0; -#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell. - for (int i = 0; i < 5; i++) { - const int test_code = first_fp8_code + test_offsets[i]; - if (test_code < 0 || test_code > 0x7e) { - continue; + float amax_sub = 0.0f; +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; ++k) { + amax_sub = fmaxf(amax_sub, fabsf(vals[k] * inv_col_scale)); } - const uint8_t code = (uint8_t) test_code; - const float test_scale = ggml_cuda_ue4m3_to_fp32(code); - const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f; - float cur_err = 0.0f; + + static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2 }; + const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_sub / 6.0f); + + uint8_t fp8_code = (uint8_t) first_fp8_code; + float subblock_scale = ggml_cuda_ue4m3_to_fp32(fp8_code); + float inv_scale_err = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; +#if CUDART_VERSION >= 12080 + float best_err = nvfp4_native_scale_error(vals, inv_col_scale, inv_scale_err, subblock_scale); +#else + float best_err = 0.0f; #pragma unroll for (int k = 0; k < QK_NVFP4_SUB; ++k) { - const float v = vals_raw[k]; - const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale); - const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale; - cur_err = fmaf(err_diff, err_diff, cur_err); + const float v = vals[k] * inv_col_scale; + const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, inv_scale_err); + const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * subblock_scale; + best_err = fmaf(err_diff, err_diff, best_err); } +#endif // CUDART_VERSION >= 12080 + +#pragma unroll + for (int i = 1; i < 5; ++i) { + const int test_code = first_fp8_code + test_offsets[i]; + if (test_code < 0 || test_code > 0x7e) { + continue; + } + + const float test_scale = ggml_cuda_ue4m3_to_fp32((uint8_t) test_code); + const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f; +#if CUDART_VERSION >= 12080 + const float cur_err = nvfp4_native_scale_error(vals, inv_col_scale, test_inv_scale, test_scale); +#else + float cur_err = 0.0f; +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; ++k) { + const float v = vals[k] * inv_col_scale; + const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale); + const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * test_scale; + cur_err = fmaf(err_diff, err_diff, cur_err); + } +#endif // CUDART_VERSION >= 12080 - if (cur_err < best_err) { - best_err = cur_err; - fp8_code = test_code; - subblock_scale = test_scale; + if (cur_err < best_err) { + best_err = cur_err; + fp8_code = (uint8_t) test_code; + subblock_scale = test_scale; + } } - } +#if CUDART_VERSION >= 12080 + const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; + const float s = inv_col_scale * inv_scale; + + __nv_fp4x4_e2m1 q0_lo(make_float4(vals[0] * s, vals[8] * s, vals[1] * s, vals[9] * s)); + __nv_fp4x4_e2m1 q0_hi(make_float4(vals[2] * s, vals[10] * s, vals[3] * s, vals[11] * s)); + __nv_fp4x4_e2m1 q1_lo(make_float4(vals[4] * s, vals[12] * s, vals[5] * s, vals[13] * s)); + __nv_fp4x4_e2m1 q1_hi(make_float4(vals[6] * s, vals[14] * s, vals[7] * s, vals[15] * s)); + + const char2 q0_lo_c = *reinterpret_cast(&q0_lo); + const char2 q0_hi_c = *reinterpret_cast(&q0_hi); + const char2 q1_lo_c = *reinterpret_cast(&q1_lo); + const char2 q1_hi_c = *reinterpret_cast(&q1_hi); + + q0 = uint32_t(uint8_t(q0_lo_c.x)) | (uint32_t(uint8_t(q0_lo_c.y)) << 8) | + (uint32_t(uint8_t(q0_hi_c.x)) << 16) | (uint32_t(uint8_t(q0_hi_c.y)) << 24); + q1 = uint32_t(uint8_t(q1_lo_c.x)) | (uint32_t(uint8_t(q1_lo_c.y)) << 8) | + (uint32_t(uint8_t(q1_hi_c.x)) << 16) | (uint32_t(uint8_t(q1_hi_c.y)) << 24); +#else + const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) { + q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 0] * inv_col_scale, inv_scale)) << (8 * k); + q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 8] * inv_col_scale, inv_scale)) << (8 * k + 4); + q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 4] * inv_col_scale, inv_scale)) << (8 * k); + q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 12] * inv_col_scale, inv_scale)) << (8 * k + 4); + } +#endif // CUDART_VERSION >= 12080 - const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; - uint32_t q0 = 0; - uint32_t q1 = 0; -#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1 - for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) { - q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 0], inv_scale) << (8 * k); - q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 8], inv_scale) << (8 * k + 4); - q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 4], inv_scale) << (8 * k); - q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4); + if constexpr (scatter) { +#pragma unroll + for (int slot = 0; slot < n_expert_used; ++slot) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + block_fp4_mmq * yb = y + (k_block * ne1 + i); + uint32_t * yqs = reinterpret_cast(yb->qs); + yqs[2 * sub + 0] = q0; + yqs[2 * sub + 1] = q1; + reinterpret_cast(yb->d4)[sub] = fp8_code; + } + } else { + block_fp4_mmq * yb = y + (blockIdx.y * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x); + uint32_t * yqs = reinterpret_cast(yb->qs); + yqs[2 * sub + 0] = q0; + yqs[2 * sub + 1] = q1; + reinterpret_cast(yb->d4)[sub] = fp8_code; + } } - - uint32_t * yqs = reinterpret_cast(yb->qs); - yqs[2 * sub + 0] = q0; - yqs[2 * sub + 1] = q1; - reinterpret_cast(yb->d4)[sub] = fp8_code; #else + GGML_UNUSED_VARS(x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, n_expert_used); NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only. #endif // defined(BLACKWELL_MMA_AVAILABLE) @@ -172,6 +333,8 @@ static __global__ void quantize_mmq_nvfp4( // quantize values in the format mxfp4 is stored which is interleaved nibbles // i.e. a block a0-a31 is represented as a0a16,a1a17 ...a15a31 +// scatter: grid over tokens, quantize once, write to all the token's compact rows +template static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, @@ -181,7 +344,8 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const int64_t s03, const int64_t ne0, const int ne1, - const int ne2) { + const int ne2, + const int n_expert_used) { constexpr int vals_per_scale = 32; constexpr int vals_per_warp = 2 * vals_per_scale; // Each warp processes 2 blocks of 32 = 64 values @@ -196,30 +360,27 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, return; } - const int64_t i1 = blockIdx.x; - const int64_t i2 = blockIdx.z % ne2; - const int64_t i3 = blockIdx.z / ne2; - - ggml_cuda_pdl_sync(); - const int64_t i01 = ids ? ids[i1] : i1; - const int64_t i02 = i2; - const int64_t i03 = i3; - - block_fp4_mmq * y = (block_fp4_mmq *) vy; - - const int64_t block_fp4_mmq_size = 8 * QK_MXFP4; // 256 values - const int64_t ib0 = blockIdx.z * ((int64_t) ne1 * (ne0 / block_fp4_mmq_size)); - const int64_t ib = ib0 + (warp_start_offset / block_fp4_mmq_size) * ne1 + blockIdx.x; + const int64_t block_fp4_mmq_size = QK_FP4_MMQ; + const int64_t k_block = warp_start_offset / block_fp4_mmq_size; const int64_t quad_idx_in_block = (warp_start_offset % block_fp4_mmq_size) / vals_per_warp; const int group_id = lane_id_32 / 4; const int lane_in_group = lane_id_32 % 4; const int base = group_id * 2; - char2 * yqs2 = (char2 *) y[ib].qs; - const int64_t base_pos = i03 * s03 + i02 * s02 + i01 * s01; + ggml_cuda_pdl_sync(); + int64_t base_pos; + if constexpr (scatter) { + base_pos = (int64_t) blockIdx.x * s02; // one physical row per token + } else { + const int64_t i2 = blockIdx.z % ne2; + const int64_t i3 = blockIdx.z / ne2; + const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x; + base_pos = i3 * s03 + i2 * s02 + i01 * s01; + } uint8_t scales[2]; + char2 packed[2]; #pragma unroll for (int b = 0; b < 2; ++b) { @@ -244,11 +405,8 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const float val2 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 1, WARP_SIZE); const float val3 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 17, WARP_SIZE); - if (lane_in_group == 0) { - __nv_fp4x4_e2m1 fp4_packed(make_float4(val0, val1, val2, val3)); - - yqs2[quad_idx_in_block * 16 + b * 8 + group_id] = *(char2 *) &fp4_packed; - } + __nv_fp4x4_e2m1 fp4_packed(make_float4(val0, val1, val2, val3)); + packed[b] = *(char2 *) &fp4_packed; #else // Fallback: manual FP4 conversion using LUT const uint8_t q_val = ggml_cuda_float_to_fp4_e2m1(xi, inv_s); @@ -258,26 +416,49 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const uint8_t q_hi_0 = __shfl_sync(0xFFFFFFFF, q_val, base + 16, WARP_SIZE); const uint8_t q_hi_1 = __shfl_sync(0xFFFFFFFF, q_val, base + 17, WARP_SIZE); - if (lane_in_group == 0) { - char2 q; - q.x = (q_hi_0 << 4) | q_lo_0; - q.y = (q_hi_1 << 4) | q_lo_1; - yqs2[quad_idx_in_block * 16 + b * 8 + group_id] = q; - } + char2 q; + q.x = (q_hi_0 << 4) | q_lo_0; + q.y = (q_hi_1 << 4) | q_lo_1; + packed[b] = q; #endif // CUDART_VERSION >= 12080 } - if (lane_id_32 == 0) { - // Store 2 scales packed into 1 uint32 - y[ib].d4[quad_idx_in_block] = (scales[1] << 8) | scales[0]; + block_fp4_mmq * y = (block_fp4_mmq *) vy; + if constexpr (scatter) { +#pragma unroll + for (int slot = 0; slot < n_expert_used; ++slot) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + block_fp4_mmq * yb = y + (k_block * ne1 + i); + char2 * yqs2 = (char2 *) yb->qs; + if (lane_in_group == 0) { + yqs2[quad_idx_in_block * 16 + 0 * 8 + group_id] = packed[0]; + yqs2[quad_idx_in_block * 16 + 1 * 8 + group_id] = packed[1]; + } + if (lane_id_32 == 0) { + yb->d4[quad_idx_in_block] = (scales[1] << 8) | scales[0]; + } + } + } else { + const int64_t ib0 = blockIdx.z * ((int64_t) ne1 * (ne0 / block_fp4_mmq_size)); + block_fp4_mmq * yb = y + (ib0 + k_block * ne1 + blockIdx.x); + char2 * yqs2 = (char2 *) yb->qs; + if (lane_in_group == 0) { + yqs2[quad_idx_in_block * 16 + 0 * 8 + group_id] = packed[0]; + yqs2[quad_idx_in_block * 16 + 1 * 8 + group_id] = packed[1]; + } + if (lane_id_32 == 0) { + yb->d4[quad_idx_in_block] = (scales[1] << 8) | scales[0]; + } } + GGML_UNUSED(n_expert_used); } -template +// scatter: grid over tokens, quantize once, write to all the token's compact rows +template static __global__ void quantize_mmq_q8_1( const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int ne1, const int ne2) { + const int64_t ne0, const int ne1, const int ne2, const int n_expert_used) { constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32; constexpr int vals_per_sum = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32; @@ -288,26 +469,27 @@ static __global__ void quantize_mmq_q8_1( return; } - const int64_t i1 = blockIdx.x; - const int64_t i2 = blockIdx.z % ne2; - const int64_t i3 = blockIdx.z / ne2; - const int64_t i00 = i0; ggml_cuda_pdl_sync(); - const int64_t i01 = ids ? ids[i1] : i1; - const int64_t i02 = i2; - const int64_t i03 = i3; - const float4 * x4 = (const float4 *) x; + int64_t base_idx; + if constexpr (scatter) { + base_idx = (int64_t) blockIdx.x * s02; // one physical row per token + } else { + const int64_t i2 = blockIdx.z % ne2; + const int64_t i3 = blockIdx.z / ne2; + const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x; + base_idx = i3*s03 + i2*s02 + i01*s01; + } + const float4 * x4 = (const float4 *) x; block_q8_1_mmq * y = (block_q8_1_mmq *) vy; - const int64_t ib0 = blockIdx.z*((int64_t)gridDim.x*gridDim.y*blockDim.x/QK8_1); // first block of channel - const int64_t ib = ib0 + (i0 / (4*QK8_1))*ne1 + blockIdx.x; // block index in channel - const int64_t iqs = i0 % (4*QK8_1); // quant index in block + const int64_t k_block = i0 / QK8_1_MMQ; // column block in the channel + const int64_t iqs = i0 % QK8_1_MMQ; // quant index in block // Load 4 floats per thread and calculate max. abs. value between them: - const float4 xi = i0 < ne00 ? x4[(i03*s03 + i02*s02 + i01*s01 + i00)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f); + const float4 xi = i0 < ne00 ? x4[(base_idx + i00)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f); float amax = fabsf(xi.x); amax = fmaxf(amax, fabsf(xi.y)); amax = fmaxf(amax, fabsf(xi.z)); @@ -336,40 +518,41 @@ static __global__ void quantize_mmq_q8_1( q.y = roundf(xi.y*d_inv); q.z = roundf(xi.z*d_inv); q.w = roundf(xi.w*d_inv); + const float d = 1.0f / d_inv; - // Write back 4 int8 values as a single 32 bit value for better memory bandwidth: - char4 * yqs4 = (char4 *) y[ib].qs; - yqs4[iqs/4] = q; - - if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) { - if (iqs % 16 != 0 || iqs >= 96) { - return; + // write the block once (normal) or to each of the token's compact rows (scatter) + const int nwrite = scatter ? n_expert_used : 1; +#pragma unroll + for (int slot = 0; slot < nwrite; ++slot) { + int64_t ib; + if constexpr (scatter) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + ib = k_block*ne1 + i; + } else { + const int64_t ib0 = blockIdx.z*((int64_t)gridDim.x*gridDim.y*blockDim.x/QK8_1); // first block of channel + ib = ib0 + k_block*ne1 + blockIdx.x; } - y[ib].d2s6[2 + iqs/16] = sum; - - if (iqs % 64 != 0) { - return; + // Write back 4 int8 values as a single 32 bit value for better memory bandwidth: + char4 * yqs4 = (char4 *) y[ib].qs; + yqs4[iqs/4] = q; + + if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) { + if (iqs % 16 == 0 && iqs < 96) { + y[ib].d2s6[2 + iqs/16] = sum; + if (iqs % 64 == 0) { + y[ib].d2s6[iqs/64] = d; + } + } + } else if (iqs % 32 == 0) { + if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) { + y[ib].ds4[iqs/32] = make_half2(d, sum); + } else { + y[ib].d4[iqs/32] = d; + } } - - const float d = 1.0f / d_inv; - - y[ib].d2s6[iqs/64] = d; - - return; - } - - if (iqs % 32 != 0) { - return; - } - - const float d = 1.0f / d_inv; - - if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) { - y[ib].ds4[iqs/32] = make_half2(d, sum); - } else { - y[ib].d4[iqs/32] = d; } + GGML_UNUSED(n_expert_used); } void quantize_row_q8_1_cuda( @@ -394,7 +577,7 @@ void quantize_mmq_q8_1_cuda( const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ne0 % (4*QK8_1) == 0); + GGML_ASSERT(ne0 % QK8_1_MMQ == 0); // ne1 tends to assume the highest values, therefore use it as the "x" dimension of the CUDA grid: const int64_t block_num_y = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ); @@ -402,16 +585,16 @@ void quantize_mmq_q8_1_cuda( const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); switch (mmq_get_q8_1_ds_layout(type_src0)) { case MMQ_Q8_1_DS_LAYOUT_D4: - quantize_mmq_q8_1 - <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_q8_1 + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); break; case MMQ_Q8_1_DS_LAYOUT_DS4: - quantize_mmq_q8_1 - <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_q8_1 + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); break; case MMQ_Q8_1_DS_LAYOUT_D2S6: - quantize_mmq_q8_1 - <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_q8_1 + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); break; default: GGML_ABORT("fatal error"); @@ -419,21 +602,85 @@ void quantize_mmq_q8_1_cuda( } } +// scatter=true reuses the quant kernel: grid over tokens, ids = inverse map (token slot -> compact row) +void quantize_scatter_mmq_q8_1_cuda( + const float * x, const int32_t * ids_src1_inv, void * vy, const ggml_type type_src0, + const int64_t ne00, const int64_t stride_token, const int64_t ne0, + const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) { + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne0 % QK8_1_MMQ == 0); + + const int64_t block_num_y = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ); + const dim3 num_blocks(n_tokens, block_num_y, 1); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); + switch (mmq_get_q8_1_ds_layout(type_src0)) { + case MMQ_Q8_1_DS_LAYOUT_D4: + quantize_mmq_q8_1<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + break; + case MMQ_Q8_1_DS_LAYOUT_DS4: + quantize_mmq_q8_1<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + break; + case MMQ_Q8_1_DS_LAYOUT_D2S6: + quantize_mmq_q8_1<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + break; + default: + GGML_ABORT("fatal error"); + break; + } +} + +// scatter=true reuses the quant kernels: grid over tokens, ids = inverse map (token slot -> compact row) +void quantize_scatter_mmq_fp4_cuda( + const float * x, const int32_t * ids_src1_inv, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8, + const int64_t ne00, const int64_t stride_token, const int64_t ne0, + const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) { + GGML_ASSERT(ne0 > 0); + if (type_src0 == GGML_TYPE_NVFP4) { + GGML_ASSERT(scale); + GGML_ASSERT(ne00 % QK_NVFP4 == 0); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); + const dim3 num_blocks(n_tokens, 1, 1); + if (use_aligned_float8) { + quantize_mmq_nvfp4<<>>( + x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used); + } else { + quantize_mmq_nvfp4<<>>( + x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used); + } + } else { + GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4); + constexpr int nwarps = 8; + constexpr int vals_per_block = nwarps * 2 * QK_MXFP4; + const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block; + const dim3 block_size(WARP_SIZE, nwarps, 1); + const dim3 num_blocks(n_tokens, block_num_y, 1); + quantize_mmq_mxfp4<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + } +} + void quantize_mmq_fp4_cuda( - const float * x, const int32_t * ids, void * vy, const ggml_type type_src0, + const float * x, const int32_t * ids, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4); GGML_ASSERT(ne0 > 0); if (type_src0 == GGML_TYPE_NVFP4) { + GGML_ASSERT(scale); GGML_ASSERT(ne00 % QK_NVFP4 == 0); - constexpr int nvfp4_block_size = 128; - const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size); - const dim3 block_size(nvfp4_block_size, 1, 1); - const dim3 num_blocks(ne1, block_num_y, ne2 * ne3); - quantize_mmq_nvfp4<<>>( - x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); + const dim3 num_blocks(ne1, ne2 * ne3, 1); + if (use_aligned_float8) { + quantize_mmq_nvfp4<<>>( + x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); + } else { + quantize_mmq_nvfp4<<>>( + x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); + } } else { GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0); @@ -445,6 +692,6 @@ void quantize_mmq_fp4_cuda( const dim3 num_blocks(ne1, block_num_y, ne2 * ne3); const dim3 block_size(WARP_SIZE, nwarps, 1); - quantize_mmq_mxfp4<<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_mxfp4<<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); } } diff --git a/ggml/src/ggml-cuda/quantize.cuh b/ggml/src/ggml-cuda/quantize.cuh index 768a3ae6de6c..5f08dcbfe331 100644 --- a/ggml/src/ggml-cuda/quantize.cuh +++ b/ggml/src/ggml-cuda/quantize.cuh @@ -29,7 +29,9 @@ void quantize_mmq_q8_1_cuda( void quantize_mmq_fp4_cuda(const float * x, const int32_t * ids, void * vy, + float * scale, ggml_type type_src0, + bool use_aligned_float8, int64_t ne00, int64_t s01, int64_t s02, @@ -39,3 +41,30 @@ void quantize_mmq_fp4_cuda(const float * x, int64_t ne2, int64_t ne3, cudaStream_t stream); + +// quantize each token once and scatter the block to its compact rows (via the inverse map) +void quantize_scatter_mmq_fp4_cuda(const float * x, + const int32_t * ids_src1_inv, + void * vy, + float * scale, + ggml_type type_src0, + bool use_aligned_float8, + int64_t ne00, + int64_t stride_token, + int64_t ne0, + int64_t n_tokens, + int64_t nrows_dst, + int n_expert_used, + cudaStream_t stream); + +void quantize_scatter_mmq_q8_1_cuda(const float * x, + const int32_t * ids_src1_inv, + void * vy, + ggml_type type_src0, + int64_t ne00, + int64_t stride_token, + int64_t ne0, + int64_t n_tokens, + int64_t nrows_dst, + int n_expert_used, + cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/topk-moe.cu b/ggml/src/ggml-cuda/topk-moe.cu index c80394e31ff3..c8cec70bb320 100644 --- a/ggml/src/ggml-cuda/topk-moe.cu +++ b/ggml/src/ggml-cuda/topk-moe.cu @@ -8,6 +8,7 @@ // Kernel config struct - passed by value to CUDA kernel struct topk_moe_config { bool use_sigmoid; + bool use_sqrt_softplus; bool with_norm; bool delayed_softmax; }; @@ -67,6 +68,16 @@ __device__ void sigmoid_warp_inplace(float (&vals)[experts_per_thread], const in } } +template +__device__ void sqrt_softplus_warp_inplace(float (&vals)[experts_per_thread], const int limit, const int lane) { +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + const int idx = lane + i * WARP_SIZE; + const bool active = !use_limit || (idx < limit); + vals[i] = active ? sqrtf(vals[i] > 20.0f ? vals[i] : logf(1.0f + expf(vals[i]))) : -INFINITY; + } +} + /* This kernel does the following: 1. optionally softmax over the logits per token [n_experts, n_tokens] @@ -115,6 +126,8 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * if (!config.delayed_softmax) { if (config.use_sigmoid) { sigmoid_warp_inplace(wt, n_experts, threadIdx.x); + } else if (config.use_sqrt_softplus) { + sqrt_softplus_warp_inplace(wt, n_experts, threadIdx.x); } else { softmax_warp_inplace(wt, n_experts, threadIdx.x); } @@ -364,9 +377,10 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, } topk_moe_config config; - config.use_sigmoid = args.sigmoid; - config.with_norm = with_norm; - config.delayed_softmax = args.delayed_softmax; + config.use_sigmoid = args.sigmoid; + config.use_sqrt_softplus = args.sqrt_softplus; + config.with_norm = with_norm; + config.delayed_softmax = args.delayed_softmax; if (bias) { launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, bias_d, n_rows, n_experts, n_expert_used, clamp_val, @@ -415,7 +429,7 @@ bool ggml_cuda_should_use_topk_moe(const ggml_tensor * gating_op, } else if (gating_op->op == GGML_OP_UNARY) { ggml_unary_op op = ggml_get_unary_op(gating_op); - if (op != GGML_UNARY_OP_SIGMOID) { + if (op != GGML_UNARY_OP_SIGMOID && op != GGML_UNARY_OP_SOFTPLUS) { return false; } } diff --git a/ggml/src/ggml-cuda/topk-moe.cuh b/ggml/src/ggml-cuda/topk-moe.cuh index 243dc2f1c41b..091ef02a415a 100644 --- a/ggml/src/ggml-cuda/topk-moe.cuh +++ b/ggml/src/ggml-cuda/topk-moe.cuh @@ -5,6 +5,7 @@ struct ggml_cuda_topk_moe_args { bool sigmoid{}; + bool sqrt_softplus{}; bool softmax{}; bool delayed_softmax{}; bool prob_bias{}; diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh index d1741cc8d7ba..b9932bce9c7f 100644 --- a/ggml/src/ggml-cuda/vecdotq.cuh +++ b/ggml/src/ggml-cuda/vecdotq.cuh @@ -681,35 +681,40 @@ static __device__ __forceinline__ float vec_dot_q1_0_q8_1( // Q8_1: 32 elements per block with individual scales // iqs selects which of the 4 chunks of 32 elements to process (0-3) - const float d1 = bq1_0->d; + const float d1 = bq1_0->d; + const int16_t * qs = (const int16_t *) bq1_0->qs + iqs * 2; // Process only the chunk specified by iqs const block_q8_1 * bq8_1_chunk = bq8_1 + iqs; - // Load 32 bits (4 bytes) for this chunk from Q1_0 - const int offset = iqs * 4; - const int v = bq1_0->qs[offset + 0] | (bq1_0->qs[offset + 1] << 8) | - (bq1_0->qs[offset + 2] << 16) | (bq1_0->qs[offset + 3] << 24); - - // Unpack 32 bits into 32 signed values (-1 or +1) - int vi_bytes[8]; -#pragma unroll - for (int j = 0; j < 8; ++j) { - const int shift = j * 4; - const int bits4 = (v >> shift) & 0x0F; - const int b0 = (bits4 & 0x01) ? 1 : -1; - const int b1 = (bits4 & 0x02) ? 1 : -1; - const int b2 = (bits4 & 0x04) ? 1 : -1; - const int b3 = (bits4 & 0x08) ? 1 : -1; - vi_bytes[j] = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24); - } - - // Compute dot product for this 32-element chunk int sumi = 0; #pragma unroll - for (int j = 0; j < 8; ++j) { - const int u = get_int_b4(bq8_1_chunk->qs, j); - sumi = ggml_cuda_dp4a(vi_bytes[j], u, sumi); + for (int j = 0; j < 2; ++j) { + const int q = qs[j]; + + const int u0 = get_int_b4(bq8_1_chunk->qs, j*4+0); + const int u1 = get_int_b4(bq8_1_chunk->qs, j*4+1); + const int u2 = get_int_b4(bq8_1_chunk->qs, j*4+2); + const int u3 = get_int_b4(bq8_1_chunk->qs, j*4+3); + + // unpack crumbs into nibble indices + const int n0 = __byte_perm(0x11100100, 0x11100100, q >> 0); // [0, 1, 4, 5] [ 8, 9, 12, 13] + const int n1 = __byte_perm(0x11100100, 0x11100100, q >> 2); // [2, 3, 6, 7] [10, 11, 14, 15] + // unpack nibbles into byte values + const int s0 = __byte_perm(0x01FF, 0x01FF, n0 >> 0); + const int s1 = __byte_perm(0x01FF, 0x01FF, n1 >> 0); + const int s2 = __byte_perm(0x01FF, 0x01FF, n0 >> 16); + const int s3 = __byte_perm(0x01FF, 0x01FF, n1 >> 16); + // unshuffle values + const int v0 = __byte_perm(s0, s1, 0x5410); + const int v1 = __byte_perm(s0, s1, 0x7632); + const int v2 = __byte_perm(s2, s3, 0x5410); + const int v3 = __byte_perm(s2, s3, 0x7632); + + sumi = ggml_cuda_dp4a(v0, u0, sumi); + sumi = ggml_cuda_dp4a(v1, u1, sumi); + sumi = ggml_cuda_dp4a(v2, u2, sumi); + sumi = ggml_cuda_dp4a(v3, u3, sumi); } // Apply Q1_0's single scale and this chunk's Q8_1 scale diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index d01f1533abb6..9aa558f3f4ca 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -6,10 +6,6 @@ #include #include -#if defined(GGML_HIP_ROCWMMA_FATTN) -#include -#endif // defined(GGML_HIP_ROCWMMA_FATTN) - #ifdef GGML_USE_NCCL #include #endif // GGML_USE_NCCL diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 76c71d7ee70e..bdb8af0820a3 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -21,6 +21,11 @@ #include #ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include # include #else # include @@ -28,7 +33,9 @@ #endif #pragma clang diagnostic ignored "-Wnested-anon-types" +#pragma clang diagnostic ignored "-Wlanguage-extension-token" #pragma clang diagnostic ignored "-Wgnu-anonymous-struct" +#pragma clang diagnostic ignored "-Wmicrosoft-enum-value" #include #include @@ -134,12 +141,14 @@ static const char * htp_event_name(uint16_t id) { case HTP_TRACE_EVT_HVX_FA_K_PREP: return "HVX_K_PREP"; case HTP_TRACE_EVT_HVX_FA_V_PREP: return "HVX_V_PREP"; case HTP_TRACE_EVT_HMX_COMP: return "HMX_COMP"; + case HTP_TRACE_EVT_L2FLUSH: return "L2FLUSH"; + case HTP_TRACE_EVT_INIT: return "INIT"; + case HTP_TRACE_EVT_BUFF: return "BUFF"; default: return "UNKNOWN"; } } -static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node, - const htp_prof_desc & pd) { +static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node, const htp_prof_desc & pd) { if (!opt_profile) return; uint32_t op_usec = pd.usecs; @@ -159,6 +168,43 @@ static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_op node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.kparams, op_usec, op_cycles, pd.cycles_start, mhz, pmu_str); } +static void ggml_hexagon_dump_batch_prof(const std::string & sess_name, const htp_opbatch_rsp & rsp) { + uint64_t batch_cycles = rsp.cycles_stop - rsp.cycles_start; + float batch_mhz = rsp.usecs > 0 ? (float) batch_cycles / rsp.usecs : 0.0f; + + char evt_str[256] = "----"; + if (opt_profile == 3) { + snprintf(evt_str, sizeof(evt_str), "evt-cnt %u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u", + rsp.n_traces[0], rsp.n_traces[1], rsp.n_traces[2], rsp.n_traces[3], + rsp.n_traces[4], rsp.n_traces[5], rsp.n_traces[6], rsp.n_traces[7], + rsp.n_traces[8], rsp.n_traces[9], rsp.n_traces[10]); + } + + GGML_LOG_DEBUG("ggml-hex: %s profile-op OPBATCH|----|n-ops %u|%s|----|----|usec %u cycles %llu start %llu mhz %.1f\n", + sess_name.c_str(), rsp.n_ops, evt_str, rsp.usecs, (unsigned long long) batch_cycles, (unsigned long long) rsp.cycles_start, batch_mhz); +} + +static void ggml_hexagon_dump_trace_events(const std::string & sess_name, const htp_opbatch_rsp & rsp, + const htp_trace_desc * trace_events, uint32_t n_traces) { + if (opt_profile == 3 && trace_events) { + uint32_t valid_cnt[HTP_MAX_NTHREADS + 1] = {0}; + for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { + uint32_t count = rsp.n_traces[t]; + valid_cnt[t] = count > n_traces ? n_traces : count; + } + + for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { + for (uint32_t idx = 0; idx < valid_cnt[t]; idx++) { + const auto & e = trace_events[t * n_traces + idx]; + bool is_stop = (e.info & 0x8000) != 0; + uint16_t info = e.info & 0x7FFF; + GGML_LOG_DEBUG("ggml-hex: %s trace-evt %s: thread %u info %u %s %u\n", + sess_name.c_str(), htp_event_name(e.id), t, info, is_stop ? "stop" : "start", e.cycles); + } + } + } +} + // ** static inline bool ggml_hexagon_is_repack_type(enum ggml_type type) { @@ -501,6 +547,8 @@ static void repack_q4_0_tiled(ggml_tensor * t, const void * data, size_t size) { } } } + + GGML_UNUSED(size); } // repack q4_0_tiled tensor into q4_0 data @@ -554,6 +602,8 @@ static void repack_tiled_q4_0(void * data, const ggml_tensor * t, size_t size) { } } } + + GGML_UNUSED(size); } // repack q4_1 data into q4_1_tiled tensor @@ -611,6 +661,8 @@ static void repack_q4_1_tiled(ggml_tensor * t, const void * data, size_t size) { } } } + + GGML_UNUSED(size); } // repack q4_1_tiled tensor into q4_1 data @@ -665,6 +717,8 @@ static void repack_tiled_q4_1(void * data, const ggml_tensor * t, size_t size) { } } } + + GGML_UNUSED(size); } // repack q8_0 data into q8_0_tiled tensor @@ -711,6 +765,8 @@ static void repack_q8_0_tiled(ggml_tensor * t, const void * data, size_t size) { } } } + + GGML_UNUSED(size); } // repack q8_0_tiled tensor into q8_0 data @@ -761,6 +817,8 @@ static void repack_tiled_q8_0(void * data, const ggml_tensor * t, size_t size) { } } } + + GGML_UNUSED(size); } // repack mxfp4 data into mxfp4_tiled tensor @@ -812,6 +870,8 @@ static void repack_mxfp4_tiled(ggml_tensor * t, const void * data, size_t size) } } } + + GGML_UNUSED(size); } // repack mxfp4_tiled tensor into mxfp4 data @@ -865,6 +925,8 @@ static void repack_tiled_mxfp4(void * data, const ggml_tensor * t, size_t size) } } } + + GGML_UNUSED(size); } static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer, @@ -965,11 +1027,12 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer, static bool ggml_backend_hexagon_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + // we might optimize this later, for now take the slow path (ie get/set_tensor) + return false; + GGML_UNUSED(buffer); GGML_UNUSED(src); GGML_UNUSED(dst); - // we might optimize this later, for now take the slow path (ie get/set_tensor) - return false; } static void ggml_backend_hexagon_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -1025,9 +1088,9 @@ static ggml_backend_buffer_t ggml_backend_hexagon_repack_buffer_type_alloc_buffe } } -static size_t ggml_backend_hexagon_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) { +static size_t ggml_backend_hexagon_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; // HVX alignment - GGML_UNUSED(buffer_type); + GGML_UNUSED(buft); } static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * t) { @@ -1039,20 +1102,24 @@ static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffe return ggml_row_size(t->type, ne0) * ne1 * ne2 * ne3; } return ggml_nbytes(t); + + GGML_UNUSED(buft); } -static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) { - auto * context = static_cast(buffer_type->context); +static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + auto * context = static_cast(buft->context); return context->sess->max_bufsize; } static bool ggml_backend_hexagon_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return opt_hostbuf; + GGML_UNUSED(buft); } static bool ggml_backend_hexagon_repack_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return false; + GGML_UNUSED(buft); } @@ -1098,6 +1165,8 @@ struct ggml_hexagon_opbatch { std::unordered_map t_map; // tensor ptr to index std::unordered_multimap d_map; // tensor data to index + + unsigned int n_bufs; // num buffers in the batch unsigned int n_tens; // num tensors ... unsigned int n_ops; // num ops ... @@ -1124,7 +1193,7 @@ struct ggml_hexagon_opbatch { n_bufs_max = HTP_OP_MAX_BUFS; n_ops_max = batch_size; - n_tens_max = n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS; + n_tens_max = std::min(n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS, HTP_OP_MAX_TENSORS); b_vmem_max = max_vmem; @@ -1170,6 +1239,8 @@ struct ggml_hexagon_opbatch { return bi; } + + bool same_shape(const htp_tensor * h, const ggml_tensor * t) const { int64_t ne0 = t->ne[0]; int64_t ne1 = t->ne[1]; @@ -1182,7 +1253,8 @@ struct ggml_hexagon_opbatch { int64_t nb2 = is_repack ? nb1 * ne1 : t->nb[2]; int64_t nb3 = is_repack ? nb2 * t->ne[2] : t->nb[3]; - return (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) && + return (h->type == t->type) && + (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) && (h->nb[0] == t->nb[0]) && (h->nb[1] == nb1) && (h->nb[2] == nb2) && (h->nb[3] == nb3); } @@ -1213,6 +1285,7 @@ struct ggml_hexagon_opbatch { htp_tensor &h = h_tens[ti]; h.bi = add_buffer(sbuf); + h.ti = ti; h.data = t_offset; h.type = t->type; @@ -1235,8 +1308,10 @@ struct ggml_hexagon_opbatch { h.nb[0] = t->nb[0]; h.nb[1] = t->nb[1]; h.nb[2] = t->nb[2]; h.nb[3] = t->nb[3]; } + + h.flags = 0; - if (ggml_backend_buffer_get_usage(t->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { + if (ggml_backend_buffer_get_usage(t->buffer) != GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { h.flags |= HTP_TENSOR_COMPUTE; } @@ -1313,6 +1388,9 @@ struct ggml_hexagon_opbatch { o.dst[i] = (i < outputs.size() && outputs[i]) ? add_tensor(outputs[i]) : 0xffff; } } + + void finalize_ranges() { + } }; struct ggml_hexagon_opqueue { @@ -1462,9 +1540,6 @@ struct ggml_hexagon_opqueue { if (opt_profile && rsp.n_ops > 0) { auto & ops = op_cache[rsp.id]; - uint64_t batch_usec = ggml_time_us() - start_usec[rsp.id]; - uint32_t htp_usec = 0; - GGML_ASSERT(rsp.n_ops <= ops.size()); const htp_prof_desc * pd = (const htp_prof_desc *) p_ptr; @@ -1475,55 +1550,13 @@ struct ggml_hexagon_opqueue { trace_events = (const htp_trace_desc *) (p_ptr + p_size); } - uint32_t trace_idx[HTP_MAX_NTHREADS + 1] = {0}; - uint32_t valid_cnt[HTP_MAX_NTHREADS + 1] = {0}; - - if (opt_profile == 3) { - for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { - uint32_t count = rsp.n_traces[t]; - valid_cnt[t] = count > n_traces ? n_traces : count; - } - } + ggml_hexagon_dump_batch_prof(shm_buf->sess->name, rsp); for (uint32_t i = 0; i < rsp.n_ops; i++) { - htp_usec += pd[i].usecs; - ggml_hexagon_dump_op_prof(shm_buf->sess->name, ops[i], pd[i]); - - if (opt_profile == 3) { - uint32_t op_duration = pd[i].cycles_stop - pd[i].cycles_start; - - for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { - while (trace_idx[t] < valid_cnt[t]) { - const auto & e = trace_events[t * n_traces + trace_idx[t]]; - uint32_t offset = e.cycles - pd[i].cycles_start; - if (offset >= 0x80000000) { - trace_idx[t]++; - continue; - } - if (offset > op_duration) { - break; - } - bool is_stop = (e.info & 0x8000) != 0; - uint16_t info = e.info & 0x7FFF; - GGML_LOG_DEBUG("ggml-hex: %s trace-op %s: thread %u event %s info %u %s %u\n", - shm_buf->sess->c_name(), ops[i].op_name().c_str(), t, htp_event_name(e.id), info, is_stop ? "stop" : "start", e.cycles); - trace_idx[t]++; - } - } - } - } - - char evt_str[256] = ""; - if (opt_profile == 3) { - snprintf(evt_str, sizeof(evt_str), " evt [%u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u]", - rsp.n_traces[0], rsp.n_traces[1], rsp.n_traces[2], rsp.n_traces[3], - rsp.n_traces[4], rsp.n_traces[5], rsp.n_traces[6], rsp.n_traces[7], - rsp.n_traces[8], rsp.n_traces[9], rsp.n_traces[10]); } - GGML_LOG_DEBUG("ggml-hex: %s profile-batch n-ops %u batch-dur-usec %lld htp-ops-usec %u%s\n", - shm_buf->sess->c_name(), rsp.n_ops, (long long) batch_usec, htp_usec, evt_str); + ggml_hexagon_dump_trace_events(shm_buf->sess->name, rsp, trace_events, n_traces); } } }; @@ -1542,7 +1575,7 @@ void ggml_hexagon_session::flush_pending(bool all) { const uint32_t timeo = opt_oppoll ? 0 : DSPQUEUE_TIMEOUT; int err = dspqueue_read(this->queue, &flags, 1, &n_dbufs, &dbuf, sizeof(rsp), &rsp_size, (uint8_t *) &rsp, timeo); - if (err == AEE_EEXPIRED) { + if (err == AEE_EEXPIRED || err == AEE_EWOULDBLOCK) { continue; } @@ -1571,6 +1604,8 @@ void ggml_hexagon_session::flush_pending(bool all) { void ggml_hexagon_session::flush_batch() { if (op_batch->empty()) { return; } + op_batch->finalize_ranges(); + htp_opbatch_req req {}; dspqueue_buffer dbuf{}; @@ -1647,7 +1682,7 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { GGML_LOG_DEBUG("ggml-hex: %s allocating new session\n", this->name.c_str()); - domain * my_domain = get_domain(this->domain_id); + domain * my_domain = htpdrv_get_domain(this->domain_id); if (my_domain == NULL) { GGML_LOG_ERROR("ggml-hex: unable to get domain struct for CDSP\n"); throw std::runtime_error("ggml-hex: failed to get CDSP domain (see log for details)"); @@ -1793,16 +1828,6 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { } } - if (opt_profile) { - htp_iface_pmu_conf pmu_conf{}; - std::copy(opt_pmu_evt.begin(), opt_pmu_evt.end(), pmu_conf.events); - - err = htp_iface_profiler(this->handle, opt_profile, &pmu_conf); - if (err != 0) { - GGML_LOG_ERROR("ggml-hex: failed to enable profiling: 0x%08x\n", (unsigned) err); - } - } - // Allocate buffers and state for op batching this->op_queue = new ggml_hexagon_opqueue(this, opt_opbatch, opt_opqueue); @@ -1821,6 +1846,16 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { throw std::runtime_error("ggml-hex: iface start failed (see log for details)"); } this->valid_iface = true; + + if (opt_profile) { + htp_iface_pmu_conf pmu_conf{}; + std::copy(opt_pmu_evt.begin(), opt_pmu_evt.end(), pmu_conf.events); + + err = htp_iface_profiler(this->handle, opt_profile, &pmu_conf); + if (err != 0) { + GGML_LOG_ERROR("ggml-hex: failed to enable profiling: 0x%08x\n", (unsigned) err); + } + } } void ggml_hexagon_session::release() noexcept(true) { @@ -1929,6 +1964,8 @@ static bool ggml_hexagon_flash_attn_is_hmx_eligible( } return true; + + GGML_UNUSED(sinks); } static bool ggml_hexagon_precompute_flash_attn_params( @@ -1990,7 +2027,7 @@ static bool ggml_hexagon_precompute_flash_attn_params( const struct ggml_tensor * sinks = op->src[4]; if (ggml_hexagon_flash_attn_is_hmx_eligible(sess, q, k, v, sinks)) { size_t Br = 0, Bc = 0; - int ret = hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, sess->vtcm_size, sess->n_threads); + int ret = hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, sess->vtcm_size, sess->n_threads, kparams->is_q_fp32 != 0); if (ret == 0) { kparams->kernel_type = HTP_FA_KERNEL_HMX; kparams->Br = Br; @@ -2000,7 +2037,7 @@ static bool ggml_hexagon_precompute_flash_attn_params( kparams->u.hmx.g_br = hex_align_up(G * Br, 32); kparams->u.hmx.pipeline = (kparams->n_kv_blocks >= 3 && sess->n_threads >= 2) ? 1 : 0; - kparams->vtcm_size = hmx_fa_compute_vtcm_usage(G, DK, DV, Br, Bc, kparams->n_threads, kparams->u.hmx.pipeline != 0); + kparams->vtcm_size = hmx_fa_compute_vtcm_usage(G, DK, DV, Br, Bc, kparams->n_threads, kparams->u.hmx.pipeline != 0, kparams->is_q_fp32 != 0); const size_t row_vec_bytes = hex_align_up(Bc * sizeof(uint16_t), 256); kparams->u.hmx.row_buf_stride = row_vec_bytes / 128; // HVX vector is 128 bytes @@ -2149,8 +2186,9 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_matmul_is_hmx_eligible( @@ -2198,6 +2236,8 @@ static bool ggml_hexagon_matmul_is_hmx_eligible( } return true; + + GGML_UNUSED(dst); } static bool ggml_hexagon_precompute_hmx_mm_params( @@ -2234,109 +2274,15 @@ static bool ggml_hexagon_precompute_hmx_mm_params( if (is_batched_val && wtype == GGML_TYPE_F16 && group_size > 1) { // Try grouped path first const bool use_dma_activation = (src1->nb[1]/sizeof(float) > (size_t)ne00_padded); - size_t best_mblocks = SIZE_MAX; - int best_act_threads = 0; - size_t best_m_chunk = 0; - size_t best_n_chunk = 0; - size_t best_vtcm_size = 0; - - int act_threads = n_threads; - while (act_threads >= 1) { - const size_t f32_scratch_size = use_dma_activation ? hex_align_up(act_threads * HTP_MM_DMA_ACT_MULTIPLIER * ne00_padded * sizeof(float), HTP_MM_HMX_TILE_SIZE) : 0; - size_t group_overhead = 256 + f32_scratch_size; - size_t group_size_per_n, group_size_per_m, group_size_per_mn; - htp_mm_hmx_get_batched_chunk_costs(ne00_padded, group_size, &group_size_per_n, &group_size_per_m, &group_size_per_mn); - - size_t m_chunk_candidate = 0; - size_t n_chunk_candidate = 0; - size_t vtcm_size_candidate = 0; - - if (htp_mm_hmx_compute_chunks(vtcm_budget, group_overhead, group_size_per_n, group_size_per_m, group_size_per_mn, hex_align_up(ne11, 32), ne01_padded, - (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) ne11 * HTP_MM_HMX_COST_A_CONVERT, - &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { - size_t exact_size = htp_mm_hmx_get_batched_vtcm_size(wtype, ne00_padded, m_chunk_candidate, n_chunk_candidate, group_size, use_dma_activation, pipeline, act_threads); - if (exact_size <= vtcm_budget) { - size_t mblocks = ((size_t) ne11 + m_chunk_candidate - 1) / m_chunk_candidate; - if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { - best_mblocks = mblocks; - best_act_threads = act_threads; - best_m_chunk = m_chunk_candidate; - best_n_chunk = n_chunk_candidate; - best_vtcm_size = exact_size; - } - } - } - if (act_threads == 1) { - act_threads = 0; - } else { - act_threads /= 2; - } - } - - if (best_act_threads > 0) { - m_chunk = best_m_chunk; - n_chunk = best_n_chunk; - vtcm_size = best_vtcm_size; - act_threads_selected = best_act_threads; + if (htp_mm_hmx_solve_batched_params(wtype, ne00_padded, ne01_padded, ne11, group_size, use_dma_activation, n_threads, pipeline, vtcm_budget, &m_chunk, &n_chunk, &act_threads_selected, &vtcm_size)) { use_grouped = true; } } if (!use_grouped) { // Fallback to simple 2D path (group_size = 1) - size_t best_mblocks = SIZE_MAX; - int best_act_threads = 0; - size_t best_m_chunk = 0; - size_t best_n_chunk = 0; - size_t best_vtcm_size = 0; - - // For MUL_MAT_ID the kernel runs one 2D matmul per expert, with M equal to the number of rows routed to that expert. - // A single expert can receive up to all routed rows (dst->ne[1]*dst->ne[2] = n_expert_used*n_tokens), so size the chunk - // search for that upper bound rather than ne12 (token positions only). - // We recompute m_chunk per expert against the actual count in the NPU kernel. - const int m_id_rows = (int) ((size_t) dst->ne[1] * dst->ne[2]); - const int m_for_chunks = is_matmul_id ? hex_align_up(m_id_rows, 32) : ne11_padded; - const int m_for_cost = is_matmul_id ? m_id_rows : ne11; - - int act_threads = n_threads; - while (act_threads >= 1) { - const size_t act_f32_size = is_matmul_id ? 0 : hex_align_up(act_threads * HTP_MM_DMA_ACT_MULTIPLIER * ne00_padded * sizeof(float), HTP_MM_HMX_TILE_SIZE); - size_t simple_2d_overhead = 256 + act_f32_size; - size_t simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn; - htp_mm_hmx_get_2d_chunk_costs(wtype, ne00_padded, pipeline, aligned_tile_size, &simple_2d_size_per_n, &simple_2d_size_per_m, &simple_2d_size_per_mn); - - size_t m_chunk_candidate = 0; - size_t n_chunk_candidate = 0; - size_t vtcm_size_candidate = 0; - - if (htp_mm_hmx_compute_chunks(vtcm_budget, simple_2d_overhead, simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn, m_for_chunks, ne01_padded, - (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) m_for_cost * HTP_MM_HMX_COST_A_CONVERT, - &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { - size_t exact_size = htp_mm_hmx_get_2d_vtcm_size(wtype, ne00_padded, m_chunk_candidate, n_chunk_candidate, pipeline, is_matmul_id ? 0 : act_threads, aligned_tile_size); - if (exact_size <= vtcm_budget) { - size_t mblocks = ((size_t) m_for_cost + m_chunk_candidate - 1) / m_chunk_candidate; - if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { - best_mblocks = mblocks; - best_act_threads = act_threads; - best_m_chunk = m_chunk_candidate; - best_n_chunk = n_chunk_candidate; - best_vtcm_size = exact_size; - } - } - } - if (act_threads == 1) { - act_threads = 0; - } else { - act_threads /= 2; - } - } - - if (best_act_threads > 0) { - m_chunk = best_m_chunk; - n_chunk = best_n_chunk; - vtcm_size = best_vtcm_size; - act_threads_selected = best_act_threads; - } else { + const int m_id_rows = (int) ((size_t) dst->ne[1] * dst->ne[2]); + if (!htp_mm_hmx_solve_2d_params(wtype, ne00_padded, m_id_rows, ne01_padded, ne11_padded, ne11, n_threads, pipeline, is_matmul_id, aligned_tile_size, vtcm_budget, &m_chunk, &n_chunk, &act_threads_selected, &vtcm_size)) { return false; } } @@ -2352,6 +2298,8 @@ static bool ggml_hexagon_precompute_hmx_mm_params( kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10); kparams->vtcm_size = vtcm_size; kparams->vtcm_src0_size = 0; + kparams->div_n_act_threads = init_fastdiv_values(act_threads_selected); + kparams->div_ne00_padded = init_fastdiv_values(ne00_padded); kparams->vtcm_src1_size = 0; kparams->vtcm_dst_size = 0; @@ -2361,6 +2309,8 @@ static bool ggml_hexagon_precompute_hmx_mm_params( kparams->kernel_type = HTP_MM_KERNEL_HMX_2D; } return true; + + GGML_UNUSED(src0); } static void ggml_hexagon_precompute_hvx_mm_params( @@ -2376,6 +2326,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( int ne12, int ne13, bool is_matmul_id, + const size_t src2_row_size, size_t vtcm_budget, struct htp_mm_kernel_params * kparams ) { @@ -2401,7 +2352,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( for (uint32_t d = max_prefetch; d >= 2; d /= 2) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0->nb[1], 0, d, true, false, false + 0, src0->nb[1], 0, src2_row_size, d, true, false, false ); if (L.total_bytes <= vtcm_budget) { best_n_prefetch = d; @@ -2411,7 +2362,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( if (best_n_prefetch == 2 && L.total_bytes > vtcm_budget) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0->nb[1], 0, 2, true, false, false + 0, src0->nb[1], 0, src2_row_size, 2, true, false, false ); } kparams->n_prefetch = best_n_prefetch; @@ -2435,7 +2386,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( for (uint32_t d = max_prefetch; d >= 2; d /= 2) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], d, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, d, false, false, false ); if (L.total_bytes <= vtcm_budget) { best_n_prefetch = d; @@ -2445,7 +2396,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( if (best_n_prefetch == 2 && L.total_bytes > vtcm_budget) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 2, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 2, false, false, false ); } @@ -2469,7 +2420,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); kparams->n_prefetch = 16; @@ -2489,7 +2440,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_F16_F16_VTCM, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); if (!is_batched && !is_permuted && L.total_bytes <= vtcm_budget) { @@ -2509,7 +2460,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( kparams->src1_row_size = src1->nb[1]; htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); kparams->vtcm_size = L.total_bytes; kparams->vtcm_src0_size = L.src0_bytes; @@ -2525,7 +2476,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_F32_F32_VTCM, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); if (!is_batched && !is_permuted && L.total_bytes <= vtcm_budget) { @@ -2541,7 +2492,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( kparams->src1_row_size = src1->nb[1]; htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); kparams->vtcm_size = L.total_bytes; kparams->vtcm_src0_size = L.src0_bytes; @@ -2552,11 +2503,12 @@ static void ggml_hexagon_precompute_hvx_mm_params( } } -static void ggml_hexagon_precompute_matmul_params( +static void ggml_hexagon_precompute_matmul_params_impl( const struct ggml_hexagon_session * sess, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst, + const size_t src2_row_size, struct htp_mm_kernel_params * kparams ) { memset(kparams, 0, sizeof(*kparams)); @@ -2591,7 +2543,7 @@ static void ggml_hexagon_precompute_matmul_params( } // Fallback to HVX parameter computation - ggml_hexagon_precompute_hvx_mm_params(sess, src0, src1, dst, wtype, ne02, ne03, ne10, ne11, ne12, ne13, is_matmul_id, vtcm_budget, kparams); + ggml_hexagon_precompute_hvx_mm_params(sess, src0, src1, dst, wtype, ne02, ne03, ne10, ne11, ne12, ne13, is_matmul_id, src2_row_size, vtcm_budget, kparams); finalize: kparams->div_ne12_ne1 = init_fastdiv_values(ne12 * ne11); @@ -2601,6 +2553,27 @@ static void ggml_hexagon_precompute_matmul_params( kparams->div_ne11 = init_fastdiv_values(ne11); } +static void ggml_hexagon_precompute_matmul_params( + const struct ggml_hexagon_session * sess, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + const struct ggml_tensor * dst, + struct htp_mm_kernel_params * kparams +) { + ggml_hexagon_precompute_matmul_params_impl(sess, src0, src1, dst, 0, kparams); +} + +static void ggml_hexagon_precompute_fused_matmul_add_params( + const struct ggml_hexagon_session * sess, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + const struct ggml_tensor * src2, + const struct ggml_tensor * dst, + struct htp_mm_kernel_params * kparams +) { + ggml_hexagon_precompute_matmul_params_impl(sess, src0, src1, dst, src2->nb[1], kparams); +} + static void ggml_hexagon_precompute_unary_params( const struct ggml_hexagon_session * sess, uint32_t op, @@ -2694,7 +2667,7 @@ static void ggml_hexagon_precompute_fused_qkv_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, d, false, true, false + 0, src0_row_size, src1_row_size, 0, d, false, true, false ); if (L.total_bytes <= sess->vtcm_size) { best_n_prefetch = d; @@ -2709,7 +2682,7 @@ static void ggml_hexagon_precompute_fused_qkv_params( // Test tiled first htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, best_n_prefetch, false, true, false + 0, src0_row_size, src1_row_size, 0, best_n_prefetch, false, true, false ); if (try_tiled && L.total_bytes <= sess->vtcm_size) { @@ -2727,7 +2700,7 @@ static void ggml_hexagon_precompute_fused_qkv_params( htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, flat_src1_row_size, best_n_prefetch, false, true, false + 0, src0_row_size, flat_src1_row_size, 0, best_n_prefetch, false, true, false ); kparams->vtcm_src0_size = L.src0_bytes; kparams->vtcm_src1_size = L.src1_bytes; @@ -2764,7 +2737,7 @@ static void ggml_hexagon_precompute_fused_ffn_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, d, false, false, true + 0, src0_row_size, src1_row_size, 0, d, false, false, true ); if (L.total_bytes <= sess->vtcm_size) { best_n_prefetch = d; @@ -2779,7 +2752,7 @@ static void ggml_hexagon_precompute_fused_ffn_params( // Test tiled first htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, best_n_prefetch, false, false, true + 0, src0_row_size, src1_row_size, 0, best_n_prefetch, false, false, true ); if (try_tiled && L.total_bytes <= sess->vtcm_size) { @@ -2796,7 +2769,7 @@ static void ggml_hexagon_precompute_fused_ffn_params( htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, flat_src1_row_size, best_n_prefetch, false, false, true + 0, src0_row_size, flat_src1_row_size, 0, best_n_prefetch, false, false, true ); kparams->vtcm_src0_size = L.src0_bytes; kparams->vtcm_src1_size = L.src1_bytes; @@ -2955,6 +2928,8 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -2981,6 +2956,8 @@ static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * se } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3006,6 +2983,8 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3025,10 +3004,11 @@ static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); } -static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess, - const struct ggml_tensor * op) { +static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * src1 = op->src[1]; const struct ggml_tensor * dst = op; @@ -3040,7 +3020,10 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session return false; } - if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) { + if (!ggml_is_contiguous_1(src0)) { + return false; + } + if (!ggml_is_contiguous(dst)) { return false; } @@ -3051,12 +3034,14 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session if (!ggml_are_same_shape(src0, src1)) { return false; } - if (!ggml_is_contiguous(src1)) { + if (!ggml_is_contiguous_1(src1)) { return false; } } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3122,6 +3107,8 @@ static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * s } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_set_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3142,6 +3129,8 @@ static bool ggml_hexagon_supported_set_rows(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3162,6 +3151,8 @@ static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3182,6 +3173,8 @@ static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * s } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3243,6 +3236,8 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess return false; } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3282,6 +3277,37 @@ static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); +} + +static bool ggml_hexagon_supported_im2col(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { + const struct ggml_tensor * src1 = op->src[1]; + const struct ggml_tensor * dst = op; + + const bool is_2D = ((const int32_t *) op->op_params)[6] == 1; + if (!is_2D) { + return false; + } + + // For now support F32->F32 and F32->F16 only. + if (src1->type != GGML_TYPE_F32 || (dst->type != GGML_TYPE_F16 && dst->type != GGML_TYPE_F32)) { + return false; + } + + if (!ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) { + return false; + } + + // For now keep padded OPs on CPU. Will revisit once we expand coverage past patch-embed OPs. + const int32_t p0 = ((const int32_t *) op->op_params)[2]; + const int32_t p1 = ((const int32_t *) op->op_params)[3]; + if (p0 != 0 || p1 != 0) { + return false; + } + + GGML_UNUSED(sess); + return true; } static bool ggml_hexagon_supported_pad(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3292,8 +3318,9 @@ static bool ggml_hexagon_supported_pad(const struct ggml_hexagon_session * sess, return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3308,8 +3335,9 @@ static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * se return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_diag(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3331,8 +3359,9 @@ static bool ggml_hexagon_supported_diag(const struct ggml_hexagon_session * sess return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_solve_tri(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3364,8 +3393,9 @@ static bool ggml_hexagon_supported_solve_tri(const struct ggml_hexagon_session * return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_tri(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3415,6 +3445,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) { case GGML_OP_RMS_NORM: return HTP_OP_RMS_NORM; case GGML_OP_CONCAT: return HTP_OP_CONCAT; case GGML_OP_SCALE: return HTP_OP_SCALE; + case GGML_OP_CLAMP: return HTP_OP_CLAMP; case GGML_OP_SQR: return HTP_OP_SQR; case GGML_OP_SQRT: return HTP_OP_SQRT; case GGML_OP_SOFT_MAX: return HTP_OP_SOFTMAX; @@ -3428,6 +3459,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) { case GGML_OP_SOLVE_TRI: return HTP_OP_SOLVE_TRI; case GGML_OP_TRI: return HTP_OP_TRI; case GGML_OP_PAD: return HTP_OP_PAD; + case GGML_OP_IM2COL: return HTP_OP_IM2COL; case GGML_OP_UNARY: switch (ggml_get_unary_op(t)) { @@ -3590,16 +3622,19 @@ static bool try_fuse_node(const ggml_hexagon_session * sess, const ggml_cgraph * if (n->op == GGML_OP_MUL_MAT && next_node) { if (next_node->op == GGML_OP_ADD && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) { if (next_node->src[0] == n || next_node->src[1] == n) { + const struct ggml_tensor * src2 = (next_node->src[0] == n) ? next_node->src[1] : next_node->src[0]; struct htp_mm_kernel_params kparams; - ggml_hexagon_precompute_matmul_params(sess, n->src[0], n->src[1], next_node, &kparams); - if ((size_t)kparams.vtcm_size <= sess->vtcm_size) { + ggml_hexagon_precompute_fused_matmul_add_params(sess, n->src[0], n->src[1], src2, next_node, &kparams); + const int src1_nrows = n->src[1]->ne[1] * n->src[1]->ne[2] * n->src[1]->ne[3]; + const bool can_fuse = (kparams.n_hmx > 0) || (src1_nrows == 1); + if (can_fuse && (size_t)kparams.vtcm_size <= sess->vtcm_size) { htp_opnode node(n, {}, HTP_OP_MUL_MAT_ADD); node.add_fused(next_node); memcpy(node.kernel_params, &kparams, sizeof(kparams)); nodes.push_back(std::move(node)); i += 1; return true; - } else { + } else if (can_fuse) { HEX_VERBOSE("ggml-hex: skip MUL_MAT_ADD fusion because VTCM needed (%d) > budget (%zu)\n", kparams.vtcm_size, sess->vtcm_size); } @@ -3812,6 +3847,8 @@ static void ggml_backend_hexagon_graph_optimize(ggml_backend_t backend, ggml_cgr } } } + + GGML_UNUSED(backend); } static struct ggml_backend_i hexagon_backend_i = { @@ -3930,6 +3967,8 @@ static bool ggml_hexagon_supported_buffers(ggml_hexagon_session *sess, const str } static bool ggml_hexagon_supported_cpy(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { + GGML_UNUSED(sess); + const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * dst = op; @@ -4000,6 +4039,7 @@ static bool ggml_hexagon_supported_concat(const struct ggml_hexagon_session * se } return true; + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_fill(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -4009,8 +4049,8 @@ static bool ggml_hexagon_supported_fill(const struct ggml_hexagon_session * sess return false; } - GGML_UNUSED(sess); return true; + GGML_UNUSED(sess); } static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { @@ -4060,6 +4100,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons case GGML_OP_L2_NORM: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: + case GGML_OP_CLAMP: supp = ggml_hexagon_supported_unary(sess, op); break; @@ -4083,12 +4124,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_TANH: - supp = ggml_hexagon_supported_unary(sess, op); - break; case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: - supp = ggml_hexagon_supported_activations(sess, op); + supp = ggml_hexagon_supported_unary(sess, op); break; default: break; @@ -4143,6 +4182,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons supp = ggml_hexagon_supported_ssm_conv(sess, op); break; + case GGML_OP_IM2COL: + supp = ggml_hexagon_supported_im2col(sess, op); + break; + case GGML_OP_GATED_DELTA_NET: supp = ggml_hexagon_supported_gated_delta_net(sess, op); break; @@ -4293,6 +4336,7 @@ static void * ggml_backend_hexagon_get_proc_address(ggml_backend_reg_t reg, cons } return NULL; + GGML_UNUSED(reg); } template std::vector str_to_vec(const char* str) { @@ -4351,10 +4395,18 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) { // Init Arch first since it affects other defaults if (!str_arch) { - int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch); + int err = htpdrv_get_arch(CDSP_DOMAIN_ID, &opt_arch); if (err != 0) { GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err); opt_arch = 73; + } else { + if (opt_arch < 73) { + GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is under supported range, capping at v73\n", opt_arch); + opt_arch = 73; + } else if (opt_arch > 81) { + GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is over supported range, capping at v81\n", opt_arch); + opt_arch = 81; + } } } else { if (str_arch[0] == 'v' || str_arch[0] == 'V') { @@ -4376,7 +4428,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) { opt_opstage = str_opstage ? strtoul(str_opstage, NULL, 0) : opt_opstage; opt_opbatch = str_opbatch ? strtoul(str_opbatch, NULL, 0) : opt_opbatch; opt_opqueue = str_opqueue ? strtoul(str_opqueue, NULL, 0) : opt_opqueue; - opt_optrace = str_optrace ? strtoul(str_optrace, NULL, 0) : (opt_opbatch * 128); + opt_optrace = str_optrace ? strtoul(str_optrace, NULL, 0) : (opt_opbatch * 256); opt_oppoll = str_oppoll ? strtoul(str_oppoll, NULL, 0) : opt_oppoll; opt_opfusion = str_opfusion ? atoi(str_opfusion) : opt_opfusion; opt_profile = str_profile ? atoi(str_profile) : 0; diff --git a/ggml/src/ggml-hexagon/htp-drv.cpp b/ggml/src/ggml-hexagon/htp-drv.cpp index 4c376b5fc918..4f0790801731 100644 --- a/ggml/src/ggml-hexagon/htp-drv.cpp +++ b/ggml/src/ggml-hexagon/htp-drv.cpp @@ -1,13 +1,8 @@ -// sample drv interface - -#pragma clang diagnostic ignored "-Wgnu-anonymous-struct" -#pragma clang diagnostic ignored "-Wmissing-prototypes" -#pragma clang diagnostic ignored "-Wsign-compare" - #include #include #include #include + #ifdef _WIN32 # define WIN32_LEAN_AND_MEAN # ifndef NOMINMAX @@ -16,9 +11,17 @@ # include # include #else -# include -# include +# include +# include #endif + +#pragma clang diagnostic ignored "-Wgnu-anonymous-struct" +#pragma clang diagnostic ignored "-Wmissing-prototypes" +#pragma clang diagnostic ignored "-Wsign-compare" +#pragma clang diagnostic ignored "-Wlanguage-extension-token" +#pragma clang diagnostic ignored "-Wmicrosoft-enum-value" +#pragma clang diagnostic ignored "-Wnested-anon-types" + #include "ggml-impl.h" #include "htp-drv.h" #include "libdl.h" @@ -56,7 +59,11 @@ typedef AEEResult (*dspqueue_read_pfn_t)(dspqueue_t queue, uint32_t *flags, uint32_t max_message_length, uint32_t *message_length, uint8_t *message, uint32_t timeout_us); - +typedef AEEResult (*dspqueue_read_noblock_pfn_t)(dspqueue_t queue, uint32_t *flags, + uint32_t max_buffers, uint32_t *num_buffers, + struct dspqueue_buffer *buffers, + uint32_t max_message_length, + uint32_t *message_length, uint8_t *message); typedef int (*fastrpc_mmap_pfn_t)(int domain, int fd, void *addr, int offset, size_t length, enum fastrpc_map_flags flags); typedef int (*fastrpc_munmap_pfn_t)(int domain, int fd, void *addr, size_t length); @@ -79,11 +86,12 @@ rpcmem_to_fd_pfn_t rpcmem_to_fd_pfn = nullptr; fastrpc_mmap_pfn_t fastrpc_mmap_pfn = nullptr; fastrpc_munmap_pfn_t fastrpc_munmap_pfn = nullptr; -dspqueue_create_pfn_t dspqueue_create_pfn = nullptr; -dspqueue_close_pfn_t dspqueue_close_pfn = nullptr; -dspqueue_export_pfn_t dspqueue_export_pfn = nullptr; -dspqueue_write_pfn_t dspqueue_write_pfn = nullptr; -dspqueue_read_pfn_t dspqueue_read_pfn = nullptr; +dspqueue_create_pfn_t dspqueue_create_pfn = nullptr; +dspqueue_close_pfn_t dspqueue_close_pfn = nullptr; +dspqueue_export_pfn_t dspqueue_export_pfn = nullptr; +dspqueue_write_pfn_t dspqueue_write_pfn = nullptr; +dspqueue_read_pfn_t dspqueue_read_pfn = nullptr; +dspqueue_read_noblock_pfn_t dspqueue_read_noblock_pfn = nullptr; remote_handle64_open_pfn_t remote_handle64_open_pfn = nullptr; remote_handle64_invoke_pfn_t remote_handle64_invoke_pfn = nullptr; @@ -164,6 +172,12 @@ AEEResult dspqueue_read(dspqueue_t queue, uint32_t * message_length, uint8_t * message, uint32_t timeout_us) { +#ifdef _WIN32 + if (timeout_us == 0) { + return dspqueue_read_noblock_pfn(queue, flags, max_buffers, num_buffers, buffers, max_message_length, + message_length, message); + } +#endif return dspqueue_read_pfn(queue, flags, max_buffers, num_buffers, buffers, max_message_length, message_length, message, timeout_us); } @@ -346,6 +360,7 @@ int htpdrv_init() { dlsym(handle.get(), dspqueue_export_pfn_t, dspqueue_export_pfn, dspqueue_export, false); dlsym(handle.get(), dspqueue_write_pfn_t, dspqueue_write_pfn, dspqueue_write, false); dlsym(handle.get(), dspqueue_read_pfn_t, dspqueue_read_pfn, dspqueue_read, false); + dlsym(handle.get(), dspqueue_read_noblock_pfn_t, dspqueue_read_noblock_pfn, dspqueue_read_noblock, false); dlsym(handle.get(), remote_handle64_open_pfn_t, remote_handle64_open_pfn, remote_handle64_open, false); dlsym(handle.get(), remote_handle64_invoke_pfn_t, remote_handle64_invoke_pfn, remote_handle64_invoke, false); dlsym(handle.get(), remote_handle_control_pfn_t, remote_handle_control_pfn, remote_handle_control, false); @@ -359,7 +374,7 @@ int htpdrv_init() { return AEE_SUCCESS; } -domain * get_domain(int domain_id) { +domain * htpdrv_get_domain(int domain_id) { int i = 0; int size = sizeof(supported_domains) / sizeof(domain); @@ -372,7 +387,7 @@ domain * get_domain(int domain_id) { return NULL; } -int get_hex_arch_ver(int domain, int * arch) { +int htpdrv_get_arch(int domain, int * arch) { if (!remote_handle_control_pfn) { GGML_LOG_ERROR("ggml-hex: remote_handle_control is not supported on this device\n"); return AEE_EUNSUPPORTEDAPI; @@ -394,25 +409,7 @@ int get_hex_arch_ver(int domain, int * arch) { return err; } - switch (arch_ver.capability & 0xff) { - case 0x68: - *arch = 68; - return 0; - case 0x69: - *arch = 69; - return 0; - case 0x73: - *arch = 73; - return 0; - case 0x75: - *arch = 75; - return 0; - case 0x79: - *arch = 79; - return 0; - case 0x81: - *arch = 81; - return 0; - } - return -1; + uint32_t val = arch_ver.capability & 0xff; + *arch = (int) ((val >> 4) * 10 + (val & 0x0f)); + return 0; } diff --git a/ggml/src/ggml-hexagon/htp-drv.h b/ggml/src/ggml-hexagon/htp-drv.h index 6eba7ba17d8d..f3cc0da75c28 100644 --- a/ggml/src/ggml-hexagon/htp-drv.h +++ b/ggml/src/ggml-hexagon/htp-drv.h @@ -96,17 +96,17 @@ extern "C" { HTPDRV_API int htpdrv_init(void); /** - * get_domain API: get domain struct from domain value. + * htpdrv_get_domain API: get domain struct from domain value. * * @param[in] domain value of a domain * @return Returns domain struct of the domain if it is supported or else * returns NULL. * */ -HTPDRV_API domain * get_domain(int domain_id); +HTPDRV_API domain * htpdrv_get_domain(int domain_id); /** - * get_hex_arch_ver API: query the Hexagon processor architecture version information + * htpdrv_get_arch API: query the Hexagon processor architecture version information * * @param[in] domain_id value of a domain * @param[out] Arch version (73, 75, ...) @@ -114,7 +114,7 @@ HTPDRV_API domain * get_domain(int domain_id); * non-zero if error, return value points to the error. * */ -HTPDRV_API int get_hex_arch_ver(int domain, int * arch); +HTPDRV_API int htpdrv_get_arch(int domain, int * arch); #ifdef __cplusplus } diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt index cf9e726c0019..b00aa2bc94c3 100644 --- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt @@ -17,9 +17,12 @@ set(HTP_LIB ggml-htp-${DSP_VERSION}) add_library(${HTP_LIB} SHARED main.c htp_iface_skel.c - worker-pool.c - hex-dma.c + work-queue.c + dma-queue.c hmx-queue.c + htp-tensor.c + matmul-ops.c + flash-attn-ops.c gated-delta-net-ops.c binary-ops.c unary-ops.c @@ -38,9 +41,8 @@ add_library(${HTP_LIB} SHARED diag-ops.c solve-tri-ops.c pad-ops.c - flash-attn-ops.c - matmul-ops.c argsort-ops.c + im2col-ops.c ) target_compile_definitions(${HTP_LIB} PRIVATE diff --git a/ggml/src/ggml-hexagon/htp/act-ops.c b/ggml/src/ggml-hexagon/htp/act-ops.c index 6416d2dfbc38..9973c088dda7 100644 --- a/ggml/src/ggml-hexagon/htp/act-ops.c +++ b/ggml/src/ggml-hexagon/htp/act-ops.c @@ -16,6 +16,8 @@ #include "htp-ctx.h" #include "htp-ops.h" #include "htp-ops.h" +#include "htp-tensor.h" +#include "htp-vtcm.h" #define htp_act_preamble \ const struct htp_tensor * src0 = actx->octx->src[0]; \ @@ -53,581 +55,413 @@ const uint32_t nb3 = dst->nb[3]; struct htp_act_context { - struct htp_ops_context * octx; + struct htp_ops_context * octx; // Precomputed values - const uint8_t * data_src0; - const uint8_t * data_src1; - uint8_t * data_dst; - - size_t src0_row_size; - size_t src1_row_size; - size_t dst_row_size; - - size_t src0_row_size_aligned; - size_t src1_row_size_aligned; - size_t dst_row_size_aligned; - - size_t src0_spad_half_size; - size_t src1_spad_half_size; - size_t dst_spad_half_size; - - uint32_t block; - uint32_t src0_nrows; - uint32_t src0_nrows_per_thread; - int nc; -}; - -static void glu_swiglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; - - size_t src0_row_size = actx->src0_row_size; - size_t src1_row_size = actx->src1_row_size; - size_t dst_row_size = actx->dst_row_size; - - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } + const uint8_t * data_src0; + const uint8_t * data_src1; + uint8_t * data_dst; - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + size_t src0_row_size; + size_t src1_row_size; + size_t dst_row_size; - const uint8_t * restrict data_src0 = actx->data_src0; - const uint8_t * restrict data_src1 = actx->data_src1; - uint8_t * restrict data_dst = actx->data_dst; + size_t src0_row_stride; + size_t src1_row_stride; - const int nc = actx->nc; + size_t src0_row_size_aligned; + size_t src1_row_size_aligned; + size_t dst_row_size_aligned; - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t src1_row_size_aligned = actx->src1_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; + size_t src0_spad_half_size; + size_t src1_spad_half_size; + size_t dst_spad_half_size; - uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); - uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + uint32_t block; + uint32_t src0_nrows; + uint32_t src0_nrows_per_thread; + int nc; - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t src1_spad_half_size = actx->src1_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; + uint8_t * vtcm_src0; + uint8_t * vtcm_src1; + uint8_t * vtcm_dst; - const int BLOCK = actx->block; - if (BLOCK == 0) { - FARF(ERROR, - "swiglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } + size_t vtcm_src0_size_per_thread; + size_t vtcm_src1_size_per_thread; + size_t vtcm_dst_size_per_thread; +}; - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; +struct htp_act_vtcm_layout { + size_t total_bytes; + size_t off_src0; + size_t off_src1; + size_t off_dst; - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); + size_t src0_bytes_per_thread; + size_t src1_bytes_per_thread; + size_t dst_bytes_per_thread; - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); + uint32_t vtcm_row_per_thread; +}; - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)), - src1_row_size_aligned, src1_row_size, block_size); - } +static inline void htp_act_vtcm_layout_build(struct htp_act_vtcm_layout * L, + size_t src0_row_size_aligned, + size_t src1_row_size_aligned, + size_t dst_row_size_aligned, + uint32_t n_threads, + size_t vtcm_size) { + const size_t spad_size_per_row = src0_row_size_aligned + src1_row_size_aligned + dst_row_size_aligned; + const uint32_t vtcm_row_per_thread = (uint32_t) (vtcm_size / (n_threads * spad_size_per_row)); - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float * dst_spad = (float *) dma_queue_pop(dma_queue).src; - float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; - float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float * src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - const float * src1_spad_ptr = src1_spad + ib * (src1_row_size_aligned / sizeof(float)); - float * dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - //swiglu(x) = x1 * sigmoid(x0) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, nc); - hvx_mul_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, - (const uint8_t *) src1_spad_ptr, nc); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size, - dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)), - src1_row_size_aligned, src1_row_size, pref_block_size); - } - } + L->vtcm_row_per_thread = vtcm_row_per_thread; - dma_queue_flush(dma_queue); + L->src0_bytes_per_thread = src0_row_size_aligned * vtcm_row_per_thread; + L->src1_bytes_per_thread = src1_row_size_aligned * vtcm_row_per_thread; + L->dst_bytes_per_thread = dst_row_size_aligned * vtcm_row_per_thread; - t2 = HAP_perf_get_qtimer_count(); + L->off_src0 = 0; + L->off_src1 = L->off_src0 + L->src0_bytes_per_thread * n_threads; + L->off_dst = L->off_src1 + L->src1_bytes_per_thread * n_threads; - FARF(HIGH, "swiglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, - ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, - (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); + L->total_bytes = L->off_dst + L->dst_bytes_per_thread * n_threads; } -static void glu_swiglu_oai_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; - - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); - - size_t src0_row_size = actx->src0_row_size; - size_t src1_row_size = actx->src1_row_size; - size_t dst_row_size = actx->dst_row_size; - - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; - - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; +#define htp_glu_op_preamble \ + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; \ + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; \ + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; \ + const int nc = actx->nc; + +// swiglu(x) = x1 * sigmoid(x0) +static void swiglu_f32(const float * restrict src0, + const float * restrict src1, + float * restrict dst, + const uint32_t num_rows, + const struct htp_act_context * actx) { + htp_glu_op_preamble; + + for (uint32_t ib = 0; ib < num_rows; ib++) { + const uint8_t * restrict src0_ptr = (const uint8_t *) src0 + (ib * src0_row_size_aligned); + const uint8_t * restrict src1_ptr = (const uint8_t *) src1 + (ib * src1_row_size_aligned); + uint8_t * restrict dst_ptr = (uint8_t *) dst + (ib * dst_row_size_aligned); + + hvx_sigmoid_f32_aa(dst_ptr, src0_ptr, nc); + hvx_mul_mul_f32_aa(dst_ptr, src0_ptr, dst_ptr, src1_ptr, nc); } +} - const uint8_t * restrict data_src0 = actx->data_src0; - const uint8_t * restrict data_src1 = actx->data_src1; - uint8_t * restrict data_dst = actx->data_dst; - - const int nc = actx->nc; - - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t src1_row_size_aligned = actx->src1_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - - uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); - uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t src1_spad_half_size = actx->src1_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; - - const int BLOCK = actx->block; - if (BLOCK == 0) { - FARF(ERROR, - "swiglu-oai-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least " - "%zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } +// out = x * sigmoid(alpha * x) * (clamp(y, -limit, limit) + 1.f) +static void swiglu_oai_f32(const float * restrict src0, + const float * restrict src1, + float * restrict dst, + const uint32_t num_rows, + const struct htp_act_context * actx) { + htp_glu_op_preamble; const float alpha = ((const float *) (actx->octx->op_params))[2]; const float limit = ((const float *) (actx->octx->op_params))[3]; - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; - - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); - - dma_queue_push_ddr_to_vtcm( - dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - dma_queue_push_ddr_to_vtcm( - dma_queue, - dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)), - src1_row_size_aligned, src1_row_size, block_size); + for (uint32_t ib = 0; ib < num_rows; ib++) { + const uint8_t * restrict src0_ptr = (const uint8_t *) src0 + (ib * src0_row_size_aligned); + const uint8_t * restrict src1_ptr = (const uint8_t *) src1 + (ib * src1_row_size_aligned); + uint8_t * restrict dst_ptr = (uint8_t *) dst + (ib * dst_row_size_aligned); + + // x (src0_ptr) = std::min(src0_p[k], limit); + hvx_min_scalar_f32((uint8_t *) src0_ptr, src0_ptr, limit, nc); + // y1 (src1_ptr) = std::clamp(src1_p[k], -limit, limit); + hvx_clamp_scalar_f32((uint8_t *) src1_ptr, src1_ptr, -limit, limit, nc); + // y (src1_ptr) = y1 + 1.f + hvx_add_scalar_f32((uint8_t *) src1_ptr, src1_ptr, 1.0, nc); + // x1 (dst_ptr) = alpha * x + hvx_mul_scalar_f32(dst_ptr, src0_ptr, alpha, nc); + // x2 (dst_ptr) = sigmoid(x1) = 1/(1+exp(-x1)) + hvx_sigmoid_f32_aa(dst_ptr, dst_ptr, nc); + // out = x * sigmoid(alpha * x) * (y + 1.f) + hvx_mul_mul_f32_aa(dst_ptr, src0_ptr, dst_ptr, src1_ptr, nc); } - - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float * dst_spad = (float *) dma_queue_pop(dma_queue).src; - float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; - float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float * src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - const float * src1_spad_ptr = src1_spad + ib * (src1_row_size_aligned / sizeof(float)); - float * dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - // x (src0_spad_data) = std::min(src0_p[k], limit); - hvx_min_scalar_f32((uint8_t *) src0_spad_ptr, (const uint8_t *) src0_spad_ptr, limit, nc); - // y1 (src1_spad_data) = std::clamp(src1_p[k], -limit, limit); - hvx_clamp_scalar_f32((uint8_t *) src1_spad_ptr, (const uint8_t *) src1_spad_ptr, -limit, limit, nc); - // y (src1_spad_data) = y1 + 1.f - hvx_add_scalar_f32((uint8_t *) src1_spad_ptr, (const uint8_t *) src1_spad_ptr, 1.0, nc); - // x1 (dst_spad_data) = alpha * (x) - hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, alpha, nc); - // x2 (dst_spad_data) = sigmoid(x1) = 1/(1+exp(-x1)) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc); - // out = x * sigmoid(alpha * x) * (y + 1.f) - hvx_mul_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, - (const uint8_t *) src1_spad_ptr, nc); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size, - dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)), - src1_row_size_aligned, src1_row_size, pref_block_size); - } - } - - dma_queue_flush(dma_queue); - - t2 = HAP_perf_get_qtimer_count(); - - FARF(HIGH, "swiglu-oai-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, src0->ne[0], - src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1], src1->ne[2], - src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } +static const float GELU_COEF_A = 0.044715f; +static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; -static void unary_gelu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; - - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); - - const size_t src0_row_size = actx->src0_row_size; - const size_t dst_row_size = actx->dst_row_size; - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; - - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } - - const uint8_t * data_src0 = actx->data_src0; - uint8_t * data_dst = actx->data_dst; - - // nc/ne0 matches. - const int ne0_val = actx->nc; // == dst->ne[0] - - uint8_t * src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; - - // In gelu = x*sigmoid(x*1.702) - const int BLOCK = actx->block; - - if (BLOCK == 0) { - FARF(ERROR, "gelu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } - - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; - - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); - - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - } +static inline void hvx_geglu_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { + assert((unsigned long) dst % 128 == 0); + assert((unsigned long) src0 % 128 == 0); + assert((unsigned long) src1 % 128 == 0); - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float* dst_spad = (float *) dma_queue_pop(dma_queue).src; - float* src0_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float* src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - // gelu = x * sigmoid(1.702 * x) // current implementation - hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0_val); - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); - hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), - dst_row_size, dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - } - } + HVX_Vector * restrict vdst = (HVX_Vector *) dst; + const HVX_Vector * restrict vsrc0 = (const HVX_Vector *) src0; + const HVX_Vector * restrict vsrc1 = (const HVX_Vector *) src1; - dma_queue_flush(dma_queue); + const uint32_t epv = 128 / sizeof(float); + const uint32_t nvec = n / epv; + const uint32_t nloe = n % epv; - t2 = HAP_perf_get_qtimer_count(); + const float GELU_COEF_A_TIMES_SQRT = GELU_COEF_A * SQRT_2_OVER_PI; - FARF(HIGH, "gelu-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, ne00, ne01, ne02, - ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} + const HVX_Vector v_coef_a_times_sqrt = hvx_vec_splat_f32(GELU_COEF_A_TIMES_SQRT); + const HVX_Vector v_sqrt_2_pi = hvx_vec_splat_f32(SQRT_2_OVER_PI); + const HVX_Vector v_half = hvx_vec_splat_f32(0.5f); + const HVX_Vector v_one = hvx_vec_splat_f32(1.0f); + const HVX_Vector v_two = hvx_vec_splat_f32(2.0f); + // Hoisted fast sigmoid / inverse constants to avoid loop-internal overhead + const HVX_Vector v_log2f = Q6_V_vsplat_R(FAST_SIGMOID_LOG2F); + const HVX_Vector v_c1 = Q6_V_vsplat_R(FAST_SIGMOID_C1); + const HVX_Vector v_c2 = Q6_V_vsplat_R(FAST_SIGMOID_C2); + const HVX_Vector v_inv_aprox = Q6_V_vsplat_R(0x7EEEEBB3); + const HVX_Vector v_max_exp = hvx_vec_splat_f32(87.0f); + const HVX_Vector v_min_exp = hvx_vec_splat_f32(-87.0f); -static void unary_silu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; + uint32_t i = 0; - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + for (; i < nvec; i++) { + HVX_Vector x = vsrc0[i]; + HVX_Vector g = vsrc1[i]; - const size_t src0_row_size = actx->src0_row_size; - const size_t dst_row_size = actx->dst_row_size; - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; + HVX_Vector x2 = hvx_vec_mul_f32_f32(x, x); + HVX_Vector coef = hvx_vec_mul_f32_f32(x2, v_coef_a_times_sqrt); + coef = hvx_vec_add_f32_f32(coef, v_sqrt_2_pi); + HVX_Vector inner = hvx_vec_mul_f32_f32(x, coef); - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; + // y2 = 2 * inner + HVX_Vector y2 = hvx_vec_mul_f32_f32(inner, v_two); - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); + // Sigmoid guard check predicates + HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(v_max_exp, y2); + HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(y2, v_min_exp); - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } + // Fast sigmoid approximation + HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(y2, v_log2f); + v = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(v), v_half); - const uint8_t * data_src0 = actx->data_src0; - uint8_t * data_dst = actx->data_dst; + HVX_Vector in_int = hvx_vec_truncate_f32(Q6_Vsf_equals_Vqf32(v)); + HVX_Vector x_sig = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int)); + HVX_Vector xx_sig = Q6_Vqf32_vmpy_Vqf32Vqf32(x_sig, x_sig); - const int ne0_val = actx->nc; // == dst->ne[0] + HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx_sig), v_c2); + v1 = Q6_Vqf32_vadd_Vqf32Vsf(v1, v_log2f); - uint8_t * src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x_sig), v_c1); + v2 = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx_sig); + v2 = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x_sig); - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; + HVX_Vector v3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1)); + v3 = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24); - const int BLOCK = actx->block; + HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1)); + HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4)); - if (BLOCK == 0) { - FARF(ERROR, "silu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } + // Fast division (Newton-Raphson with 2 iterations) + HVX_Vector i_sf = Q6_Vw_vsub_VwVw(v_inv_aprox, v5); + HVX_Vector r_qf = Q6_Vqf32_vmpy_VsfVsf( + i_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(i_sf, v5))))); + r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32( + r_qf, Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v5)))); + HVX_Vector res_inv = Q6_Vsf_equals_Vqf32(r_qf); - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; + HVX_Vector sig2y = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v3, res_inv)); - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); + // Sigmoid guards + sig2y = Q6_V_vmux_QVV(pred_max, sig2y, v_one); + sig2y = Q6_V_vmux_QVV(pred_min, sig2y, Q6_V_vzero()); - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); + // tanh(inner) = 2 * sigmoid(2 * inner) - 1 + HVX_Vector tanh_val = hvx_vec_mul_f32_f32(sig2y, v_two); + tanh_val = hvx_vec_sub_f32_f32(tanh_val, v_one); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - } + HVX_Vector tanh_plus_one = hvx_vec_add_f32_f32(tanh_val, v_one); + HVX_Vector half_x = hvx_vec_mul_f32_f32(x, v_half); + HVX_Vector gelu_x = hvx_vec_mul_f32_f32(half_x, tanh_plus_one); - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float* dst_spad = (float *) dma_queue_pop(dma_queue).src; - float* src0_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float* src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - // silu = x * sigmoid(x) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0_val); - hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), - dst_row_size, dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - } + vdst[i] = hvx_vec_mul_f32_f32(gelu_x, g); } - dma_queue_flush(dma_queue); + if (nloe) { + HVX_Vector x = vsrc0[i]; + HVX_Vector g = vsrc1[i]; - t2 = HAP_perf_get_qtimer_count(); + HVX_Vector x2 = hvx_vec_mul_f32_f32(x, x); + HVX_Vector coef = hvx_vec_mul_f32_f32(x2, v_coef_a_times_sqrt); + coef = hvx_vec_add_f32_f32(coef, v_sqrt_2_pi); + HVX_Vector inner = hvx_vec_mul_f32_f32(x, coef); - FARF(HIGH, "silu-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, ne00, ne01, ne02, - ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} + HVX_Vector y2 = hvx_vec_mul_f32_f32(inner, v_two); -static const float GELU_COEF_A = 0.044715f; -static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(v_max_exp, y2); + HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(y2, v_min_exp); -static void glu_geglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; + HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(y2, v_log2f); + v = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(v), v_half); - size_t src0_row_size = actx->src0_row_size; - size_t src1_row_size = actx->src1_row_size; - size_t dst_row_size = actx->dst_row_size; + HVX_Vector in_int = hvx_vec_truncate_f32(Q6_Vsf_equals_Vqf32(v)); + HVX_Vector x_sig = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int)); + HVX_Vector xx_sig = Q6_Vqf32_vmpy_Vqf32Vqf32(x_sig, x_sig); - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx_sig), v_c2); + v1 = Q6_Vqf32_vadd_Vqf32Vsf(v1, v_log2f); - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; + HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x_sig), v_c1); + v2 = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx_sig); + v2 = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x_sig); - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); + HVX_Vector v3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1)); + v3 = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24); - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } + HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1)); + HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4)); - const uint8_t * restrict data_src0 = actx->data_src0; - const uint8_t * restrict data_src1 = actx->data_src1; - uint8_t * restrict data_dst = actx->data_dst; + HVX_Vector i_sf = Q6_Vw_vsub_VwVw(v_inv_aprox, v5); + HVX_Vector r_qf = Q6_Vqf32_vmpy_VsfVsf( + i_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(i_sf, v5))))); + r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32( + r_qf, Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v5)))); + HVX_Vector res_inv = Q6_Vsf_equals_Vqf32(r_qf); - const int nc = actx->nc; + HVX_Vector sig2y = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v3, res_inv)); - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t src1_row_size_aligned = actx->src1_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; + sig2y = Q6_V_vmux_QVV(pred_max, sig2y, v_one); + sig2y = Q6_V_vmux_QVV(pred_min, sig2y, Q6_V_vzero()); - uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); - uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + HVX_Vector tanh_val = hvx_vec_mul_f32_f32(sig2y, v_two); + tanh_val = hvx_vec_sub_f32_f32(tanh_val, v_one); - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t src1_spad_half_size = actx->src1_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; + HVX_Vector tanh_plus_one = hvx_vec_add_f32_f32(tanh_val, v_one); + HVX_Vector half_x = hvx_vec_mul_f32_f32(x, v_half); + HVX_Vector gelu_x = hvx_vec_mul_f32_f32(half_x, tanh_plus_one); - const int BLOCK = actx->block; - if (BLOCK == 0) { - FARF(ERROR, - "geglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; + HVX_Vector res = hvx_vec_mul_f32_f32(gelu_x, g); + hvx_vec_store_a((void *) &vdst[i], nloe * sizeof(float), res); } +} - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; - - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); +// geglu(x, g) = gelu(x) * g +static void geglu_f32(const float * restrict src0, + const float * restrict src1, + float * restrict dst, + const uint32_t num_rows, + const struct htp_act_context * actx) { + htp_glu_op_preamble; - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); + for (uint32_t ib = 0; ib < num_rows; ib++) { + const uint8_t * restrict src0_ptr = (const uint8_t *) src0 + (ib * src0_row_size_aligned); + const uint8_t * restrict src1_ptr = (const uint8_t *) src1 + (ib * src1_row_size_aligned); + uint8_t * restrict dst_ptr = (uint8_t *) dst + (ib * dst_row_size_aligned); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)), - src1_row_size_aligned, src1_row_size, block_size); + hvx_geglu_f32_aa(dst_ptr, src0_ptr, src1_ptr, nc); } +} - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float * dst_spad = (float *) dma_queue_pop(dma_queue).src; - float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; - float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const uint8_t * src0_spad_ptr = (const uint8_t *)(src0_spad + ib * (src0_row_size_aligned / sizeof(float))); - const uint8_t * src1_spad_ptr = (const uint8_t *)(src1_spad + ib * (src1_row_size_aligned / sizeof(float))); - uint8_t * dst_spad_ptr = (uint8_t *)(dst_spad + ib * (dst_row_size_aligned / sizeof(float))); - - // geglu tanh implementation - // geglu(x, g) = gelu(x) * g - // gelu(x) = 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))) - hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, src0_spad_ptr, nc); // res = x*x - hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, GELU_COEF_A, nc); // res = res * GELU_COEF_A - hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 1.0f, nc); // res = res + 1.0f - hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc); // res = res * x - hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, SQRT_2_OVER_PI, nc); // res = result * SQRT_2_OVER_PI - hvx_tanh_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc); // res = tanh(res) - hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, 1.0f, nc); // res = res + 1.0f - hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc); // res = res * x - hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 0.5f, nc); // res = res + 0.5f - hvx_mul_f32_aaa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, src1_spad_ptr, nc); // res = res * g - } - - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size, - dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)), - src1_row_size_aligned, src1_row_size, pref_block_size); - } +#define DEFINE_GLU_PER_THREAD(NAME, OP_STR, CORE_EXPR) \ + static void glu_##NAME##_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { \ + struct htp_act_context * actx = (struct htp_act_context *) data; \ + htp_act_preamble; \ + \ + struct htp_thread_trace * tr = actx->octx->ctx ? &actx->octx->ctx->trace[ith] : NULL; \ + \ + size_t src0_row_size = actx->src0_row_size; \ + size_t src1_row_size = actx->src1_row_size; \ + size_t dst_row_size = actx->dst_row_size; \ + \ + size_t src0_row_stride = actx->src0_row_stride; \ + size_t src1_row_stride = actx->src1_row_stride; \ + \ + const uint32_t src0_nrows = actx->src0_nrows; \ + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; \ + \ + const uint32_t src0_start_row = src0_nrows_per_thread * ith; \ + const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); \ + \ + /* no work for this thread */ \ + if (src0_start_row >= src0_end_row) { \ + return; \ + } \ + \ + const uint8_t * restrict data_src0 = actx->data_src0; \ + const uint8_t * restrict data_src1 = actx->data_src1; \ + uint8_t * restrict data_dst = actx->data_dst; \ + \ + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; \ + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; \ + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; \ + \ + uint8_t * restrict src0_spad_data = actx->vtcm_src0 + (ith * actx->vtcm_src0_size_per_thread); \ + uint8_t * restrict src1_spad_data = actx->vtcm_src1 + (ith * actx->vtcm_src1_size_per_thread); \ + uint8_t * restrict dst_spad_data = actx->vtcm_dst + (ith * actx->vtcm_dst_size_per_thread); \ + \ + size_t src0_spad_half_size = actx->src0_spad_half_size; \ + size_t src1_spad_half_size = actx->src1_spad_half_size; \ + size_t dst_spad_half_size = actx->dst_spad_half_size; \ + \ + const int BLOCK = actx->block; \ + if (BLOCK == 0) { \ + FARF(ERROR, \ + OP_STR \ + " : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", \ + actx->vtcm_src0_size_per_thread, src0_row_size_aligned); \ + return; \ + } \ + \ + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; \ + \ + /* See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 */ \ + for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { \ + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); \ + \ + /* Dummy DMA transation for sequencing (interleaving dst,src,dst,...) */ \ + dma_queue_push_vtcm_to_ddr(dma_queue, \ + dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), \ + dst_row_size, dst_row_size_aligned, 0); \ + \ + dma_queue_push( \ + dma_queue, \ + dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_stride)), \ + src0_row_size_aligned, src0_row_stride, src0_row_size, block_size); \ + dma_queue_push( \ + dma_queue, \ + dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_stride)), \ + src1_row_size_aligned, src1_row_stride, src1_row_size, block_size); \ + } \ + \ + for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { \ + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); \ + \ + float * dst_spad = (float *) dma_queue_pop(dma_queue).src; \ + float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; \ + float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; \ + \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir); \ + CORE_EXPR; \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir); \ + \ + dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), \ + dst_row_size, dst_row_size_aligned, block_size); \ + \ + /* prefetch N+2 loop iteration if any */ \ + const uint32_t pref_block = (ir + BLOCK * 2); \ + if (pref_block < src0_end_row) { \ + const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); \ + dma_queue_push(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_stride)), \ + src0_row_size_aligned, src0_row_stride, src0_row_size, pref_block_size); \ + dma_queue_push(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_stride)), \ + src1_row_size_aligned, src1_row_stride, src1_row_size, pref_block_size); \ + } \ + } \ + \ + dma_queue_flush(dma_queue); \ + \ } - dma_queue_flush(dma_queue); - - t2 = HAP_perf_get_qtimer_count(); - - FARF(HIGH, "geglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, - ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, - (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} +DEFINE_GLU_PER_THREAD(swiglu, "swiglu-f32", swiglu_f32(src0_spad, src1_spad, dst_spad, block_size, actx)) +DEFINE_GLU_PER_THREAD(swiglu_oai, "swiglu-oai-f32", swiglu_oai_f32(src0_spad, src1_spad, dst_spad, block_size, actx)) +DEFINE_GLU_PER_THREAD(geglu, "geglu-f32", geglu_f32(src0_spad, src1_spad, dst_spad, block_size, actx)) static int execute_op_activations_f32(struct htp_ops_context * octx) { const struct htp_tensor * src0 = octx->src[0]; const struct htp_tensor * src1 = octx->src[1]; const struct htp_tensor * dst = octx->dst; - if (((src0->ne[0] * SIZEOF_FP32) != src0->nb[1]) || ((dst->ne[0] * SIZEOF_FP32) != dst->nb[1])) { - FARF(ERROR, "Non-contiguous tensors are not supported at this time \n"); + if ((dst->ne[0] * SIZEOF_FP32) != dst->nb[1]) { + FARF(ERROR, "Non-contiguous dst is not supported at this time \n"); return HTP_STATUS_NO_SUPPORT; } @@ -635,11 +469,6 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { const char * op_type = NULL; switch (octx->op) { - case HTP_OP_UNARY_SILU: - act_op_func = (worker_callback_t)unary_silu_f32_per_thread; - op_type = "silu-f32"; - break; - case HTP_OP_GLU_SWIGLU: act_op_func = (worker_callback_t)glu_swiglu_f32_per_thread; op_type = "swiglu-f32"; @@ -649,10 +478,6 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { act_op_func = (worker_callback_t)glu_swiglu_oai_f32_per_thread; op_type = "swiglu-oai-f32"; break; - case HTP_OP_UNARY_GELU: - act_op_func = (worker_callback_t)unary_gelu_f32_per_thread; - op_type = "gelu-f32"; - break; case HTP_OP_GLU_GEGLU: act_op_func = (worker_callback_t)glu_geglu_f32_per_thread; @@ -666,51 +491,39 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); - size_t src0_row_size = src0->nb[1]; - size_t src1_row_size = src1 ? src1->nb[1] : src0->nb[1]; - size_t dst_row_size = dst->nb[1]; + // row_size = bytes of useful data per row (what the kernel touches / what DMA copies). + // row_stride = bytes between successive rows in DDR (may exceed row_size for non-contig src). + const size_t nc_bytes = dst->ne[0] * SIZEOF_FP32; + const size_t src0_row_size = nc_bytes; + const size_t src1_row_size = nc_bytes; + const size_t dst_row_size = nc_bytes; + const size_t src0_row_stride = src0->nb[1]; + const size_t src1_row_stride = src1 ? src1->nb[1] : src0->nb[1]; const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN); const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); - // VTCM scratchpads for all tensors - // N rows per thread, padded to HVX vector size - size_t spad_size_per_row = (src0_row_size_aligned + src1_row_size_aligned) + dst_row_size_aligned; - size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads* spad_size_per_row); + struct htp_act_vtcm_layout L; + htp_act_vtcm_layout_build(&L, src0_row_size_aligned, src1_row_size_aligned, dst_row_size_aligned, n_threads, + octx->ctx->vtcm_size); // Make sure the reserved vtcm size is sufficient - if (vtcm_row_per_thread == 0) { + if (L.vtcm_row_per_thread == 0) { FARF(ERROR, "act-%s : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", op_type, octx->ctx->vtcm_size, - spad_size_per_row * n_threads); + (src0_row_size_aligned + src1_row_size_aligned + dst_row_size_aligned) * n_threads); return HTP_STATUS_VTCM_TOO_SMALL; } - octx->src0_spad.size_per_thread = src0_row_size_aligned * vtcm_row_per_thread; - octx->src1_spad.size_per_thread = src1_row_size_aligned * vtcm_row_per_thread; - octx->dst_spad.size_per_thread = dst_row_size_aligned * vtcm_row_per_thread; - - octx->dst_spad.size = n_threads* octx->dst_spad.size_per_thread; - octx->src0_spad.size = n_threads* octx->src0_spad.size_per_thread; - octx->src1_spad.size = n_threads* octx->src1_spad.size_per_thread; - - octx->src0_spad.data = octx->ctx->vtcm_base; - octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; - octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; - - octx->src0_spad.src = NULL; - octx->src1_spad.src = NULL; - octx->dst_spad.src = NULL; - if (src1) { - FARF(HIGH, "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", + FARF(HIGH, "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-vtcm-size %zu src1-vtcm-size %zu dst-vtcm-size %zu\n", op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], - src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size, - octx->dst_spad.size); + src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], L.src0_bytes_per_thread * n_threads, + L.src1_bytes_per_thread * n_threads, L.dst_bytes_per_thread * n_threads); } else { - FARF(HIGH, "%s: %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", op_type, + FARF(HIGH, "%s: %ux%ux%ux%u -> %ux%ux%ux%u : src0-vtcm-size %zu src1-vtcm-size %zu dst-vtcm-size %zu\n", op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], - octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); + L.src0_bytes_per_thread * n_threads, L.src1_bytes_per_thread * n_threads, L.dst_bytes_per_thread * n_threads); } if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { @@ -731,9 +544,21 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { actx.src1_row_size_aligned = src1_row_size_aligned; actx.dst_row_size_aligned = dst_row_size_aligned; - actx.src0_spad_half_size = octx->src0_spad.size_per_thread / 2; - actx.src1_spad_half_size = octx->src1_spad.size_per_thread / 2; - actx.dst_spad_half_size = octx->dst_spad.size_per_thread / 2; + actx.src0_row_stride = src0_row_stride; + actx.src1_row_stride = src1_row_stride; + + uint8_t * const base = (uint8_t *) octx->ctx->vtcm_base; + actx.vtcm_src0 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src0); + actx.vtcm_src1 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src1); + actx.vtcm_dst = VTCM_LAYOUT_PTR(uint8_t, base, L.off_dst); + + actx.vtcm_src0_size_per_thread = L.src0_bytes_per_thread; + actx.vtcm_src1_size_per_thread = L.src1_bytes_per_thread; + actx.vtcm_dst_size_per_thread = L.dst_bytes_per_thread; + + actx.src0_spad_half_size = L.src0_bytes_per_thread / 2; + actx.src1_spad_half_size = L.src1_bytes_per_thread / 2; + actx.dst_spad_half_size = L.dst_bytes_per_thread / 2; actx.block = actx.src0_spad_half_size / actx.src0_row_size_aligned; actx.src0_nrows = src0_nrows; @@ -766,17 +591,11 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { } int op_activations(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - switch (octx->src[0]->type) { case HTP_TYPE_F32: - err = execute_op_activations_f32(octx); - break; + return execute_op_activations_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/argsort-ops.c b/ggml/src/ggml-hexagon/htp/argsort-ops.c index 13f12a39e685..774faef5f388 100644 --- a/ggml/src/ggml-hexagon/htp/argsort-ops.c +++ b/ggml/src/ggml-hexagon/htp/argsort-ops.c @@ -345,7 +345,7 @@ static void htp_argsort_f32_##ne00##_##order_name(unsigned int n, unsigned int i int32_t * indices_buf = (int32_t *) (spad + values_size); \ uint32_t nb01 = src0->nb[1]; \ uint32_t nb1 = dst->nb[1]; \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[i] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[i]; \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start_row); \ for (uint32_t r = start_row; r < end_row; r++) { \ uint32_t src_offset = r * nb01; \ @@ -411,7 +411,7 @@ static void htp_argsort_f32_fallback(unsigned int n, unsigned int i, void * data const HVX_Vector ind_init_vec = *(HVX_Vector *)argosrt_ramp_lut; const HVX_Vector ind_diff_vec = Q6_V_vsplat_R(32); - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start_row); for (uint32_t r = start_row; r < end_row; r++) { diff --git a/ggml/src/ggml-hexagon/htp/binary-ops.c b/ggml/src/ggml-hexagon/htp/binary-ops.c index 52013ad0fec5..db6177963541 100644 --- a/ggml/src/ggml-hexagon/htp/binary-ops.c +++ b/ggml/src/ggml-hexagon/htp/binary-ops.c @@ -16,6 +16,7 @@ #include "htp-ctx.h" #include "htp-ops.h" #include "htp-ops.h" +#include "htp-tensor.h" #ifndef MIN #define MIN(a, b) ((a) < (b) ? (a) : (b)) diff --git a/ggml/src/ggml-hexagon/htp/cumsum-ops.c b/ggml/src/ggml-hexagon/htp/cumsum-ops.c index 2ced19712362..2d45c39f23b5 100644 --- a/ggml/src/ggml-hexagon/htp/cumsum-ops.c +++ b/ggml/src/ggml-hexagon/htp/cumsum-ops.c @@ -9,6 +9,7 @@ #include "ggml-common.h" #include "htp-ctx.h" #include "htp-ops.h" +#include "htp-tensor.h" #include "hvx-types.h" #include "hvx-utils.h" #include "hex-dma.h" @@ -255,16 +256,10 @@ int op_cumsum_f32(struct htp_ops_context * octx) { int op_cumsum(struct htp_ops_context * octx) { const struct htp_tensor * dst = octx->dst; - int err = HTP_STATUS_OK; - switch (dst->type) { case HTP_TYPE_F32: - err = op_cumsum_f32(octx); - break; + return op_cumsum_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/dma-queue.c b/ggml/src/ggml-hexagon/htp/dma-queue.c new file mode 100644 index 000000000000..4beded1de508 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/dma-queue.c @@ -0,0 +1,104 @@ +#include "dma-queue.h" + +#include +#include +#include + +#pragma clang diagnostic ignored "-Wunused-function" + +static inline uint32_t pow2_ceil(uint32_t x) { + if (x <= 1) { + return 1; + } + int p = 2; + x--; + while (x >>= 1) { + p <<= 1; + } + return p; +} + +static inline uintptr_t align_up(uintptr_t addr, size_t align) { + return (addr + align - 1) & ~(align - 1); +} + +size_t dma_queue_sizeof(size_t capacity) { + capacity = pow2_ceil(capacity); + + size_t size_q = sizeof(dma_queue); + size_t offset_r = align_up(size_q, HEX_L2_LINE_SIZE); + size_t size_r = sizeof(dma_ring); + size_t offset_desc = align_up(offset_r + size_r, HEX_L2_LINE_SIZE); + size_t size_desc = capacity * sizeof(dma_descriptor_2d); + size_t offset_dptr = align_up(offset_desc + size_desc, HEX_L2_LINE_SIZE); + size_t size_dptr = capacity * sizeof(dma_ptr); + + return offset_dptr + size_dptr; +} + +size_t dma_queue_alignof(void) { + return HEX_L2_LINE_SIZE; +} + +dma_queue_t dma_queue_init(void * ptr, size_t capacity, uintptr_t vtcm_base, size_t vtcm_size, struct htp_thread_trace * trace) { + capacity = pow2_ceil(capacity); + + size_t size_q = sizeof(dma_queue); + size_t offset_r = align_up(size_q, HEX_L2_LINE_SIZE); + size_t size_r = sizeof(dma_ring); + size_t offset_desc = align_up(offset_r + size_r, HEX_L2_LINE_SIZE); + size_t size_desc = capacity * sizeof(dma_descriptor_2d); + size_t offset_dptr = align_up(offset_desc + size_desc, HEX_L2_LINE_SIZE); + size_t size_dptr = capacity * sizeof(dma_ptr); + + size_t total_size = offset_dptr + size_dptr; + memset(ptr, 0, total_size); + + dma_queue * q = (dma_queue *) ptr; + dma_ring * r = (dma_ring *) ((uintptr_t) ptr + offset_r); + + q->ring = r; + q->nocache = 0; + q->alias = false; + + r->trace = trace; + r->vtcm_base = vtcm_base; + r->vtcm_end = vtcm_base + vtcm_size; + r->capacity = capacity; + r->idx_mask = capacity - 1; + r->push_idx = 0; + r->pop_idx = 0; + + r->desc = (dma_descriptor_2d *) ((uintptr_t) ptr + offset_desc); + r->dptr = (dma_ptr *) ((uintptr_t) ptr + offset_dptr); + r->tail = &r->desc[capacity - 1]; + + FARF(HIGH, "dma-queue: capacity %u, unified memory size %zu\n", capacity, total_size); + + return q; +} + +void dma_queue_free(dma_queue_t q) { + (void) q; +} + +size_t dma_queue_alias_sizeof(void) { + return sizeof(dma_queue); +} + +dma_queue_t dma_queue_alias_init(void * ptr, dma_queue_t main_q, uint8_t nocache) { + dma_queue * q = (dma_queue *) ptr; + memset(q, 0, sizeof(dma_queue)); + + q->ring = main_q->ring; + q->nocache = nocache; + q->alias = true; + + return q; +} + +void dma_queue_alias_free(dma_queue_t q) { + (void) q; +} + + diff --git a/ggml/src/ggml-hexagon/htp/dma-queue.h b/ggml/src/ggml-hexagon/htp/dma-queue.h new file mode 100644 index 000000000000..264284bda828 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/dma-queue.h @@ -0,0 +1,407 @@ +#ifndef HTP_DMA_H +#define HTP_DMA_H + +#include +#include +#include +#include +#include "hex-utils.h" + +#include "hex-profile.h" + +#ifdef __cplusplus +extern "C" { +#endif + +// Define the HW descriptor structs here since the ones in HexSDK are a bit out of date +typedef struct dma_descriptor_1d_s { + void * next; + uint32_t size:24; + uint32_t desc_size:2; + uint32_t dst_comp:1; + uint32_t src_comp:1; + uint32_t dst_bypass:1; + uint32_t src_bypass:1; + uint32_t order:1; + uint32_t done:1; + void * src; + void * dst; +} dma_descriptor_1d; + +#if __HVX_ARCH__ < 75 + +typedef struct dma_descriptor_2d_s { + void * next; + uint32_t reserved0:24; + uint32_t desc_size:2; + uint32_t dst_comp:1; + uint32_t src_comp:1; + uint32_t dst_bypass:1; + uint32_t src_bypass:1; + uint32_t order:1; + uint32_t done:1; + void * src; + void * dst; + uint32_t desc_type:8; + uint32_t reserved1:24; + uint32_t row_size:16; + uint32_t nrows:16; + uint32_t src_stride:16; + uint32_t dst_stride:16; + uint32_t src_offset:16; + uint32_t dst_offset:16; +} dma_descriptor_2d; + +#else + +typedef struct dma_descriptor_2d_s { + void * next; + uint32_t dst_stride:24; + uint32_t desc_size:2; + uint32_t dst_comp:1; + uint32_t src_comp:1; + uint32_t dst_bypass:1; + uint32_t src_bypass:1; + uint32_t order:1; + uint32_t done:1; + void * src; + void * dst; + uint32_t desc_type:8; + uint32_t reserved0:24; + uint32_t row_size:24; + uint32_t nrows_lo:8; + uint32_t nrows_hi:8; + uint32_t src_stride:24; + uint32_t offset:24; + uint32_t reserved1:8; +} dma_descriptor_2d; + +#endif + +typedef struct { + void *dst; + const void *src; +} dma_ptr; + +typedef struct dma_ring_s dma_ring; +struct dma_ring_s { + dma_descriptor_2d * desc; // descriptor pointers + dma_descriptor_2d * tail; // tail pointer + dma_ptr * dptr; // dst/src pointers + uint32_t push_idx; + uint32_t pop_idx; + uint32_t capacity; + uint32_t idx_mask; + struct htp_thread_trace * trace; + uintptr_t vtcm_base; + uintptr_t vtcm_end; +}; + +typedef struct dma_queue_s dma_queue; +typedef dma_queue * dma_queue_t; + +struct dma_queue_s { + dma_ring * ring; // Points to the descriptor ring state + uint8_t nocache; // Queue-specific bypass flag + bool alias; // When set, dma_queue_delete will not free the ring +}; + + + +size_t dma_queue_sizeof(size_t capacity); +size_t dma_queue_alignof(void); +dma_queue_t dma_queue_init(void * ptr, size_t capacity, uintptr_t vtcm_base, size_t vtcm_size, struct htp_thread_trace * trace); +void dma_queue_free(dma_queue_t q); + +size_t dma_queue_alias_sizeof(void); +dma_queue_t dma_queue_alias_init(void * ptr, dma_queue_t main_q, uint8_t nocache); +void dma_queue_alias_free(dma_queue_t q); + +// TODO: technically we don't need these and could use Q6_dmstart/wait/etc instead +// but those do not seem to always compiler properly. +static inline void dmstart(void * next) { + asm volatile(" release(%0):at" : : "r"(next)); + asm volatile(" dmstart(%0)" : : "r"(next)); +} + +static inline void dmlink(void * cur, void * next) { + asm volatile(" release(%0):at" : : "r"(next)); + asm volatile(" dmlink(%0, %1)" : : "r"(cur), "r"(next)); +} + +static inline unsigned int dmpoll(void) { + unsigned int ret = 0; + asm volatile(" %0 = dmpoll" : "=r"(ret) : : "memory"); + return ret; +} + +static inline unsigned int dmwait(void) { + unsigned int ret = 0; + asm volatile(" %0 = dmwait" : "=r"(ret) : : "memory"); + return ret; +} + +static inline dma_ptr dma_make_ptr(void *dst, const void *src) +{ + dma_ptr p = { dst, src }; + return p; +} + +static inline bool dma_is_vtcm(const dma_queue * q, const void * ptr) { + return (uintptr_t) ptr >= q->ring->vtcm_base && (uintptr_t) ptr < q->ring->vtcm_end; +} + +static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t size) { + dma_ring * r = q->ring; + if (((r->push_idx + 1) & r->idx_mask) == r->pop_idx) { + return false; + } + + dma_descriptor_1d * desc = (dma_descriptor_1d *) &r->desc[r->push_idx]; + desc->src = (void *) dptr.src; + desc->dst = (void *) dptr.dst; + desc->size = size; + + r->dptr[r->push_idx] = dptr; + + htp_trace_event_start(r->trace, HTP_TRACE_EVT_DMA, r->push_idx); + + if (size) { + desc->next = NULL; + desc->desc_size = 0; // 1D mode + desc->src_bypass = dma_is_vtcm(q, dptr.src) ? 1 : q->nocache; + desc->dst_bypass = dma_is_vtcm(q, dptr.dst) ? 1 : q->nocache; + desc->order = 0; + desc->done = 0; + + dmlink(r->tail, desc); + r->tail = (dma_descriptor_2d *) desc; + } else { + desc->desc_size = 0; + desc->done = 1; + } + + r->push_idx = (r->push_idx + 1) & r->idx_mask; + return true; +} + +static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { + dma_ring * r = q->ring; + if (((r->push_idx + 1) & r->idx_mask) == r->pop_idx) { + return false; + } + + dma_descriptor_2d * desc = &r->desc[r->push_idx]; + + desc->next = NULL; + desc->reserved0 = 0; + desc->reserved1 = 0; + desc->desc_size = 1; // 2d mode + desc->src_bypass = dma_is_vtcm(q, dptr.src) ? 1 : q->nocache; + desc->dst_bypass = dma_is_vtcm(q, dptr.dst) ? 1 : q->nocache; + desc->src_comp = 0; + desc->dst_comp = 0; + desc->order = 0; + desc->done = 0; + desc->src_stride = src_stride; + desc->dst_stride = dst_stride; + desc->src = (void *) dptr.src; + desc->dst = (void *) dptr.dst; + desc->row_size = row_size; + +#if __HVX_ARCH__ < 75 + desc->desc_type = 0; // 2d (16-bit) mode + desc->nrows = nrows; + desc->src_offset = 0; + desc->dst_offset = 0; +#else + desc->desc_type = 9; // 2d (24-bit) mode + desc->nrows_lo = (nrows & 0xff); + desc->nrows_hi = (nrows >> 8); + desc->offset = 0; +#endif + + r->dptr[r->push_idx] = dptr; + + htp_trace_event_start(r->trace, HTP_TRACE_EVT_DMA, r->push_idx); + + if (nrows) { + dmlink(r->tail, desc); + r->tail = desc; + } else { + desc->done = 1; + } + + r->push_idx = (r->push_idx + 1) & r->idx_mask; + return true; +} + +static inline dma_ptr dma_queue_pop(dma_queue * q) { + dma_ring * r = q->ring; + dma_ptr dptr = { NULL }; + + if (r->push_idx == r->pop_idx) { + return dptr; + } + + dma_descriptor_2d * desc = &r->desc[r->pop_idx]; + + // Wait for desc to complete + if (!desc->done) { + while (!desc->done) { + dmpoll(); + } + } + + dptr = r->dptr[r->pop_idx]; + + htp_trace_event_stop(r->trace, HTP_TRACE_EVT_DMA, r->pop_idx); + + r->pop_idx = (r->pop_idx + 1) & r->idx_mask; + return dptr; +} + +static inline dma_ptr dma_queue_pop_nowait(dma_queue * q) { + dma_ring * r = q->ring; + dma_ptr dptr = { NULL }; + + if (r->push_idx == r->pop_idx) { + return dptr; + } + + dptr = r->dptr[r->pop_idx]; + + htp_trace_event_stop(r->trace, HTP_TRACE_EVT_DMA, r->pop_idx); + + r->pop_idx = (r->pop_idx + 1) & r->idx_mask; + return dptr; +} + +static inline bool dma_queue_empty(dma_queue * q) { + return q->ring->push_idx == q->ring->pop_idx; +} + +static inline void dma_queue_flush(dma_queue * q) { + while (dma_queue_pop(q).dst != NULL) ; +} + +static inline uint32_t dma_queue_depth(dma_queue * q) { + return (q->ring->push_idx - q->ring->pop_idx) & q->ring->idx_mask; +} + +static inline uint32_t dma_queue_capacity(dma_queue * q) { + return q->ring->capacity; +} + +#if __HVX_ARCH__ < 75 + +// Overflow-safe DMA push: all 2d descriptor fields (row_size, nrows, src_stride, dst_stride) are 16-bit, max 65535. +// This version transparently handles values that exceed the 16-bit limit and submits chained DMA transtions. + +#define DMA_MAX_FIELD_VAL 65535u + +static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { + // Fast path: everything fits in 16 bits + if (nrows == 0 || __builtin_expect( + row_size <= DMA_MAX_FIELD_VAL && + nrows <= DMA_MAX_FIELD_VAL && + src_stride <= DMA_MAX_FIELD_VAL && + dst_stride <= DMA_MAX_FIELD_VAL, 1)) { + return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); + } + + // Contiguous block + // Use 1d DMA mode which supports sizes up to 24-bits (16MB) + if (nrows == 1 || (row_size == src_stride && row_size == dst_stride)) { + size_t total = row_size * nrows; + return dma_queue_push_single_1d(q, dptr, total); + } + + // Stride overflow - fall back to row-by-row. + { + const uint8_t *src = (const uint8_t *) dptr.src; + uint8_t *dst = (uint8_t *) dptr.dst; + size_t r = 0; + while (r + 1 < nrows) { + dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride); + if (!dma_queue_push_single_1d(q, p, row_size)) { + dma_queue_flush(q); + } else { + r++; + } + } + dma_queue_flush(q); + dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride); + return dma_queue_push_single_1d(q, p, row_size); + } +} + +#else // HVX_ARCH >= 75 + +static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { + // On v75 and up we always use 2d 24-bit mode + return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); +} + +#endif + +static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { + return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows); +} + +static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { + return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows); +} + +#define DMA_CACHE_MAX_SIZE 256U + +typedef struct { + uint8_t *base; + uint32_t line_size; + uint32_t capacity; + uint32_t src[DMA_CACHE_MAX_SIZE]; + uint16_t age[DMA_CACHE_MAX_SIZE]; +} dma_cache; + +static inline void dma_cache_init(dma_cache *c, uint8_t *base, uint32_t line_size, uint32_t capacity) +{ + c->capacity = (capacity > DMA_CACHE_MAX_SIZE) ? DMA_CACHE_MAX_SIZE : capacity; + c->base = base; + c->line_size = line_size; + + for (unsigned i=0; i < c->capacity; i++) { + c->src[i] = 0; + c->age[i] = 0; + } +} + +static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * src, uint32_t dst_stride, uint32_t src_stride, uint32_t row_size, uint32_t nrows) +{ + uint32_t o_idx = 0; + uint16_t o_age = 0; + uint8_t * dst = 0; + + for (unsigned i=0; i < c->capacity; i++) { + if (c->src[i] == (uint32_t) src) { + c->age[i] = 0; + dst = c->base + (i * c->line_size); nrows = 0; // dummy dma + } else { + c->age[i]++; + if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; } + } + } + if (!dst) { + c->age[o_idx] = 0; + c->src[o_idx] = (uint32_t) src; + dst = c->base + o_idx * c->line_size; // normal nrows dma + return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows); + } + + return dma_queue_push_single_1d(q, dma_make_ptr(dst, src), 0); +} + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif /* HTP_DMA_H */ diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c index 6f2a643e69da..fe78718c6197 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c @@ -24,7 +24,7 @@ #include "hvx-reduce.h" #include "hvx-flash-attn.h" #include "htp-vtcm.h" -#include "worker-pool.h" +#include "work-queue.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -123,15 +123,17 @@ struct hmx_fa_context { uint32_t g_br; // hex_align_up(G * Br, 32) - actual tile row dim // VTCM buffers (allocated by vtcm_seq_alloc) + __fp16 * vtcm_q_dma; // Q DMA fetch buffer __fp16 * vtcm_q_tiles; // Q tile format [g_br, D] __fp16 * vtcm_o_tiles[2]; // O ping-pong [g_br, D] __fp16 * vtcm_k_fp16[2]; // K DMA double-buffer [Bc, D] __fp16 * vtcm_v_fp16[2]; // V DMA double-buffer [Bc, D] - __fp16 * vtcm_k_tiles; // K tiles (transposed) + __fp16 * vtcm_k_tiles[2]; // K tiles (transposed, double-buffered) __fp16 * vtcm_v_tiles[2]; // V tiles (column-major, double-buffered) - __fp16 * vtcm_s_tiles; // S = QK^T [g_br, Bc] - __fp16 * vtcm_p_tiles; // P = softmax(S) [g_br, Bc] + __fp16 * vtcm_s_tiles[2]; // S = QK^T [g_br, Bc] (double-buffered) + __fp16 * vtcm_p_tiles[2]; // P = softmax(S) [g_br, Bc] __fp16 * vtcm_d_tiles; // Diagonal rescale [g_br, g_br] + __fp16 * vtcm_d_inv_l; // Diagonal rescale (1/l) [g_br, g_br] HVX_Vector * vtcm_m_vec; // Row max [g_br] HVX_Vector * vtcm_l_vec; // Row sum [g_br] HVX_Vector * vtcm_s_rowmax; // Softmax intermediate [g_br] @@ -204,7 +206,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * if (ir0 >= ir1) return; - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; dma_queue * dma = octx->ctx->dma[ith]; @@ -236,10 +238,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const uint32_t iv3 = fastdiv(iq3, &factx->broadcast_rv3); const uint32_t iv2 = fastdiv(iq2, &factx->broadcast_rv2); - // Fetch Q row - const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3); - dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); - const __fp16 * mp_base = NULL; if (mask) { const uint32_t im2 = fastmodulo(iq2, mask->ne[2], &factx->src3_div2); @@ -247,26 +245,91 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * mp_base = (const __fp16 *) ((const uint8_t *) mask->data + iq1*mask->nb[1] + im2*mask->nb[2] + im3*mask->nb[3]); } - // Prefetch first two blocks - for (uint32_t ib = 0; ib < MIN(factx->n_blocks, 2); ++ib) { - const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE; - const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + // Precalculate next row variables if there is a next row + bool has_next_ir = (ir + 1 < ir1); + uint32_t next_ik2 = 0, next_ik3 = 0, next_iv2 = 0, next_iv3 = 0; + const uint8_t * next_q_row_ptr = NULL; + const __fp16 * next_mp_base = NULL; + + const uint8_t * next_k_src0 = NULL; + const uint8_t * next_v_src0 = NULL; + const uint8_t * next_m_src0 = NULL; + uint32_t next_block_size0 = 0; + + const uint8_t * next_k_src1 = NULL; + const uint8_t * next_v_src1 = NULL; + const uint8_t * next_m_src1 = NULL; + uint32_t next_block_size1 = 0; + + if (has_next_ir) { + const uint32_t next_ir = ir + 1; + const uint32_t next_iq3 = fastdiv(next_ir, &factx->src0_div21); + const uint32_t next_iq2 = fastdiv(next_ir - next_iq3*neq2*neq1, &factx->src0_div1); + const uint32_t next_iq1 = (next_ir - next_iq3*neq2*neq1 - next_iq2 * neq1); - // K - const uint8_t * k_src = (const uint8_t *) k->data + (ic_start*nbk1 + ik2*nbk2 + ik3*nbk3); - uint8_t * k_dst = spad_k + (ib % 2) * factx->size_k_block; - dma_queue_push(dma, dma_make_ptr(k_dst, k_src), factx->size_k_row_padded, nbk1, size_k_row, current_block_size); + next_ik3 = fastdiv(next_iq3, &factx->broadcast_rk3); + next_ik2 = fastdiv(next_iq2, &factx->broadcast_rk2); - // V - const uint8_t * v_src = (const uint8_t *) v->data + (ic_start*nbv1 + iv2*nbv2 + iv3*nbv3); - uint8_t * v_dst = spad_v + (ib % 2) * factx->size_v_block; - dma_queue_push(dma, dma_make_ptr(v_dst, v_src), factx->size_v_row_padded, nbv1, size_v_row, current_block_size); + next_iv3 = fastdiv(next_iq3, &factx->broadcast_rv3); + next_iv2 = fastdiv(next_iq2, &factx->broadcast_rv2); + + next_q_row_ptr = (const uint8_t *) q->data + (next_iq1*nbq1 + next_iq2*nbq2 + next_iq3*nbq3); - // Mask if (mask) { - const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start); - // Mask is 1D contiguous for this row - dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1); + const uint32_t next_im2 = fastmodulo(next_iq2, mask->ne[2], &factx->src3_div2); + const uint32_t next_im3 = fastmodulo(next_iq3, mask->ne[3], &factx->src3_div3); + next_mp_base = (const __fp16 *) ((const uint8_t *) mask->data + next_iq1*mask->nb[1] + next_im2*mask->nb[2] + next_im3*mask->nb[3]); + } + + // Precalculate next K/V block 0 source pointers + { + const uint32_t ic_start = 0; + next_block_size0 = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + next_k_src0 = (const uint8_t *) k->data + (ic_start*nbk1 + next_ik2*nbk2 + next_ik3*nbk3); + next_v_src0 = (const uint8_t *) v->data + (ic_start*nbv1 + next_iv2*nbv2 + next_iv3*nbv3); + if (mask) { + next_m_src0 = (const uint8_t *) (next_mp_base + ic_start); + } + } + + // Precalculate next K/V block 1 source pointers (if n_blocks > 1) + if (factx->n_blocks > 1) { + const uint32_t ic_start = 1 * FLASH_ATTN_BLOCK_SIZE; + next_block_size1 = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + next_k_src1 = (const uint8_t *) k->data + (ic_start*nbk1 + next_ik2*nbk2 + next_ik3*nbk3); + next_v_src1 = (const uint8_t *) v->data + (ic_start*nbv1 + next_iv2*nbv2 + next_iv3*nbv3); + if (mask) { + next_m_src1 = (const uint8_t *) (next_mp_base + ic_start); + } + } + } + + if (ir == ir0) { + // Fetch Q row + const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3); + dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); + + // Prefetch first two blocks + for (uint32_t ib = 0; ib < MIN(factx->n_blocks, 2); ++ib) { + const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE; + const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + + // K + const uint8_t * k_src = (const uint8_t *) k->data + (ic_start*nbk1 + ik2*nbk2 + ik3*nbk3); + uint8_t * k_dst = spad_k + (ib % 2) * factx->size_k_block; + dma_queue_push(dma, dma_make_ptr(k_dst, k_src), factx->size_k_row_padded, nbk1, size_k_row, current_block_size); + + // V + const uint8_t * v_src = (const uint8_t *) v->data + (ic_start*nbv1 + iv2*nbv2 + iv3*nbv3); + uint8_t * v_dst = spad_v + (ib % 2) * factx->size_v_block; + dma_queue_push(dma, dma_make_ptr(v_dst, v_src), factx->size_v_row_padded, nbv1, size_v_row, current_block_size); + + // Mask + if (mask) { + const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start); + // Mask is 1D contiguous for this row + dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1); + } } } @@ -287,6 +350,11 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const HVX_Vector slope_vec = hvx_vec_splat_f16(slope); const HVX_Vector v_neg_inf = Q6_Vh_vsplat_R(0xfbff); + const HVX_Vector v_cap = (factx->logit_softcap != 0.0f) ? hvx_vec_splat_f16(factx->logit_softcap) : Q6_V_vzero(); + const HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00); + const HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF); + const HVX_Vector v_log2e = hvx_vec_splat_f16(EXP_LOG2E_F); + const uint32_t stride_v2 = factx->size_v_row_padded * 2; for (uint32_t ib = 0; ib < factx->n_blocks; ++ib) { const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE; const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); @@ -309,7 +377,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * // 2. Softcap (in FP16) if (factx->logit_softcap != 0.0f) { - const HVX_Vector v_cap = hvx_vec_splat_f16(factx->logit_softcap); scores_f16 = hvx_vec_tanh_f16(scores_f16); scores_f16 = hvx_vec_mul_f16_f16(scores_f16, v_cap); } @@ -319,8 +386,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * // 3. Mask (in FP16) if (mask) { HVX_Vector m_vals_f16 = *(const HVX_UVector *) m_base; - HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00); - HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF); HVX_VectorPred is_inf = Q6_Q_vcmp_eq_VhVh(m_vals_f16, vinf); m_vals_f16 = Q6_V_vmux_QVV(is_inf, vmin, m_vals_f16); @@ -335,10 +400,30 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * HVX_Vector v_max = Q6_V_lo_W(hvx_vec_f16_to_f32(v_max_f16)); // splat block max in FP32 htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_QK, ir); + if (ib + 1 == factx->n_blocks && has_next_ir) { + // Queue next row's Q row! + dma_queue_push(dma, dma_make_ptr(spad_q, next_q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); + + if (factx->n_blocks % 2 == 0) { + // Queue next row's block 0 (into buffer slot 0) + uint8_t * k_dst = spad_k + 0 * factx->size_k_block; + uint8_t * v_dst = spad_v + 0 * factx->size_v_block; + + // K (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src0), factx->size_k_row_padded, nbk1, size_k_row, next_block_size0); + + // V (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src0), factx->size_v_row_padded, nbv1, size_v_row, next_block_size0); + + // Mask (block 0 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src0, next_block_size0 * 2, next_block_size0 * 2, next_block_size0 * 2, 1); + } + } + } + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_SFM, ir); { - const HVX_Vector v_log2e = hvx_vec_splat_f16(EXP_LOG2E_F); - // 4. Online Softmax Update HVX_Vector M_new_vec = Q6_Vsf_vmax_VsfVsf(v_max, M_vec); HVX_Vector diff_vec = HVX_OP_SUB_F32(M_vec, M_new_vec); @@ -370,24 +455,20 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * S_vec = HVX_OP_ADD_F32(HVX_OP_MUL_F32(S_vec, ms_vec), p_sum_vec); // 5. Accumulate V (F16 * F16 -> F32 accumulator) - __fp16 __attribute__((aligned(128))) p_arr[VLEN_FP16]; - hvx_vec_store_a(p_arr, 128, P); + const uint8_t * v_ptr = v_base; for (uint32_t j = 0; j < current_block_size; j += 2) { if (j + 1 == current_block_size) { - if (p_arr[j] != 0.0f) { - const uint8_t * v_ptr = v_base + j * factx->size_v_row_padded; - hvx_mad_f32_f16_aa(VKQ32, v_ptr, (p_arr + j), DV); - } + HVX_Vector S0 = hvx_vec_repl_f16(Q6_V_vror_VR(P, j * 2)); + hvx_mad_f32_f16_aa_vec(VKQ32, v_ptr, S0, DV); break; } - if (p_arr[j] == 0.0f && p_arr[j + 1] == 0.0f) { - continue; - } + HVX_Vector S0 = hvx_vec_repl_f16(Q6_V_vror_VR(P, j * 2)); + HVX_Vector S1 = hvx_vec_repl_f16(Q6_V_vror_VR(P, (j + 1) * 2)); - const uint8_t * v_ptr = v_base + j * factx->size_v_row_padded; - hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, (p_arr + j), (p_arr + j + 1), DV); + hvx_mad_f32_f16_aa_rx2_vec(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, S0, S1, DV); + v_ptr += stride_v2; } } htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_SFM, ir); @@ -414,6 +495,61 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * } } + if (has_next_ir) { + if (factx->n_blocks % 2 == 0) { + // Queue next row's block 1 (into buffer slot 1, if n_blocks > 1) + if (factx->n_blocks > 1) { + uint8_t * k_dst = spad_k + 1 * factx->size_k_block; + uint8_t * v_dst = spad_v + 1 * factx->size_v_block; + + // K (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src1), factx->size_k_row_padded, nbk1, size_k_row, next_block_size1); + + // V (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src1), factx->size_v_row_padded, nbv1, size_v_row, next_block_size1); + + // Mask (block 1 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src1, next_block_size1 * 2, next_block_size1 * 2, next_block_size1 * 2, 1); + } + } + } else { + // Queue next row's block 0 (into buffer slot 0) + { + uint8_t * k_dst = spad_k + 0 * factx->size_k_block; + uint8_t * v_dst = spad_v + 0 * factx->size_v_block; + + // K (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src0), factx->size_k_row_padded, nbk1, size_k_row, next_block_size0); + + // V (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src0), factx->size_v_row_padded, nbv1, size_v_row, next_block_size0); + + // Mask (block 0 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src0, next_block_size0 * 2, next_block_size0 * 2, next_block_size0 * 2, 1); + } + } + + // Queue next row's block 1 (into buffer slot 1, if n_blocks > 1) + if (factx->n_blocks > 1) { + uint8_t * k_dst = spad_k + 1 * factx->size_k_block; + uint8_t * v_dst = spad_v + 1 * factx->size_v_block; + + // K (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src1), factx->size_k_row_padded, nbk1, size_k_row, next_block_size1); + + // V (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src1), factx->size_v_row_padded, nbv1, size_v_row, next_block_size1); + + // Mask (block 1 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src1, next_block_size1 * 2, next_block_size1 * 2, next_block_size1 * 2, 1); + } + } + } + } + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, ir); // sinks float M = hvx_vec_get_f32(M_vec); @@ -471,6 +607,7 @@ typedef struct { void * curr_k; uint32_t kv_start; uint32_t rows_per_t; + size_t buf_idx; } fa_k_int_args_t; static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) { @@ -486,23 +623,23 @@ static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_K_PREP, (uint16_t) (args->kv_start + start)); - hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles, (const __fp16 *) args->curr_k, total_rows, factx->DK, + hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles[args->buf_idx], (const __fp16 *) args->curr_k, total_rows, factx->DK, args->src_stride, start, end); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_K_PREP, (uint16_t) (args->kv_start + start)); } -static void fa_phase_k_interleave(struct hmx_fa_context * factx, uint32_t kv_rows, size_t src_stride, void * curr_k, uint32_t kv_start) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; +static void fa_phase_k_interleave(struct hmx_fa_context * factx, uint32_t kv_rows, size_t src_stride, void * curr_k, uint32_t kv_start, size_t buf_idx) { + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && kv_rows >= factx->n_threads * 2) { n = factx->n_threads; } uint32_t rows_per_t = hex_align_up(hmx_ceil_div(kv_rows, n), 2); - fa_k_int_args_t args = { factx, kv_rows, src_stride, curr_k, kv_start, rows_per_t }; + fa_k_int_args_t args = { factx, kv_rows, src_stride, curr_k, kv_start, rows_per_t, buf_idx }; if (n > 1) { - worker_pool_run_func(wp, fa_k_interleave_thread, &args, n); + work_queue_run(wp, fa_k_interleave_thread, &args, n); } else { fa_k_interleave_thread(1, 0, &args); } @@ -534,7 +671,7 @@ static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data) __fp16 * v_tiles_dst = (__fp16 *) args->v_tiles_dst; - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_V_PREP, (uint16_t) (args->kv_start + start)); hmx_interleave_cols_to_tiles(v_tiles_dst, (const __fp16 *) args->v_src, total_rows, factx->DV, args->src_stride, (uint32_t) args->n_col_tiles, start, end); @@ -548,7 +685,7 @@ static void fa_phase_v_interleave(struct hmx_fa_context * factx, void * v_tiles_dst, size_t n_col_tiles, uint32_t kv_start) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && kv_rows >= factx->n_threads * 2) { n = factx->n_threads; @@ -556,7 +693,7 @@ static void fa_phase_v_interleave(struct hmx_fa_context * factx, uint32_t rows_per_t = hex_align_up(hmx_ceil_div(kv_rows, n), 2); fa_v_int_args_t args = { factx, kv_rows, src_stride, v_src, v_tiles_dst, n_col_tiles, kv_start, rows_per_t }; if (n > 1) { - worker_pool_run_func(wp, fa_v_interleave_thread, &args, n); + work_queue_run(wp, fa_v_interleave_thread, &args, n); } else { fa_v_interleave_thread(1, 0, &args); } @@ -589,7 +726,7 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) { const size_t start = (size_t) i * rows_per_t; const size_t end = hex_smin(start + rows_per_t, factx->g_br); - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_Q_PREP, (uint16_t) (args->q_start * G + start)); // Parallel initialization of per-block state @@ -645,12 +782,13 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) { } } - // Initialize vtcm_d_tiles to 0 + // Initialize vtcm_d_tiles and vtcm_d_inv_l to 0 const size_t d_bytes_per_t = hex_align_up(d_tile_bytes / n, 128); const size_t d_start = i * d_bytes_per_t; const size_t d_end = hex_smin(d_start + d_bytes_per_t, d_tile_bytes); if (d_start < d_tile_bytes) { hvx_splat_u8_a((char *) factx->vtcm_d_tiles + d_start, 0, d_end - d_start); + hvx_splat_u8_a((char *) factx->vtcm_d_inv_l + d_start, 0, d_end - d_start); } } @@ -662,15 +800,14 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) { assert(factx->DK == factx->DV); - const size_t o_tile_bytes = factx->o_tile_bytes; - const bool use_q_dma = (2 * o_tile_bytes >= factx->g_br * DK * (factx->is_q_fp32 ? 4 : 2)); + const bool use_q_dma = (factx->vtcm_q_dma != NULL); __fp16 * q_tiles = factx->vtcm_q_tiles; if (use_q_dma) { const size_t g_rows_end = hex_smin(end, n_rows_g); const uint32_t d_limit = factx->is_q_fp32 ? DK / 32 : DK / 64; - uint8_t * q_flat = (uint8_t *) factx->vtcm_o_tiles[0]; + uint8_t * q_flat = (uint8_t *) factx->vtcm_q_dma; if (factx->is_q_fp32) { switch (d_limit) { case 2: hmx_fa_q_prep_fp32_d2(q_tiles, q_flat, start, end, g_rows_end, DK, G, args->n_rows_q, &factx->div_G, args->q_transposed); break; @@ -720,7 +857,7 @@ static void fa_phase_q_load(struct hmx_fa_context * factx, uint32_t kv_head, uint32_t ib3, size_t n_rows_g) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) { n = factx->n_threads; @@ -739,7 +876,7 @@ static void fa_phase_q_load(struct hmx_fa_context * factx, args.q_transposed = q->nb[1] < q->nb[2]; atomic_init(&args.barrier, n); if (n > 1) { - worker_pool_run_func(wp, fa_q_load_thread, &args, n); + work_queue_run(wp, fa_q_load_thread, &args, n); } else { fa_q_load_thread(1, 0, &args); } @@ -772,7 +909,7 @@ static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) { return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); const struct htp_tensor * dst = args->dst; @@ -781,10 +918,10 @@ static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) { const uint32_t kv_head = args->kv_head; const uint32_t ib3 = args->ib3; - for (size_t r = start; r < end; ++r) { - const size_t q_idx = fastdiv(r, &factx->div_G); - const size_t h_idx = fastmodulo(r, G, &factx->div_G); + size_t q_idx = fastdiv(start, &factx->div_G); + size_t h_idx = fastmodulo(start, G, &factx->div_G); + for (size_t r = start; r < end; ++r) { float * out = (float *) ((uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] + (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3]); @@ -801,6 +938,12 @@ static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) { *(HVX_UVector *) (out + d * 32) = Q6_V_hi_W(vp); } } + + h_idx++; + if (h_idx == G) { + h_idx = 0; + q_idx++; + } } htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); } @@ -820,7 +963,7 @@ static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) { return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); const struct htp_tensor * dst = args->dst; @@ -829,10 +972,10 @@ static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) { const uint32_t kv_head = args->kv_head; const uint32_t ib3 = args->ib3; - for (size_t r = start; r < end; ++r) { - const size_t q_idx = fastdiv(r, &factx->div_G); - const size_t h_idx = fastmodulo(r, G, &factx->div_G); + size_t q_idx = fastdiv(start, &factx->div_G); + size_t h_idx = fastmodulo(start, G, &factx->div_G); + for (size_t r = start; r < end; ++r) { __fp16 * out = (__fp16 *) ((uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] + (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3]); @@ -851,6 +994,12 @@ static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) { *(HVX_UVector *) (out + d * 64) = Q6_V_hi_W(vp); } } + + h_idx++; + if (h_idx == G) { + h_idx = 0; + q_idx++; + } } htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); } @@ -862,7 +1011,7 @@ static void fa_phase_o_store(struct hmx_fa_context * factx, uint32_t kv_head, uint32_t ib3, size_t n_rows_g) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) { n = factx->n_threads; @@ -871,7 +1020,7 @@ static void fa_phase_o_store(struct hmx_fa_context * factx, fa_o_store_args_t args = { factx, dst, o_tile_src, q_start, kv_head, ib3, n_rows_g, rows_per_t }; worker_callback_t store_fn = factx->is_dst_fp32 ? fa_o_store_thread_f32 : fa_o_store_thread_f16; if (n > 1) { - worker_pool_run_func(wp, store_fn, &args, n); + work_queue_run(wp, store_fn, &args, n); } else { store_fn(1, 0, &args); } @@ -879,6 +1028,7 @@ static void fa_phase_o_store(struct hmx_fa_context * factx, typedef struct { struct hmx_fa_context * factx; + size_t buf_idx; size_t kv_rows; size_t n_rows_g; size_t n_col_tiles; @@ -930,7 +1080,7 @@ static inline void fa_softmax_impl( return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_SFM, (uint16_t) (args->q_start * G + vec_start * 64)); // Per-thread row scratch: thread i uses bufs at offset i * 2 * stride @@ -960,8 +1110,8 @@ static inline void fa_softmax_impl( uint32_t r0 = r / HMX_FP16_TILE_N_ROWS; uint32_t r1 = r % HMX_FP16_TILE_N_ROWS; - const __fp16 * s_ld_base = factx->vtcm_s_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc; - __fp16 * p_st_base = factx->vtcm_p_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc; + const __fp16 * s_ld_base = factx->vtcm_s_tiles[args->buf_idx] + r0 * HMX_FP16_TILE_N_ROWS * Bc; + __fp16 * p_st_base = factx->vtcm_p_tiles[args->buf_idx] + r0 * HMX_FP16_TILE_N_ROWS * Bc; // Decode 2 rows from S tiles into per-thread row buffers if (has_softcap) { @@ -983,7 +1133,26 @@ static inline void fa_softmax_impl( my_row_buf1[ci] = hvx_vec_mul_f16_f16(t1, v_cap); } } else { - for (size_t c = 0; c < kv_rows; c += 64) { + size_t c = 0; + for (; c + 64 < kv_rows; c += 128) { + size_t ci0 = c / 64; + size_t ci1 = ci0 + 1; + const __fp16 * in_dtile0 = s_ld_base + ci0 * HMX_FP16_TILE_N_ELMS * 2; + const __fp16 * in_dtile1 = s_ld_base + ci1 * HMX_FP16_TILE_N_ELMS * 2; + const HVX_Vector * pv_s_in0_0 = ((const HVX_Vector *) in_dtile0) + r1 / 2; + const HVX_Vector * pv_s_in1_0 = pv_s_in0_0 + 16; + const HVX_Vector * pv_s_in0_1 = ((const HVX_Vector *) in_dtile1) + r1 / 2; + const HVX_Vector * pv_s_in1_1 = pv_s_in0_1 + 16; + + HVX_VectorPair vp_s_drow0 = Q6_W_vdeal_VVR(*pv_s_in1_0, *pv_s_in0_0, -2); + my_row_buf0[ci0] = Q6_V_lo_W(vp_s_drow0); + my_row_buf1[ci0] = Q6_V_hi_W(vp_s_drow0); + + HVX_VectorPair vp_s_drow1 = Q6_W_vdeal_VVR(*pv_s_in1_1, *pv_s_in0_1, -2); + my_row_buf0[ci1] = Q6_V_lo_W(vp_s_drow1); + my_row_buf1[ci1] = Q6_V_hi_W(vp_s_drow1); + } + for (; c < kv_rows; c += 64) { size_t ci = c / 64; const __fp16 * in_dtile = s_ld_base + ci * HMX_FP16_TILE_N_ELMS * 2; const HVX_Vector * pv_s_in0 = ((const HVX_Vector *) in_dtile) + r1 / 2; @@ -1007,12 +1176,12 @@ static inline void fa_softmax_impl( HVX_Vector v_s_rowmax0 = v_neg_inf; HVX_Vector v_s_rowmax1 = v_neg_inf; - for (size_t c = 0; c < kv_rows; c += 64) { - size_t ci = c / 64; - const size_t ne = hex_smin(kv_rows - c, 64); - HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16)); + if (has_mask) { + for (size_t c = 0; c < kv_rows; c += 64) { + size_t ci = c / 64; + const size_t ne = hex_smin(kv_rows - c, 64); + HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16)); - if (has_mask) { HVX_Vector v_mask0, v_mask1; if (mask_broadcast) { @@ -1066,15 +1235,31 @@ static inline void fa_softmax_impl( my_row_buf0[ci] = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_mask0_scaled), v_neg_inf); my_row_buf1[ci] = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_mask1_scaled), v_neg_inf); } - } else { + + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]); + } + } else { + size_t c = 0; + for (; c + 64 < kv_rows; c += 128) { + size_t ci0 = c / 64; + size_t ci1 = ci0 + 1; + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci0]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci0]); + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci1]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci1]); + } + for (; c < kv_rows; c += 64) { + size_t ci = c / 64; + const size_t ne = hex_smin(kv_rows - c, 64); + HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16)); if (ne < 64) { my_row_buf0[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf0[ci], v_neg_inf); my_row_buf1[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf1[ci], v_neg_inf); } + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]); } - - v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]); - v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]); } v_s_rowmax0 = hvx_vec_reduce_max_f16(v_s_rowmax0); @@ -1121,8 +1306,48 @@ static inline void fa_softmax_impl( HVX_Vector v_p_rowsum0 = v_zero; HVX_Vector v_p_rowsum1 = v_zero; - for (size_t c = 0; c < kv_rows; c += 64) { - size_t ci = c / 64; + size_t c = 0; + for (; c + 64 < kv_rows; c += 128) { + size_t ci0 = c / 64; + size_t ci1 = ci0 + 1; + + HVX_Vector v_s_minus_m0_0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci0], v_dup_m0); + HVX_Vector v_s_minus_m1_0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci0], v_dup_m1); + HVX_Vector v_s_minus_m0_1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci1], v_dup_m0); + HVX_Vector v_s_minus_m1_1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci1], v_dup_m1); + + HVX_Vector v_p_row0_hf_0 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m0_0)); + HVX_Vector v_p_row1_hf_0 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m1_0)); + HVX_Vector v_p_row0_hf_1 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m0_1)); + HVX_Vector v_p_row1_hf_1 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m1_1)); + + __fp16 * out_dtile0 = p_st_base + ci0 * HMX_FP16_TILE_N_ELMS * 2; + __fp16 * out_dtile1 = p_st_base + ci1 * HMX_FP16_TILE_N_ELMS * 2; + HVX_Vector * pv_p_out0_0 = ((HVX_Vector *) out_dtile0) + r1 / 2; + HVX_Vector * pv_p_out1_0 = pv_p_out0_0 + 16; + HVX_Vector * pv_p_out0_1 = ((HVX_Vector *) out_dtile1) + r1 / 2; + HVX_Vector * pv_p_out1_1 = pv_p_out0_1 + 16; + + HVX_VectorPair vp_p_dual0 = Q6_W_vshuff_VVR(v_p_row1_hf_0, v_p_row0_hf_0, -2); + *pv_p_out0_0 = Q6_V_lo_W(vp_p_dual0); + *pv_p_out1_0 = Q6_V_hi_W(vp_p_dual0); + + HVX_VectorPair vp_p_dual1 = Q6_W_vshuff_VVR(v_p_row1_hf_1, v_p_row0_hf_1, -2); + *pv_p_out0_1 = Q6_V_lo_W(vp_p_dual1); + *pv_p_out1_1 = Q6_V_hi_W(vp_p_dual1); + + HVX_VectorPair vp_p0_0 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf_0); + HVX_VectorPair vp_p1_0 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf_0); + HVX_VectorPair vp_p0_1 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf_1); + HVX_VectorPair vp_p1_1 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf_1); + + v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0_0), Q6_V_hi_W(vp_p0_0))); + v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0_1), Q6_V_hi_W(vp_p0_1))); + v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1_0), Q6_V_hi_W(vp_p1_0))); + v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1_1), Q6_V_hi_W(vp_p1_1))); + } + for (size_t c_rem = c; c_rem < kv_rows; c_rem += 64) { + size_t ci = c_rem / 64; HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0); HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1); @@ -1281,7 +1506,7 @@ static __attribute__((noinline)) void fa_build_d_diag_inv_l(struct hmx_fa_contex v_content = Q6_V_vror_VR(v_content, 64); } - __fp16 * out_base = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS; + __fp16 * out_base = factx->vtcm_d_inv_l + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS; Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content); } } @@ -1290,7 +1515,7 @@ static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx, fa_softmax_args_t * sargs, size_t n_row_tiles, size_t n_row_tiles_g_br) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; const size_t n_row_vec_cnt = hmx_ceil_div(sargs->n_rows_g, 64); worker_callback_t softmax_fn = fa_softmax_thread; @@ -1307,7 +1532,7 @@ static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx, if (factx->n_threads > 1 && n_row_vec_cnt >= 2) { uint32_t n_use = (uint32_t) hex_smin((size_t) factx->n_threads, n_row_vec_cnt); sargs->thread_div = init_fastdiv_values(n_use); - worker_pool_run_func(wp, softmax_fn, sargs, n_use); + work_queue_run(wp, softmax_fn, sargs, n_use); } else { softmax_fn(1, 0, sargs); } @@ -1514,13 +1739,34 @@ static void fa_pop_mask_dma_gqa(dma_queue * dma, uint32_t G) { } } +static inline void fa_prefetch_block(dma_queue * dma, const struct htp_tensor * k, const struct htp_tensor * v, const struct htp_tensor * mask, + uint32_t b, size_t Bc, size_t size_k_row_padded, size_t size_k_row, size_t size_v_row_padded, size_t size_v_row, + uint32_t ik2, uint32_t ik3, uint32_t iv2, uint32_t iv3, uint32_t q_start, uint32_t im3, uint32_t kv_head, uint32_t G, + size_t m_line_bytes, size_t n_rows_q, size_t nek1, size_t prefetch_buf, struct hmx_fa_context * factx) { + const uint32_t prefetch_start = b * Bc; + const uint32_t prefetch_rows = hex_smin(Bc, nek1 - prefetch_start); + const uint8_t * k_prefetch_src = (const uint8_t *) k->data + prefetch_start * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx->vtcm_k_fp16[prefetch_buf], k_prefetch_src), size_k_row_padded, k->nb[1], size_k_row, prefetch_rows); + const uint8_t * v_prefetch_src = (const uint8_t *) v->data + prefetch_start * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx->vtcm_v_fp16[prefetch_buf], v_prefetch_src), size_v_row_padded, v->nb[1], size_v_row, prefetch_rows); + + if (mask) { + if (__builtin_expect(factx->mask_broadcast, true)) { + const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + prefetch_start * sizeof(__fp16); + dma_cache_push(dma, &factx->m_cache, ms_src, m_line_bytes, mask->nb[1], prefetch_rows * sizeof(__fp16), n_rows_q); + } else { + fa_push_mask_dma_gqa(dma, mask, q_start, im3, prefetch_start, kv_head, G, m_line_bytes, prefetch_rows, n_rows_q, factx); + } + } +} + // ============================================================================ // Core HMX flash attention algorithm (GQA-merged) // ============================================================================ int hmx_flash_attn_ext(struct htp_ops_context * octx) { - struct htp_thread_trace * tr_hvx = octx->ctx ? &octx->ctx->trace[0] : NULL; - struct htp_thread_trace * tr_hmx = octx->ctx ? &octx->ctx->trace[HTP_MAX_NTHREADS] : NULL; + struct htp_thread_trace * tr_hvx = &octx->ctx->trace[0]; + struct htp_thread_trace * tr_hmx = &octx->ctx->trace[HTP_MAX_NTHREADS]; const struct htp_tensor * q = octx->src[0]; const struct htp_tensor * k = octx->src[1]; const struct htp_tensor * v = octx->src[2]; @@ -1612,7 +1858,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { // Build the VTCM layout once (shared with the host estimator) and place every // scratch buffer at its computed offset. struct hmx_fa_vtcm_layout L; - hmx_fa_vtcm_layout_build(&L, G, DK, DV, Br, Bc, n_threads, pipeline); + hmx_fa_vtcm_layout_build(&L, G, DK, DV, Br, Bc, n_threads, pipeline, factx.is_q_fp32); if (L.total_bytes > ctx->vtcm_size) { return HTP_STATUS_VTCM_TOO_SMALL; @@ -1620,6 +1866,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { uint8_t * const base = ctx->vtcm_base; + factx.vtcm_q_dma = VTCM_LAYOUT_PTR(__fp16, base, L.off_q_dma); factx.vtcm_q_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_q_tiles); factx.vtcm_o_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_o_tiles[0]); factx.vtcm_o_tiles[1] = VTCM_LAYOUT_PTR(__fp16, base, L.off_o_tiles[1]); @@ -1627,12 +1874,16 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { factx.vtcm_k_fp16[1] = VTCM_LAYOUT_PTR(__fp16, base, L.off_k_fp16[1]); factx.vtcm_v_fp16[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_v_fp16[0]); factx.vtcm_v_fp16[1] = VTCM_LAYOUT_PTR(__fp16, base, L.off_v_fp16[1]); - factx.vtcm_k_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_k_tiles); + factx.vtcm_k_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_k_tiles[0]); + factx.vtcm_k_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_k_tiles[1], pipeline); factx.vtcm_v_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_v_tiles[0]); factx.vtcm_v_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_v_tiles[1], pipeline); - factx.vtcm_s_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_s_tiles); - factx.vtcm_p_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_p_tiles); + factx.vtcm_s_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_s_tiles[0]); + factx.vtcm_s_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_s_tiles[1], pipeline); + factx.vtcm_p_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_p_tiles[0]); + factx.vtcm_p_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_p_tiles[1], pipeline); factx.vtcm_d_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_d_tiles); + factx.vtcm_d_inv_l = VTCM_LAYOUT_PTR(__fp16, base, L.off_d_inv_l); factx.vtcm_m_vec = VTCM_LAYOUT_PTR(HVX_Vector, base, L.off_m_vec); factx.vtcm_l_vec = VTCM_LAYOUT_PTR(HVX_Vector, base, L.off_l_vec); factx.vtcm_s_rowmax = VTCM_LAYOUT_PTR(HVX_Vector, base, L.off_s_rowmax); @@ -1670,6 +1921,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { const size_t qo_element_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16); + const bool q_transposed = q->nb[1] < q->nb[2]; + const size_t q_src_stride = q_transposed ? q->nb[2] : q->nb[1]; + const size_t q_row_bytes_untransposed = factx.G * factx.DK * qo_element_size; + const size_t q_row_bytes_trans_factor = factx.DK * qo_element_size; + const uint32_t kv_rows0 = hex_smin(Bc, nek1); + // ======== Reusable job descriptors for pipeline ======== hmx_fa_qk_job_t qk_job; hmx_fa_o_update_job_t ou_job; @@ -1690,34 +1947,34 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { const uint32_t iv2 = kv_head; const uint32_t iv3 = fastdiv(ib3, &kparams->broadcast_rv3); - // 1. Push Q DMA (if Q DMA is used) - const size_t o_tile_bytes = factx.o_tile_bytes; - const bool use_q_dma = (2 * o_tile_bytes >= factx.g_br * factx.DK * (factx.is_q_fp32 ? 4 : 2)); - if (use_q_dma) { - const bool q_transposed = q->nb[1] < q->nb[2]; - const uint8_t * q_ptr = (const uint8_t *) q->data + q_start * q->nb[1] + (kv_head * factx.G) * q->nb[2] + ib3 * q->nb[3]; - const size_t el_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16); - const size_t q_row_bytes = q_transposed ? n_rows_q * factx.DK * el_size : factx.G * factx.DK * el_size; - const size_t src_stride = q_transposed ? q->nb[2] : q->nb[1]; + // 1. Push Q and KV DMAs for the very first iteration. + // Subsequent iterations are enqueued early at the end of the previous iteration. + if (ib3 == 0 && q_start == 0 && kv_head == 0) { + const uint8_t * q_ptr = (const uint8_t *) q->data; + const size_t q_row_bytes = q_transposed ? n_rows_q * q_row_bytes_trans_factor : q_row_bytes_untransposed; const size_t n_rows = q_transposed ? factx.G : n_rows_q; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_o_tiles[0], q_ptr), q_row_bytes, hex_smax(src_stride, q_row_bytes), q_row_bytes, n_rows); - } + dma_queue_push(dma, dma_make_ptr(factx.vtcm_q_dma, q_ptr), q_row_bytes, hex_smax(q_src_stride, q_row_bytes), q_row_bytes, n_rows); - // 2. Prefetch first KV block - if (factx.n_kv_blocks > 0) { - const uint32_t kv_rows0 = hex_smin(Bc, nek1); + if (factx.n_kv_blocks > 0) { + const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1], size_k_row, kv_rows0); - const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1], size_k_row, kv_rows0); + const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1], size_v_row, kv_rows0); - const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1], size_v_row, kv_rows0); + if (factx.pipeline && mask) { + if (__builtin_expect(factx.mask_broadcast, true)) { + const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + 0; + dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows0 * sizeof(__fp16), n_rows_q); + } else { + fa_push_mask_dma_gqa(dma, mask, q_start, im3, 0, kv_head, G, m_line_bytes, kv_rows0, n_rows_q, &factx); + } + } + } } - // 3. Pop Q DMA (blocks until Q is loaded) - if (use_q_dma) { - dma_queue_pop(dma); - } + // 2. Pop Q DMA (blocks until Q is loaded) + dma_queue_pop(dma); // ---- Load Q block & Initialize per-block state ---- fa_phase_q_load(&factx, q, q_start, kv_head, ib3, n_rows_g); @@ -1735,79 +1992,43 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { const size_t k_src_stride = size_k_row_padded / sizeof(__fp16); const size_t v_src_stride = size_v_row_padded / sizeof(__fp16); - struct hmx_queue * hmx_q = ctx->hmx_queue; + hmx_queue_t hmx_q = ctx->hmx_queue; if (factx.pipeline) { - // Pipeline path + // Double-buffered job structs because HMX queue runs asynchronously + hmx_fa_qk_job_t qk_job[2]; + hmx_fa_o_update_job_t ou_job[2]; + + // Prefetch block 1 early if there are multiple blocks + if (factx.n_kv_blocks > 1) { + fa_prefetch_block(dma, k, v, mask, 1, Bc, size_k_row_padded, size_k_row, size_v_row_padded, size_v_row, + ik2, ik3, iv2, iv3, q_start, im3, kv_head, G, m_line_bytes, n_rows_q, nek1, 1, &factx); + } + + // Prep and start QK-dot(0) + void * curr_k0 = dma_queue_pop(dma).dst; + fa_phase_k_interleave(&factx, kv_rows0, k_src_stride, curr_k0, 0, 0); + + qk_job[0].q_tiles = factx.vtcm_q_tiles; + qk_job[0].k_tiles = factx.vtcm_k_tiles[0]; + qk_job[0].s_tiles = factx.vtcm_s_tiles[0]; + qk_job[0].n_row_tiles = n_row_tiles; + qk_job[0].n_col_tiles = hmx_ceil_div(kv_rows0, HMX_FP16_TILE_N_COLS); + qk_job[0].n_dot_tiles = DK / 32; + qk_job[0].n_tiles_per_bc = n_tiles_per_bc; + qk_job[0].hmx_scales = factx.vtcm_hmx_scales_qk; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job[0])); + for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) { const uint32_t kv_start = kv_blk * Bc; const uint32_t kv_rows = hex_smin(Bc, nek1 - kv_start); const size_t n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS); - // Push mask DMA - if (mask) { - if (__builtin_expect(factx.mask_broadcast, true)) { - const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + kv_start * sizeof(__fp16); - dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows * sizeof(__fp16), n_rows_q); - } else { - fa_push_mask_dma_gqa(dma, mask, q_start, im3, kv_start, kv_head, G, m_line_bytes, kv_rows, n_rows_q, &factx); - } - } - - // Prefetch next KV block early - if (kv_blk + 1 < factx.n_kv_blocks) { - const uint32_t prefetch_start = (kv_blk + 1) * Bc; - const uint32_t prefetch_rows = hex_smin(Bc, nek1 - prefetch_start); - const size_t prefetch_buf = 1 - buf_idx; - const uint8_t * k_prefetch_src = (const uint8_t *) k->data + prefetch_start * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[prefetch_buf], k_prefetch_src), size_k_row_padded, k->nb[1], size_k_row, prefetch_rows); - const uint8_t * v_prefetch_src = (const uint8_t *) v->data + prefetch_start * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[prefetch_buf], v_prefetch_src), size_v_row_padded, v->nb[1], size_v_row, prefetch_rows); - } - - // ---- Phase 1: K_int ---- - if (kv_blk > 0) { - ou_job.o_curr = o_tile_curr; - ou_job.o_prev = o_tile_prev; - ou_job.p_tiles = factx.vtcm_p_tiles; - ou_job.v_tiles = factx.vtcm_v_tiles[1 - buf_idx]; - ou_job.d_tiles = factx.vtcm_d_tiles; - ou_job.hmx_scales = factx.vtcm_hmx_scales_id; - ou_job.n_row_tiles = n_row_tiles; - ou_job.n_col_tiles = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS); - ou_job.n_row_tiles_g_br = n_row_tiles_g_br; - ou_job.n_tiles_per_bc = n_tiles_per_bc; - ou_job.DV = DV; - hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job)); - } - - // Wait for current K DMA and interleave - void * curr_k = dma_queue_pop(dma).dst; - fa_phase_k_interleave(&factx, kv_rows, k_src_stride, curr_k, kv_start); - - // ---- Phase 2: qk_dot ---- - qk_job.q_tiles = factx.vtcm_q_tiles; - qk_job.k_tiles = factx.vtcm_k_tiles; - qk_job.s_tiles = factx.vtcm_s_tiles; - qk_job.n_row_tiles = n_row_tiles; - qk_job.n_col_tiles = n_col_tiles; - qk_job.n_dot_tiles = DK / 32; - qk_job.n_tiles_per_bc = n_tiles_per_bc; - qk_job.hmx_scales = factx.vtcm_hmx_scales_qk; - hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job)); - - // Wait for current V DMA and interleave + // ---- 1. Pop and run V-prep for current block ---- void * curr_v = dma_queue_pop(dma).dst; fa_phase_v_interleave(&factx, kv_rows, v_src_stride, curr_v, factx.vtcm_v_tiles[buf_idx], n_tiles_per_bc, kv_start); - if (kv_blk > 0) { - hmx_queue_pop(hmx_q); - hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); - } - - hmx_queue_pop(hmx_q); - - // ---- Phase 3: softmax + build_D ---- + // ---- 2. Pop and run mask-prep for current block ---- __fp16 * current_mask_vtcm = NULL; if (mask) { if (__builtin_expect(factx.mask_broadcast, true)) { @@ -1818,9 +2039,34 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { } } + // ---- 3. Pop and run K-prep for next block & push next QK-dot ---- + if (kv_blk + 1 < factx.n_kv_blocks) { + const uint32_t next_start = (kv_blk + 1) * Bc; + const uint32_t next_rows = hex_smin(Bc, nek1 - next_start); + const size_t next_buf = 1 - buf_idx; + + void * next_k = dma_queue_pop(dma).dst; + fa_phase_k_interleave(&factx, next_rows, k_src_stride, next_k, next_start, next_buf); + + qk_job[next_buf].q_tiles = factx.vtcm_q_tiles; + qk_job[next_buf].k_tiles = factx.vtcm_k_tiles[next_buf]; + qk_job[next_buf].s_tiles = factx.vtcm_s_tiles[next_buf]; + qk_job[next_buf].n_row_tiles = n_row_tiles; + qk_job[next_buf].n_col_tiles = hmx_ceil_div(next_rows, HMX_FP16_TILE_N_COLS); + qk_job[next_buf].n_dot_tiles = DK / 32; + qk_job[next_buf].n_tiles_per_bc = n_tiles_per_bc; + qk_job[next_buf].hmx_scales = factx.vtcm_hmx_scales_qk; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job[next_buf])); + } + + // ---- 4. Wait for current block's QK-dot to finish ---- + hmx_queue_pop(hmx_q); + + // ---- 5. Phase 2: softmax + build_D ---- fa_softmax_args_t sargs; memset(&sargs, 0, sizeof(sargs)); sargs.factx = &factx; + sargs.buf_idx = buf_idx; sargs.kv_rows = kv_rows; sargs.n_rows_g = n_rows_g; sargs.n_col_tiles = n_col_tiles; @@ -1838,8 +2084,39 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { sargs.mask_vtcm = current_mask_vtcm; sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride; sargs.slopes = factx.vtcm_slopes; + + // Start HMX O update for block kv_blk - 1 (reads P[1 - buf_idx], V[1 - buf_idx]) + if (kv_blk > 0) { + const size_t prev_buf = 1 - buf_idx; + ou_job[prev_buf].o_curr = o_tile_curr; + ou_job[prev_buf].o_prev = o_tile_prev; + ou_job[prev_buf].p_tiles = factx.vtcm_p_tiles[prev_buf]; + ou_job[prev_buf].v_tiles = factx.vtcm_v_tiles[prev_buf]; + ou_job[prev_buf].d_tiles = factx.vtcm_d_tiles; + ou_job[prev_buf].hmx_scales = factx.vtcm_hmx_scales_id; + ou_job[prev_buf].n_row_tiles = n_row_tiles; + ou_job[prev_buf].n_col_tiles = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS); + ou_job[prev_buf].n_row_tiles_g_br = n_row_tiles_g_br; + ou_job[prev_buf].n_tiles_per_bc = n_tiles_per_bc; + ou_job[prev_buf].DV = DV; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job[prev_buf])); + } + + // Run Softmax on HVX (blocking call) fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br); + // Wait for HMX O update for block kv_blk - 1 to finish + if (kv_blk > 0) { + hmx_queue_pop(hmx_q); + hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); + } + + // Prefetch block kv_blk + 2 + if (kv_blk + 2 < factx.n_kv_blocks) { + fa_prefetch_block(dma, k, v, mask, kv_blk + 2, Bc, size_k_row_padded, size_k_row, size_v_row_padded, size_v_row, + ik2, ik3, iv2, iv3, q_start, im3, kv_head, G, m_line_bytes, n_rows_q, nek1, buf_idx, &factx); + } + buf_idx = 1 - buf_idx; } @@ -1847,18 +2124,23 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { if (factx.n_kv_blocks > 0) { const uint32_t last_blk = factx.n_kv_blocks - 1; const size_t last_cols = hmx_ceil_div(hex_smin(Bc, nek1 - last_blk * Bc), HMX_FP16_TILE_N_COLS); - ou_job.o_curr = o_tile_curr; - ou_job.o_prev = o_tile_prev; - ou_job.p_tiles = factx.vtcm_p_tiles; - ou_job.v_tiles = factx.vtcm_v_tiles[1 - buf_idx]; - ou_job.d_tiles = factx.vtcm_d_tiles; - ou_job.hmx_scales = factx.vtcm_hmx_scales_id; - ou_job.n_row_tiles = n_row_tiles; - ou_job.n_col_tiles = last_cols; - ou_job.n_row_tiles_g_br = n_row_tiles_g_br; - ou_job.n_tiles_per_bc = n_tiles_per_bc; - ou_job.DV = DV; - hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job)); + ou_job[0].o_curr = o_tile_curr; + ou_job[0].o_prev = o_tile_prev; + ou_job[0].p_tiles = factx.vtcm_p_tiles[1 - buf_idx]; + ou_job[0].v_tiles = factx.vtcm_v_tiles[1 - buf_idx]; + ou_job[0].d_tiles = factx.vtcm_d_tiles; + ou_job[0].hmx_scales = factx.vtcm_hmx_scales_id; + ou_job[0].n_row_tiles = n_row_tiles; + ou_job[0].n_col_tiles = last_cols; + ou_job[0].n_row_tiles_g_br = n_row_tiles_g_br; + ou_job[0].n_tiles_per_bc = n_tiles_per_bc; + ou_job[0].DV = DV; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job[0])); + + // Overlapped: run HVX build diag inv L while HMX is busy executing the update + htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); + fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br); + htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); hmx_queue_pop(hmx_q); hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); @@ -1892,12 +2174,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { // Wait for current K DMA and interleave void * curr_k = dma_queue_pop(dma).dst; - fa_phase_k_interleave(&factx, kv_rows, k_src_stride, curr_k, kv_start); + fa_phase_k_interleave(&factx, kv_rows, k_src_stride, curr_k, kv_start, 0); { qk_job.q_tiles = factx.vtcm_q_tiles; - qk_job.k_tiles = factx.vtcm_k_tiles; - qk_job.s_tiles = factx.vtcm_s_tiles; + qk_job.k_tiles = factx.vtcm_k_tiles[0]; + qk_job.s_tiles = factx.vtcm_s_tiles[0]; qk_job.n_row_tiles = n_row_tiles; qk_job.n_col_tiles = n_col_tiles; qk_job.n_dot_tiles = (size_t) (DK / 32); @@ -1948,7 +2230,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { { ou_job.o_curr = o_tile_curr; ou_job.o_prev = o_tile_prev; - ou_job.p_tiles = factx.vtcm_p_tiles; + ou_job.p_tiles = factx.vtcm_p_tiles[0]; ou_job.v_tiles = factx.vtcm_v_tiles[0]; ou_job.d_tiles = factx.vtcm_d_tiles; ou_job.hmx_scales = factx.vtcm_hmx_scales_id; @@ -1959,6 +2241,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { ou_job.DV = DV; hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job)); + if (kv_blk + 1 == factx.n_kv_blocks) { + // Overlapped: run HVX build diag inv L while HMX is busy executing the update + htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); + fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br); + htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); + } hmx_queue_pop(ctx->hmx_queue); hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); @@ -1968,15 +2256,63 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { } } + // Enqueue DMAs for the next iteration early so they overlap with O-PROC + uint32_t next_kv_head = kv_head + 1; + uint32_t next_q_start = q_start; + uint32_t next_ib3 = ib3; + if (next_kv_head >= n_kv_heads) { + next_kv_head = 0; + next_q_start = q_start + Br; + if (next_q_start >= neq1) { + next_q_start = 0; + next_ib3 = ib3 + 1; + } + } + bool has_next = (next_ib3 < neq3); + + if (has_next) { + const uint32_t next_n_rows_q = hex_smin(Br, neq1 - next_q_start); + const uint8_t * next_q_ptr = (const uint8_t *) q->data + next_q_start * q->nb[1] + (next_kv_head * factx.G) * q->nb[2] + next_ib3 * q->nb[3]; + const size_t next_q_row_bytes = q_transposed ? next_n_rows_q * q_row_bytes_trans_factor : q_row_bytes_untransposed; + const size_t next_n_rows = q_transposed ? factx.G : next_n_rows_q; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_q_dma, next_q_ptr), next_q_row_bytes, hex_smax(q_src_stride, next_q_row_bytes), next_q_row_bytes, next_n_rows); + + if (factx.n_kv_blocks > 0) { + const uint32_t next_ik2 = next_kv_head; + const uint32_t next_iv2 = next_kv_head; + uint32_t next_ik3 = ik3; + uint32_t next_iv3 = iv3; + if (next_ib3 != ib3) { + next_ik3 = fastdiv(next_ib3, &kparams->broadcast_rk3); + next_iv3 = fastdiv(next_ib3, &kparams->broadcast_rv3); + } + + const uint8_t * next_k_src = (const uint8_t *) k->data + next_ik2 * k->nb[2] + next_ik3 * k->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], next_k_src), size_k_row_padded, k->nb[1], size_k_row, kv_rows0); + + const uint8_t * next_v_src = (const uint8_t *) v->data + next_iv2 * v->nb[2] + next_iv3 * v->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], next_v_src), size_v_row_padded, v->nb[1], size_v_row, kv_rows0); + + if (factx.pipeline && mask) { + uint32_t next_im3 = im3; + if (next_ib3 != ib3) { + next_im3 = fastmodulo(next_ib3, mask->ne[3], &factx.src3_div3); + } + if (__builtin_expect(factx.mask_broadcast, true)) { + const uint8_t * ms_src = (const uint8_t *) mask->data + next_q_start * mask->nb[1] + next_im3 * mask->nb[3] + 0; + dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows0 * sizeof(__fp16), next_n_rows_q); + } else { + fa_push_mask_dma_gqa(dma, mask, next_q_start, next_im3, 0, next_kv_head, G, m_line_bytes, kv_rows0, next_n_rows_q, &factx); + } + } + } + } + // ---- Final normalization ---- { - htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); - fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br); - htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); - on_job.o_curr = o_tile_curr; on_job.o_prev = o_tile_prev; - on_job.d_tiles = factx.vtcm_d_tiles; + on_job.d_tiles = factx.vtcm_d_inv_l; on_job.hmx_scales = factx.vtcm_hmx_scales_id; on_job.n_row_tiles = n_row_tiles; on_job.n_row_tiles_g_br = n_row_tiles_g_br; @@ -2084,7 +2420,7 @@ int op_flash_attn_ext(struct htp_ops_context * octx) { } if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { - worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads); + work_queue_run(octx->ctx->work_queue, flash_attn_ext_f16_thread, &factx, octx->n_threads); } return HTP_STATUS_OK; diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.h b/ggml/src/ggml-hexagon/htp/flash-attn-ops.h index 16822f22bf6e..efe5ce548173 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.h +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.h @@ -101,14 +101,16 @@ static_assert(sizeof(struct htp_fa_kernel_params) <= 128, "htp_fa_kernel_params struct hmx_fa_vtcm_layout { // Byte offsets from vtcm_base for each region. size_t off_q_tiles; + size_t off_q_dma; size_t off_o_tiles[2]; size_t off_k_fp16[2]; size_t off_v_fp16[2]; - size_t off_k_tiles; - size_t off_v_tiles[2]; // [1] allocated only when pipeline, else 0 - size_t off_s_tiles; - size_t off_p_tiles; + size_t off_k_tiles[2]; + size_t off_v_tiles[2]; + size_t off_s_tiles[2]; + size_t off_p_tiles[2]; size_t off_d_tiles; + size_t off_d_inv_l; size_t off_m_vec; size_t off_l_vec; size_t off_s_rowmax; @@ -140,7 +142,7 @@ struct hmx_fa_vtcm_layout { static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, size_t gqa_factor, size_t DK, size_t DV, - size_t Br, size_t Bc, size_t n_threads, bool pipeline) { + size_t Br, size_t Bc, size_t n_threads, bool pipeline, bool is_q_fp32) { const size_t g_br = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS); const size_t q_tile_size = hex_align_up(g_br * DK * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); const size_t o_tile_size = hex_align_up(g_br * DV * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); @@ -149,6 +151,7 @@ static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, const size_t s_tile_size = hex_align_up(g_br * Bc * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); const size_t d_tile_size = hex_align_up(g_br * g_br * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); + const size_t q_dma_size = hex_align_up(g_br * DK * (is_q_fp32 ? sizeof(float) : sizeof(__fp16)), 128); const size_t k_dma_size = hex_align_up(Bc * hex_round_up(DK * sizeof(__fp16), 128), 128); const size_t v_dma_size = hex_align_up(Bc * hex_round_up(DV * sizeof(__fp16), 128), 128); const size_t col_vec_size = hex_align_up(g_br * sizeof(float), 256); @@ -160,27 +163,47 @@ static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, size_t off = 0; - // Section 1: HMX Tiled Buffers (FA_HMX_TILE_SIZE = 2KB Aligned) + // Group A (Part 1 - HMX Tiled buffers) VTCM_LAYOUT_ALLOC(off, off_q_tiles, q_tile_size); VTCM_LAYOUT_ALLOC(off, off_o_tiles[0], o_tile_size); VTCM_LAYOUT_ALLOC(off, off_o_tiles[1], o_tile_size); - VTCM_LAYOUT_ALLOC(off, off_k_tiles, k_tile_size); - VTCM_LAYOUT_ALLOC(off, off_v_tiles[0], v_tile_size); - VTCM_LAYOUT_ALLOC_OPTIONAL(off, off_v_tiles[1], v_tile_size, pipeline); - VTCM_LAYOUT_ALLOC(off, off_s_tiles, s_tile_size); - VTCM_LAYOUT_ALLOC(off, off_p_tiles, s_tile_size); VTCM_LAYOUT_ALLOC(off, off_d_tiles, d_tile_size); + VTCM_LAYOUT_ALLOC(off, off_d_inv_l, d_tile_size); - // Section 2: HVX/DMA flat and vector buffers (128B / 256B Aligned) + // Group B & C share start offset (Group B tiles must be 2KB aligned) + size_t off_group_b_c = hex_align_up(off, HTP_FA_HMX_TILE_SIZE); + + // Group B: Compute-only buffers + size_t off_group_b = off_group_b_c; + VTCM_LAYOUT_ALLOC(off_group_b, off_k_tiles[0], k_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_k_tiles[1], k_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_v_tiles[0], v_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_v_tiles[1], v_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_s_tiles[0], s_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_s_tiles[1], s_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_p_tiles[0], s_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_p_tiles[1], s_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_s_rowmax, col_vec_size); + VTCM_LAYOUT_ALLOC(off_group_b, off_p_rowsum, col_vec_size); + VTCM_LAYOUT_ALLOC(off_group_b, off_row_bufs, row_vec_size * 2 * n_threads); + + const size_t group_b_size = off_group_b - off_group_b_c; + + // Group C: Q fetch DMA buffer + size_t off_group_c = off_group_b_c; + VTCM_LAYOUT_ALLOC(off_group_c, off_q_dma, q_dma_size); + + const size_t group_c_size = off_group_c - off_group_b_c; + + off = off_group_b_c + hex_smax(group_b_size, group_c_size); + + // Group A (Part 2 - remaining non-HMX buffers) VTCM_LAYOUT_ALLOC(off, off_k_fp16[0], k_dma_size); VTCM_LAYOUT_ALLOC(off, off_k_fp16[1], k_dma_size); VTCM_LAYOUT_ALLOC(off, off_v_fp16[0], v_dma_size); VTCM_LAYOUT_ALLOC(off, off_v_fp16[1], v_dma_size); VTCM_LAYOUT_ALLOC(off, off_m_vec, col_vec_size); VTCM_LAYOUT_ALLOC(off, off_l_vec, col_vec_size); - VTCM_LAYOUT_ALLOC(off, off_s_rowmax, col_vec_size); - VTCM_LAYOUT_ALLOC(off, off_p_rowsum, col_vec_size); - VTCM_LAYOUT_ALLOC(off, off_row_bufs, row_vec_size * 2 * n_threads); VTCM_LAYOUT_ALLOC(off, off_hmx_scales_id, 256); VTCM_LAYOUT_ALLOC(off, off_hmx_scales_qk, 256); VTCM_LAYOUT_ALLOC(off, off_mask_buf, m_buf_size); @@ -200,9 +223,9 @@ static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, } // Exact VTCM usage for a given (gqa_factor, DK, DV, Br, Bc) configuration. -static inline size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline) { +static inline size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline, bool is_q_fp32) { struct hmx_fa_vtcm_layout L; - hmx_fa_vtcm_layout_build(&L, gqa_factor, DK, DV, Br, Bc, n_threads, pipeline); + hmx_fa_vtcm_layout_build(&L, gqa_factor, DK, DV, Br, Bc, n_threads, pipeline, is_q_fp32); return L.total_bytes; } @@ -239,7 +262,8 @@ static inline int hmx_fa_find_chunk_size(size_t * Br_out, size_t qo_len, size_t kv_len, size_t vtcm_budget, - size_t n_threads) { + size_t n_threads, + bool is_q_fp32) { const size_t T = HMX_FP16_TILE_N_ROWS; // 32 const size_t br_unit = hmx_ceil_div(T, gqa_factor); const size_t bc_unit = HMX_FP16_TILE_N_COLS * 2; // 64 @@ -253,8 +277,9 @@ static inline int hmx_fa_find_chunk_size(size_t * Br_out, const size_t Bc_limit = can_pipeline ? hex_align_down(kv_len / FA_MIN_KV_BLOCKS, bc_unit) : (kv_len >= bc_unit ? hex_align_down(kv_len, bc_unit) : bc_unit); // Cost coefficients calibrated from profiling - const size_t c_q_fixed = 1400; // per-Q-block: q_load + epilogue o_update + o_norm + o_store - const size_t c_iter_fixed = 200; // per-KV-iter: HMX queue push/pop + DMA pop + barriers + const size_t c_q_fixed = 800; // per-Q-block: q_load + epilogue o_update + o_norm + o_store + const size_t c_iter_base = 200; // per-KV-iter base (HMX dot/update + DMA) + const size_t c_softmax = 600; // per 64-row vector chunk on HVX size_t best_cost = SIZE_MAX, best_mn = 0; size_t best_Br = 0, best_Bc = 0; @@ -262,13 +287,20 @@ static inline int hmx_fa_find_chunk_size(size_t * Br_out, for (size_t Br = Br_max; Br >= br_unit; Br -= br_unit) { // Try all Bc candidates from Bc_limit down to bc_unit for (size_t Bc = Bc_limit; Bc >= bc_unit; Bc -= bc_unit) { - size_t vtcm_needed = hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline); + size_t vtcm_needed = hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline, is_q_fp32); if (vtcm_needed <= vtcm_budget) { // This Bc fits for this Br! - const size_t q_blocks = (qo_len + Br - 1) / Br; - const size_t kv_blocks = (kv_len + Bc - 1) / Bc; - const size_t cost = q_blocks * (c_q_fixed + kv_blocks * c_iter_fixed); - const size_t mn = Br * Bc; + const size_t q_blocks = (qo_len + Br - 1) / Br; + const size_t kv_blocks = (kv_len + Bc - 1) / Bc; + const size_t actual_threads = (kv_blocks >= 3 && n_threads >= 2) ? n_threads : 1; + const size_t n_rows_g = Br * gqa_factor; + const size_t n_row_vec_cnt = (n_rows_g + 63) / 64; + const size_t n_use = n_row_vec_cnt < actual_threads ? n_row_vec_cnt : actual_threads; + const size_t vecs_per_t = n_use > 0 ? (n_row_vec_cnt + n_use - 1) / n_use : 1; + + const size_t c_iter_actual = c_iter_base + c_softmax * vecs_per_t; + const size_t cost = q_blocks * (c_q_fixed + kv_blocks * c_iter_actual); + const size_t mn = Br * Bc; if (cost < best_cost || (cost == best_cost && mn > best_mn)) { best_cost = cost; diff --git a/ggml/src/ggml-hexagon/htp/hex-bitmap.h b/ggml/src/ggml-hexagon/htp/hex-bitmap.h new file mode 100644 index 000000000000..140898852a11 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/hex-bitmap.h @@ -0,0 +1,24 @@ +#ifndef HEX_BITMAP_H +#define HEX_BITMAP_H + +#include +#include +#include + +static inline void bitmap_set(uint32_t * bitmap, uint32_t idx) { + bitmap[idx / 32] |= (1U << (idx % 32)); +} + +static inline void bitmap_clear(uint32_t * bitmap, uint32_t idx) { + bitmap[idx / 32] &= ~(1U << (idx % 32)); +} + +static inline bool bitmap_test(const uint32_t * bitmap, uint32_t idx) { + return (bitmap[idx / 32] & (1U << (idx % 32))) != 0; +} + +static inline void bitmap_reset(uint32_t * bitmap, size_t size_in_bits) { + memset(bitmap, 0, ((size_in_bits + 31) / 32) * sizeof(uint32_t)); +} + +#endif // HEX_BITMAP_H diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.c b/ggml/src/ggml-hexagon/htp/hex-dma.c deleted file mode 100644 index b66e2d2603ce..000000000000 --- a/ggml/src/ggml-hexagon/htp/hex-dma.c +++ /dev/null @@ -1,63 +0,0 @@ -#include "hex-dma.h" - -#include -#include -#include - -#pragma clang diagnostic ignored "-Wunused-function" - -static inline uint32_t pow2_ceil(uint32_t x) { - if (x <= 1) { - return 1; - } - int p = 2; - x--; - while (x >>= 1) { - p <<= 1; - } - return p; -} - -dma_queue * dma_queue_create(size_t capacity) { - dma_queue * q = (dma_queue *) memalign(32, sizeof(dma_queue)); - if (q == NULL) { - FARF(ERROR, "%s: failed to allocate DMA queue\n", __FUNCTION__); - return NULL; - } - - capacity = pow2_ceil(capacity); - - memset(q, 0, sizeof(dma_queue)); - q->capacity = capacity; - q->idx_mask = capacity - 1; - - q->desc = (dma_descriptor_2d *) memalign(64, capacity * sizeof(dma_descriptor_2d)); - memset(q->desc, 0, capacity * sizeof(dma_descriptor_2d)); - - q->dptr = (dma_ptr *) memalign(4, capacity * sizeof(dma_ptr)); - memset(q->dptr, 0, capacity * sizeof(dma_ptr)); - - q->tail = &q->desc[capacity - 1]; - - if (!q->desc && !q->dptr) { - FARF(ERROR, "%s: failed to allocate DMA queue items\n", __FUNCTION__); - return NULL; - } - - FARF(HIGH, "dma-queue: capacity %u\n", capacity); - - return q; -} - -void dma_queue_delete(dma_queue * q) { - if (!q) { - return; - } - free(q->desc); - free(q->dptr); - free(q); -} - -void dma_queue_flush(dma_queue * q) { - while (dma_queue_pop(q).dst != NULL) ; -} diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.h b/ggml/src/ggml-hexagon/htp/hex-dma.h index 98fcc9fda63f..9e9a5f9502a0 100644 --- a/ggml/src/ggml-hexagon/htp/hex-dma.h +++ b/ggml/src/ggml-hexagon/htp/hex-dma.h @@ -1,375 +1,2 @@ -#ifndef HTP_DMA_H -#define HTP_DMA_H - -#include -#include -#include -#include -#include "hex-utils.h" - -#include "hex-profile.h" - -#ifdef __cplusplus -extern "C" { -#endif - -// Define the HW descriptor structs here since the ones in HexSDK are a bit out of date -typedef struct dma_descriptor_1d_s { - void * next; - uint32_t size:24; - uint32_t desc_size:2; - uint32_t dst_comp:1; - uint32_t src_comp:1; - uint32_t dst_bypass:1; - uint32_t src_bypass:1; - uint32_t order:1; - uint32_t done:1; - void * src; - void * dst; -} dma_descriptor_1d; - -#if __HVX_ARCH__ < 75 - -typedef struct dma_descriptor_2d_s { - void * next; - uint32_t reserved0:24; - uint32_t desc_size:2; - uint32_t dst_comp:1; - uint32_t src_comp:1; - uint32_t dst_bypass:1; - uint32_t src_bypass:1; - uint32_t order:1; - uint32_t done:1; - void * src; - void * dst; - uint32_t desc_type:8; - uint32_t reserved1:24; - uint32_t row_size:16; - uint32_t nrows:16; - uint32_t src_stride:16; - uint32_t dst_stride:16; - uint32_t src_offset:16; - uint32_t dst_offset:16; -} dma_descriptor_2d; - -#else - -typedef struct dma_descriptor_2d_s { - void * next; - uint32_t dst_stride:24; - uint32_t desc_size:2; - uint32_t dst_comp:1; - uint32_t src_comp:1; - uint32_t dst_bypass:1; - uint32_t src_bypass:1; - uint32_t order:1; - uint32_t done:1; - void * src; - void * dst; - uint32_t desc_type:8; - uint32_t reserved0:24; - uint32_t row_size:24; - uint32_t nrows_lo:8; - uint32_t nrows_hi:8; - uint32_t src_stride:24; - uint32_t offset:24; - uint32_t reserved1:8; -} dma_descriptor_2d; - -#endif - -typedef struct { - void *dst; - const void *src; -} dma_ptr; - -typedef struct { - dma_descriptor_2d * desc; // descriptor pointers - dma_descriptor_2d * tail; // tail pointer - dma_ptr * dptr; // dst/src pointers - uint32_t push_idx; - uint32_t pop_idx; - uint32_t capacity; - uint32_t idx_mask; - struct htp_thread_trace * trace; -} dma_queue; - -dma_queue * dma_queue_create(size_t capacity); -void dma_queue_delete(dma_queue * q); -void dma_queue_flush(dma_queue * q); - -// TODO: technically we don't need these and could use Q6_dmstart/wait/etc instead -// but those do not seem to always compiler properly. -static inline void dmstart(void * next) { - asm volatile(" release(%0):at" : : "r"(next)); - asm volatile(" dmstart(%0)" : : "r"(next)); -} - -static inline void dmlink(void * cur, void * next) { - asm volatile(" release(%0):at" : : "r"(next)); - asm volatile(" dmlink(%0, %1)" : : "r"(cur), "r"(next)); -} - -static inline unsigned int dmpoll(void) { - unsigned int ret = 0; - asm volatile(" %0 = dmpoll" : "=r"(ret) : : "memory"); - return ret; -} - -static inline unsigned int dmwait(void) { - unsigned int ret = 0; - asm volatile(" %0 = dmwait" : "=r"(ret) : : "memory"); - return ret; -} - -static inline dma_ptr dma_make_ptr(void *dst, const void *src) -{ - dma_ptr p = { dst, src }; - return p; -} - -static const uint32_t dma_src_l2_bypass_on = 1; -static const uint32_t dma_dst_l2_bypass_on = 1; - -static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t size) { - if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) { - FARF(HIGH, "dma-push: queue full\n"); - return false; - } - - dma_descriptor_1d * desc = (dma_descriptor_1d *) &q->desc[q->push_idx]; - desc->src = (void *) dptr.src; - desc->dst = (void *) dptr.dst; - desc->size = size; - - q->dptr[q->push_idx] = dptr; - - if (size) { - desc->next = NULL; - desc->desc_size = 0; // 1D mode - desc->src_bypass = dma_src_l2_bypass_on; - desc->dst_bypass = dma_dst_l2_bypass_on; - desc->order = 0; - desc->done = 0; - - htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx); - dmlink(q->tail, desc); - q->tail = (dma_descriptor_2d *) desc; - } else { - desc->desc_size = 0; - desc->done = 1; - } - - q->push_idx = (q->push_idx + 1) & q->idx_mask; - return true; -} - -static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { - if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) { - FARF(HIGH, "dma-push: queue full\n"); - return false; - } - - dma_descriptor_2d * desc = &q->desc[q->push_idx]; - - desc->next = NULL; - desc->reserved0 = 0; - desc->reserved1 = 0; - desc->desc_size = 1; // 2d mode - desc->src_bypass = dma_src_l2_bypass_on; - desc->dst_bypass = dma_dst_l2_bypass_on; - desc->src_comp = 0; - desc->dst_comp = 0; - desc->order = 0; - desc->done = 0; - desc->src_stride = src_stride; - desc->dst_stride = dst_stride; - desc->src = (void *) dptr.src; - desc->dst = (void *) dptr.dst; - desc->row_size = row_size; - -#if __HVX_ARCH__ < 75 - desc->desc_type = 0; // 2d (16-bit) mode - desc->nrows = nrows; - desc->src_offset = 0; - desc->dst_offset = 0; -#else - desc->desc_type = 9; // 2d (24-bit) mode - desc->nrows_lo = (nrows & 0xff); - desc->nrows_hi = (nrows >> 8); - desc->offset = 0; -#endif - - q->dptr[q->push_idx] = dptr; - - if (nrows) { - htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx); - dmlink(q->tail, desc); - q->tail = desc; - } else { - desc->done = 1; - } - - // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src); - q->push_idx = (q->push_idx + 1) & q->idx_mask; - return true; -} - -static inline dma_ptr dma_queue_pop(dma_queue * q) { - dma_ptr dptr = { NULL }; - - if (q->push_idx == q->pop_idx) { - return dptr; - } - - dma_descriptor_2d * desc = &q->desc[q->pop_idx]; - - // Wait for desc to complete - if (!desc->done) { - while (!desc->done) { - dmpoll(); - } - } - htp_trace_event_stop(q->trace, HTP_TRACE_EVT_DMA, q->pop_idx); - - dptr = q->dptr[q->pop_idx]; - - // FARF(ERROR, "dma-pop: i %u dst %p src %p\n", q->pop_idx, dptr.dst, dptr.src); - q->pop_idx = (q->pop_idx + 1) & q->idx_mask; - return dptr; -} - -static inline dma_ptr dma_queue_pop_nowait(dma_queue * q) { - dma_ptr dptr = { NULL }; - - if (q->push_idx == q->pop_idx) { - return dptr; - } - - dptr = q->dptr[q->pop_idx]; - - // FARF(ERROR, "dma-pop-nowait: i %u dst %p src %p\n", q->pop_idx, dptr.dst, dptr.src); - q->pop_idx = (q->pop_idx + 1) & q->idx_mask; - return dptr; -} - -static inline bool dma_queue_empty(dma_queue * q) { - return q->push_idx == q->pop_idx; -} - -static inline uint32_t dma_queue_depth(dma_queue * q) { - return (q->push_idx - q->pop_idx) & q->idx_mask; -} - -static inline uint32_t dma_queue_capacity(dma_queue * q) { - return q->capacity; -} - -#if __HVX_ARCH__ < 75 - -// Overflow-safe DMA push: all 2d descriptor fields (row_size, nrows, src_stride, dst_stride) are 16-bit, max 65535. -// This version transparently handles values that exceed the 16-bit limit and submits chained DMA transtions. - -#define DMA_MAX_FIELD_VAL 65535u - -static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { - // Fast path: everything fits in 16 bits - if (nrows == 0 || __builtin_expect( - row_size <= DMA_MAX_FIELD_VAL && - nrows <= DMA_MAX_FIELD_VAL && - src_stride <= DMA_MAX_FIELD_VAL && - dst_stride <= DMA_MAX_FIELD_VAL, 1)) { - return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); - } - - // Contiguous block - // Use 1d DMA mode which supports sizes up to 24-bits (16MB) - if (nrows == 1 || (row_size == src_stride && row_size == dst_stride)) { - size_t total = row_size * nrows; - return dma_queue_push_single_1d(q, dptr, total); - } - - // Stride overflow — fall back to row-by-row. - { - const uint8_t *src = (const uint8_t *) dptr.src; - uint8_t *dst = (uint8_t *) dptr.dst; - for (size_t r = 0; r < nrows; ++r) { - dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride); - if (!dma_queue_push_single_1d(q, p, row_size)) - return false; - if (r + 1 < nrows) - dma_queue_pop(q); - } - return true; - } -} - -#else // HVX_ARCH >= 75 - -static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { - // On v75 and up we always use 2d 24-bit mode - return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); -} - -#endif - -static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { - return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows); -} - -static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { - return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows); -} - -#define DMA_CACHE_MAX_SIZE 256U - -typedef struct { - uint8_t *base; - uint32_t line_size; - uint32_t capacity; - uint32_t src[DMA_CACHE_MAX_SIZE]; - uint16_t age[DMA_CACHE_MAX_SIZE]; -} dma_cache; - -static inline void dma_cache_init(dma_cache *c, uint8_t *base, uint32_t line_size, uint32_t capacity) -{ - c->capacity = (capacity > DMA_CACHE_MAX_SIZE) ? DMA_CACHE_MAX_SIZE : capacity; - c->base = base; - c->line_size = line_size; - - for (unsigned i=0; i < c->capacity; i++) { - c->src[i] = 0; - c->age[i] = 0; - } -} - -static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * src, uint32_t dst_stride, uint32_t src_stride, uint32_t row_size, uint32_t nrows) -{ - uint32_t o_idx = 0; - uint16_t o_age = 0; - uint8_t * dst = 0; - - for (unsigned i=0; i < c->capacity; i++) { - if (c->src[i] == (uint32_t) src) { - c->age[i] = 0; - dst = c->base + (i * c->line_size); nrows = 0; // dummy dma - } else { - c->age[i]++; - if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; } - } - } - if (!dst) { - c->age[o_idx] = 0; - c->src[o_idx] = (uint32_t) src; - dst = c->base + o_idx * c->line_size; // normal nrows dma - return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows); - } - - return dma_queue_push_single_1d(q, dma_make_ptr(dst, src), 0); -} - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif /* HTP_DMA_H */ +#pragma once +#include "dma-queue.h" diff --git a/ggml/src/ggml-hexagon/htp/hex-profile.h b/ggml/src/ggml-hexagon/htp/hex-profile.h index 8a37a4a06664..a26961fc93b8 100644 --- a/ggml/src/ggml-hexagon/htp/hex-profile.h +++ b/ggml/src/ggml-hexagon/htp/hex-profile.h @@ -44,11 +44,11 @@ struct htp_thread_trace { }; static inline void htp_trace_event(struct htp_thread_trace * tr, uint16_t id, uint16_t info, uint32_t type) { - if (tr && tr->events && tr->count < tr->max_events) { - uint32_t idx = tr->count; - tr->events[idx].id = id; - tr->events[idx].info = info | (type == HTP_TRACE_EVT_STOP ? 0x8000 : 0); - tr->events[idx].cycles = (uint32_t) hex_get_cycles(); + if (tr->count < tr->max_events) { + uint32_t i = tr->count; + tr->events[i].id = id; + tr->events[i].info = info | (type == HTP_TRACE_EVT_STOP ? 0x8000 : 0); + tr->events[i].cycles = (uint32_t) hex_get_cycles(); tr->count++; } } diff --git a/ggml/src/ggml-hexagon/htp/hex-utils.h b/ggml/src/ggml-hexagon/htp/hex-utils.h index 07930bef6ec1..93e87efcb4c4 100644 --- a/ggml/src/ggml-hexagon/htp/hex-utils.h +++ b/ggml/src/ggml-hexagon/htp/hex-utils.h @@ -30,21 +30,26 @@ static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride, Q6_l2fetch_AP((void *) p, control); } -#define HEX_L2_LINE_SIZE 64 -#define HEX_L2_FLUSH_SIZE (128 * 1024) +static inline void hex_l2fetch_block(const void * addr, size_t size) { + if (size == 0) return; + const uint32_t width = 16384; // 16KB rows + const uint32_t height = (size + width - 1) / width; + hex_l2fetch(addr, width, width, height); +} + +#define HEX_L2_LINE_SIZE 128 +#define HEX_L2_BLOCK_SIZE (HEX_L2_LINE_SIZE * 4) // flush granularity (lines per loop iteration) +#define HEX_L2_FLUSH_WQ_THRESHOLD (4 * 1024) +#define HEX_L2_FLUSH_ALL_THRESHOLD (4 * 1024 * 1024) static inline void hex_l2flush(void * addr, size_t size) { - if (size > HEX_L2_FLUSH_SIZE) { - qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); - } else { - const uint32_t s = (uint32_t) addr; - const uint32_t e = s + size; - for (uint32_t i = s; i < e; i += HEX_L2_LINE_SIZE * 4) { - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 0); - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 1); - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 2); - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 3); - } + const uint32_t s = ((uint32_t) addr) & ~(HEX_L2_LINE_SIZE - 1); + const uint32_t e = (((uint32_t) addr) + size + HEX_L2_LINE_SIZE - 1) & ~(HEX_L2_LINE_SIZE - 1); + for (uint32_t i = s; i < e; i += HEX_L2_BLOCK_SIZE) { + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 0); + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 1); + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 2); + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 3); } } diff --git a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h index 740a8f87d61f..0011abba5a8a 100644 --- a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h +++ b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h @@ -767,23 +767,25 @@ static void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, co // output : fp16 -> f32p -static void transfer_output_chunk_fp16_to_fp32( +static void transfer_output_chunk_fp16_to_fp32_col_chunk( float *restrict dst, const float *restrict src2, const __fp16 *restrict vtcm_src, uint32_t start_row, uint32_t n_rows, - uint32_t n_cols, + uint32_t c_len, + uint32_t total_n_cols, uint32_t dst_stride, uint32_t src2_stride, uint32_t dst_cols ) { - assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0); - const size_t tile_row_stride = (n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS; + assert(c_len % HTP_MM_HMX_TILE_N_COLS == 0); + assert(total_n_cols % HTP_MM_HMX_TILE_N_COLS == 0); + const size_t tile_row_stride = (total_n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS; const HVX_Vector one = hvx_vec_splat_f16(1.0); - const size_t limit_c = hex_smin(n_cols, dst_cols); + const size_t limit_c = hex_smin(c_len, dst_cols); const size_t limit_c_aligned = (limit_c & ~31); for (size_t r = 0; r < n_rows; r += 2) { @@ -848,6 +850,22 @@ static void transfer_output_chunk_fp16_to_fp32( } } +static inline void transfer_output_chunk_fp16_to_fp32( + float *restrict dst, + const float *restrict src2, + const __fp16 *restrict vtcm_src, + uint32_t start_row, + uint32_t n_rows, + uint32_t n_cols, + uint32_t dst_stride, + uint32_t src2_stride, + uint32_t dst_cols +) { + transfer_output_chunk_fp16_to_fp32_col_chunk( + dst, src2, vtcm_src, start_row, n_rows, n_cols, n_cols, dst_stride, src2_stride, dst_cols + ); +} + typedef struct { const __fp16 *vtcm_src; float *dst; @@ -1005,10 +1023,62 @@ static void transfer_activation_row_pair_fp32_to_fp16( } } +static void transfer_activation_row_pair_fp32_to_fp16_col_chunk( + __fp16 *restrict vtcm_dst, + const float *restrict row0, // offset by c_first + const float *restrict row1, // offset by c_first + uint32_t r, + uint32_t k_block, + uint32_t c_first, + uint32_t c_len, + uint32_t k_chunk_valid, + bool row0_valid, + bool row1_valid) { + + uint32_t r0 = r / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = r % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + + uint32_t c = 0; + for (; c + 32 <= k_chunk_valid; c += 32) { + HVX_Vector v0 = Q6_V_vzero(); + HVX_Vector v1 = Q6_V_vzero(); + if (row0_valid) v0 = *(const HVX_Vector *)(row0 + c); + if (row1_valid) v1 = *(const HVX_Vector *)(row1 + c); + + HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1); + + uint32_t c0 = (c_first + c) / HTP_MM_HMX_TILE_N_COLS; // tile column index + uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0; + + HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS); + tile[r1 / 2] = v_out; + } + if (c < c_len) { + HVX_Vector v0 = Q6_V_vzero(); + HVX_Vector v1 = Q6_V_vzero(); + if (row0_valid) v0 = *(const HVX_Vector *)(row0 + c); + if (row1_valid) v1 = *(const HVX_Vector *)(row1 + c); + + uint32_t rem = (k_chunk_valid > c) ? (k_chunk_valid - c) : 0; + HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0); + v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero()); + v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero()); + + HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1); + + uint32_t c0 = (c_first + c) / HTP_MM_HMX_TILE_N_COLS; // tile column index + uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0; + + HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS); + tile[r1 / 2] = v_out; + } +} + static void transfer_activation_chunk_fp32_to_fp16_gathered( __fp16 *restrict vtcm_dst, const float *restrict src, uint32_t start_row, + uint32_t vtcm_start_row, uint32_t n_rows, uint32_t k_block, const struct mmid_row_mapping *matrix_rows, @@ -1029,8 +1099,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered( for (r = 0; r < n_rows_tiled; r += 2) { uint32_t r_idx0 = start_row + r + 0; uint32_t r_idx1 = start_row + r + 1; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0]; struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1]; @@ -1073,9 +1144,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered( } for (; r < n_rows_padded; r += 2) { - uint32_t r_idx0 = start_row + r; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx const bool row0_valid = (start_row + r + 0) < cne1; const bool row1_valid = (start_row + r + 1) < cne1; @@ -1135,6 +1206,7 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered_flat( __fp16 *restrict vtcm_dst, const float *restrict src, uint32_t start_row, + uint32_t vtcm_start_row, uint32_t n_rows, uint32_t k_block, const struct mmid_row_mapping *matrix_rows, @@ -1152,8 +1224,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered_flat( for (r = 0; r < n_rows_tiled; r += 2) { uint32_t r_idx0 = start_row + r + 0; uint32_t r_idx1 = start_row + r + 1; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0]; struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1]; @@ -1193,9 +1266,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered_flat( } for (; r < n_rows_padded; r += 2) { - uint32_t r_idx0 = start_row + r; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx const bool row0_valid = (start_row + r + 0) < cne1; const bool row1_valid = (start_row + r + 1) < cne1; @@ -1253,6 +1326,7 @@ static void transfer_output_chunk_fp16_to_fp32_scattered( float *restrict dst, const __fp16 *restrict vtcm_src, uint32_t start_row, + uint32_t vtcm_start_row, uint32_t n_rows, uint32_t n_cols, const struct mmid_row_mapping *matrix_rows, @@ -1269,8 +1343,9 @@ static void transfer_output_chunk_fp16_to_fp32_scattered( for (size_t r = 0; r < n_rows; r += 2) { uint32_t r_idx0 = start_row + r + 0; uint32_t r_idx1 = start_row + r + 1; - const size_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; - const size_t r1 = (r_idx0 % HTP_MM_HMX_TILE_N_ROWS) / 2; // index of the row pair within the tile + uint32_t lr = vtcm_start_row + r; // vtcm-local row + const size_t r0 = (lr / HTP_MM_HMX_TILE_N_ROWS); + const size_t r1 = (lr % HTP_MM_HMX_TILE_N_ROWS) / 2; // index of the row pair within the tile const __fp16 *row_base = vtcm_src + r0 * tile_row_stride; if (r_idx0 >= cne1) break; diff --git a/ggml/src/ggml-hexagon/htp/hmx-queue.c b/ggml/src/ggml-hexagon/htp/hmx-queue.c index 3add542bae7c..c369d3dd23f0 100644 --- a/ggml/src/ggml-hexagon/htp/hmx-queue.c +++ b/ggml/src/ggml-hexagon/htp/hmx-queue.c @@ -14,7 +14,7 @@ #define QURT_LOWEST_PRIO (254) -static inline void hmx_lock(struct hmx_queue *q) +static inline void hmx_lock(hmx_queue_t q) { if (!q->hmx_locked) { HAP_compute_res_hmx_lock(q->hap_rctx); @@ -22,7 +22,7 @@ static inline void hmx_lock(struct hmx_queue *q) } } -static inline void hmx_unlock(struct hmx_queue *q) +static inline void hmx_unlock(hmx_queue_t q) { if (q->hmx_locked) { HAP_compute_res_hmx_unlock(q->hap_rctx); @@ -30,7 +30,7 @@ static inline void hmx_unlock(struct hmx_queue *q) } } -static inline void hmx_queue_process(struct hmx_queue *q, bool* killed) { +static inline void hmx_queue_process(hmx_queue_t q, bool* killed) { unsigned int ir = atomic_load(&q->idx_read); while (ir != atomic_load(&q->idx_write)) { @@ -61,7 +61,7 @@ static inline void hmx_queue_process(struct hmx_queue *q, bool* killed) { } static void hmx_queue_thread(void * arg) { - struct hmx_queue * q = (struct hmx_queue *) arg; + hmx_queue_t q = (hmx_queue_t) arg; FARF(HIGH, "hmx-queue-thread: started"); @@ -93,34 +93,41 @@ static void hmx_queue_thread(void * arg) { FARF(HIGH, "hmx-queue-thread: stopped"); } -struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx) { +size_t hmx_queue_sizeof(size_t capacity, uint32_t stack_size) { capacity = hex_ceil_pow2(capacity); + size_t size_q = hex_align_up(sizeof(struct hmx_queue_s), HEX_L2_LINE_SIZE); + size_t size_desc = hex_align_up(capacity * sizeof(struct hmx_queue_desc), HEX_L2_LINE_SIZE); + size_t size_stack = stack_size; + return size_q + size_desc + size_stack; +} + +size_t hmx_queue_alignof(void) { + return HEX_L2_LINE_SIZE; +} + +hmx_queue_t hmx_queue_init(void * ptr, size_t capacity, uint32_t stack_size, uint32_t hap_rctx, struct htp_thread_trace * trace) { + capacity = hex_ceil_pow2(capacity); + size_t size_q = hex_align_up(sizeof(struct hmx_queue_s), HEX_L2_LINE_SIZE); + size_t size_desc = hex_align_up(capacity * sizeof(struct hmx_queue_desc), HEX_L2_LINE_SIZE); + + uint8_t * block = (uint8_t *) ptr; + + hmx_queue_t q = (hmx_queue_t) block; block += size_q; + memset(q, 0, sizeof(struct hmx_queue_s)); - struct hmx_queue * q = (struct hmx_queue *) memalign(32, sizeof(struct hmx_queue)); - if (q == NULL) { - FARF(ERROR, "%s: failed to allocate DMA queue\n", __FUNCTION__); - return NULL; - } - memset(q, 0, sizeof(struct hmx_queue)); q->capacity = capacity; q->idx_mask = capacity - 1; q->hap_rctx = hap_rctx; + q->external_mem = true; - q->desc = (struct hmx_queue_desc *) memalign(64, capacity * sizeof(struct hmx_queue_desc)); - if (!q->desc) { - FARF(ERROR, "hmx-queue: failed to allocate HMX queue descriptors\n"); - return NULL; - } + q->desc = (struct hmx_queue_desc *) block; block += size_desc; memset(q->desc, 0, capacity * sizeof(struct hmx_queue_desc)); - const size_t stack_size = HMX_QUEUE_THREAD_STACK_SIZE; - q->stack = (unsigned char *) memalign(64, stack_size); - if (!q->stack) { - FARF(ERROR, "hmx-queue: thread stack allocation failed (%zu bytes)", stack_size); - return NULL; - } + q->stack = block; memset(q->stack, 0, stack_size); + q->trace = trace; + // Match caller thread priority (same pattern as worker-pool.c). int prio = qurt_thread_get_priority(qurt_thread_get_id()); if (prio < 1) { @@ -148,7 +155,7 @@ struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx) { return q; } -void hmx_queue_delete(struct hmx_queue * q) { +void hmx_queue_free(hmx_queue_t q) { if (!q) { return; } @@ -160,8 +167,4 @@ void hmx_queue_delete(struct hmx_queue * q) { int status; qurt_thread_join(q->thread, &status); - - free(q->desc); - free(q->stack); - free(q); } diff --git a/ggml/src/ggml-hexagon/htp/hmx-queue.h b/ggml/src/ggml-hexagon/htp/hmx-queue.h index b176fa179611..c2b1859a2813 100644 --- a/ggml/src/ggml-hexagon/htp/hmx-queue.h +++ b/ggml/src/ggml-hexagon/htp/hmx-queue.h @@ -17,8 +17,6 @@ extern "C" { #endif -#define HMX_QUEUE_THREAD_STACK_SIZE (16 * 1024) - #if __HVX_ARCH__ > 79 #define HMX_QUEUE_POLL_COUNT 2000 #else @@ -41,7 +39,7 @@ struct hmx_queue_desc { atomic_uint done; }; -struct hmx_queue { +struct hmx_queue_s { struct hmx_queue_desc * desc; atomic_uint idx_write; // updated by producer (push) atomic_uint idx_read; // updated by consumer (process) @@ -55,19 +53,24 @@ struct hmx_queue { uint32_t hap_rctx; bool hmx_locked; struct htp_thread_trace * trace; + bool external_mem; // memory owned externally }; -struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx); -void hmx_queue_delete(struct hmx_queue * q); +typedef struct hmx_queue_s * hmx_queue_t; + +size_t hmx_queue_sizeof(size_t capacity, uint32_t stack_size); +size_t hmx_queue_alignof(void); +hmx_queue_t hmx_queue_init(void * ptr, size_t capacity, uint32_t stack_size, uint32_t hap_rctx, struct htp_thread_trace * trace); +void hmx_queue_free(hmx_queue_t q); static inline struct hmx_queue_desc hmx_queue_make_desc(hmx_queue_func func, void * data) { struct hmx_queue_desc d = { func, data }; return d; } -static inline bool hmx_queue_push(struct hmx_queue * q, struct hmx_queue_desc d) { +static inline bool hmx_queue_push(hmx_queue_t q, struct hmx_queue_desc d) { unsigned int ir = atomic_load(&q->idx_read); - unsigned int iw = q->idx_write; + unsigned int iw = atomic_load(&q->idx_write); if (((iw + 1) & q->idx_mask) == ir) { FARF(HIGH, "hmx-queue-push: queue is full\n"); @@ -87,25 +90,25 @@ static inline bool hmx_queue_push(struct hmx_queue * q, struct hmx_queue_desc d) return true; } -static inline bool hmx_queue_signal(struct hmx_queue *q, enum hmx_queue_signal sig) { +static inline bool hmx_queue_signal(hmx_queue_t q, enum hmx_queue_signal sig) { return hmx_queue_push(q, hmx_queue_make_desc((hmx_queue_func) sig, NULL)); } -static inline bool hmx_queue_empty(struct hmx_queue * q) { - return q->idx_pop == q->idx_write; +static inline bool hmx_queue_empty(hmx_queue_t q) { + return q->idx_pop == atomic_load(&q->idx_write); } -static inline uint32_t hmx_queue_depth(struct hmx_queue * q) { - return (q->idx_read - q->idx_read) & q->idx_mask; +static inline uint32_t hmx_queue_depth(hmx_queue_t q) { + return (atomic_load(&q->idx_write) - atomic_load(&q->idx_read)) & q->idx_mask; } -static inline uint32_t hmx_queue_capacity(struct hmx_queue * q) { +static inline uint32_t hmx_queue_capacity(hmx_queue_t q) { return q->capacity; } -static inline struct hmx_queue_desc hmx_queue_pop_one(struct hmx_queue * q) { +static inline struct hmx_queue_desc hmx_queue_pop_one(hmx_queue_t q) { unsigned int ip = q->idx_pop; - unsigned int iw = q->idx_write; + unsigned int iw = atomic_load(&q->idx_write); struct hmx_queue_desc rd = { NULL, NULL }; if (ip == iw) { @@ -126,7 +129,7 @@ static inline struct hmx_queue_desc hmx_queue_pop_one(struct hmx_queue * q) { return rd; } -static inline struct hmx_queue_desc hmx_queue_pop(struct hmx_queue * q) { +static inline struct hmx_queue_desc hmx_queue_pop(hmx_queue_t q) { while (1) { struct hmx_queue_desc d = hmx_queue_pop_one(q); @@ -138,15 +141,15 @@ static inline struct hmx_queue_desc hmx_queue_pop(struct hmx_queue * q) { } } -static inline void hmx_queue_flush(struct hmx_queue * q) { +static inline void hmx_queue_flush(hmx_queue_t q) { while (hmx_queue_pop_one(q).func != NULL) ; } -static inline void hmx_queue_wakeup(struct hmx_queue * q) { +static inline void hmx_queue_wakeup(hmx_queue_t q) { hmx_queue_signal(q, HMX_QUEUE_WAKEUP); } -static inline void hmx_queue_suspend(struct hmx_queue *q) { +static inline void hmx_queue_suspend(hmx_queue_t q) { hmx_queue_signal(q, HMX_QUEUE_SUSPEND); } diff --git a/ggml/src/ggml-hexagon/htp/htp-ctx.h b/ggml/src/ggml-hexagon/htp/htp-ctx.h index e13103fb1887..e0f9a0c40d19 100644 --- a/ggml/src/ggml-hexagon/htp/htp-ctx.h +++ b/ggml/src/ggml-hexagon/htp/htp-ctx.h @@ -5,7 +5,8 @@ #include "hmx-queue.h" #include "htp-ops.h" #include "hex-profile.h" -#include "worker-pool.h" +#include "work-queue.h" +#include "hex-fastdiv.h" #include #include @@ -18,6 +19,8 @@ #endif #define HTP_MAX_MMAPS 16 +#define HTP_MAX_DIRTY_RANGES 16 + // Memory mapping struct htp_mmap { uint64_t size; @@ -52,6 +55,9 @@ struct htp_ops_context { const struct htp_tensor * dsts[HTP_OP_MAX_OUTPUTS]; }; + dma_queue ** src_dma[HTP_OP_MAX_INPUTS]; + dma_queue ** dst_dma[HTP_OP_MAX_OUTPUTS]; + // TODO convert these to an array struct htp_spad src0_spad; struct htp_spad src1_spad; @@ -65,11 +71,16 @@ struct htp_ops_context { // Main context for htp DSP backend struct htp_context { - dspqueue_t queue; - dma_queue * dma[HTP_MAX_NTHREADS]; + dspqueue_t dsp_queue; + struct htp_mmap mmap[HTP_MAX_MMAPS]; - worker_pool_context_t worker_pool; + dma_queue_t dma[HTP_MAX_NTHREADS]; + dma_queue_t dma_cached[HTP_MAX_NTHREADS]; + work_queue_t work_queue; + hmx_queue_t hmx_queue; + uint32_t n_threads; + struct fastdiv_values n_threads_div; int thread_id; int thread_prio; @@ -86,6 +97,11 @@ struct htp_context { atomic_bool vtcm_needs_release; uint64_t max_vmem; + struct htp_dirty_range { + uint32_t start; + uint32_t end; + uint32_t bi; + } dirty_ranges[HTP_MAX_DIRTY_RANGES]; // Persistent DDR scratchpad for MUL_MAT_ID mappings void * ddr_spad_base; @@ -93,7 +109,10 @@ struct htp_context { struct htp_ops_context octx; - struct hmx_queue * hmx_queue; // Async HMX queue for pipeline overlap + qurt_thread_t main_thread; + void * main_stack; + atomic_bool killed; + size_t footprint; }; int op_matmul(struct htp_ops_context * octx); @@ -121,5 +140,6 @@ int op_diag(struct htp_ops_context * octx); int op_solve_tri(struct htp_ops_context * octx); int op_gated_delta_net(struct htp_ops_context * octx); int op_pad(struct htp_ops_context * octx); +int op_im2col(struct htp_ops_context * octx); #endif /* HTP_CTX_H */ diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h index c9d0b3539a95..a138f062aa68 100644 --- a/ggml/src/ggml-hexagon/htp/htp-ops.h +++ b/ggml/src/ggml-hexagon/htp/htp-ops.h @@ -97,6 +97,8 @@ enum htp_op_code { HTP_OP_PAD, HTP_OP_NORM, HTP_OP_CONCAT, + HTP_OP_CLAMP, + HTP_OP_IM2COL, HTP_OP_INVALID }; @@ -108,8 +110,7 @@ enum htp_op_code { #define HTP_OP_MAX_KERN_PARAMS 32 #define HTP_OP_MAX_BUFS 16 -#define HTP_OP_MAX_REQS 256 -#define HTP_OP_MAX_TENSORS (HTP_OP_MAX_REQS * HTP_OP_MAX_INPUTS + HTP_OP_MAX_REQS) +#define HTP_OP_MAX_TENSORS 8192 // must stay under 64K (uint16) #define HTP_OP_MAX_VMEM_DEFAULT (3355443200u) @@ -117,16 +118,18 @@ enum htp_op_code { enum htp_tensor_flags { HTP_TENSOR_COMPUTE = (1U << 0), // Tensor buffer temporal compute data (not weights) - HTP_TENSOR_FLUSHED = (1U << 1) // Tensor buffer has been flushed (set by the NPU) + HTP_TENSOR_DIRTY = (1U << 1) // Tensor buffer is dirty and needs to be flushed }; // Tensor descriptor struct htp_tensor { uint32_t data; // Buffer offset in the messages, and data pointer on the NPU + uint32_t reserved; // Reserved for alignment padding (must be multiple of 8) uint32_t size; // Data size in bytes uint32_t flags; // Buffer / tensor flags - uint16_t type; // Data type + uint32_t type; // Data type uint16_t bi; // Buffer index + uint16_t ti; // Tensor index uint32_t ne[HTP_OP_MAX_DIMS]; // Number of elements uint32_t nb[HTP_OP_MAX_DIMS]; // Stride in bytes (see ggml.h ggml_tensor) }; @@ -169,6 +172,9 @@ enum htp_profiler_mode { enum htp_trace_event_id { HTP_TRACE_EVT_DMA = 0, + HTP_TRACE_EVT_L2FLUSH = 1, + HTP_TRACE_EVT_INIT = 2, + HTP_TRACE_EVT_BUFF = 3, HTP_TRACE_EVT_HVX_COMP = 20, HTP_TRACE_EVT_HVX_A_QUANT = 21, @@ -221,7 +227,10 @@ struct htp_opbatch_rsp { uint32_t n_tensors; // Number of tensors uint32_t n_ops; // Number of op profile descriptors uint32_t n_traces[HTP_MAX_NTHREADS + 1]; - uint8_t pad[8]; // align to 8 bytes + uint32_t usecs; // Number of usec + uint32_t pad; // align to 8 bytes + uint64_t cycles_start; // Start cycle counter + uint64_t cycles_stop; // Stop cycle counter // struct htp_prof_desc profs[]; -- dspqueue buf 0 }; diff --git a/ggml/src/ggml-hexagon/htp/htp-tensor.c b/ggml/src/ggml-hexagon/htp/htp-tensor.c new file mode 100644 index 000000000000..39436e26dfff --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/htp-tensor.c @@ -0,0 +1,293 @@ +#include "htp-tensor.h" + +#include +#include +#include + +#include "hex-common.h" +#include "hex-utils.h" +#include "hex-fastdiv.h" +#include "hex-profile.h" +#include "htp-ctx.h" +#include "work-queue.h" + +struct l2flush_range { + uint32_t start; // line-aligned start address + uint32_t end; // line-aligned end address + uint32_t block_first; // global block index of this range's first block + uint32_t n_blocks; // number of HEX_L2_BLOCK_SIZE chunks (last may be partial) +}; + +struct l2flush_multi_task { + struct htp_thread_trace * trace; + struct l2flush_range ranges[HTP_OP_MAX_INPUTS]; + uint32_t n_ranges; + uint32_t total_blocks; + uint32_t blocks_per_thread; +}; + +static void flush_all_dcache(struct htp_context * ctx) { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, 0); + qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); + hex_l2fetch_block(ctx, ctx->footprint); + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, 0); + memset(ctx->dirty_ranges, 0, sizeof(ctx->dirty_ranges)); +} + +static void l2flush_multi_worker(unsigned int n, unsigned int i, void * data) { + struct l2flush_multi_task * task = (struct l2flush_multi_task *) data; + (void) n; + + const uint32_t gb_first = i * task->blocks_per_thread; + uint32_t gb_last = gb_first + task->blocks_per_thread; + if (gb_last > task->total_blocks) { + gb_last = task->total_blocks; + } + if (gb_first >= gb_last) { + return; + } + + struct htp_thread_trace * tr = &task->trace[i]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, gb_first); + + for (uint32_t r = 0; r < task->n_ranges; r++) { + const struct l2flush_range * rg = &task->ranges[r]; + const uint32_t rb_first = rg->block_first; + const uint32_t rb_last = rg->block_first + rg->n_blocks; + + const uint32_t lo = gb_first > rb_first ? gb_first : rb_first; + const uint32_t hi = gb_last < rb_last ? gb_last : rb_last; + if (lo >= hi) { + continue; + } + + const uint32_t s = rg->start + (lo - rb_first) * HEX_L2_BLOCK_SIZE; + uint32_t e = rg->start + (hi - rb_first) * HEX_L2_BLOCK_SIZE; + if (e > rg->end) { + e = rg->end; + } + hex_l2flush((void *) (uintptr_t) s, e - s); + } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, gb_first); +} + +void htp_tensor_dirty_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n) { + const struct htp_tensor * pending[HTP_OP_MAX_OUTPUTS]; + uint32_t n_pending = 0; + + for (uint32_t i = 0; i < n; i++) { + const struct htp_tensor * t = tensors[i]; + if (!t) continue; + + uint32_t t_start = t->data; + uint32_t t_end = t_start + t->size; + + bool merged = false; + for (uint32_t j = 0; j < HTP_MAX_DIRTY_RANGES; j++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[j]; + if (!r->start) continue; + + if (r->start <= t_end && t_start <= r->end) { + uint32_t new_start = (t_start < r->start) ? t_start : r->start; + uint32_t new_end = (t_end > r->end) ? t_end : r->end; + r->start = new_start; + r->end = new_end; + merged = true; + } + } + + if (!merged) { + pending[n_pending++] = t; + } + } + + if (n_pending == 0) { + return; + } + + uint32_t empty_indices[HTP_MAX_DIRTY_RANGES]; + uint32_t active_indices[HTP_MAX_DIRTY_RANGES]; + uint32_t n_active = 0; + uint32_t n_empty = 0; + for (uint32_t j = 0; j < HTP_MAX_DIRTY_RANGES; j++) { + if (ctx->dirty_ranges[j].start) { + active_indices[n_active++] = j; + } else { + empty_indices[n_empty++] = j; + } + } + + if (n_pending <= n_empty) { + for (uint32_t i = 0; i < n_pending; i++) { + uint32_t idx = empty_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + r->start = pending[i]->data; + r->end = pending[i]->data + pending[i]->size; + r->bi = pending[i]->bi; + } + return; + } + + uint32_t n_evict = n_pending - n_empty; + uint32_t total_evict_size = 0; + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + total_evict_size += r->end - r->start; + } + + if (total_evict_size > HEX_L2_FLUSH_ALL_THRESHOLD) { + flush_all_dcache(ctx); + for (uint32_t i = 0; i < n_pending; i++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[i]; + r->start = pending[i]->data; + r->end = pending[i]->data + pending[i]->size; + r->bi = pending[i]->bi; + } + return; + } + + if (total_evict_size > HEX_L2_FLUSH_WQ_THRESHOLD && ctx->n_threads > 1 && n_evict <= HTP_OP_MAX_INPUTS) { + struct l2flush_multi_task task; + task.trace = ctx->trace; + task.n_ranges = n_evict; + + uint32_t block_acc = 0; + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + + struct l2flush_range * rg = &task.ranges[i]; + rg->start = hex_align_down((size_t) r->start, HEX_L2_LINE_SIZE); + rg->end = hex_align_up((size_t) r->end, HEX_L2_LINE_SIZE); + rg->block_first = block_acc; + rg->n_blocks = (rg->end - rg->start + HEX_L2_BLOCK_SIZE - 1) / HEX_L2_BLOCK_SIZE; + block_acc += rg->n_blocks; + } + + task.total_blocks = block_acc; + task.blocks_per_thread = fastdiv(block_acc + ctx->n_threads - 1, &ctx->n_threads_div); + + work_queue_run(ctx->work_queue, l2flush_multi_worker, &task, ctx->n_threads); + } else { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, 0); + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + uint32_t size = r->end - r->start; + hex_l2flush((void *) (uintptr_t) r->start, size); + } + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, 0); + } + + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + r->start = pending[i]->data; + r->end = pending[i]->data + pending[i]->size; + r->bi = pending[i]->bi; + } + + for (uint32_t i = 0; i < n_empty; i++) { + uint32_t idx = empty_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + r->start = pending[n_evict + i]->data; + r->end = pending[n_evict + i]->data + pending[n_evict + i]->size; + r->bi = pending[n_evict + i]->bi; + } +} + +static void make_tensor_clean(struct htp_context * ctx, const struct htp_tensor * t) { + uint32_t t_start = t->data; + uint32_t t_end = t_start + t->size; + + for (uint32_t i = 0; i < HTP_MAX_DIRTY_RANGES; i++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[i]; + if (!r->start) continue; + + if (r->start < t_end && t_start < r->end) { + if (t_start <= r->start && r->end <= t_end) { + r->start = 0; + } else if (t_start <= r->start) { + r->start = t_end; + } else if (r->end <= t_end) { + r->end = t_start; + } + } + } +} + +static inline bool is_tensor_dirty(struct htp_context * ctx, const struct htp_tensor * t) { + uint32_t t_start = t->data; + uint32_t t_end = t_start + t->size; + + for (uint32_t i = 0; i < HTP_MAX_DIRTY_RANGES; i++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[i]; + if (!r->start) continue; + + if (r->start < t_end && t_start < r->end) { + return true; + } + } + return false; +} + +void htp_tensor_flush_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n) { + const struct htp_tensor * dirty_tensors[HTP_OP_MAX_INPUTS]; + uint32_t n_dirty = 0; + uint64_t total_dirty = 0; + + for (uint32_t i = 0; i < n; i++) { + const struct htp_tensor * t = tensors[i]; + if (t && (t->flags & HTP_TENSOR_COMPUTE) && is_tensor_dirty(ctx, t)) { + dirty_tensors[n_dirty++] = t; + total_dirty += t->size; + } + } + + if (total_dirty == 0) { + return; + } + + if (total_dirty > HEX_L2_FLUSH_ALL_THRESHOLD) { + flush_all_dcache(ctx); + return; + } + + if (total_dirty >= HEX_L2_FLUSH_WQ_THRESHOLD && ctx->n_threads > 1) { + struct l2flush_multi_task task; + task.trace = ctx->trace; + task.n_ranges = 0; + + uint32_t block_acc = 0; + for (uint32_t i = 0; i < n_dirty; i++) { + const struct htp_tensor * t = dirty_tensors[i]; + make_tensor_clean(ctx, t); + + struct l2flush_range * rg = &task.ranges[task.n_ranges++]; + rg->start = hex_align_down((size_t) t->data, HEX_L2_LINE_SIZE); + rg->end = hex_align_up((size_t) t->data + t->size, HEX_L2_LINE_SIZE); + rg->block_first = block_acc; + rg->n_blocks = (rg->end - rg->start + HEX_L2_BLOCK_SIZE - 1) / HEX_L2_BLOCK_SIZE; + block_acc += rg->n_blocks; + } + + task.total_blocks = block_acc; + task.blocks_per_thread = fastdiv(block_acc + ctx->n_threads - 1, &ctx->n_threads_div); + + work_queue_run(ctx->work_queue, l2flush_multi_worker, &task, ctx->n_threads); + return; + } + + struct htp_thread_trace * tr = &ctx->trace[0]; + for (uint32_t i = 0; i < n_dirty; i++) { + const struct htp_tensor * t = dirty_tensors[i]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, t->ti); + hex_l2flush((void *) (uintptr_t) t->data, t->size); + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, t->ti); + make_tensor_clean(ctx, t); + } +} diff --git a/ggml/src/ggml-hexagon/htp/htp-tensor.h b/ggml/src/ggml-hexagon/htp/htp-tensor.h new file mode 100644 index 000000000000..2c3fc54c748f --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/htp-tensor.h @@ -0,0 +1,20 @@ +#ifndef HTP_TENSOR_H +#define HTP_TENSOR_H + +#include +#include "htp-ops.h" +#include "hex-bitmap.h" + +static inline void * htp_tensor_data(const struct htp_tensor * t) { + return (void *) (uintptr_t) t->data; +} + +static inline uint32_t * htp_tensor_flags(const struct htp_tensor * t) { + return (uint32_t *) &t->flags; +} + +struct htp_context; +void htp_tensor_flush_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n); +void htp_tensor_dirty_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n); + +#endif // HTP_TENSOR_H diff --git a/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h b/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h index c05bd0b85260..5b18f163c57e 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h +++ b/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h @@ -208,6 +208,77 @@ static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y, const void * restr } } } +static inline void hvx_mad_f32_f16_aa_vec(float * restrict y, const void * restrict x, HVX_Vector S0, uint32_t n) { + const HVX_Vector * restrict vx0 = (const HVX_Vector *) x; + + HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y; + HVX_Vector * restrict vy = (HVX_Vector *) y; + + uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + uint32_t nloe = n % VLEN_FP16; // leftover elements + + uint32_t i = 0; + + #pragma unroll(2) + for (i = 0; i < nvec; ++i) { + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0); + } + + if (nloe) { + HVX_VectorPair xy_p = vy_p[i]; + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0); + + HVX_Vector xy = Q6_V_lo_W(xy_p); + i = 2 * i; // index for vy + + if (nloe >= VLEN_FP32) { + vy[i] = xy; + nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p); + } + + if (nloe) { + hvx_vec_store_a(&vy[i], nloe * 4, xy); + } + } +} + +static inline void hvx_mad_f32_f16_aa_rx2_vec(float * restrict y, const void * restrict x0, const void * restrict x1, + HVX_Vector S0, HVX_Vector S1, uint32_t n) { + const HVX_Vector * restrict vx0 = (const HVX_Vector *) x0; + const HVX_Vector * restrict vx1 = (const HVX_Vector *) x1; + + HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y; + HVX_Vector * restrict vy = (HVX_Vector *) y; + + uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + uint32_t nloe = n % VLEN_FP16; // leftover elements + + uint32_t i = 0; + + #pragma unroll(2) + for (i = 0; i < nvec; ++i) { + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0); + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx1[i]), S1); + } + + if (nloe) { + HVX_VectorPair xy_p = vy_p[i]; + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0); + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx1[i]), S1); + + HVX_Vector xy = Q6_V_lo_W(xy_p); + i = 2 * i; // index for vy + + if (nloe >= VLEN_FP32) { + vy[i] = xy; + nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p); + } + + if (nloe) { + hvx_vec_store_a(&vy[i], nloe * 4, xy); + } + } +} static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t n, HVX_Vector vs) { assert((size_t) dst % 128 == 0); diff --git a/ggml/src/ggml-hexagon/htp/hvx-reduce.h b/ggml/src/ggml-hexagon/htp/hvx-reduce.h index 3c0073ef6d80..76d712dc8981 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-reduce.h +++ b/ggml/src/ggml-hexagon/htp/hvx-reduce.h @@ -286,6 +286,46 @@ static inline float hvx_sum_of_squares_f32(const uint8_t * restrict src, const i } } +// Signed 32-bit Integer Max variants + +static inline HVX_Vector hvx_vec_reduce_max_n_i32(HVX_Vector in, unsigned int n) { + unsigned int total = n * 4; // total vec nbytes + unsigned int width = 4; // int32 nbytes + + HVX_Vector max_val = in, max_t; + while (width < total) { + max_t = Q6_V_vror_VR(max_val, width); // rotate right + max_val = Q6_Vw_vmax_VwVw(max_t, max_val); // elementwise signed max + width = width << 1; + } + return max_val; +} + +static inline HVX_Vector hvx_vec_reduce_max_i32(HVX_Vector in) { + return hvx_vec_reduce_max_n_i32(in, 32); +} + +static inline int32_t hvx_reduce_max_i32_a(const uint8_t * restrict src, const int num_elems) { + HVX_Vector init_vec = Q6_V_vsplat_R(((const int32_t *) src)[0]); + HVX_Vector pad_vec = Q6_V_vsplat_R(0x80000000); + assert((uintptr_t) src % 128 == 0); + hvx_reduce_loop_body(HVX_Vector, init_vec, pad_vec, Q6_Vw_vmax_VwVw, hvx_vec_reduce_max_i32, hvx_vec_get_i32); +} + +static inline int32_t hvx_reduce_max_i32_u(const uint8_t * restrict src, const int num_elems) { + HVX_Vector init_vec = Q6_V_vsplat_R(((const int32_t *) src)[0]); + HVX_Vector pad_vec = Q6_V_vsplat_R(0x80000000); + hvx_reduce_loop_body(HVX_UVector, init_vec, pad_vec, Q6_Vw_vmax_VwVw, hvx_vec_reduce_max_i32, hvx_vec_get_i32); +} + +static inline int32_t hvx_reduce_max_i32(const uint8_t * restrict src, const int num_elems) { + if (hex_is_aligned((void *) src, 128)) { + return hvx_reduce_max_i32_a(src, num_elems); + } else { + return hvx_reduce_max_i32_u(src, num_elems); + } +} + #undef hvx_reduce_loop_body #undef HVX_REDUCE_MAX_OP #undef HVX_REDUCE_SUM_OP diff --git a/ggml/src/ggml-hexagon/htp/im2col-ops.c b/ggml/src/ggml-hexagon/htp/im2col-ops.c new file mode 100644 index 000000000000..35fc103df8fe --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/im2col-ops.c @@ -0,0 +1,306 @@ +#pragma clang diagnostic ignored "-Wunused-variable" +#pragma clang diagnostic ignored "-Wunused-function" +#pragma clang diagnostic ignored "-Wunused-but-set-variable" + +#include +#include +#include +#include +#include + +#define GGML_COMMON_DECL_C +#include "ggml-common.h" +#include "htp-ctx.h" +#include "htp-ops.h" +#include "hvx-utils.h" +#include "hex-dma.h" +#include "hex-profile.h" +#include "htp-vtcm.h" + +struct htp_im2col_context { + struct htp_ops_context * octx; + uint32_t npatches_per_thread; // patches = N*OH*OW (pure-DDR kernel) + + uint32_t pe_rows_per_thread; // N*OH rows per worker + uint32_t pe_src_row_bytes; // one output row's source: IC*KH*IW*4, rounded 256 + uint32_t pe_dst_row_bytes; // one output row's dst: OW*patch_stride*2, rounded 256 + + // Patch-embed DMA path VTCM ping-pong. + uint8_t * pe_vtcm_src; // base of the 2x src buffers region + uint8_t * pe_vtcm_dst; // base of the 2x dst buffers region + uint32_t pe_src_size_per_thread; // 2 * pe_src_row_bytes + uint32_t pe_dst_size_per_thread; // 2 * pe_dst_row_bytes +}; + +// Per-op VTCM layout for the patch-embed DMA path +struct htp_im2col_vtcm_layout { + size_t off_src; + size_t off_dst; + size_t src_bytes_per_thread; + size_t dst_bytes_per_thread; + size_t total_bytes; +}; + +static inline void htp_im2col_vtcm_layout_build(struct htp_im2col_vtcm_layout * L, + size_t src_row_bytes, + size_t dst_row_bytes, + uint32_t n_threads) { + L->src_bytes_per_thread = 2 * src_row_bytes; + L->dst_bytes_per_thread = 2 * dst_row_bytes; + + L->off_src = 0; + L->off_dst = L->off_src + L->src_bytes_per_thread * n_threads; + L->total_bytes = L->off_dst + L->dst_bytes_per_thread * n_threads; +} + +#define IM2COL_PATCHEMBED_BODY(FNAME, DST_CTYPE, COPY_FN, SPLAT_FN, DST_ELEM, TAG) \ + static void FNAME(unsigned int nth, unsigned int ith, void * data) { \ + struct htp_im2col_context * ictx = (struct htp_im2col_context *) data; \ + struct htp_ops_context * octx = ictx->octx; \ + struct htp_thread_trace * restrict tr = &octx->ctx->trace[ith]; \ + const struct htp_tensor * restrict src1 = octx->src[1]; \ + const struct htp_tensor * restrict dst = octx->dst; \ + const int32_t s0 = octx->op_params[0]; \ + const int32_t s1 = octx->op_params[1]; \ + const int32_t p0 = octx->op_params[2]; \ + const int32_t p1 = octx->op_params[3]; \ + const int32_t d0 = octx->op_params[4]; \ + const int32_t d1 = octx->op_params[5]; \ + const uint32_t N = src1->ne[3]; \ + const uint32_t IC = src1->ne[2]; \ + const uint32_t IH = src1->ne[1]; \ + const uint32_t IW = src1->ne[0]; \ + const uint32_t KH = octx->src[0]->ne[1]; \ + const uint32_t KW = octx->src[0]->ne[0]; \ + const uint32_t OH = dst->ne[2]; \ + const uint32_t OW = dst->ne[1]; \ + const uint32_t patch_stride = IC * KH * KW; \ + const float * restrict src_data = (const float *) src1->data; \ + DST_CTYPE * restrict dst_data = (DST_CTYPE *) dst->data; \ + const uint32_t npatches = N * OH * OW; \ + const uint32_t patch_start = ictx->npatches_per_thread * ith; \ + const uint32_t patch_end = MIN(patch_start + ictx->npatches_per_thread, npatches); \ + if (patch_start >= patch_end) { \ + return; \ + } \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, patch_start); \ + for (uint32_t p = patch_start; p < patch_end; p++) { \ + const uint32_t iow = p % OW; \ + const uint32_t ioh = (p / OW) % OH; \ + const uint32_t in = p / (OW * OH); \ + DST_CTYPE * restrict dst_patch = dst_data + (uint64_t) p * patch_stride; \ + for (uint32_t iic = 0; iic < IC; iic++) { \ + const float * restrict src_plane = src_data + ((uint64_t) in * IC + iic) * IH * IW; \ + for (uint32_t ikh = 0; ikh < KH; ikh++) { \ + const int32_t iih = (int32_t) ioh * s1 + (int32_t) ikh * d1 - p1; \ + DST_CTYPE * restrict out_run = dst_patch + iic * (KH * KW) + ikh * KW; \ + if (iih < 0 || iih >= (int32_t) IH) { \ + SPLAT_FN(out_run, 0.0f, KW); \ + continue; \ + } \ + const int32_t iiw0 = (int32_t) iow * s0 - p0; \ + const float * restrict src_run = src_plane + (uint64_t) iih * IW + iiw0; \ + if (d0 == 1) { \ + /* contiguous source run: [lo,hi) is in-bounds, tails are zero pad */ \ + const int32_t lo = iiw0 < 0 ? -iiw0 : 0; \ + int32_t hi = (int32_t) IW - iiw0; \ + if (hi > (int32_t) KW) { \ + hi = (int32_t) KW; \ + } \ + if (hi <= lo) { \ + SPLAT_FN(out_run, 0.0f, KW); \ + } else { \ + if (lo > 0) { \ + SPLAT_FN(out_run, 0.0f, (uint32_t) lo); \ + } \ + COPY_FN((uint8_t *) (out_run + lo), (const uint8_t *) (src_run + lo), \ + (uint32_t) (hi - lo)); \ + if (hi < (int32_t) KW) { \ + SPLAT_FN(out_run + hi, 0.0f, (KW - (uint32_t) hi)); \ + } \ + } \ + continue; \ + } \ + for (uint32_t ikw = 0; ikw < KW; ikw++) { \ + const int32_t iiw = (int32_t) iow * s0 + (int32_t) ikw * d0 - p0; \ + out_run[ikw] = (iiw < 0 || iiw >= (int32_t) IW) ? \ + (DST_CTYPE) 0.0f : \ + (DST_CTYPE) src_plane[(uint64_t) iih * IW + iiw]; \ + } \ + } \ + } \ + } \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, patch_start); \ + } + +IM2COL_PATCHEMBED_BODY(im2col_patchembed_thread, __fp16, hvx_copy_f16_f32_uu, hvx_splat_f16_u, sizeof(__fp16), "f32-f16") +IM2COL_PATCHEMBED_BODY(im2col_patchembed_f32_thread, float, hvx_copy_f32_uu, hvx_splat_f32_u, sizeof(float), "f32-f32") + +#define IM2COL_PATCHEMBED_DMA_BODY(FNAME, DST_CTYPE, COPY_FN, SPLAT_FN, DST_ELEM, TAG) \ + static void FNAME(unsigned int nth, unsigned int ith, void * data) { \ + struct htp_im2col_context * ictx = (struct htp_im2col_context *) data; \ + struct htp_ops_context * octx = ictx->octx; \ + struct htp_thread_trace * restrict tr = &octx->ctx->trace[ith]; \ + const struct htp_tensor * restrict src1 = octx->src[1]; \ + const struct htp_tensor * restrict dst = octx->dst; \ + const uint32_t N = src1->ne[3], IC = src1->ne[2], IH = src1->ne[1], IW = src1->ne[0]; \ + const uint32_t KH = octx->src[0]->ne[1], KW = octx->src[0]->ne[0]; \ + const uint32_t OH = dst->ne[2], OW = dst->ne[1]; \ + const uint32_t patch_stride = IC * KH * KW; \ + const float * restrict src_data = (const float *) src1->data; \ + DST_CTYPE * restrict dst_data = (DST_CTYPE *) dst->data; \ + dma_queue * dmaq = octx->ctx->dma[ith]; \ + uint8_t * src_base = ictx->pe_vtcm_src + ith * ictx->pe_src_size_per_thread; \ + uint8_t * dst_base = ictx->pe_vtcm_dst + ith * ictx->pe_dst_size_per_thread; \ + float * srcb = (float *) src_base; \ + DST_CTYPE * dstb = (DST_CTYPE *) dst_base; \ + const uint32_t nrows = N * OH; \ + const uint32_t per_thread = ictx->pe_rows_per_thread; \ + const uint32_t row_start = per_thread * ith; \ + const uint32_t row_end = MIN(row_start + per_thread, nrows); \ + if (row_start >= row_end) \ + return; \ + for (uint32_t r = row_start; r < row_end; r++) { \ + const uint32_t in = r / OH; \ + const uint32_t ioh = r % OH; \ + for (uint32_t ikh = 0; ikh < KH; ikh++) { \ + int32_t iih = (int32_t) ioh * (int32_t) KH + (int32_t) ikh; \ + int ok = (iih >= 0 && iih < (int32_t) IH); \ + for (uint32_t iic = 0; iic < IC; iic++) { \ + float * vdst = srcb + ((uint64_t) (iic * KH + ikh)) * IW; \ + const float * _vsrc = \ + ok ? (src_data + ((uint64_t) (in * IC + iic) * IH + iih) * IW) : (const float *) vdst; \ + dma_queue_push_ddr_to_vtcm( \ + dmaq, dma_make_ptr((uint8_t *) vdst, ok ? (const uint8_t *) _vsrc : (const uint8_t *) vdst), \ + IW * sizeof(float), IW * sizeof(float), ok ? 1 : 0); \ + } \ + } \ + for (uint32_t i = 0; i < IC * KH; i++) \ + dma_queue_pop(dmaq); \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, r); \ + for (uint32_t iow = 0; iow < OW; iow++) { \ + DST_CTYPE * dst_patch = dstb + (uint64_t) iow * patch_stride; \ + for (uint32_t ikh = 0; ikh < KH; ikh++) { \ + int32_t iih = (int32_t) ioh * (int32_t) KH + (int32_t) ikh; \ + for (uint32_t iic = 0; iic < IC; iic++) { \ + DST_CTYPE * out_run = dst_patch + iic * (KH * KW) + ikh * KW; \ + if (iih < 0 || iih >= (int32_t) IH) { \ + SPLAT_FN(out_run, 0.0f, KW); \ + continue; \ + } \ + const float * src_run = srcb + ((uint64_t) (iic * KH + ikh)) * IW + (uint64_t) iow * KW; \ + COPY_FN((uint8_t *) out_run, (const uint8_t *) src_run, KW); \ + } \ + } \ + } \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, r); \ + DST_CTYPE * ddr_row = dst_data + ((uint64_t) (in * OH + ioh) * OW) * patch_stride; \ + dma_queue_push_vtcm_to_ddr(dmaq, dma_make_ptr((uint8_t *) ddr_row, (uint8_t *) dstb), \ + OW * patch_stride * (DST_ELEM), OW * patch_stride * (DST_ELEM), 1); \ + dma_queue_flush(dmaq); \ + } \ + } + +IM2COL_PATCHEMBED_DMA_BODY(im2col_patchembed_dma_thread, __fp16, hvx_copy_f16_f32_uu, hvx_splat_f16_u, sizeof(__fp16), "pe-dma-f16") +IM2COL_PATCHEMBED_DMA_BODY(im2col_patchembed_dma_f32_thread, float, hvx_copy_f32_uu, hvx_splat_f32_u, sizeof(float), "pe-dma-f32") + +static bool im2col_use_patchembed_dma(const struct htp_ops_context * octx) { + const int32_t s0 = octx->op_params[0], s1 = octx->op_params[1]; + const int32_t p0 = octx->op_params[2], p1 = octx->op_params[3]; + const int32_t d0 = octx->op_params[4], d1 = octx->op_params[5]; + const int is_2D = octx->op_params[6] == 1; + if (!is_2D) { + return false; + } + if (octx->dst->type != HTP_TYPE_F16 && octx->dst->type != HTP_TYPE_F32) { + return false; + } + const uint32_t KH = octx->src[0]->ne[1], KW = octx->src[0]->ne[0]; + if (s0 != (int32_t) KW || s1 != (int32_t) KH) { + return false; // non-overlapping + } + if (p0 != 0 || p1 != 0) { + return false; // no padding + } + if (d0 != 1 || d1 != 1) { + return false; // no dilation + } + return true; +} + +// Sizes the per-thread 2x(src,dst) VTCM ping-pong for the patch-embed DMA path. +// Returns false if it doesn't fit the VTCM budget (caller falls back). +static bool im2col_patchembed_dma_fits(struct htp_ops_context * octx, + struct htp_im2col_context * ictx, + uint32_t n_threads) { + const uint32_t IC = octx->src[1]->ne[2], IW = octx->src[1]->ne[0]; + const uint32_t KH = octx->src[0]->ne[1], KW = octx->src[0]->ne[0]; + const uint32_t OW = octx->dst->ne[1]; + const uint32_t patch_stride = IC * KH * KW; + + ictx->pe_src_row_bytes = hex_round_up(IC * KH * IW * sizeof(float), 256); + const uint32_t dst_elem = (octx->dst->type == HTP_TYPE_F16) ? sizeof(__fp16) : sizeof(float); + ictx->pe_dst_row_bytes = hex_round_up(OW * patch_stride * dst_elem, 256); + + // 2 src + 2 dst buffers per thread (ping-pong), src region first then dst. + struct htp_im2col_vtcm_layout L; + htp_im2col_vtcm_layout_build(&L, ictx->pe_src_row_bytes, ictx->pe_dst_row_bytes, n_threads); + if (L.total_bytes > octx->ctx->vtcm_size) { + return false; + } + + uint8_t * const base = octx->ctx->vtcm_base; + ictx->pe_vtcm_src = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src); + ictx->pe_vtcm_dst = VTCM_LAYOUT_PTR(uint8_t, base, L.off_dst); + ictx->pe_src_size_per_thread = (uint32_t) L.src_bytes_per_thread; + ictx->pe_dst_size_per_thread = (uint32_t) L.dst_bytes_per_thread; + return true; +} + +int op_im2col(struct htp_ops_context * octx) { + const struct htp_tensor * src1 = octx->src[1]; + const struct htp_tensor * dst = octx->dst; + + if (src1->type != HTP_TYPE_F32 || (dst->type != HTP_TYPE_F16 && dst->type != HTP_TYPE_F32)) { + FARF(ERROR, "im2col: only (F32 image -> F16/F32 columns) supported"); + return HTP_STATUS_NO_SUPPORT; + } + + const uint32_t N = src1->ne[3]; + const uint32_t OH = dst->ne[2]; + const uint32_t OW = dst->ne[1]; + const uint32_t npatches = N * OH * OW; + const uint32_t n_threads = MIN(octx->n_threads, npatches); + + if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) || n_threads == 0) { + return HTP_STATUS_OK; + } + + struct htp_im2col_context ictx = { 0 }; + ictx.octx = octx; + ictx.npatches_per_thread = (npatches + n_threads - 1) / n_threads; + + // Clean non-overlapping patch-embed -> DMA kernel (if it fits VTCM); + // everything else (padding/dilation/stride edges) -> pure-DDR kernel. + if (im2col_use_patchembed_dma(octx)) { + const uint32_t nrows = N * OH; + const uint32_t pth = MIN(octx->n_threads, nrows); + if (pth > 0 && im2col_patchembed_dma_fits(octx, &ictx, pth)) { + ictx.pe_rows_per_thread = (nrows + pth - 1) / pth; + if (dst->type == HTP_TYPE_F16) { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_dma_thread, &ictx, pth); + } else { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_dma_f32_thread, &ictx, pth); + } + return HTP_STATUS_OK; + } + // else: doesn't fit -> fall through to the pure-DDR kernel below. + } + + if (dst->type == HTP_TYPE_F16) { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_thread, &ictx, n_threads); + } else { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_f32_thread, &ictx, n_threads); + } + return HTP_STATUS_OK; +} diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c index d971b60f3a9c..880e20c99597 100644 --- a/ggml/src/ggml-hexagon/htp/main.c +++ b/ggml/src/ggml-hexagon/htp/main.c @@ -25,112 +25,44 @@ #define GGML_COMMON_DECL_C #include "ggml-common.h" +#include "hex-bitmap.h" #include "htp-ctx.h" #include "htp-ops.h" -#include "htp-ops.h" +#include "htp-tensor.h" #include "htp_iface.h" -#include "worker-pool.h" - -AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) { - struct htp_context * ctx; - int err = 0; +#include "work-queue.h" +#include "hex-profile.h" - ctx = calloc(1, sizeof(*ctx)); - if (ctx == NULL) { - return AEE_ENOMEMORY; - } - - // Use the context structure as the handle - *handle = (remote_handle64) ctx; - - // Enable FARF logs - HAP_setFARFRuntimeLoggingParams(0xffff, NULL, 0); - - // Set client class - { - HAP_power_request_t request; - memset(&request, 0, sizeof(HAP_power_request_t)); - request.type = HAP_power_set_apptype; - request.apptype = HAP_POWER_COMPUTE_CLIENT_CLASS; - - if ((err = HAP_power_set((void *) ctx, &request)) != 0) { - return err; - } - } +#define HMX_QUEUE_CAPACITY 16 +#define HMX_QUEUE_STACK_SIZE 16384 +#define WORK_QUEUE_CAPACITY 16 +#define WORK_QUEUE_STACK_SIZE 16384 +#define MAIN_THREAD_STACK_SIZE 32768 - { - HAP_power_request_t request; - memset(&request, 0, sizeof(request)); - - request.type = HAP_power_set_DCVS_v3; - request.dcvs_v3.set_dcvs_enable = TRUE; - request.dcvs_v3.dcvs_enable = FALSE; - request.dcvs_v3.set_bus_params = TRUE; - request.dcvs_v3.bus_params.min_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.bus_params.max_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.bus_params.target_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.set_core_params = TRUE; - request.dcvs_v3.core_params.min_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.core_params.max_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.core_params.target_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.set_sleep_disable = TRUE; - request.dcvs_v3.sleep_disable = TRUE; - -#if (__HEXAGON_ARCH__ >= 79) - HAP_set_dcvs_v3_protected_bus_corners(&request, 1); -#endif - if ((err = HAP_power_set((void *) ctx, &request)) != 0) { - return err; - } +_Static_assert(WORK_QUEUE_MAX_N_THREADS >= HTP_MAX_NTHREADS, + "work-queue thread cap must be >= HTP_MAX_NTHREADS"); - memset(&request, 0, sizeof(request)); - request.type = HAP_power_set_HVX; - request.hvx.power_up = TRUE; - if ((err = HAP_power_set((void *) ctx, &request)) != 0) { - return err; - } - } +struct htp_handle { + struct htp_context * ctx; +}; -#if __HVX_ARCH__ >= 75 - { - // Power on HMX and set HMX clock - HAP_power_request_t request; - memset(&request, 0, sizeof(HAP_power_request_t)); - request.type = HAP_power_set_HMX_v2; - request.hmx_v2.set_power = TRUE; - request.hmx_v2.power_up = TRUE; - request.hmx_v2.set_clock = TRUE; - request.hmx_v2.target_corner = HAP_DCVS_EXP_VCORNER_MAX; - request.hmx_v2.min_corner = HAP_DCVS_EXP_VCORNER_MAX; - request.hmx_v2.max_corner = HAP_DCVS_EXP_VCORNER_MAX; - request.hmx_v2.perf_mode = HAP_CLK_PERF_HIGH; - FARF(ALWAYS, "Setting HMX clock\n"); - err = HAP_power_set((void *) ctx, &request); - if (err != AEE_SUCCESS) { - FARF(ERROR, "ggml-hex: error setting HMX clock."); - return err; - } - } -#else - { - // Power on HMX - HAP_power_request_t request; - memset(&request, 0, sizeof(HAP_power_request_t)); - request.type = HAP_power_set_HMX; - request.hmx.power_up = TRUE; - FARF(ALWAYS, "Powering HMX on\n"); - err = HAP_power_set((void *) ctx, &request); - if (err != AEE_SUCCESS) { - FARF(ERROR, "ggml-hex: error powering on HMX."); - return err; - } +AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) { + (void) uri; + struct htp_handle * h = calloc(1, sizeof(*h)); + if (h == NULL) { + return AEE_ENOMEMORY; } -#endif + *handle = (remote_handle64) h; return AEE_SUCCESS; } AEEResult htp_iface_etm(remote_handle64 handle, uint32_t enable) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h) { + return AEE_EBADPARM; + } + int err = enable ? HAP_user_etm_enable() : HAP_user_etm_disable(); if (err) { if (err == AEE_EVERSIONNOTSUPPORT) { @@ -143,10 +75,11 @@ AEEResult htp_iface_etm(remote_handle64 handle, uint32_t enable) { } AEEResult htp_iface_profiler(remote_handle64 handle, uint32_t mode, const htp_iface_pmu_conf* pmu_conf) { - struct htp_context * ctx = (struct htp_context *) handle; - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h || !h->ctx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; if (mode == HTP_PROF_PMU) { const uint32_t* events = pmu_conf->events; @@ -179,48 +112,55 @@ AEEResult htp_iface_profiler(remote_handle64 handle, uint32_t mode, const htp_if } AEEResult htp_iface_close(remote_handle64 handle) { - struct htp_context * ctx = (struct htp_context *) handle; - - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h) { return AEE_EBADPARM; } - if (ctx->queue) { - FARF(ERROR, "Closing handle with queue still open"); - return AEE_EITEMBUSY; - } + struct htp_context * ctx = h->ctx; + if (ctx) { + if (ctx->dsp_queue) { + FARF(ERROR, "Closing handle with queue still open"); + return AEE_EITEMBUSY; + } - // release the mmaps (if any) - for (uint32_t i=0; immap[i].size) { + // release the mmaps (if any) + for (uint32_t i=0; immap[i].size) { #if __HVX_ARCH__ > 73 - HAP_munmap2((void *) ctx->mmap[i].base, ctx->mmap[i].size); + HAP_munmap2((void *) ctx->mmap[i].base, ctx->mmap[i].size); #else - HAP_munmap((void *) ctx->mmap[i].base, ctx->mmap[i].size); + HAP_munmap((void *) ctx->mmap[i].base, ctx->mmap[i].size); #endif - ctx->mmap[i].size = 0; - ctx->mmap[i].base = NULL; - ctx->mmap[i].fd = -1; + ctx->mmap[i].size = 0; + ctx->mmap[i].base = NULL; + ctx->mmap[i].fd = -1; + } } - } - if (ctx->profiler) { - qurt_pmu_enable(1); - } + if (ctx->profiler) { + qurt_pmu_enable(1); + } - if (ctx->etm) { - HAP_user_etm_disable(); + if (ctx->etm) { + HAP_user_etm_disable(); + } + + // Free the unified block (ctx is the base address of the block) + free(ctx); + h->ctx = NULL; } - free(ctx); + free(h); return AEE_SUCCESS; } AEEResult htp_iface_mmap(remote_handle64 handle, uint32_t fd, uint32_t size) { - struct htp_context * ctx = (struct htp_context *) handle; - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h || !h->ctx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; // See if we already have this mapping for (uint32_t i=0; ictx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; for (uint32_t i=0; immap[i]; @@ -358,91 +299,268 @@ static void vtcm_free(struct htp_context * ctx) { } } +static void htp_main_thread(void * context); static void htp_packet_callback(dspqueue_t queue, int error, void * context); static void htp_error_callback(dspqueue_t queue, int error, void * context); AEEResult htp_iface_start(remote_handle64 handle, uint32_t sess_id, uint64_t dsp_queue_id, uint32_t n_hvx, uint32_t n_hmx, uint64_t max_vmem) { - struct htp_context * ctx = (struct htp_context *) handle; - - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h) { return AEE_EBADPARM; } - if (ctx->queue) { + if (h->ctx) { FARF(ERROR, "Queue already open"); return AEE_EITEMBUSY; } - // Import queue created on the CPU - int err = dspqueue_import(dsp_queue_id, // Queue ID from dspqueue_export - htp_packet_callback, // Packet callback - htp_error_callback, // Error callback; no errors expected on the DSP - (void *) ctx, // Callback context - &ctx->queue); + // Cache the original FastRPC thread priority, then calculate compute priority + int fastrpc_tid = qurt_thread_get_id(); + int fastrpc_prio = qurt_thread_get_priority(fastrpc_tid); + int main_prio = fastrpc_prio - 10; + if (main_prio < 1) main_prio = 1; + + dspqueue_t dsp_queue = NULL; + bool use_callbacks = false; + + // Import queue with NULL callbacks to avoid starting dspueue internal threads + int err = dspqueue_import(dsp_queue_id, NULL, NULL, (void *) h, &dsp_queue); + if (err == AEE_EBADPARM) { + // Fallback for devices that don't support NULL callbacks + FARF(HIGH, "dspqueue import with NULL callbacks failed, trying with callbacks"); + use_callbacks = true; + err = dspqueue_import(dsp_queue_id, htp_packet_callback, htp_error_callback, (void *) h, &dsp_queue); + } + if (err) { FARF(ERROR, "Queue import failed with 0x%08x", (unsigned) err); return err; } + qurt_sysenv_max_hthreads_t hw_threads; + qurt_sysenv_get_max_hw_threads(&hw_threads); + uint32_t hw_nhvx = (qurt_hvx_get_units() >> 8) & 0xFF; + + if (n_hvx == 0) { + n_hvx = hw_nhvx; + } + if (n_hvx > hw_threads.max_hthreads) { + n_hvx = hw_threads.max_hthreads; + } + if (n_hvx > HTP_MAX_NTHREADS) { + n_hvx = HTP_MAX_NTHREADS; + } + + // layout segments of our contiguous block + + // 1. htp_context : sits at the base (block is 4K-aligned via memalign below) + size_t offset = sizeof(struct htp_context); + + // 2. main_stack + size_t offset_main_stack = 0; + size_t size_main_stack = 0; + if (!use_callbacks) { + offset_main_stack = hex_align_up(offset, 4096); + size_main_stack = MAIN_THREAD_STACK_SIZE; + offset = offset_main_stack + size_main_stack; + } + + // 3. work_queue + size_t wq_align = work_queue_alignof(); + size_t offset_wq = hex_align_up(offset, wq_align); + size_t size_wq = work_queue_sizeof(n_hvx, WORK_QUEUE_CAPACITY, WORK_QUEUE_STACK_SIZE); + offset = offset_wq + size_wq; + + // 4. dma_queue + size_t dma_align = dma_queue_alignof(); + size_t offset_dma = hex_align_up(offset, dma_align); + size_t size_dma = 0; + for (uint32_t i = 0; i < n_hvx; i++) { + size_dma = hex_align_up(size_dma, dma_queue_alignof()); + size_dma += dma_queue_sizeof(256); + size_dma = hex_align_up(size_dma, dma_queue_alignof()); + size_dma += dma_queue_alias_sizeof(); + } + offset = offset_dma + size_dma; + + // 5. hmx_queue + size_t offset_hmx = 0; + size_t size_hmx = 0; + if (n_hmx) { + size_t hmx_align = hmx_queue_alignof(); + offset_hmx = hex_align_up(offset, hmx_align); + size_hmx = hmx_queue_sizeof(HMX_QUEUE_CAPACITY, HMX_QUEUE_STACK_SIZE); + offset = offset_hmx + size_hmx; + } + + size_t footprint = hex_align_up(offset, 128); + + void * block = memalign(4096, footprint); + if (!block) { + FARF(ERROR, "Unable to allocate unified block of size %zu\n", footprint); + dspqueue_close(dsp_queue); + return AEE_ENOMEMORY; + } + memset(block, 0, footprint); + + h->ctx = (struct htp_context *) block; + struct htp_context * ctx = h->ctx; + ctx->footprint = footprint; + + ctx->thread_id = fastrpc_tid; + ctx->thread_prio = main_prio; ctx->max_vmem = max_vmem; - ctx->thread_id = qurt_thread_get_id(); - ctx->thread_prio = qurt_thread_get_priority(ctx->thread_id); + ctx->dsp_queue = dsp_queue; - // allocate VTCM err = vtcm_alloc(ctx); if (err != AEE_SUCCESS) { FARF(ERROR, "Unable to allocate VTCM"); + htp_iface_stop(handle); return AEE_ENOMEMORY; } - ctx->hmx_enabled = n_hmx; - ctx->hmx_queue = NULL; - if (n_hmx) { - ctx->hmx_queue = hmx_queue_create(16, ctx->vtcm_rctx); - if (ctx->hmx_queue) { - ctx->hmx_queue->trace = &ctx->trace[HTP_MAX_NTHREADS]; - } else { - FARF(ERROR, "hmx-queue-create failed"); - ctx->hmx_enabled = false; + HAP_setFARFRuntimeLoggingParams(0xffff, NULL, 0); + + // Set client class + { + HAP_power_request_t request; + memset(&request, 0, sizeof(HAP_power_request_t)); + request.type = HAP_power_set_apptype; + request.apptype = HAP_POWER_COMPUTE_CLIENT_CLASS; + + if ((err = HAP_power_set((void *) ctx, &request)) != 0) { + htp_iface_stop(handle); + return err; } } - FARF(HIGH, "HMX %s (n_hmx=%d)", ctx->hmx_enabled ? "enabled" : "disabled", n_hmx); - qurt_sysenv_max_hthreads_t hw_threads; - qurt_sysenv_get_max_hw_threads(&hw_threads); - uint32_t hw_nhvx = (qurt_hvx_get_units() >> 8) & 0xFF; + // DCVS setup + { + HAP_power_request_t request; + memset(&request, 0, sizeof(request)); - if (n_hvx == 0) { - n_hvx = hw_nhvx; + request.type = HAP_power_set_DCVS_v3; + request.dcvs_v3.set_dcvs_enable = TRUE; + request.dcvs_v3.dcvs_enable = FALSE; + request.dcvs_v3.set_bus_params = TRUE; + request.dcvs_v3.bus_params.min_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.bus_params.max_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.bus_params.target_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.set_core_params = TRUE; + request.dcvs_v3.core_params.min_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.core_params.max_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.core_params.target_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.set_sleep_disable = TRUE; + request.dcvs_v3.sleep_disable = TRUE; + +#if (__HEXAGON_ARCH__ >= 79) + HAP_set_dcvs_v3_protected_bus_corners(&request, 1); +#endif + if ((err = HAP_power_set((void *) ctx, &request)) != 0) { + htp_iface_stop(handle); + return err; + } + + memset(&request, 0, sizeof(request)); + request.type = HAP_power_set_HVX; + request.hvx.power_up = TRUE; + if ((err = HAP_power_set((void *) ctx, &request)) != 0) { + htp_iface_stop(handle); + return err; + } } - if (n_hvx > hw_threads.max_hthreads) { - n_hvx = hw_threads.max_hthreads; + +#if __HVX_ARCH__ >= 75 + { + // Power on HMX and set HMX clock + HAP_power_request_t request; + memset(&request, 0, sizeof(HAP_power_request_t)); + request.type = HAP_power_set_HMX_v2; + request.hmx_v2.set_power = TRUE; + request.hmx_v2.power_up = TRUE; + request.hmx_v2.set_clock = TRUE; + request.hmx_v2.target_corner = HAP_DCVS_EXP_VCORNER_MAX; + request.hmx_v2.min_corner = HAP_DCVS_EXP_VCORNER_MAX; + request.hmx_v2.max_corner = HAP_DCVS_EXP_VCORNER_MAX; + request.hmx_v2.perf_mode = HAP_CLK_PERF_HIGH; + FARF(ALWAYS, "Setting HMX clock\n"); + err = HAP_power_set((void *) ctx, &request); + if (err != AEE_SUCCESS) { + FARF(ERROR, "ggml-hex: error setting HMX clock."); + htp_iface_stop(handle); + return err; + } } - if (n_hvx > HTP_MAX_NTHREADS) { - n_hvx = HTP_MAX_NTHREADS; +#else + { + // Power on HMX + HAP_power_request_t request; + memset(&request, 0, sizeof(HAP_power_request_t)); + request.type = HAP_power_set_HMX; + request.hmx.power_up = TRUE; + FARF(ALWAYS, "Powering HMX on\n"); + err = HAP_power_set((void *) ctx, &request); + if (err != AEE_SUCCESS) { + FARF(ERROR, "ggml-hex: error powering on HMX."); + htp_iface_stop(handle); + return err; + } + } +#endif + + ctx->hmx_enabled = n_hmx; + ctx->hmx_queue = NULL; + if (n_hmx) { + void * hmx_ptr = (void *) ((uintptr_t) block + offset_hmx); + ctx->hmx_queue = hmx_queue_init(hmx_ptr, HMX_QUEUE_CAPACITY, HMX_QUEUE_STACK_SIZE, ctx->vtcm_rctx, &ctx->trace[HTP_MAX_NTHREADS]); } + FARF(HIGH, "HMX %s (n_hmx=%d)", ctx->hmx_enabled ? "enabled" : "disabled", n_hmx); ctx->n_threads = n_hvx; + ctx->n_threads_div = init_fastdiv_values(ctx->n_threads); + + // Initialize DMA queues + uint8_t * dma_ptr_curr = (uint8_t *) ((uintptr_t) block + offset_dma); + size_t size_dma_q = dma_queue_sizeof(256); + size_t size_dma_alias = dma_queue_alias_sizeof(); + for (int i = 0; i < ctx->n_threads; i++) { - ctx->dma[i] = dma_queue_create(256); // queue depth - if (ctx->dma[i]) { - ctx->dma[i]->trace = &ctx->trace[i]; - } + dma_ptr_curr = (uint8_t *) hex_align_up((uintptr_t) dma_ptr_curr, dma_queue_alignof()); + ctx->dma_cached[i] = dma_queue_init(dma_ptr_curr, 256, (uintptr_t) ctx->vtcm_base, ctx->vtcm_size, &ctx->trace[i]); + dma_ptr_curr += size_dma_q; + + dma_ptr_curr = (uint8_t *) hex_align_up((uintptr_t) dma_ptr_curr, dma_queue_alignof()); + ctx->dma[i] = dma_queue_alias_init(dma_ptr_curr, ctx->dma_cached[i], 1); + dma_ptr_curr += size_dma_alias; } ctx->ddr_spad_size = 512 * 1024; // 512 KB ctx->ddr_spad_base = memalign(128, ctx->ddr_spad_size); - // init worker pool - err = worker_pool_init(&ctx->worker_pool, n_hvx); - if (err != AEE_SUCCESS) { - FARF(ERROR, "Unable to create worker pool"); - if (ctx->ddr_spad_base) { - free(ctx->ddr_spad_base); - ctx->ddr_spad_base = NULL; - ctx->ddr_spad_size = 0; + void * wq_ptr = (void *) ((uintptr_t) block + offset_wq); + ctx->work_queue = work_queue_init(wq_ptr, n_hvx, WORK_QUEUE_CAPACITY, WORK_QUEUE_STACK_SIZE); + + ctx->main_stack = NULL; + ctx->main_thread = 0; + atomic_store(&ctx->killed, false); + + if (!use_callbacks) { + // Start main compute thread + ctx->main_stack = (void *) ((uintptr_t) block + offset_main_stack); + + qurt_thread_attr_t attr; + qurt_thread_attr_init(&attr); + qurt_thread_attr_set_stack_addr(&attr, ctx->main_stack); + qurt_thread_attr_set_stack_size(&attr, size_main_stack); + qurt_thread_attr_set_priority(&attr, main_prio); + qurt_thread_attr_set_name(&attr, "htp-main"); + + int err_thread = qurt_thread_create(&ctx->main_thread, &attr, htp_main_thread, ctx); + if (err_thread) { + FARF(ERROR, "Unable to create htp main thread: %d", err_thread); + htp_iface_stop(handle); + return AEE_ENOMEMORY; } - return err; } FARF(HIGH, "session %u started: n-hvx %u vtcm-size %zu vtcm-rctx %u n-threads %u thread-id %d thread-prio %d \n", @@ -452,35 +570,34 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32_t sess_id, uint64_t dsp } AEEResult htp_iface_stop(remote_handle64 handle) { - struct htp_context * ctx = (struct htp_context *) handle; - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h || !h->ctx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; - if (!ctx->queue) { - FARF(ERROR, "Queue not open"); - return AEE_EBADSTATE; + if (ctx->main_thread) { + atomic_store(&ctx->killed, true); + int status; + (void) qurt_thread_join(ctx->main_thread, &status); + ctx->main_thread = 0; } - // Close queue. dspqueue_close() will also wait for callbacks to finish. - int err = dspqueue_close(ctx->queue); - ctx->queue = NULL; + int err = dspqueue_close(ctx->dsp_queue); ctx->dsp_queue = NULL; if (err != 0) { FARF(ERROR, "Queue close failed with 0x%08x", (unsigned) err); return err; } - if (ctx->worker_pool) { - // Release worker pool - worker_pool_release(&ctx->worker_pool); - } + work_queue_free(ctx->work_queue); for (int i = 0; i < ctx->n_threads; i++) { - dma_queue_delete(ctx->dma[i]); + dma_queue_alias_free(ctx->dma[i]); + dma_queue_free(ctx->dma_cached[i]); } if (ctx->hmx_queue) { - hmx_queue_delete(ctx->hmx_queue); + hmx_queue_free(ctx->hmx_queue); ctx->hmx_queue = NULL; } ctx->hmx_enabled = false; @@ -493,6 +610,9 @@ AEEResult htp_iface_stop(remote_handle64 handle) { ctx->ddr_spad_size = 0; } + free(ctx); + h->ctx = NULL; + return AEE_SUCCESS; } @@ -598,18 +718,19 @@ static int execute_op(struct htp_ops_context * octx) { case HTP_OP_RMS_NORM: case HTP_OP_RMS_NORM_MUL: case HTP_OP_SCALE: + case HTP_OP_CLAMP: case HTP_OP_SQR: case HTP_OP_SQRT: case HTP_OP_UNARY_SOFTPLUS: case HTP_OP_UNARY_SIGMOID: + case HTP_OP_UNARY_SILU: + case HTP_OP_UNARY_GELU: case HTP_OP_UNARY_NEG: case HTP_OP_UNARY_EXP: case HTP_OP_UNARY_TANH: case HTP_OP_L2_NORM: return op_unary(octx); - case HTP_OP_UNARY_SILU: - case HTP_OP_UNARY_GELU: case HTP_OP_GLU_SWIGLU: case HTP_OP_GLU_SWIGLU_OAI: case HTP_OP_GLU_GEGLU: @@ -660,6 +781,9 @@ static int execute_op(struct htp_ops_context * octx) { case HTP_OP_PAD: return op_pad(octx); + case HTP_OP_IM2COL: + return op_im2col(octx); + case HTP_OP_CONCAT: return op_concat(octx); @@ -671,8 +795,6 @@ static int execute_op(struct htp_ops_context * octx) { case HTP_OP_INVALID: break; - - // No default to catch missing cases } FARF(ERROR, "Unknown Op %u", octx->op); @@ -778,12 +900,12 @@ static void prep_op_bufs(struct htp_context *ctx, struct htp_buf_desc *bufs, uin } } -static void prep_tensor(struct htp_context *ctx, struct htp_buf_desc *bufs, uint32_t idx, struct htp_tensor *t) { +static void prep_tensor(struct htp_context *ctx, struct htp_buf_desc *bufs, struct htp_tensor *tens, uint32_t idx, struct htp_tensor *t) { uint32_t offset = t->data; uint32_t size = t->size; uint32_t bi = t->bi; - t->data = bufs[bi].base + offset; // update data to the actual pointer + t->data = (uint32_t) (bufs[bi].base + offset); // update data to the actual pointer FARF(HIGH, "prep-tensor #%u: bi %u offset %u size %u data %p : %u:%u:%u:%u", idx, t->bi, offset, t->size, (void*) t->data, t->ne[0], t->ne[1], t->ne[3], t->ne[3]); @@ -791,7 +913,7 @@ static void prep_tensor(struct htp_context *ctx, struct htp_buf_desc *bufs, uint static void prep_tensors(struct htp_context *ctx, struct htp_buf_desc *bufs, struct htp_tensor *tens, uint32_t n_tens) { for (uint32_t i=0; i < n_tens; i++) { - prep_tensor(ctx, bufs, i, tens + i); + prep_tensor(ctx, bufs, tens, i, tens + i); } } @@ -805,29 +927,34 @@ static int proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens, u // Prep input tensors for (uint32_t i=0; isrc[i] == 0xffff ? NULL : tens + op->src[i]; - - octx->src[i] = src; - if (!src) continue; - - if (!(src->flags & HTP_TENSOR_FLUSHED) && (src->flags & HTP_TENSOR_COMPUTE)) { - // flush compute buffers on input - hex_l2flush((void *) src->data, src->size); + uint16_t src_idx = op->src[i]; + if (src_idx == 0xffff) { + octx->src[i] = NULL; + octx->src_dma[i] = NULL; + continue; } + struct htp_tensor *src = tens + src_idx; + octx->src[i] = src; + octx->src_dma[i] = octx->ctx->dma; // FIXME: ? octx->ctx->dma_cached : octx->ctx->dma; + FARF(HIGH, "prep-src #%u: data %p size %u : %u:%u:%u:%u", op->src[i], (void*) src->data, src->size, src->ne[0], src->ne[1], src->ne[3], src->ne[3]); } + htp_tensor_flush_all(octx->ctx, octx->src, HTP_OP_MAX_INPUTS); + // Prep output tensors for (uint32_t i = 0; i < HTP_OP_MAX_OUTPUTS; i++) { uint16_t dst_idx = op->dst[i]; if (dst_idx == 0xffff) { - octx->dsts[i] = NULL; + octx->dsts[i] = NULL; + octx->dst_dma[i] = NULL; continue; } struct htp_tensor *dst = tens + dst_idx; - octx->dsts[i] = dst; + octx->dsts[i] = dst; + octx->dst_dma[i] = octx->ctx->dma; // FIXME: ? octx->ctx->dma_cached : octx->ctx->dma; FARF(HIGH, "prep-dst[%u] #%u: data %p size %u : %u:%u:%u:%u", i, dst_idx, (void*) dst->data, dst->size, dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3]); @@ -835,40 +962,154 @@ static int proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens, u int status = execute_op(octx); + htp_tensor_dirty_all(octx->ctx, octx->dsts, HTP_OP_MAX_OUTPUTS); + octx->src0_spad.src = NULL; octx->src1_spad.src = NULL; octx->src2_spad.src = NULL; octx->src3_spad.src = NULL; octx->dst_spad.src = NULL; - // flush buffers on output - for (uint32_t i = 0; i < HTP_OP_MAX_OUTPUTS; i++) { - if (octx->dsts[i]) { - struct htp_tensor *dst = (struct htp_tensor *)octx->dsts[i]; - hex_l2flush((void *) dst->data, dst->size); - dst->flags |= HTP_TENSOR_FLUSHED; + return status; +} + +static void process_opbatch(struct htp_context * ctx, const struct htp_opbatch_req * req, const struct dspqueue_buffer * dbuf) { + dspqueue_t queue = ctx->dsp_queue; + int err; + + const uint32_t n_bufs = req->n_bufs; + const uint32_t n_tens = req->n_tensors; + const uint32_t n_ops = req->n_ops; + + const uint32_t b_size = sizeof(struct htp_buf_desc) * n_bufs; + const uint32_t t_size = sizeof(struct htp_tensor) * n_tens; + const uint32_t o_size = sizeof(struct htp_op_desc) * n_ops; + const uint32_t p_size = sizeof(struct htp_prof_desc) * n_ops; + const uint32_t tr_size = (HTP_MAX_NTHREADS + 1) * req->n_traces * sizeof(struct htp_trace_desc); - FARF(HIGH, "post-dst[%u] #%u: data %p size %u : %u:%u:%u:%u", i, op->dst[i], (void*) dst->data, dst->size, - dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3]); + if (dbuf->size < b_size + t_size + o_size + p_size + tr_size) { + FARF(ERROR, "invalid opbatch memory block size %u (req %u)", dbuf->size, b_size + t_size + o_size + p_size + tr_size); + return; + } + + FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u n-traces %u : m-size %u b-size %u t-size %u o-size %u", req->id, + n_bufs, n_tens, n_ops, req->n_traces, dbuf->size, b_size, t_size, o_size); + + // Setup descriptor pointers + uint8_t * m_ptr = dbuf->ptr; + struct htp_buf_desc* bufs = (struct htp_buf_desc*) m_ptr; m_ptr += b_size; + struct htp_tensor* tens = (struct htp_tensor*) m_ptr; m_ptr += t_size; + struct htp_op_desc* ops = (struct htp_op_desc*) m_ptr; m_ptr += o_size; + struct htp_prof_desc* pds = (struct htp_prof_desc*) m_ptr; + + struct profile_data batch_prof; + profile_start(HTP_PROF_BASIC, &batch_prof); + + memset(ctx->trace, 0, sizeof(ctx->trace)); + if (ctx->profiler == HTP_PROF_TRACE) { + struct htp_trace_desc * trace_events = (struct htp_trace_desc *) (m_ptr + p_size); + for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { + ctx->trace[t].events = &trace_events[t * req->n_traces]; + ctx->trace[t].max_events = req->n_traces; } } - return status; + // Clean cache at the start of the batch + htp_trace_event_start(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); + hex_l2fetch_block(ctx, ctx->footprint); + memset(ctx->dirty_ranges, 0, sizeof(ctx->dirty_ranges)); + htp_trace_event_stop(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + + htp_trace_event_start(&ctx->trace[0], HTP_TRACE_EVT_BUFF, 0); + prep_op_bufs(ctx, bufs, n_bufs); + htp_trace_event_stop(&ctx->trace[0], HTP_TRACE_EVT_BUFF, 0); + + prep_tensors(ctx, bufs, tens, n_tens); + + struct htp_ops_context *octx = &ctx->octx; + memset(octx, 0, sizeof(*octx)); + octx->n_threads = ctx->n_threads; + octx->ctx = ctx; + + work_queue_wakeup(ctx->work_queue); + if (ctx->hmx_queue) { + hmx_queue_wakeup(ctx->hmx_queue); + } + + int op_status = HTP_STATUS_OK; + for (uint32_t i = 0; i < n_ops && op_status == HTP_STATUS_OK; i++) { + struct profile_data prof; + + profile_start(ctx->profiler, &prof); + + op_status = proc_op_req(octx, tens, i, &ops[i]); + + profile_stop(ctx->profiler, &prof); + + if (ctx->profiler) { + pds[i].opcode = ops[i].opcode; + pds[i].usecs = prof.usecs; + pds[i].cycles_start = prof.cycles_start; + pds[i].cycles_stop = prof.cycles_stop; + for (int j = 0; j < HEX_NUM_PMU_COUNTERS; j++) { + pds[i].pmu[j] = prof.pmu_counters[j]; + } + } + } + + if (ctx->hmx_queue) { + hmx_queue_suspend(ctx->hmx_queue); + hmx_queue_flush(ctx->hmx_queue); + } + work_queue_suspend(ctx->work_queue); + + // Flush remaining dirty tensors at the end of the batch + htp_trace_event_start(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); + htp_trace_event_stop(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + + profile_stop(HTP_PROF_BASIC, &batch_prof); + + struct htp_opbatch_rsp rsp; + memset(&rsp, 0, sizeof(rsp)); + rsp.id = req->id; + rsp.status = op_status; + rsp.n_bufs = n_bufs; + rsp.n_tensors = n_tens; + rsp.n_ops = n_ops; + rsp.usecs = batch_prof.usecs; + rsp.cycles_start = batch_prof.cycles_start; + rsp.cycles_stop = batch_prof.cycles_stop; + + if (ctx->profiler == HTP_PROF_TRACE) { + for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { + rsp.n_traces[t] = ctx->trace[t].count; + } + } + + struct dspqueue_buffer write_dbuf = *dbuf; + write_dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT; + + err = dspqueue_write(queue, 0, 1, &write_dbuf, sizeof(rsp), (const uint8_t *) &rsp, DSPQUEUE_TIMEOUT_NONE); + if (err != 0) { + FARF(ERROR, "dspqueue_write failed: 0x%08x", (unsigned) err); + } } +#define DSPQUEUE_READ_TIMEOUT_USEC 5000 #define DSPQUEUE_POLL_TIMEOUT_USEC 100 #define DSPQUEUE_POLL_COUNT 100 -static void htp_packet_callback(dspqueue_t queue, int error, void * context) { - struct htp_context * ctx = (struct htp_context *) context; - +static void process_ops(struct htp_context * ctx) { + dspqueue_t queue = ctx->dsp_queue; int err; uint32_t poll_count = DSPQUEUE_POLL_COUNT; vtcm_acquire(ctx); - while (!ctx->vtcm_needs_release) { + while (!ctx->vtcm_needs_release && !atomic_load(&ctx->killed)) { struct htp_opbatch_req req; uint32_t r_size = sizeof(req); @@ -898,111 +1139,41 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) { // Reset poll count for valid requests poll_count = DSPQUEUE_POLL_COUNT; - const uint32_t n_bufs = req.n_bufs; - const uint32_t n_tens = req.n_tensors; - const uint32_t n_ops = req.n_ops; - - const uint32_t b_size = sizeof(struct htp_buf_desc) * n_bufs; - const uint32_t t_size = sizeof(struct htp_tensor) * n_tens; - const uint32_t o_size = sizeof(struct htp_op_desc) * n_ops; - const uint32_t p_size = sizeof(struct htp_prof_desc) * n_ops; - const uint32_t tr_size = (HTP_MAX_NTHREADS + 1) * req.n_traces * sizeof(struct htp_trace_desc); - - if (dbuf.size < b_size + t_size + o_size + p_size + tr_size) { - FARF(ERROR, "invalid opbatch memory block size %u (req %u)", dbuf.size, b_size + t_size + o_size + p_size + tr_size); - break; - } - - FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u n-traces %u : m-size %u b-size %u t-size %u o-size %u", req.id, - n_bufs, n_tens, n_ops, req.n_traces, dbuf.size, b_size, t_size, o_size); - - // Setup descriptor pointers - uint8_t * m_ptr = dbuf.ptr; - struct htp_buf_desc* bufs = (struct htp_buf_desc*) m_ptr; m_ptr += b_size; - struct htp_tensor* tens = (struct htp_tensor*) m_ptr; m_ptr += t_size; - struct htp_op_desc* ops = (struct htp_op_desc*) m_ptr; m_ptr += o_size; - struct htp_prof_desc* pds = (struct htp_prof_desc*) m_ptr; - - prep_op_bufs(ctx, bufs, n_bufs); - prep_tensors(ctx, bufs, tens, n_tens); - - struct htp_ops_context *octx = &ctx->octx; - memset(octx, 0, sizeof(*octx)); - octx->n_threads = ctx->n_threads; - octx->ctx = ctx; - - if (ctx->profiler == HTP_PROF_TRACE) { - memset(ctx->trace, 0, sizeof(ctx->trace)); - struct htp_trace_desc * trace_events = (struct htp_trace_desc *) (m_ptr + p_size); - for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { - ctx->trace[t].events = &trace_events[t * req.n_traces]; - ctx->trace[t].max_events = req.n_traces; - } - } else { - for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { - ctx->trace[t].events = NULL; - ctx->trace[t].max_events = 0; - } - } - - int op_status = HTP_STATUS_OK; - uint32_t op_wakeup = n_ops / 2; // half-way throgh the batch - - hmx_queue_wakeup(ctx->hmx_queue); - - for (uint32_t i=0; i < n_ops; i++) { - struct profile_data prof; - - if (i == op_wakeup) { - dspqueue_write_early_wakeup_noblock(queue, 0, 0); - } - - profile_start(ctx->profiler, &prof); + process_opbatch(ctx, &req, &dbuf); + } - op_status = proc_op_req(octx, tens, i, &ops[i]); + vtcm_release(ctx); +} - profile_stop(ctx->profiler, &prof); +static void htp_packet_callback(dspqueue_t queue, int error, void * context) { + (void) queue; + (void) error; + struct htp_handle * h = (struct htp_handle *) context; + if (h && h->ctx) { + process_ops(h->ctx); + } +} - if (op_status != HTP_STATUS_OK) { - break; - } +static void htp_main_thread(void * context) { + struct htp_context * ctx = (struct htp_context *) context; - if (ctx->profiler) { - pds[i].opcode = ops[i].opcode; - pds[i].usecs = prof.usecs; - pds[i].cycles_start = prof.cycles_start; - pds[i].cycles_stop = prof.cycles_stop; - for (int j = 0; j < HEX_NUM_PMU_COUNTERS; j++) { - pds[i].pmu[j] = prof.pmu_counters[j]; - } - } - } + FARF(HIGH, "htp-main-thread: started"); - hmx_queue_suspend(ctx->hmx_queue); + while (!atomic_load(&ctx->killed)) { + uint32_t flags = 0; + uint32_t num_buffers = 0; + uint32_t message_length = 0; - struct htp_opbatch_rsp rsp; - rsp.id = req.id; - rsp.status = op_status; - rsp.n_bufs = n_bufs; - rsp.n_tensors = n_tens; - rsp.n_ops = n_ops; - memset(rsp.pad, 0, sizeof(rsp.pad)); - if (ctx->profiler == HTP_PROF_TRACE) { - for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { - rsp.n_traces[t] = ctx->trace[t].count; - } + int err = dspqueue_peek(ctx->dsp_queue, &flags, &num_buffers, &message_length, 50000); + if (err == 0) { + process_ops(ctx); + } else if (err == AEE_EWOULDBLOCK || err == AEE_EEXPIRED) { + continue; } else { - memset(rsp.n_traces, 0, sizeof(rsp.n_traces)); - } - - dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT; - - err = dspqueue_write(queue, 0, 1, &dbuf, sizeof(rsp), (const uint8_t *) &rsp, DSPQUEUE_TIMEOUT_NONE); - if (err != 0) { - FARF(ERROR, "dspqueue_write failed: 0x%08x", (unsigned) err); + FARF(ERROR, "dspqueue_peek failed: 0x%08x", (unsigned) err); break; } } - vtcm_release(ctx); + FARF(HIGH, "htp-main-thread: stopped"); } diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c index 1683131a813a..9d385469ae9f 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.c +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c @@ -14,6 +14,8 @@ #include "hex-dma.h" #include "hvx-utils.h" #include "hvx-dump.h" +#include "hvx-arith.h" +#include "hvx-reduce.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -82,6 +84,8 @@ struct htp_mm_context { // Precomputed values uint32_t src0_nrows_per_thread; + uint32_t src0_row_size_padded; + uint32_t src1_nrows; struct fastdiv_values mm_div_ne12_ne1; struct fastdiv_values mm_div_ne1; @@ -92,10 +96,10 @@ struct htp_mm_context { // Per thread quant tasks // Precomputed block-parallel quantization values worker_callback_t quant_task_func; - uint32_t quant_ib_first[MAX_NUM_WORKERS]; - uint32_t quant_ib_last[MAX_NUM_WORKERS]; - uint32_t quant_r[MAX_NUM_WORKERS]; - uint32_t quant_c[MAX_NUM_WORKERS]; + uint32_t quant_ib_first[WORK_QUEUE_MAX_N_THREADS]; + uint32_t quant_ib_last[WORK_QUEUE_MAX_N_THREADS]; + uint32_t quant_r[WORK_QUEUE_MAX_N_THREADS]; + uint32_t quant_c[WORK_QUEUE_MAX_N_THREADS]; uint32_t n_quant_tasks; uint32_t n_quant_rows_per_thread; atomic_uint quant_barrier; @@ -103,6 +107,7 @@ struct htp_mm_context { // Fields for scattered mapping & HMX support in MUL_MAT_ID const uint32_t * matrix_row_counts; const struct mmid_row_mapping * matrix_rows; + uint32_t mapping_stride; // Dynamic VTCM pointers allocated sequentially uint8_t * vtcm_src0; @@ -154,8 +159,6 @@ static const uint8_t __attribute__((aligned(VLEN))) kvalues_mxfp4_lut[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }; - - #define htp_matmul_tensors_preamble \ const struct htp_tensor * restrict src0 = octx->src[0]; \ const struct htp_tensor * restrict src1 = octx->src[1]; \ @@ -254,7 +257,7 @@ static void hvx_mm_4d(unsigned int nth, unsigned int ith, void * data) { return; } - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0_start); const uint32_t blck_0 = 64; @@ -309,7 +312,7 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void const uint32_t src0_start_row = src0_nrows_per_thread * ith; \ const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; \ const uint32_t n_prefetch = kparams->n_prefetch; \ @@ -410,7 +413,7 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void const uint32_t src0_start_row = src0_nrows_per_thread * ith; \ const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; \ const uint32_t n_prefetch = kparams->n_prefetch; \ @@ -444,6 +447,16 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void \ uint32_t push_ct = ct_start; \ if (src0_start_row < src0_end_row) { \ + if (src2) { \ + float * vtcm_src2_ptr = (float *) mmctx->vtcm_src2 + src0_start_row; \ + const float * src2_ptr = (const float *) src2->data + src0_start_row; \ + int slice_size = (int)MIN(src0_end_row, ne0) - (int)src0_start_row; \ + if (slice_size > 0) { \ + dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr, src2_ptr), \ + slice_size * sizeof(float), slice_size * sizeof(float), slice_size * sizeof(float), 1); \ + dma_queue_pop_nowait(dma_queue); \ + } \ + } \ for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) { \ dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned, \ src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a); \ @@ -465,7 +478,7 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct); \ DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows, NULL); \ - htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct); \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct); \ \ if (push_ct < ct_end) { \ dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile, src0_row + push_ct * tile_row_stride), \ @@ -476,24 +489,16 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void \ int copy_cnt = (int)MIN(src0_end_row, ne0) - (int)src0_start_row; \ if (copy_cnt > 0) { \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct_end); \ if (src2) { \ - float * dst_ptr = &dst_col[src0_start_row]; \ - const float * src2_ptr = (const float *) src2->data + src0_start_row; \ - float * tmp_ptr = tmp; \ - int remaining = copy_cnt; \ - while (remaining > 0) { \ - int n = MIN(remaining, 32); \ - HVX_Vector v_out = hvx_vmemu(tmp_ptr); \ - HVX_Vector v_z = hvx_vmemu(src2_ptr); \ - hvx_vec_store_u(dst_ptr, n * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z)); \ - dst_ptr += n; \ - src2_ptr += n; \ - tmp_ptr += n; \ - remaining -= n; \ - } \ + hvx_add_f32_uaa((uint8_t *) &dst_col[src0_start_row], \ + (const uint8_t *) tmp, \ + (const uint8_t *) ((const float *) mmctx->vtcm_src2 + src0_start_row), \ + copy_cnt); \ } else { \ hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt); \ } \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct_end); \ } \ } @@ -523,7 +528,7 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, uint8_t * restrict vtcm_src3_ptr = mmctx->vtcm_src3 + mmctx->vtcm_src3_size_per_thread * ith; \ uint8_t * restrict src1_data = mmctx->vtcm_src1; \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; \ const uint32_t n_prefetch = kparams->n_prefetch; \ @@ -699,7 +704,7 @@ static void hvx_mm_ffn_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, uint8_t * restrict vtcm_src2_ptr = mmctx->vtcm_src2 + mmctx->vtcm_src2_size_per_thread * ith; \ uint8_t * restrict src1_data = mmctx->vtcm_src1; \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const uint8_t * restrict src0_row = (const uint8_t *) src0->data; \ const uint8_t * restrict src2_row = (const uint8_t *) src2->data; \ @@ -820,7 +825,7 @@ static void name(unsigned int nth, unsigned int ith, void * data) { return; \ } \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first); \ \ uint8_t * restrict dst = mmctx->vtcm_src1; \ @@ -846,7 +851,7 @@ QUANTIZE_IMPL(quantize_f16_f16_flat, "quantize-f16-f16", quantize_f16_f static void quantize_f32_q8_0_tiled_block(unsigned int nth, unsigned int ith, void * data) { struct htp_mm_context * mmctx = data; struct htp_ops_context * octx = mmctx->octx; - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]); const struct htp_tensor * src = octx->src[1]; @@ -870,7 +875,7 @@ static void quantize_f32_q8_0_tiled_block(unsigned int nth, unsigned int ith, vo static void quantize_f32_q8_1_tiled_block(unsigned int nth, unsigned int ith, void * data) { struct htp_mm_context * mmctx = data; struct htp_ops_context * octx = mmctx->octx; - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]); const struct htp_tensor * src = octx->src[1]; @@ -944,7 +949,7 @@ static void hvx_mm_2d(unsigned int nth, unsigned int ith, void * data) { const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U); - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const size_t dst_row_size = nb1; const size_t src0_row_size = nb01; @@ -1040,7 +1045,7 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) { const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const size_t dst_row_size = nb1; const size_t src0_row_size = nb01; @@ -1069,6 +1074,16 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) { // Prefill vtcm with 2x src0 rows if (src0_start_row < src0_end_row) { + if (src2) { + float * vtcm_src2_ptr = (float *) mmctx->vtcm_src2 + src0_start_row; + const float * src2_ptr = (const float *) src2->data + src0_start_row; + int slice_size = (int)src0_end_row - (int)src0_start_row; + if (slice_size > 0) { + dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr, src2_ptr), + slice_size * sizeof(float), slice_size * sizeof(float), slice_size * sizeof(float), 1); + dma_queue_pop_nowait(dma_queue); + } + } for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) { const uint32_t is0 = (ir0 - src0_start_row); if (is0 >= n_prefetch) { @@ -1114,27 +1129,21 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) { } int copy_cnt = src0_end_row - src0_start_row; - if (src2) { - float * dst_ptr = &dst_col[src0_start_row]; - const float * src2_ptr = (const float *) src2->data + src0_start_row; - float * tmp_ptr = tmp; - int remaining = copy_cnt; - while (remaining > 0) { - int n = MIN(remaining, 32); - HVX_Vector v_out = hvx_vmemu(tmp_ptr); - HVX_Vector v_z = hvx_vmemu(src2_ptr); - hvx_vec_store_u(dst_ptr, n * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z)); - dst_ptr += n; - src2_ptr += n; - tmp_ptr += n; - remaining -= n; + if (copy_cnt > 0) { + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, src0_end_row); + if (src2) { + hvx_add_f32_uaa((uint8_t *) &dst_col[src0_start_row], + (const uint8_t *) tmp, + (const uint8_t *) ((const float *) mmctx->vtcm_src2 + src0_start_row), + copy_cnt); + } else { + hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt); } - } else { - hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt); + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, src0_end_row); } } -#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * ids->ne[0] * ids->ne[1] + (i1)] +#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * mmctx->mapping_stride + (i1)] static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) { htp_matmul_preamble; @@ -1155,7 +1164,7 @@ static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) { return; } - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const uint32_t n_prefetch = kparams->n_prefetch; @@ -1244,7 +1253,7 @@ static void hvx_mv_id(unsigned int nth, unsigned int ith, void * data) { return; } - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const uint32_t n_prefetch = kparams->n_prefetch; @@ -1338,6 +1347,9 @@ static int hvx_mm_init_vec_dot(struct htp_mm_context * mmctx, enum htp_data_type static int hvx_mm_matmul(struct htp_ops_context * octx) { htp_matmul_tensors_preamble; + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + struct htp_mm_context mmctx_struct = {0}; struct htp_mm_context * mmctx = &mmctx_struct; mmctx->octx = octx; @@ -1516,7 +1528,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, ne10, src1_nrows, octx->n_threads, - dst_row_size, src0_row_size, src1_row_size, kparams->n_prefetch, false, false, false); + dst_row_size, src0_row_size, src1_row_size, src2 ? src2->nb[1] : 0, kparams->n_prefetch, false, false, false); if (kparams->kernel_type == HTP_MM_KERNEL_HVX_F16_F16_VTCM || kparams->kernel_type == HTP_MM_KERNEL_HVX_F32_F32_VTCM || @@ -1548,6 +1560,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { uint8_t * const base = (uint8_t *) octx->ctx->vtcm_base; mmctx->vtcm_src1 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src1); mmctx->vtcm_src0 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src0); + mmctx->vtcm_src2 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src2); mmctx->vtcm_dst = VTCM_LAYOUT_PTR(uint8_t, base, L.off_dst); octx->src1_spad.src = NULL; @@ -1557,9 +1570,6 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { mmctx->vtcm_src0_stride = src0_row_size_padded; mmctx->vtcm_src1_stride = src1_row_size; - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) - return HTP_STATUS_OK; - if (need_quant) { mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks; mmctx->quant_task_func = quant_task_func; @@ -1570,8 +1580,9 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { mmctx->n_quant_tasks = 0; } - const uint32_t n_matmul_jobs = octx->n_threads; - worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + + worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, octx->n_threads); return HTP_STATUS_OK; } @@ -1874,7 +1885,7 @@ static void hvx_mm_ffn_2d(unsigned int nth, unsigned int ith, void * data) { #define DEQUANTIZE_WORKER_LOOP_IMPL(SUFFIX) \ static void dequantize_tiled_worker_loop_##SUFFIX(unsigned int n, unsigned int i, void *data) { \ tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data; \ - struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL; \ + struct htp_thread_trace * tr = &state->traces[i]; \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i); \ for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) { \ int start = task_id * state->n_tiles_per_task; \ @@ -1892,7 +1903,7 @@ DEQUANTIZE_WORKER_LOOP_IMPL(q8_0) static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) { tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data; - struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL; + struct htp_thread_trace * tr = &state->traces[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i); for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) { int start = task_id * state->n_tiles_per_task; @@ -1905,7 +1916,7 @@ static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) { tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data; - struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL; + struct htp_thread_trace * tr = &state->traces[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, i); for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) { @@ -1920,7 +1931,7 @@ static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { output_transfer_task_state_t *st = (output_transfer_task_state_t *) data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int start_chunk_idx = i * st->n_chunks_per_task; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, start_chunk_idx); @@ -1955,6 +1966,170 @@ typedef struct { uint32_t dma_step_rows_shift; } activation_transfer_task_state_t; +typedef struct { + __fp16 *dst; + const float *src; + uint32_t n_rows; + uint32_t k_block; + uint32_t k_stride; + uint32_t k_valid; + uint32_t n_col_chunks; + struct fastdiv_values n_threads_div; + float *vtcm_f32_act; + size_t vtcm_f32_act_bytes; + struct htp_thread_trace *traces; + struct htp_context *ctx; + uint32_t dma_step_rows; + uint32_t dma_step_rows_shift; +} activation_transfer_col_chunk_state_t; + +static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined_col_chunk( + dma_queue *dma_q, + __fp16 *restrict vtcm_dst, + const float *restrict src, + uint32_t n_rows, + uint32_t k_block, + uint32_t k_stride, + uint32_t k_chunk_valid, + uint32_t c_first, + uint32_t c_len, + float *thread_f32_act, + struct htp_thread_trace *tr, + uint32_t dma_step_rows, + uint32_t dma_step_rows_shift) { + + const uint32_t R = dma_step_rows; + const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS); + + const uint32_t n_steps = n_rows_padded >> dma_step_rows_shift; + + // Push step 0 + if (n_steps > 0 && n_rows > 0) { + uint32_t nrows_to_fetch = hex_smin(n_rows, R); + dma_queue_push(dma_q, dma_make_ptr(thread_f32_act, src + c_first), + c_len * sizeof(float), k_stride * sizeof(float), k_chunk_valid * sizeof(float), nrows_to_fetch); + } + // Push step 1 + if (n_steps > 1) { + uint32_t next_r = R * 1; + if (next_r < n_rows) { + uint32_t nrows_to_fetch = hex_smin(n_rows - next_r, R); + const float *next_src = src + next_r * k_stride + c_first; + float *next_buf = thread_f32_act + 1 * R * c_len; + dma_queue_push(dma_q, dma_make_ptr(next_buf, next_src), + c_len * sizeof(float), k_stride * sizeof(float), k_chunk_valid * sizeof(float), nrows_to_fetch); + } + } + for (uint32_t s = 0; s < n_steps; ++s) { + uint32_t r = s << dma_step_rows_shift; + float *curr_buf = thread_f32_act; + + if (r < n_rows) { + curr_buf = (float *) dma_queue_pop(dma_q).dst; + } + + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, r); + for (uint32_t p = 0; p < (R >> 1); ++p) { + uint32_t row_idx = r + (p << 1); + float *pair_buf = curr_buf + (p << 1) * c_len; + bool r0_valid = ((row_idx + 0) < n_rows); + bool r1_valid = ((row_idx + 1) < n_rows); + + transfer_activation_row_pair_fp32_to_fp16_col_chunk( + vtcm_dst, pair_buf, pair_buf + c_len, row_idx, k_block, c_first, c_len, k_chunk_valid, r0_valid, r1_valid + ); + } + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, r); + + // Push step s + 2 + uint32_t next_s = s + 2; + uint32_t next_r = next_s << dma_step_rows_shift; + if (next_r < n_rows) { + uint32_t nrows_to_fetch = hex_smin(n_rows - next_r, R); + const float *next_src = src + next_r * k_stride + c_first; + dma_queue_push(dma_q, dma_make_ptr(curr_buf, next_src), + c_len * sizeof(float), k_stride * sizeof(float), k_chunk_valid * sizeof(float), nrows_to_fetch); + } + } +} + +static void transfer_activation_chunk_fp32_to_fp16_col_chunk( + __fp16 *restrict vtcm_dst, + const float *restrict src, + uint32_t n_rows, + uint32_t k_block, + uint32_t k_stride, + uint32_t c_first, + uint32_t c_len, + uint32_t k_chunk_valid) { + const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS); + const uint32_t n_rows_tiled = (n_rows / HTP_MM_HMX_TILE_N_ROWS) * HTP_MM_HMX_TILE_N_ROWS; + + uint32_t r = 0; + + #pragma unroll(2) + for (r = 0; r < n_rows_tiled; r += 2) { + const float *ptr_in0 = src + (r + 0) * k_stride + c_first; + const float *ptr_in1 = src + (r + 1) * k_stride + c_first; + + transfer_activation_row_pair_fp32_to_fp16_col_chunk( + vtcm_dst, ptr_in0, ptr_in1, r, k_block, c_first, c_len, k_chunk_valid, true, true + ); + } + + for (; r < n_rows_padded; r += 2) { + const bool row0_valid = r < n_rows; + const bool row1_valid = (r + 1) < n_rows; + + const float *ptr_in0 = row0_valid ? (src + (r + 0) * k_stride + c_first) : NULL; + const float *ptr_in1 = row1_valid ? (src + (r + 1) * k_stride + c_first) : NULL; + + transfer_activation_row_pair_fp32_to_fp16_col_chunk( + vtcm_dst, ptr_in0, ptr_in1, r, k_block, c_first, c_len, k_chunk_valid, row0_valid, row1_valid + ); + } +} + +static void transfer_activation_chunk_col_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { + activation_transfer_col_chunk_state_t *st = (activation_transfer_col_chunk_state_t *) data; + struct htp_thread_trace * tr = &st->traces[i]; + + uint32_t n_blocks = st->k_block / 32; + uint32_t b_first = fastdiv(n_blocks * i, &st->n_threads_div); + uint32_t b_last = fastdiv(n_blocks * (i + 1), &st->n_threads_div); + uint32_t c_first = b_first * 32; + uint32_t c_last = b_last * 32; + uint32_t c_len = c_last - c_first; + + if (c_len == 0) { + return; + } + + uint32_t k_chunk_valid = 0; + if (st->k_valid > c_first) { + k_chunk_valid = hex_smin(st->k_valid, c_last) - c_first; + } + + __fp16 *dst = st->dst; + const float *src = st->src; + + if (st->vtcm_f32_act) { + size_t thread_scratch_bytes = hex_align_down(fastdiv(st->vtcm_f32_act_bytes, &st->n_threads_div), 128); + float *thread_f32_act = (float *)((char *)st->vtcm_f32_act + i * thread_scratch_bytes); + + transfer_activation_chunk_fp32_to_fp16_dma_pipelined_col_chunk( + st->ctx->dma[i], dst, src, st->n_rows, st->k_block, st->k_stride, k_chunk_valid, + c_first, c_len, thread_f32_act, tr, st->dma_step_rows, st->dma_step_rows_shift + ); + } else { + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, c_first); + transfer_activation_chunk_fp32_to_fp16_col_chunk( + dst, src, st->n_rows, st->k_block, st->k_stride, c_first, c_len, k_chunk_valid + ); + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, c_first); + } +} + static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined( dma_queue *dma_q, __fp16 *restrict vtcm_dst, @@ -2024,7 +2199,7 @@ static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined( static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) { int chunk_idx = task_id * st->n_chunks_per_task; @@ -2085,15 +2260,16 @@ typedef struct { static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigned int i, void *data) { activation_transfer_gathered_task_state_t *st = data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int chunk_idx = i; int chunk_size = st->n_chunks_per_task; - int start_row = st->start_row + chunk_idx * chunk_size; + int vtcm_start_row = chunk_idx * chunk_size; + int start_row = st->start_row + vtcm_start_row; int n_rows = hex_smin(st->cne1 - start_row, chunk_size); if (n_rows > 0) { htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); transfer_activation_chunk_fp32_to_fp16_gathered( - st->dst, st->src, start_row, n_rows, st->k_block, + st->dst, st->src, start_row, vtcm_start_row, n_rows, st->k_block, st->matrix_rows, st->cur_a, st->mapping_stride, st->ne11, &st->ne11_div, st->nb11, st->nb12, st->cne1, st->k_valid); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); @@ -2102,15 +2278,16 @@ static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigne static void transfer_activation_chunk_gathered_worker_flat_fn(unsigned int n, unsigned int i, void *data) { activation_transfer_gathered_task_state_t *st = data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int chunk_idx = i; int chunk_size = st->n_chunks_per_task; - int start_row = st->start_row + chunk_idx * chunk_size; + int vtcm_start_row = chunk_idx * chunk_size; + int start_row = st->start_row + vtcm_start_row; int n_rows = hex_smin(st->cne1 - start_row, chunk_size); if (n_rows > 0) { htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); transfer_activation_chunk_fp32_to_fp16_gathered_flat( - st->dst, st->src, start_row, n_rows, st->k_block, + st->dst, st->src, start_row, vtcm_start_row, n_rows, st->k_block, st->matrix_rows, st->cur_a, st->mapping_stride, st->nb12, st->cne1, st->k_valid); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); @@ -2119,15 +2296,16 @@ static void transfer_activation_chunk_gathered_worker_flat_fn(unsigned int n, un static void transfer_output_chunk_scattered_worker_fn(unsigned int n, unsigned int i, void *data) { output_transfer_scattered_task_state_t *st = data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int chunk_idx = i; int chunk_size = st->n_chunks_per_task; - int start_row = st->start_row + chunk_idx * chunk_size; + int vtcm_start_row = chunk_idx * chunk_size; + int start_row = st->start_row + vtcm_start_row; int n_rows = hex_smin(st->cne1 - start_row, chunk_size); if (n_rows > 0) { htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, chunk_idx); transfer_output_chunk_fp16_to_fp32_scattered( - st->dst, st->vtcm_src, start_row, n_rows, st->n_cols, + st->dst, st->vtcm_src, start_row, vtcm_start_row, n_rows, st->n_cols, st->matrix_rows, st->cur_a, st->mapping_stride, st->dst_nb1, st->dst_nb2, st->cne1); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, chunk_idx); @@ -2178,12 +2356,77 @@ static void dequantize_tiled_weight_chunk_to_fp16_tiles( } } +typedef struct { + float *dst; + const float *src2; + const __fp16 *vtcm_src; + uint32_t n_rows; + uint32_t n_cols; + uint32_t dst_stride; + uint32_t src2_stride; + uint32_t dst_cols; + struct fastdiv_values n_threads_div; + struct htp_thread_trace *traces; + struct htp_context *ctx; +} output_transfer_col_chunk_state_t; + +static void transfer_output_chunk_col_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { + (void) n; + output_transfer_col_chunk_state_t *st = (output_transfer_col_chunk_state_t *) data; + struct htp_thread_trace * tr = &st->traces[i]; + + uint32_t n_blocks = st->n_cols / 32; + uint32_t b_first = fastdiv(n_blocks * i, &st->n_threads_div); + uint32_t b_last = fastdiv(n_blocks * (i + 1), &st->n_threads_div); + uint32_t c_first = b_first * 32; + uint32_t c_last = b_last * 32; + uint32_t c_len = c_last - c_first; + + if (c_len == 0) return; + + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, c_first); + + float *dst = st->dst + c_first; + const float *src2 = st->src2 ? (st->src2 + c_first) : NULL; + const __fp16 *vtcm_src = st->vtcm_src + b_first * HTP_MM_HMX_TILE_N_ELMS; + + int chunk_dst_cols = (int)st->dst_cols - (int)c_first; + if (chunk_dst_cols > 0) { + transfer_output_chunk_fp16_to_fp32_col_chunk( + dst, src2, vtcm_src, 0, st->n_rows, c_len, st->n_cols, + st->dst_stride, st->src2_stride, (uint32_t)chunk_dst_cols + ); + } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, c_first); +} + static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const float *src2, const __fp16 *vtcm_src, int n_rows, int n_cols, int dst_stride, uint32_t src2_stride, int dst_cols, int n_threads) { assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0); if (n_rows <= 0) return; + uint32_t n_blocks = (uint32_t)n_cols / 32; + if (n_threads > 1 && n_blocks >= (uint32_t)n_threads) { + struct fastdiv_values n_threads_div = init_fastdiv_values(n_threads); + output_transfer_col_chunk_state_t col_state; + col_state.dst = dst; + col_state.src2 = src2; + col_state.vtcm_src = vtcm_src; + col_state.n_rows = (uint32_t)n_rows; + col_state.n_cols = (uint32_t)n_cols; + col_state.dst_stride = (uint32_t)dst_stride; + col_state.src2_stride = src2_stride; + col_state.dst_cols = (uint32_t)dst_cols; + col_state.n_threads_div = n_threads_div; + col_state.traces = ctx->trace; + col_state.ctx = ctx; + + worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_col_chunk_worker_fn, &col_state, n_threads); + return; + } + size_t n_tot_chunks = n_rows; size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : hmx_ceil_div(n_rows, n_threads); n_chunks_per_task = hex_align_up(n_chunks_per_task, 2); @@ -2210,42 +2453,81 @@ static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, } } -static void transfer_activation_chunk_threaded( - struct htp_context *ctx, - __fp16 *dst, - const float *src, - int n_rows, - int k_block, - int k_stride, - int n_threads, - int k_valid, - float *vtcm_f32_act, - size_t vtcm_f32_act_bytes) { +struct activation_transfer_params { + struct htp_context * ctx; + __fp16 * dst; + const float * src; + int n_rows; + int k_block; + int k_stride; + int n_threads; + const struct fastdiv_values * act_threads_div; + const struct fastdiv_values * k_div; + int k_valid; + float * vtcm_f32_act; + size_t vtcm_f32_act_bytes; +}; + +static void transfer_activation_chunk_threaded(const struct activation_transfer_params * params) { + struct htp_context * ctx = params->ctx; + __fp16 * dst = params->dst; + const float * src = params->src; + int n_rows = params->n_rows; + int k_block = params->k_block; + int k_stride = params->k_stride; + int n_threads = params->n_threads; + const struct fastdiv_values * act_threads_div = params->act_threads_div; + const struct fastdiv_values * k_div = params->k_div; + int k_valid = params->k_valid; + float * vtcm_f32_act = params->vtcm_f32_act; + size_t vtcm_f32_act_bytes = params->vtcm_f32_act_bytes; + if (n_rows <= 0) { return; } + const size_t n_tasks = (n_rows + 31) >> 5; + if (n_threads > 1 && k_block > 32 && n_tasks < (size_t) n_threads) { + // Calculate step rows parameters for column-chunked dma pipelining + uint32_t dma_step_rows = 2; + uint32_t dma_step_rows_shift = 1; + if (vtcm_f32_act && vtcm_f32_act_bytes > 0 && k_block > 0) { + size_t thread_scratch_bytes = hex_align_down(fastdiv(vtcm_f32_act_bytes, act_threads_div), 128); + size_t thread_scratch_elements = thread_scratch_bytes / sizeof(float); + size_t dma_step_rows_max = fastdiv(thread_scratch_elements / 2, k_div); + if (dma_step_rows_max >= 4) { + dma_step_rows = 4; + dma_step_rows_shift = 2; + } + } + + activation_transfer_col_chunk_state_t col_state; + col_state.dst = dst; + col_state.src = src; + col_state.n_rows = n_rows; + col_state.k_block = k_block; + col_state.k_stride = k_stride; + col_state.k_valid = k_valid; + col_state.n_col_chunks = n_threads; + col_state.n_threads_div = *act_threads_div; + col_state.vtcm_f32_act = vtcm_f32_act; + col_state.vtcm_f32_act_bytes = vtcm_f32_act_bytes; + col_state.traces = ctx->trace; + col_state.ctx = ctx; + col_state.dma_step_rows = dma_step_rows; + col_state.dma_step_rows_shift = dma_step_rows_shift; + + worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_col_chunk_worker_fn, &col_state, n_threads); + return; + } + assert(k_block % HTP_MM_HMX_TILE_N_COLS == 0 && k_stride % HTP_MM_HMX_TILE_N_COLS == 0); size_t n_tot_chunks = n_rows; size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : 32; // must be multiple of 32 to ensure correct destination address - uint32_t dma_step_rows = 2; - uint32_t dma_step_rows_shift = 1; - if (vtcm_f32_act && vtcm_f32_act_bytes > 0 && k_block > 0) { - size_t thread_scratch_elements = vtcm_f32_act_bytes / (n_threads * sizeof(float)); - size_t dma_step_rows_max = (thread_scratch_elements / 2) / k_block; - if (dma_step_rows_max >= 4) { - dma_step_rows = 4; - dma_step_rows_shift = 2; - } else { - dma_step_rows = 2; - dma_step_rows_shift = 1; - } - } - activation_transfer_task_state_t state; - state.n_tasks = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task; + state.n_tasks = (n_threads == 1) ? 1 : hmx_ceil_div(n_tot_chunks, 32); state.n_tot_chunks = n_tot_chunks; state.n_chunks_per_task = n_chunks_per_task; state.dst = dst; @@ -2258,7 +2540,18 @@ static void transfer_activation_chunk_threaded( state.vtcm_f32_act = vtcm_f32_act; int active_threads = hex_smin(n_threads, (int)state.n_tasks); - state.vtcm_f32_act_bytes_per_thread = (vtcm_f32_act_bytes / active_threads) & ~127u; + state.vtcm_f32_act_bytes_per_thread = hex_align_down(vtcm_f32_act_bytes / active_threads, 128); + + uint32_t dma_step_rows = 2; + uint32_t dma_step_rows_shift = 1; + if (vtcm_f32_act && state.vtcm_f32_act_bytes_per_thread > 0 && k_block > 0) { + size_t thread_scratch_elements = state.vtcm_f32_act_bytes_per_thread / sizeof(float); + size_t dma_step_rows_max = fastdiv(thread_scratch_elements / 2, k_div); + if (dma_step_rows_max >= 4) { + dma_step_rows = 4; + dma_step_rows_shift = 2; + } + } state.dma_step_rows = dma_step_rows; state.dma_step_rows_shift = dma_step_rows_shift; @@ -2321,9 +2614,14 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, int pipeline, int n_threads, int act_threads, + const struct fastdiv_values * act_threads_div, + const struct fastdiv_values * k_div, int tile_size, int aligned_tile_size, int vtcm_size) { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + if (k % 32 != 0 || n % 32 != 0) { return -1; } if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN)) { return -1; } @@ -2393,6 +2691,8 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, int n_chunk_cnt = hmx_ceil_div(n, n_chunk_n_cols); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + if (pipeline) { // --- Asynchronous Pipelined Loop --- hmx_matmul_job_t job_slots[2]; // persistent double-buffered job descriptors @@ -2403,7 +2703,21 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, void *vtcm_weight_bufs[2] = { vtcm_scratch0, vtcm_scratch1 }; void *vtcm_output_bufs[2] = { vtcm_output, vtcm_scratch2 }; - transfer_activation_chunk_threaded(ctx, vtcm_f16_act, activation + mr * act_stride, n_rows, k, act_stride, act_threads, k_valid, vtcm_f32_act, L.act_f32_bytes); + struct activation_transfer_params act_params = { + .ctx = ctx, + .dst = vtcm_f16_act, + .src = activation + mr * act_stride, + .n_rows = (int) n_rows, + .k_block = k, + .k_stride = act_stride, + .n_threads = act_threads, + .act_threads_div = act_threads_div, + .k_div = k_div, + .k_valid = k_valid, + .vtcm_f32_act = vtcm_f32_act, + .vtcm_f32_act_bytes = L.act_f32_bytes, + }; + transfer_activation_chunk_threaded(&act_params); // Prologue: push A0 and optionally A1 (if n_chunk_cnt > 1) const size_t n_cols_A0 = hex_smin(n - 0 * n_chunk_n_cols, n_chunk_n_cols); @@ -2480,7 +2794,21 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) { const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows); - transfer_activation_chunk_threaded(ctx, vtcm_f16_act, activation + mr * act_stride, n_rows, k, act_stride, act_threads, k_valid, vtcm_f32_act, L.act_f32_bytes); + struct activation_transfer_params act_params = { + .ctx = ctx, + .dst = vtcm_f16_act, + .src = activation + mr * act_stride, + .n_rows = (int) n_rows, + .k_block = k, + .k_stride = act_stride, + .n_threads = act_threads, + .act_threads_div = act_threads_div, + .k_div = k_div, + .k_valid = k_valid, + .vtcm_f32_act = vtcm_f32_act, + .vtcm_f32_act_bytes = L.act_f32_bytes, + }; + transfer_activation_chunk_threaded(&act_params); // A0: Pre-fetch the first weight chunk (nc = 0) if (n > 0) { @@ -2570,7 +2898,8 @@ static inline const float *hmx_mm_src2_batch_ptr(const hmx_mm_f16_f32_batched_pa static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx, const hmx_mm_f16_f32_batched_params_t *params, - int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) { + int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size, + const struct fastdiv_values * act_threads_div, const struct fastdiv_values * k_div) { int ret = 0; for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) { for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) { @@ -2582,14 +2911,17 @@ static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx, params->act_stride, params->weight_stride * (int)sizeof(__fp16), HTP_TYPE_F16, params->k, params->dst_stride, params->src2_stride, params->n, m_chunk, n_chunk, pipeline, n_threads, act_threads, - 0, 0, vtcm_size); + act_threads_div, k_div, 0, 0, vtcm_size); } } return ret; } static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_batched_params_t *params, - int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) { + int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, + const struct fastdiv_values * act_threads_div, + const struct fastdiv_values * k_div, + int vtcm_size) { if (params->act_stride < params->k || params->weight_stride < params->k || params->dst_stride < params->n) { return -1; } if (params->ne02 <= 0 || params->ne03 <= 0 || params->ne12 <= 0 || params->ne13 <= 0) { return -1; } if (params->ne12 % params->ne02 != 0 || params->ne13 % params->ne03 != 0) { return -1; } @@ -2604,9 +2936,12 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ // Grouped path is only valid if group_size > 1 and it fits within VTCM budget. bool run_grouped = (group_size > 1 && (size_t)vtcm_size <= vtcm_budget); if (!run_grouped) { - return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size); + return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size, act_threads_div, k_div); } + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const size_t vec_dot_size = params->k * sizeof(__fp16); const bool use_dma_activation = (params->act_stride > params->k); @@ -2622,7 +2957,8 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ if (L.total_bytes > vtcm_budget) { FARF(HIGH, "%s: grouped layout overflowed VTCM, falling back to simple batched loop", __func__); - return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size, act_threads_div, k_div); } uint8_t * const base = (uint8_t *) ctx->vtcm_base; @@ -2644,6 +2980,8 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ const size_t fp16_row_bytes = (size_t) params->k * sizeof(__fp16); const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + hmx_matmul_job_t job; for (int b3 = 0; b3 < params->ne13; ++b3) { @@ -2662,9 +3000,21 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ for (int g = 0; g < group_size; ++g) { const float *activation_chunk = hmx_mm_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride; __fp16 *vtcm_act_g = vtcm_f16_act + (size_t) g * L.act_head_stride; - transfer_activation_chunk_threaded(ctx, vtcm_act_g, - activation_chunk, (int) n_rows, - params->k, params->act_stride, act_threads, params->k, vtcm_f32_act, L.act_f32_bytes); + struct activation_transfer_params act_params = { + .ctx = ctx, + .dst = vtcm_act_g, + .src = activation_chunk, + .n_rows = (int) n_rows, + .k_block = params->k, + .k_stride = params->act_stride, + .n_threads = act_threads, + .act_threads_div = act_threads_div, + .k_div = k_div, + .k_valid = params->k, + .vtcm_f32_act = vtcm_f32_act, + .vtcm_f32_act_bytes = L.act_f32_bytes, + }; + transfer_activation_chunk_threaded(&act_params); } // Prologue: Push A0 and A1 (if exists) @@ -2835,6 +3185,9 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx, const struct mmid_row_mapping *matrix_rows, int cur_a, int mapping_stride) { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const int cne1 = m; const int m_padded = hex_align_up(m, 32); @@ -2913,6 +3266,8 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx, hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00)); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + hmx_matmul_job_t job; for (size_t mr = 0; mr < (size_t) m_padded; mr += m_chunk_n_rows) { @@ -2980,10 +3335,6 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k const int act_stride = (int)(src1->nb[1] / sizeof(float)); const int wgt_stride = (int)(src0->nb[1] / sizeof(__fp16)); - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) { - return HTP_STATUS_OK; - } - const float * src2_ptr = NULL; uint32_t src2_stride = 0; size_t src2_nb2 = 0; @@ -3027,6 +3378,8 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k kparams->m_chunk, kparams->n_chunk, kparams->pipeline, n_threads, kparams->n_act_threads, + &kparams->div_n_act_threads, + &kparams->div_ne00_padded, kparams->vtcm_size); } else { ret = hmx_mm_2d_f32( @@ -3035,6 +3388,8 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k (int)(dst->nb[1] / sizeof(float)), src2_stride, (int)dst->ne[0], kparams->m_chunk, kparams->n_chunk, kparams->pipeline, n_threads, kparams->n_act_threads, + &kparams->div_n_act_threads, + &kparams->div_ne00_padded, kparams->tile_size, kparams->aligned_tile_size, kparams->vtcm_size ); } @@ -3058,12 +3413,10 @@ int op_matmul(struct htp_ops_context * octx) { static int hmx_mm_op_matmul_id( struct htp_ops_context * octx, - struct htp_mm_context * mmctx, - const uint32_t * matrix_row_counts, - const struct mmid_row_mapping * matrix_rows, - void * mapping_buf, - bool must_free_mapping + struct htp_mm_context * mmctx ) { + const uint32_t * matrix_row_counts = mmctx->matrix_row_counts; + const struct mmid_row_mapping * matrix_rows = mmctx->matrix_rows; htp_matmul_tensors_preamble; const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const int n_ids = octx->src[2]->ne[0]; @@ -3081,28 +3434,28 @@ static int hmx_mm_op_matmul_id( nb11, nb12, nb1, nb2, (int) src0->nb[1], (int) src0->type, - matrix_rows, cur_a, n_ids * octx->src[2]->ne[1]); + matrix_rows, cur_a, mmctx->mapping_stride); if (ret != 0) { FARF(ERROR, "HMX matmul failed for expert %u, error %d\n", cur_a, ret); - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_NO_SUPPORT; } } - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_OK; } static int hvx_mm_matmul_id( struct htp_ops_context * octx, struct htp_mm_context * mmctx, - size_t src0_row_size_padded, - uint32_t src1_nrows, - worker_callback_t matmul_id_job_func, - void * mapping_buf, - bool must_free_mapping + work_queue_func_t hvx_mmid_task_func ) { htp_matmul_tensors_preamble; + const uint32_t src0_row_size_padded = mmctx->src0_row_size_padded; + const uint32_t src1_nrows = mmctx->src1_nrows; + + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const struct htp_tensor * restrict ids = octx->src[2]; const size_t src0_row_size = nb01; @@ -3111,7 +3464,7 @@ static int hvx_mm_matmul_id( const uint32_t nb = (ne10 + qk - 1) / qk; const uint32_t total_nb = src1_nrows * nb; - worker_callback_t quant_task_func; + work_queue_func_t quant_task_func; uint32_t n_quant_tasks = 1; if (src1_nrows < octx->n_threads) { n_quant_tasks = MIN(total_nb, octx->n_threads); @@ -3132,7 +3485,7 @@ static int hvx_mm_matmul_id( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, ne10, src1_nrows, octx->n_threads, - 0, src0_row_size, src1_row_size, kparams->n_prefetch, true, false, false); + 0, src0_row_size, src1_row_size, 0, kparams->n_prefetch, true, false, false); size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : L.total_bytes; @@ -3147,7 +3500,6 @@ static int hvx_mm_matmul_id( // Make sure the reserved vtcm size is sufficient if (octx->ctx->vtcm_size < vtcm_size) { FARF(ERROR, "matmul-id-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type, octx->ctx->vtcm_size, vtcm_size); - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_VTCM_TOO_SMALL; } @@ -3175,16 +3527,86 @@ static int hvx_mm_matmul_id( mmctx->n_quant_tasks = n_quant_tasks; atomic_init(&mmctx->quant_barrier, n_quant_tasks); - const uint32_t n_matmul_jobs = octx->n_threads; - worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + + worker_pool_run_func(octx->ctx->worker_pool, hvx_mmid_task_func, mmctx, octx->n_threads); - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_OK; } +static inline void scan_expert_ids_n( + const struct htp_tensor * ids, + const uint32_t n_ids, + uint32_t n_as, + uint32_t * counts, + struct mmid_row_mapping * matrix_rows, + uint32_t mapping_stride +) { + const size_t ids_nb1 = ids->nb[1]; + const uint8_t * ids_data = (const uint8_t *) ids->data; + + for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + const int32_t * row_ptr = (const int32_t *) (ids_data + iid1 * ids_nb1); + for (uint32_t id = 0; id < n_ids; ++id) { + const int32_t i02 = row_ptr[id]; + if (i02 < 0) { + continue; + } + assert(i02 < n_as); + + if (matrix_rows) { + matrix_rows[i02 * mapping_stride + counts[i02]] = (struct mmid_row_mapping) { id, iid1 }; + } + counts[i02] += 1; + } + } +} + +static inline void scan_expert_ids( + const struct htp_tensor * ids, + uint32_t n_ids, + uint32_t n_as, + uint32_t * counts, + struct mmid_row_mapping * matrix_rows, + uint32_t mapping_stride +) { + const size_t ids_nb0 = ids->nb[0]; + + if (ids_nb0 == 4) { + switch (n_ids) { + case 8: scan_expert_ids_n(ids, 8, n_as, counts, matrix_rows, mapping_stride); break; + case 4: scan_expert_ids_n(ids, 4, n_as, counts, matrix_rows, mapping_stride); break; + case 2: scan_expert_ids_n(ids, 2, n_as, counts, matrix_rows, mapping_stride); break; + default: scan_expert_ids_n(ids, n_ids, n_as, counts, matrix_rows, mapping_stride); break; + } + } else { + // Strided fallback + const size_t ids_nb1 = ids->nb[1]; + const uint8_t * ids_data = (const uint8_t *) ids->data; + for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + const int32_t * row_ptr = (const int32_t *) (ids_data + iid1 * ids_nb1); + for (uint32_t id = 0; id < n_ids; ++id) { + const int32_t i02 = *(const int32_t *) ((const uint8_t *) row_ptr + id * ids_nb0); + if (i02 < 0) { + continue; + } + assert(i02 < n_as); + + if (matrix_rows) { + matrix_rows[i02 * mapping_stride + counts[i02]] = (struct mmid_row_mapping) { id, iid1 }; + } + counts[i02] += 1; + } + } + } +} + int op_matmul_id(struct htp_ops_context * octx) { htp_matmul_tensors_preamble; + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + struct htp_mm_context mmctx_struct = {0}; struct htp_mm_context * mmctx = &mmctx_struct; mmctx->octx = octx; @@ -3201,80 +3623,78 @@ int op_matmul_id(struct htp_ops_context * octx) { const uint32_t src0_nrows = ne01; // per expert const uint32_t src1_nrows = ne11 * ne12 * ne13; - worker_callback_t quant_task_func; - worker_callback_t matmul_id_job_func = src1_nrows > 1 ? hvx_mm_id : hvx_mv_id; - - // Compute src0_nrows_per_thread - mmctx->src0_nrows_per_thread = (src0_nrows + octx->n_threads - 1) / octx->n_threads; - mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32); + mmctx->src0_nrows_per_thread = (src0_nrows + octx->n_threads - 1) / octx->n_threads; + mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32); // row groups const int n_ids = ids->ne[0]; // n_expert_used const int n_as = ne02; // n_expert - size_t matrix_row_counts_size = n_as * sizeof(uint32_t); - size_t matrix_row_map_size = n_as * ids->ne[0] * ids->ne[1] * sizeof(struct mmid_row_mapping); - const size_t total_map_size = matrix_row_counts_size + matrix_row_map_size; + uint8_t * mapping_buf = octx->ctx->ddr_spad_base; + uint32_t mapping_stride = 1; + uint32_t * matrix_row_counts = (uint32_t *) mapping_buf; + struct mmid_row_mapping * matrix_rows = NULL; - void * mapping_buf = NULL; - bool must_free_mapping = false; + if (src1_nrows > 1) { + const size_t matrix_row_counts_size = n_as * sizeof(uint32_t); + assert(octx->ctx->ddr_spad_size >= matrix_row_counts_size); - if (octx->ctx->ddr_spad_base && total_map_size <= octx->ctx->ddr_spad_size) { - mapping_buf = octx->ctx->ddr_spad_base; - } else { - mapping_buf = memalign(128, total_map_size); - if (mapping_buf) { - must_free_mapping = true; - } else { - return HTP_STATUS_INTERNAL_ERR; - } - } + hex_l2fetch_block((const void *) ids->data, ids->ne[1] * ids->nb[1]); - uint32_t * matrix_row_counts = (uint32_t *) mapping_buf; - struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) ((uint8_t *) mapping_buf + matrix_row_counts_size); + memset(matrix_row_counts, 0, matrix_row_counts_size); + scan_expert_ids(ids, n_ids, n_as, matrix_row_counts, NULL, 0); - mmctx->matrix_row_counts = matrix_row_counts; - mmctx->matrix_rows = matrix_rows; - mmctx->mm_div_ne11 = kparams->div_ne11; + uint32_t max_count = hvx_reduce_max_i32((const uint8_t *) matrix_row_counts, n_as); + mapping_stride = max_count > 0 ? max_count : 1; - if (hvx_mm_init_vec_dot(mmctx, src0->type) != 0) { - if (must_free_mapping) free(mapping_buf); - return HTP_STATUS_NO_SUPPORT; - } + size_t matrix_row_map_size = n_as * mapping_stride * sizeof(struct mmid_row_mapping); + const size_t total_map_size = matrix_row_counts_size + matrix_row_map_size; + + if (total_map_size > octx->ctx->ddr_spad_size) { + mapping_buf = memalign(128, total_map_size); + if (!mapping_buf) { + return HTP_STATUS_INTERNAL_ERR; + } + } + + matrix_row_counts = (uint32_t *) mapping_buf; + matrix_rows = (struct mmid_row_mapping *) (mapping_buf + matrix_row_counts_size); - if (src1_nrows > 1) { - // initialize matrix_row_counts and map memset(matrix_row_counts, 0, n_as * sizeof(uint32_t)); + scan_expert_ids(ids, n_ids, n_as, matrix_row_counts, matrix_rows, mapping_stride); + } - // group rows by src0 matrix - for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { // token idx - for (uint32_t id = 0; id < n_ids; ++id) { // expert idx - const int32_t i02 = *(const int32_t *) ((const uint8_t *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]); + mmctx->matrix_row_counts = matrix_row_counts; + mmctx->matrix_rows = matrix_rows; + mmctx->mapping_stride = mapping_stride; + mmctx->mm_div_ne11 = kparams->div_ne11; + mmctx->src0_row_size_padded = src0_row_size_padded; + mmctx->src1_nrows = src1_nrows; - if (i02 < 0) { - continue; - } - assert(i02 < n_as); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); - matrix_rows[i02 * n_ids * ids->ne[1] + matrix_row_counts[i02]] = (struct mmid_row_mapping) { id, iid1 }; - matrix_row_counts[i02] += 1; - } + int s; + if (kparams->n_hmx) { + s = hmx_mm_op_matmul_id(octx, mmctx); + } else { + if (hvx_mm_init_vec_dot(mmctx, src0->type) == 0) { + s = hvx_mm_matmul_id(octx, mmctx, src1_nrows > 1 ? hvx_mm_id : hvx_mv_id); + } else { + s = HTP_STATUS_NO_SUPPORT; } } - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) { - if (must_free_mapping) free(mapping_buf); - return HTP_STATUS_OK; + if (mapping_buf != octx->ctx->ddr_spad_base) { + free(mapping_buf); } - if (kparams->n_hmx) { - return hmx_mm_op_matmul_id(octx, mmctx, matrix_row_counts, matrix_rows, mapping_buf, must_free_mapping); - } - - return hvx_mm_matmul_id(octx, mmctx, src0_row_size_padded, src1_nrows, matmul_id_job_func, mapping_buf, must_free_mapping); + return s; } int op_matmul_qkv(struct htp_ops_context * octx) { + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const struct htp_tensor * restrict src0 = octx->src[0]; // Wk const struct htp_tensor * restrict src1 = octx->src[1]; // x const struct htp_tensor * restrict src2 = octx->src[2]; // Wv @@ -3345,7 +3765,7 @@ int op_matmul_qkv(struct htp_ops_context * octx) { struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, src1->ne[0], src1_nrows, octx->n_threads, - 0, src0_row_size, src1_row_size, kparams->n_prefetch, false, true, false); + 0, src0_row_size, src1_row_size, 0, kparams->n_prefetch, false, true, false); size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : L.total_bytes; @@ -3379,9 +3799,6 @@ int op_matmul_qkv(struct htp_ops_context * octx) { mmctx->vtcm_src3_size_per_thread = L.src3_bytes / octx->n_threads; mmctx->vtcm_dst_size_per_thread = L.dst_bytes / octx->n_threads; - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) - return HTP_STATUS_OK; - mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks; mmctx->quant_task_func = quant_task_func; mmctx->n_quant_tasks = n_quant_tasks; @@ -3413,12 +3830,18 @@ int op_matmul_qkv(struct htp_ops_context * octx) { } else { matmul_job_func = hvx_mm_qkv_2d; } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs); return HTP_STATUS_OK; } int op_matmul_ffn(struct htp_ops_context * octx) { + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const struct htp_tensor * restrict src0 = octx->src[0]; // Wgate const struct htp_tensor * restrict src1 = octx->src[1]; // y const struct htp_tensor * restrict src2 = octx->src[2]; // Wup @@ -3487,7 +3910,7 @@ int op_matmul_ffn(struct htp_ops_context * octx) { struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, src1->ne[0], src1_nrows, octx->n_threads, - 0, src0_row_size, src1_row_size, kparams->n_prefetch, false, false, true); + 0, src0_row_size, src1_row_size, 0, kparams->n_prefetch, false, false, true); size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : L.total_bytes; @@ -3516,9 +3939,6 @@ int op_matmul_ffn(struct htp_ops_context * octx) { mmctx->vtcm_src2_size_per_thread = L.src2_bytes / octx->n_threads; mmctx->vtcm_dst_size_per_thread = L.dst_bytes / octx->n_threads; - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) - return HTP_STATUS_OK; - mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks; mmctx->quant_task_func = quant_task_func; mmctx->n_quant_tasks = n_quant_tasks; @@ -3550,6 +3970,9 @@ int op_matmul_ffn(struct htp_ops_context * octx) { } else { matmul_job_func = hvx_mm_ffn_2d; } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs); return HTP_STATUS_OK; diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.h b/ggml/src/ggml-hexagon/htp/matmul-ops.h index 2e131bc3d025..6c393664c6e8 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.h +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.h @@ -95,6 +95,8 @@ struct htp_mm_kernel_params { struct fastdiv_values div_r2; struct fastdiv_values div_r3; struct fastdiv_values div_ne11; + struct fastdiv_values div_n_act_threads; + struct fastdiv_values div_ne00_padded; }; #if defined(__cplusplus) @@ -458,6 +460,7 @@ static inline void htp_mm_hvx_vtcm_layout_build( size_t dst_row_size, size_t src0_row_size, size_t src1_row_size, + size_t src2_row_size, uint32_t n_prefetch, bool is_matmul_id, bool is_fused_qkv, @@ -465,7 +468,7 @@ static inline void htp_mm_hvx_vtcm_layout_build( ) { size_t src0_sz = 0; size_t src1_sz = 0; - size_t src2_sz = 0; + size_t src2_sz = src2_row_size > 0 ? htp_mm_round_up(src2_row_size, 128) : 0; size_t src3_sz = 0; size_t dst_sz = 0; @@ -643,6 +646,136 @@ static inline size_t htp_mm_hmx_get_batched_vtcm_size( return L.total_bytes; } +static inline bool htp_mm_hmx_solve_batched_params( + int wtype, + uint32_t k, + uint32_t ne01_padded, + uint32_t ne11, + uint32_t group_size, + bool use_dma_activation, + int n_threads, + bool pipeline, + size_t vtcm_budget, + size_t * m_chunk_out, + size_t * n_chunk_out, + int * act_threads_out, + size_t * vtcm_size_out +) { + size_t best_mblocks = SIZE_MAX; + int best_act_threads = 0; + size_t best_m_chunk = 0; + size_t best_n_chunk = 0; + size_t best_vtcm_size = 0; + + int act_threads = n_threads; + while (act_threads >= 1) { + size_t group_overhead = 256; + size_t group_size_per_n, group_size_per_m, group_size_per_mn; + htp_mm_hmx_get_batched_chunk_costs(k, group_size, &group_size_per_n, &group_size_per_m, &group_size_per_mn); + + size_t m_chunk_candidate = 0; + size_t n_chunk_candidate = 0; + size_t vtcm_size_candidate = 0; + + if (htp_mm_hmx_compute_chunks(vtcm_budget, group_overhead, group_size_per_n, group_size_per_m, group_size_per_mn, hex_align_up(ne11, 32), ne01_padded, + (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) ne11 * HTP_MM_HMX_COST_A_CONVERT, + &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { + size_t exact_size = htp_mm_hmx_get_batched_vtcm_size(wtype, k, m_chunk_candidate, n_chunk_candidate, group_size, use_dma_activation, pipeline, act_threads); + if (exact_size <= vtcm_budget) { + size_t mblocks = ((size_t) ne11 + m_chunk_candidate - 1) / m_chunk_candidate; + if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { + best_mblocks = mblocks; + best_act_threads = act_threads; + best_m_chunk = m_chunk_candidate; + best_n_chunk = n_chunk_candidate; + best_vtcm_size = exact_size; + } + } + } + if (act_threads == 1) { + act_threads = 0; + } else { + act_threads /= 2; + } + } + + if (best_act_threads > 0) { + *m_chunk_out = best_m_chunk; + *n_chunk_out = best_n_chunk; + *vtcm_size_out = best_vtcm_size; + *act_threads_out = best_act_threads; + return true; + } + return false; +} + +static inline bool htp_mm_hmx_solve_2d_params( + int wtype, + uint32_t k, + uint32_t m_id_rows, + uint32_t ne01_padded, + uint32_t ne11_padded, + uint32_t m_for_cost, + int n_threads, + bool pipeline, + bool is_matmul_id, + uint32_t aligned_tile_size, + size_t vtcm_budget, + size_t * m_chunk_out, + size_t * n_chunk_out, + int * act_threads_out, + size_t * vtcm_size_out +) { + size_t best_mblocks = SIZE_MAX; + int best_act_threads = 0; + size_t best_m_chunk = 0; + size_t best_n_chunk = 0; + size_t best_vtcm_size = 0; + + const int m_for_chunks = is_matmul_id ? hex_align_up(m_id_rows, 32) : ne11_padded; + + int act_threads = n_threads; + while (act_threads >= 1) { + size_t simple_2d_overhead = 256; + size_t simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn; + htp_mm_hmx_get_2d_chunk_costs(wtype, k, pipeline, aligned_tile_size, &simple_2d_size_per_n, &simple_2d_size_per_m, &simple_2d_size_per_mn); + + size_t m_chunk_candidate = 0; + size_t n_chunk_candidate = 0; + size_t vtcm_size_candidate = 0; + + if (htp_mm_hmx_compute_chunks(vtcm_budget, simple_2d_overhead, simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn, m_for_chunks, ne01_padded, + (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) m_for_cost * HTP_MM_HMX_COST_A_CONVERT, + &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { + size_t exact_size = htp_mm_hmx_get_2d_vtcm_size(wtype, k, m_chunk_candidate, n_chunk_candidate, pipeline, is_matmul_id ? 0 : act_threads, aligned_tile_size); + if (exact_size <= vtcm_budget) { + size_t mblocks = ((size_t) m_for_cost + m_chunk_candidate - 1) / m_chunk_candidate; + if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { + best_mblocks = mblocks; + best_act_threads = act_threads; + best_m_chunk = m_chunk_candidate; + best_n_chunk = n_chunk_candidate; + best_vtcm_size = exact_size; + } + } + } + if (act_threads == 1) { + act_threads = 0; + } else { + act_threads /= 2; + } + } + + if (best_act_threads > 0) { + *m_chunk_out = best_m_chunk; + *n_chunk_out = best_n_chunk; + *vtcm_size_out = best_vtcm_size; + *act_threads_out = best_act_threads; + return true; + } + return false; +} + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-hexagon/htp/rope-ops.c b/ggml/src/ggml-hexagon/htp/rope-ops.c index d16dc7d38ef4..5bc7d74f5e21 100644 --- a/ggml/src/ggml-hexagon/htp/rope-ops.c +++ b/ggml/src/ggml-hexagon/htp/rope-ops.c @@ -18,6 +18,7 @@ #include "htp-ctx.h" #include "htp-ops.h" #include "htp-ops.h" +#include "htp-tensor.h" // Redefined the rope type constants as we can't include ggml.h #define HTP_ROPE_TYPE_NORMAL 0 @@ -712,17 +713,11 @@ static int execute_op_rope_f32(struct htp_ops_context * octx) { } int op_rope(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - switch (octx->src[0]->type) { case HTP_TYPE_F32: - err = execute_op_rope_f32(octx); - break; + return execute_op_rope_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.c b/ggml/src/ggml-hexagon/htp/unary-ops.c index a71107f10476..b21415a67d64 100644 --- a/ggml/src/ggml-hexagon/htp/unary-ops.c +++ b/ggml/src/ggml-hexagon/htp/unary-ops.c @@ -19,6 +19,7 @@ #include "ggml-common.h" #include "htp-ctx.h" #include "htp-ops.h" +#include "htp-tensor.h" #include "htp-vtcm.h" #include "hex-profile.h" @@ -137,6 +138,24 @@ static void scale_f32(const float * restrict src, } } +static void clamp_f32(const float * restrict src, + float * restrict dst, + const uint32_t num_rows, + const struct htp_unary_context * uctx) { + htp_unary_op_preamble; + float min = 0.f; + float max = 0.f; + memcpy(&min, &op_params[0], sizeof(float)); + memcpy(&max, &op_params[1], sizeof(float)); + + for (uint32_t ir = 0; ir < num_rows; ir++) { + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * dst_row_size_aligned); + + hvx_clamp_scalar_f32(dst_local, src_local, min, max, ne0); + } +} + static void rms_norm_f32(const float * restrict src, float * restrict dst, const uint32_t num_rows, @@ -257,6 +276,39 @@ static void sigmoid_f32(const float * restrict src, } } +// silu(x) = x * sigmoid(x) +static void silu_f32(const float * restrict src, + float * restrict dst, + const uint32_t num_rows, + const struct htp_unary_context * uctx) { + htp_unary_op_preamble; + + for (uint32_t ir = 0; ir < num_rows; ir++) { + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * dst_row_size_aligned); + + hvx_sigmoid_f32_aa(dst_local, src_local, ne0); + hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0); + } +} + +// gelu(x) = x * sigmoid(1.702 * x) (quick/sigmoid approximation, matches CPU GELU_QUICK reference) +static void gelu_f32(const float * restrict src, + float * restrict dst, + const uint32_t num_rows, + const struct htp_unary_context * uctx) { + htp_unary_op_preamble; + + for (uint32_t ir = 0; ir < num_rows; ir++) { + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * dst_row_size_aligned); + + hvx_mul_scalar_f32(dst_local, src_local, 1.702f, ne0); + hvx_sigmoid_f32_aa(dst_local, dst_local, ne0); + hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0); + } +} + static void tri_f32(const float * restrict src, float * restrict dst, const uint32_t num_rows, @@ -397,7 +449,7 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat struct htp_ops_context * octx = uctx->octx; \ const struct htp_tensor * src = octx->src[0]; \ const struct htp_tensor * dst = octx->dst; \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ htp_unary_preamble; \ \ @@ -541,11 +593,14 @@ DEFINE_UNARY_TASK(norm, false, false, norm_f32(src0_vtcm, dst_vtcm, bl DEFINE_UNARY_TASK(rms_norm, false, false, rms_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(rms_norm_mul, true, false, rms_norm_mul_f32(src0_vtcm, uctx->broadcast_weight ? (const float *) src1_vtcm_data : src1_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(scale, false, false, scale_f32(src0_vtcm, dst_vtcm, block_size, uctx)) +DEFINE_UNARY_TASK(clamp, false, false, clamp_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(sqr, false, false, sqr_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(sqrt, false, false, sqrt_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_neg, false, false, neg_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_exp, false, false, exp_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_sigmoid, false, false, sigmoid_f32(src0_vtcm, dst_vtcm, block_size, uctx)) +DEFINE_UNARY_TASK(unary_silu, false, false, silu_f32(src0_vtcm, dst_vtcm, block_size, uctx)) +DEFINE_UNARY_TASK(unary_gelu, false, false, gelu_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_softplus, false, false, softplus_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_tanh, false, false, tanh_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(l2_norm, false, false, l2_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx)) @@ -558,7 +613,7 @@ static void unary_task_f32_tiled_##NAME(unsigned int nth, unsigned int ith, void struct htp_ops_context * octx = uctx->octx; \ const struct htp_tensor * src = octx->src[0]; \ const struct htp_tensor * dst = octx->dst; \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ htp_unary_preamble; \ \ @@ -680,6 +735,14 @@ static inline void tile_scale_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, hvx_scale_offset_f32_aa(dst_vtcm, src_vtcm, tw, scale, bias); } +static inline void tile_clamp_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw, const int32_t * op_params) { + float min = 0.f; + float max = 0.f; + memcpy(&min, &op_params[0], sizeof(float)); + memcpy(&max, &op_params[1], sizeof(float)); + hvx_clamp_scalar_f32(dst_vtcm, src_vtcm, min, max, tw); +} + static inline void tile_unary_softplus_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) { const float * restrict sf = (const float *) src_vtcm; float * restrict df = (float *) dst_vtcm; @@ -689,6 +752,19 @@ static inline void tile_unary_softplus_f32(uint8_t * dst_vtcm, const uint8_t * s } } +// silu(x) = x * sigmoid(x) +static inline void tile_silu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) { + hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw); + hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw); +} + +// gelu(x) = x * sigmoid(1.702 * x) (quick/sigmoid approximation, matches CPU GELU_QUICK reference) +static inline void tile_gelu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) { + hvx_mul_scalar_f32(dst_vtcm, src_vtcm, 1.702f, tw); + hvx_sigmoid_f32_aa(dst_vtcm, dst_vtcm, tw); + hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw); +} + // Triangular mask applied to one column tile. Boundary is an absolute column index, so // each vector compares against its absolute column position (col_start + i*VLEN_FP32). static inline void tri_apply_tile_f32(const uint8_t * restrict src, uint8_t * restrict dst, @@ -764,11 +840,14 @@ static inline void tri_apply_tile_f32(const uint8_t * restrict src, uint8_t * re } DEFINE_UNARY_TILED_TASK(scale, false, tile_scale_f32(dst_vtcm, src_vtcm, tw, op_params)) +DEFINE_UNARY_TILED_TASK(clamp, false, tile_clamp_f32(dst_vtcm, src_vtcm, tw, op_params)) DEFINE_UNARY_TILED_TASK(sqr, false, hvx_sqr_f32_aa(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(sqrt, false, hvx_sqrt_f32_aa(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(unary_neg, false, hvx_scale_f32_aa(dst_vtcm, src_vtcm, tw, -1.0f)) DEFINE_UNARY_TILED_TASK(unary_exp, false, hvx_exp_f32(dst_vtcm, src_vtcm, tw, false)) DEFINE_UNARY_TILED_TASK(unary_sigmoid, false, hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw)) +DEFINE_UNARY_TILED_TASK(unary_silu, false, tile_silu_f32(dst_vtcm, src_vtcm, tw)) +DEFINE_UNARY_TILED_TASK(unary_gelu, false, tile_gelu_f32(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(unary_softplus, false, tile_unary_softplus_f32(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(unary_tanh, false, hvx_tanh_f32_aa(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(tri, true, tri_apply_tile_f32(src_vtcm, dst_vtcm, tw, col, i01, ne0, tri_ttype)) @@ -786,11 +865,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { case HTP_OP_RMS_NORM: op_type = "rmsnorm-f32"; break; case HTP_OP_RMS_NORM_MUL: op_type = "rmsnorm-mul-f32"; break; case HTP_OP_SCALE: op_type = "scale-f32"; break; + case HTP_OP_CLAMP: op_type = "clamp-f32"; break; case HTP_OP_SQR: op_type = "sqr-f32"; break; case HTP_OP_SQRT: op_type = "sqrt-f32"; break; case HTP_OP_UNARY_NEG: op_type = "neg-f32"; break; case HTP_OP_UNARY_EXP: op_type = "exp-f32"; break; case HTP_OP_UNARY_SIGMOID: op_type = "sigmoid-f32"; break; + case HTP_OP_UNARY_SILU: op_type = "silu-f32"; break; + case HTP_OP_UNARY_GELU: op_type = "gelu-f32"; break; case HTP_OP_UNARY_SOFTPLUS: op_type = "softplus-f32"; break; case HTP_OP_UNARY_TANH: op_type = "tanh-f32"; break; case HTP_OP_L2_NORM: op_type = "l2norm-f32"; break; @@ -881,11 +963,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { if (col_tile) { switch (octx->op) { case HTP_OP_SCALE: task_func = unary_task_f32_tiled_scale; break; + case HTP_OP_CLAMP: task_func = unary_task_f32_tiled_clamp; break; case HTP_OP_SQR: task_func = unary_task_f32_tiled_sqr; break; case HTP_OP_SQRT: task_func = unary_task_f32_tiled_sqrt; break; case HTP_OP_UNARY_NEG: task_func = unary_task_f32_tiled_unary_neg; break; case HTP_OP_UNARY_EXP: task_func = unary_task_f32_tiled_unary_exp; break; case HTP_OP_UNARY_SIGMOID: task_func = unary_task_f32_tiled_unary_sigmoid; break; + case HTP_OP_UNARY_SILU: task_func = unary_task_f32_tiled_unary_silu; break; + case HTP_OP_UNARY_GELU: task_func = unary_task_f32_tiled_unary_gelu; break; case HTP_OP_UNARY_SOFTPLUS: task_func = unary_task_f32_tiled_unary_softplus; break; case HTP_OP_UNARY_TANH: task_func = unary_task_f32_tiled_unary_tanh; break; case HTP_OP_TRI: task_func = unary_task_f32_tiled_tri; break; @@ -897,11 +982,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { case HTP_OP_RMS_NORM: task_func = unary_task_f32_rms_norm; break; case HTP_OP_RMS_NORM_MUL: task_func = unary_task_f32_rms_norm_mul; break; case HTP_OP_SCALE: task_func = unary_task_f32_scale; break; + case HTP_OP_CLAMP: task_func = unary_task_f32_clamp; break; case HTP_OP_SQR: task_func = unary_task_f32_sqr; break; case HTP_OP_SQRT: task_func = unary_task_f32_sqrt; break; case HTP_OP_UNARY_NEG: task_func = unary_task_f32_unary_neg; break; case HTP_OP_UNARY_EXP: task_func = unary_task_f32_unary_exp; break; case HTP_OP_UNARY_SIGMOID: task_func = unary_task_f32_unary_sigmoid; break; + case HTP_OP_UNARY_SILU: task_func = unary_task_f32_unary_silu; break; + case HTP_OP_UNARY_GELU: task_func = unary_task_f32_unary_gelu; break; case HTP_OP_UNARY_SOFTPLUS: task_func = unary_task_f32_unary_softplus; break; case HTP_OP_UNARY_TANH: task_func = unary_task_f32_unary_tanh; break; case HTP_OP_L2_NORM: task_func = unary_task_f32_l2_norm; break; @@ -922,17 +1010,11 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { } int op_unary(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - switch (octx->src[0]->type) { case HTP_TYPE_F32: - err = execute_op_unary_f32(octx); - break; + return execute_op_unary_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.h b/ggml/src/ggml-hexagon/htp/unary-ops.h index b90b095ac9ad..1f4c3a5c4d96 100644 --- a/ggml/src/ggml-hexagon/htp/unary-ops.h +++ b/ggml/src/ggml-hexagon/htp/unary-ops.h @@ -41,6 +41,7 @@ _Static_assert(sizeof(struct htp_unary_kernel_params) <= 128, "htp_unary_kernel_ static inline bool htp_op_is_unary(uint32_t opcode) { switch (opcode) { + case HTP_OP_CLAMP: case HTP_OP_NORM: case HTP_OP_RMS_NORM: case HTP_OP_RMS_NORM_MUL: @@ -50,6 +51,8 @@ static inline bool htp_op_is_unary(uint32_t opcode) { case HTP_OP_UNARY_NEG: case HTP_OP_UNARY_EXP: case HTP_OP_UNARY_SIGMOID: + case HTP_OP_UNARY_SILU: + case HTP_OP_UNARY_GELU: case HTP_OP_UNARY_SOFTPLUS: case HTP_OP_UNARY_TANH: case HTP_OP_L2_NORM: diff --git a/ggml/src/ggml-hexagon/htp/work-queue.c b/ggml/src/ggml-hexagon/htp/work-queue.c new file mode 100644 index 000000000000..bb73e205a4a9 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/work-queue.c @@ -0,0 +1,244 @@ +#include "work-queue.h" +#include "hex-utils.h" + +#include +#include + +#include +#include +#include +#include +#include + +#include "HAP_farf.h" + +#define LOWEST_USABLE_QURT_PRIO (254) + +// internal structure kept in thread-local storage per instance of work queue +typedef struct { + work_queue_t queue; + unsigned int id; +} worker_context_t; + +struct work_queue_task_s { + work_queue_func_t func; + void * data; + unsigned int n_threads; + atomic_uint barrier; +}; + +// internal structure kept in thread-local storage per instance of work queue +struct work_queue_s { + atomic_uint seqn; // seqno used to detect new jobs + atomic_uint idx_read; // Updated by producer (pop/reclaim) + unsigned int idx_write; // Updated by producer (push) + uint32_t idx_mask; + uint32_t capacity; + + qurt_thread_t thread[WORK_QUEUE_MAX_N_THREADS]; // thread ID's of the workers + worker_context_t context[WORK_QUEUE_MAX_N_THREADS]; // worker contexts + void * stack[WORK_QUEUE_MAX_N_THREADS]; // thread stack pointers + unsigned int n_threads; // total threads (workers + main) + unsigned int n_workers; // number of active threads (just workers) + + atomic_bool active; // workers are polling/active + atomic_bool killed; // threads need to exit + bool external_mem; // memory owned externally + + struct work_queue_task_s queue[] __attribute__((aligned(HEX_L2_LINE_SIZE))); +}; + +static void work_queue_thread(void * context) { + worker_context_t * me = (worker_context_t *) context; + work_queue_t q = me->queue; + + FARF(HIGH, "work-queue: thread %u started", me->id); + + unsigned int prev_seqn = 0; + + while (!atomic_load_explicit(&q->killed, memory_order_relaxed)) { + unsigned int seqn = atomic_load_explicit(&q->seqn, memory_order_acquire); + if (seqn == prev_seqn) { + if (atomic_load_explicit(&q->active, memory_order_relaxed)) { + hex_pause(); + } else { + qurt_futex_wait(&q->seqn, prev_seqn); + } + continue; + } + + prev_seqn = seqn; + + // Process all active tasks in the queue + unsigned int ir = atomic_load_explicit(&q->idx_read, memory_order_relaxed); + unsigned int iw = q->idx_write; + + while (ir != iw) { + struct work_queue_task_s * task = &q->queue[ir]; + + unsigned int n = task->n_threads; + unsigned int i = me->id; + if (i < n) { + task->func(n, i, task->data); + + atomic_fetch_sub_explicit(&task->barrier, 1, memory_order_release); + } else { + while (atomic_load_explicit(&task->barrier, memory_order_relaxed) > 0) { + hex_pause(); + } + } + + ir = (ir + 1) & q->idx_mask; + } + } + + FARF(HIGH, "work-queue: thread %u stopped", me->id); +} + +bool work_queue_run_async(work_queue_t q, work_queue_func_t func, void * data, unsigned int n) { + if (n > q->n_threads) { + FARF(ERROR, "work-queue: invalid number of jobs %u for n-threads %u", n, q->n_threads); + return false; + } + + unsigned int ir = atomic_load_explicit(&q->idx_read, memory_order_relaxed); + unsigned int iw = q->idx_write; + + if (((iw + 1) & q->idx_mask) == ir) { + FARF(ERROR, "work-queue-push: queue is full\n"); + return false; + } + + struct work_queue_task_s * task = &q->queue[iw]; + task->func = func; + task->data = data; + task->n_threads = n; + atomic_store_explicit(&task->barrier, n, memory_order_relaxed); + + q->idx_write = (iw + 1) & q->idx_mask; + + // publish job to workers (already awake and polling) + atomic_fetch_add_explicit(&q->seqn, 1, memory_order_release); + + // main thread runs job #0 + func(n, 0, data); + + atomic_fetch_sub_explicit(&task->barrier, 1, memory_order_release); + + while (atomic_load_explicit(&task->barrier, memory_order_relaxed) > 0) { + hex_pause(); + } + + atomic_thread_fence(memory_order_acquire); + + atomic_store_explicit(&q->idx_read, (ir + 1) & q->idx_mask, memory_order_relaxed); + + return true; +} + +size_t work_queue_sizeof(uint32_t n_threads, uint32_t capacity, uint32_t stack_size) { + capacity = hex_ceil_pow2(capacity); + uint32_t n_workers = n_threads > 1 ? n_threads - 1 : 0; + size_t size_stacks = stack_size * n_workers; + size_t size_q = hex_align_up(sizeof(struct work_queue_s) + capacity * sizeof(struct work_queue_task_s), HEX_L2_LINE_SIZE); + return size_stacks + size_q; +} + +size_t work_queue_alignof(void) { + return 4096; +} + +work_queue_t work_queue_init(void * ptr, uint32_t n_threads, uint32_t capacity, uint32_t stack_size) { + capacity = hex_ceil_pow2(capacity); + uint32_t n_workers = n_threads > 1 ? n_threads - 1 : 0; + unsigned char * mem_blob = (unsigned char *) ptr; + + work_queue_t q = (work_queue_t) (mem_blob + stack_size * n_workers); + memset(q, 0, sizeof(struct work_queue_s) + capacity * sizeof(struct work_queue_task_s)); + + q->n_threads = n_threads; + q->n_workers = n_workers; + q->external_mem = true; + q->capacity = capacity; + + for (unsigned int i = 0; i < n_workers; i++) { + q->stack[i] = mem_blob; mem_blob += stack_size; + q->thread[i] = 0; + q->context[i].id = i + 1; + q->context[i].queue = q; + } + + atomic_init(&q->idx_read, 0); + atomic_init(&q->seqn, 0); + atomic_init(&q->active, false); + q->idx_write = 0; + q->idx_mask = capacity - 1; + q->killed = 0; + for (int i = 0; i < (int) capacity; i++) { + atomic_init(&q->queue[i].barrier, 0); + q->queue[i].func = NULL; + q->queue[i].data = NULL; + q->queue[i].n_threads = 0; + } + + // launch the workers + qurt_thread_attr_t attr; + qurt_thread_attr_init(&attr); + + for (unsigned int i = 0; i < n_workers; i++) { + qurt_thread_attr_set_stack_addr(&attr, q->stack[i]); + qurt_thread_attr_set_stack_size(&attr, stack_size); + + char thread_name[32]; + snprintf(thread_name, sizeof(thread_name), "work-queue:%u", i); + qurt_thread_attr_set_name(&attr, thread_name); + + // set up priority - by default, match the creating thread's prio + int prio = qurt_thread_get_priority(qurt_thread_get_id()); + if (prio < 1) { + prio = 1; + } + if (prio > LOWEST_USABLE_QURT_PRIO) { + prio = LOWEST_USABLE_QURT_PRIO; + } + + qurt_thread_attr_set_priority(&attr, prio); + + int err = qurt_thread_create(&q->thread[i], &attr, work_queue_thread, (void *) &q->context[i]); + if (err) { + FARF(ERROR, "Could not launch worker threads!"); + work_queue_free(q); + return NULL; + } + } + + return q; +} + +void work_queue_free(work_queue_t q) { + if (!q) { return; } + + atomic_store_explicit(&q->killed, 1, memory_order_relaxed); + atomic_fetch_add_explicit(&q->seqn, 1, memory_order_release); + qurt_futex_wake(&q->seqn, q->n_workers); + + for (unsigned int i = 0; i < q->n_workers; i++) { + if (q->thread[i]) { + int status; + (void) qurt_thread_join(q->thread[i], &status); + } + } +} + +void work_queue_wakeup(work_queue_t q) { + if (!atomic_load_explicit(&q->active, memory_order_relaxed)) { + atomic_store_explicit(&q->active, true, memory_order_release); + // Increment seqn and wake workers to transition them out of sleep + atomic_fetch_add_explicit(&q->seqn, 1, memory_order_release); + qurt_futex_wake(&q->seqn, q->n_workers); + } +} + +void work_queue_suspend(work_queue_t q) { + atomic_store_explicit(&q->active, false, memory_order_release); +} diff --git a/ggml/src/ggml-hexagon/htp/work-queue.h b/ggml/src/ggml-hexagon/htp/work-queue.h new file mode 100644 index 000000000000..09ca4b1f4392 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/work-queue.h @@ -0,0 +1,38 @@ +#ifndef HTP_WORK_QUEUE_H +#define HTP_WORK_QUEUE_H + +#include +#include +#include + +typedef void (*work_queue_func_t)(unsigned int n, unsigned int i, void *); + +struct work_queue_s; +typedef struct work_queue_s * work_queue_t; + +#define WORK_QUEUE_MAX_N_THREADS 10 + +size_t work_queue_sizeof(uint32_t n_threads, uint32_t capacity, uint32_t stack_size); +size_t work_queue_alignof(void); +work_queue_t work_queue_init(void * ptr, uint32_t n_threads, uint32_t capacity, uint32_t stack_size); +void work_queue_free(work_queue_t q); + +void work_queue_wakeup(work_queue_t q); +void work_queue_suspend(work_queue_t q); + +bool work_queue_run_async(work_queue_t q, work_queue_func_t func, void * data, unsigned int n); + +static inline bool work_queue_run(work_queue_t q, work_queue_func_t func, void * data, unsigned int n) { + if (n <= 1) { + func(n, 0, data); + return true; + } + return work_queue_run_async(q, func, data, n); +} + +// Legacy compatibility +typedef work_queue_func_t worker_callback_t; +#define worker_pool_run_func work_queue_run +#define worker_pool work_queue + +#endif // #ifndef HTP_WORK_QUEUE_H diff --git a/ggml/src/ggml-hexagon/htp/worker-pool.c b/ggml/src/ggml-hexagon/htp/worker-pool.c deleted file mode 100644 index 50960d2c75d7..000000000000 --- a/ggml/src/ggml-hexagon/htp/worker-pool.c +++ /dev/null @@ -1,305 +0,0 @@ -#include "worker-pool.h" -#include "hex-utils.h" - -#include -#include - -#include -#include -#include -#include -#include - -#include "HAP_farf.h" - -#define LOWEST_USABLE_QURT_PRIO (254) - -struct worker_pool_s; - -// internal structure kept in thread-local storage per instance of worker pool -typedef struct { - struct worker_pool_s * pool; - unsigned int id; -} worker_context_t; - -// internal structure kept in thread-local storage per instance of worker pool -typedef struct worker_pool_s { - worker_pool_job_t job[MAX_NUM_WORKERS]; // list of job descriptors - qurt_thread_t thread[MAX_NUM_WORKERS]; // thread ID's of the workers - worker_context_t context[MAX_NUM_WORKERS]; // worker contexts - void * stack[MAX_NUM_WORKERS]; // thread stack pointers - unsigned int n_threads; // number of workers in this pool - - atomic_uint seqn; // seqno used to detect new jobs - atomic_uint next_job; // next job index - atomic_uint n_pending; // number of pending jobs - atomic_uint n_jobs; // number of current jobs - atomic_bool killed; // threads need to exit -} worker_pool_t; - -static void worker_pool_main(void * context) { - worker_context_t * me = (worker_context_t *) context; - worker_pool_t * pool = me->pool; - - FARF(HIGH, "worker-pool: thread %u started", me->id); - - unsigned int prev_seqn = 0; - unsigned int poll_cnt = WORKER_POOL_POLL_COUNT; - while (!atomic_load(&pool->killed)) { - unsigned int seqn = atomic_load(&pool->seqn); - if (seqn == prev_seqn) { - // drop HVX context while spinning - if (poll_cnt > 1 && poll_cnt == WORKER_POOL_POLL_COUNT) { - qurt_hvx_unlock(); - } - if (--poll_cnt) { - hex_pause(); - continue; - } - qurt_futex_wait(&pool->seqn, prev_seqn); - poll_cnt = WORKER_POOL_POLL_COUNT; - continue; - } - - prev_seqn = seqn; - poll_cnt = WORKER_POOL_POLL_COUNT; - - // New job - unsigned int n = atomic_load(&pool->n_jobs); - unsigned int i = atomic_fetch_add(&pool->next_job, 1); - if (i >= n) { - // Spurious wakeup - continue; - } - - pool->job[i].func(n, i, pool->job[i].data); - - atomic_fetch_sub(&pool->n_pending, 1); - } - - FARF(HIGH, "worker-pool: thread %u stopped", me->id); -} - -AEEResult worker_pool_init_with_stack_size(worker_pool_context_t * context, uint32_t n_threads, uint32_t stack_size) { - int err = 0; - - if (NULL == context) { - FARF(ERROR, "NULL context passed to worker_pool_init()."); - return AEE_EBADPARM; - } - - // Allocations - int size = (stack_size * n_threads) + (sizeof(worker_pool_t)); - - unsigned char * mem_blob = (unsigned char *) malloc(size); - if (!mem_blob) { - FARF(ERROR, "Could not allocate memory for worker pool!!"); - return AEE_ENOMEMORY; - } - - worker_pool_t * me = (worker_pool_t *) (mem_blob + stack_size * n_threads); - - // name for the first worker, useful in debugging threads - char name[19]; - snprintf(name, 12, "0x%8x:", (int) me); - strcat(name, "worker0"); - me->n_threads = n_threads; - - // initializations - for (unsigned int i = 0; i < me->n_threads; i++) { - me->stack[i] = NULL; - me->thread[i] = 0; - - me->context[i].id = i; - me->context[i].pool = me; - } - - // initialize job queue - me->n_pending = 0; - me->n_jobs = 0; - me->next_job = 0; - me->seqn = 0; - me->killed = 0; - - // launch the workers - qurt_thread_attr_t attr; - qurt_thread_attr_init(&attr); - - for (unsigned int i = 0; i < me->n_threads; i++) { - // set up stack - me->stack[i] = mem_blob; - mem_blob += stack_size; - qurt_thread_attr_set_stack_addr(&attr, me->stack[i]); - qurt_thread_attr_set_stack_size(&attr, stack_size); - - // set up name - qurt_thread_attr_set_name(&attr, name); - name[17] = (name[17] + 1); - // name threads context:worker0, context:worker1, .. (recycle at 9, but num threads should be less than that anyway) - if (name[17] > '9') { - name[17] = '0'; - } - - // set up priority - by default, match the creating thread's prio - int prio = qurt_thread_get_priority(qurt_thread_get_id()); - - if (prio < 1) { - prio = 1; - } - if (prio > LOWEST_USABLE_QURT_PRIO) { - prio = LOWEST_USABLE_QURT_PRIO; - } - - qurt_thread_attr_set_priority(&attr, prio); - - // launch - err = qurt_thread_create(&me->thread[i], &attr, worker_pool_main, (void *) &me->context[i]); - if (err) { - FARF(ERROR, "Could not launch worker threads!"); - worker_pool_release((worker_pool_context_t *) &me); - return AEE_EQURTTHREADCREATE; - } - } - *context = (worker_pool_context_t *) me; - return AEE_SUCCESS; -} - -AEEResult worker_pool_init(worker_pool_context_t * context, uint32_t n_threads) { - return worker_pool_init_with_stack_size(context, n_threads, WORKER_THREAD_STACK_SZ); -} - -// clean up worker pool -void worker_pool_release(worker_pool_context_t * context) { - worker_pool_t * me = (worker_pool_t *) *context; - - // if no worker pool exists, return error. - if (NULL == me) { - return; - } - - atomic_store(&me->killed, 1); - atomic_fetch_add(&me->seqn, 1); - qurt_futex_wake(&me->seqn, me->n_threads); - - // de-initializations - for (unsigned int i = 0; i < me->n_threads; i++) { - if (me->thread[i]) { - int status; - (void) qurt_thread_join(me->thread[i], &status); - } - } - - // free allocated memory (were allocated as a single buffer starting at stack[0]) - if (me->stack[0]) { - free(me->stack[0]); - } - - *context = NULL; -} - -// run jobs -AEEResult worker_pool_run_jobs(worker_pool_context_t context, worker_pool_job_t * job, unsigned int n) { - worker_pool_t * me = (worker_pool_t *) context; - if (NULL == me) { - FARF(ERROR, "worker-pool: invalid context"); - return AEE_EBADPARM; - } - - if (n > me->n_threads) { - FARF(ERROR, "worker-pool: invalid number of jobs %u for n-threads %u", n, me->n_threads); - return AEE_EBADPARM; - } - - memcpy(me->job, job, sizeof(worker_pool_job_t) * n); - - if (n > 1) { - atomic_store(&me->next_job, 1); - atomic_store(&me->n_jobs, n); - atomic_store(&me->n_pending, n - 1); - - // wake up workers - atomic_fetch_add(&me->seqn, 1); - qurt_futex_wake(&me->seqn, n - 1); - } - - // main thread runs job #0 - me->job[0].func(n, 0, me->job[0].data); - - if (n > 1) { - while (atomic_load(&me->n_pending)) - ; - } - - return 0; -} - -// run func -AEEResult worker_pool_run_func(worker_pool_context_t context, worker_callback_t func, void * data, unsigned int n) { - worker_pool_job_t job[n]; - - for (unsigned int i = 0; i < n; i++) { - job[i].func = func; - job[i].data = data; - } - - return worker_pool_run_jobs(context, job, n); -} - -AEEResult worker_pool_set_thread_priority(worker_pool_context_t context, unsigned int prio) { - worker_pool_t * me = (worker_pool_t *) context; - - // if no worker pool exists, return error. - if (!me) { - return AEE_ENOMORE; - } - - int result = AEE_SUCCESS; - if (prio < 1) { - prio = 1; - } - if (prio > LOWEST_USABLE_QURT_PRIO) { - prio = LOWEST_USABLE_QURT_PRIO; - } - - for (unsigned int i = 0; i < me->n_threads; i++) { - int res = qurt_thread_set_priority(me->thread[i], (unsigned short) prio); - if (0 != res) { - result = AEE_EBADPARM; - FARF(ERROR, "QURT failed to set priority of thread %d, ERROR = %d", me->thread[i], res); - } - } - - return result; -} - -AEEResult worker_pool_retrieve_thread_id(worker_pool_context_t context, unsigned int * tids) { - worker_pool_t * me = (worker_pool_t *) context; - if (!me) { - FARF(ERROR, "worker-pool: invalid context"); - return AEE_EBADPARM; - ; - } - - for (int i = 0; i < me->n_threads; i++) { - tids[i] = me->thread[i]; - } - - return AEE_SUCCESS; -} - -AEEResult worker_pool_get_thread_priority(worker_pool_context_t context, unsigned int * prio) { - worker_pool_t * me = (worker_pool_t *) context; - if (!me) { - FARF(ERROR, "worker-pool: invalid context"); - return AEE_EBADPARM; - } - - int priority = qurt_thread_get_priority(me->thread[0]); - if (priority > 0) { - *prio = priority; - return 0; - } else { - *prio = 0; - return AEE_EBADSTATE; - } -} diff --git a/ggml/src/ggml-hexagon/htp/worker-pool.h b/ggml/src/ggml-hexagon/htp/worker-pool.h deleted file mode 100644 index cba692126ad0..000000000000 --- a/ggml/src/ggml-hexagon/htp/worker-pool.h +++ /dev/null @@ -1,65 +0,0 @@ -#ifndef HTP_WORKER_POOL_H -#define HTP_WORKER_POOL_H - -// MACRO enables function to be visible in shared-library case. -#define WORKERPOOL_API __attribute__((visibility("default"))) - -#include -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -/// signature of callbacks to be invoked by worker threads -typedef void (*worker_callback_t)(unsigned int n, unsigned int i, void *); - -/// Typedef of worker_pool context -typedef void * worker_pool_context_t; - -/// descriptor for requested callback -typedef struct { - worker_callback_t func; - void * data; -} worker_pool_job_t; - -#define WORKER_THREAD_STACK_SZ (2 * 16384) - -/// Maximum supported number of worker threads. -#define MAX_NUM_WORKERS 10 - -#if __HVX_ARCH__ > 79 -#define WORKER_POOL_POLL_COUNT 2000 -#else -#define WORKER_POOL_POLL_COUNT 1 -#endif - -// Initialize worker pool. -WORKERPOOL_API AEEResult worker_pool_init(worker_pool_context_t * context, uint32_t n_threads); - -// Initialize worker pool with custom stack size -WORKERPOOL_API AEEResult worker_pool_init_with_stack_size(worker_pool_context_t * context, - uint32_t n_threads, - uint32_t stack_size); - -// Kill worker threads and release worker pool resources -WORKERPOOL_API void worker_pool_release(worker_pool_context_t * context); - -// Run jobs with the worker pool. -WORKERPOOL_API AEEResult worker_pool_run_jobs(worker_pool_context_t context, worker_pool_job_t * job, unsigned int n); - -WORKERPOOL_API AEEResult worker_pool_run_func(worker_pool_context_t context, - worker_callback_t func, - void * data, - unsigned int n); - -WORKERPOOL_API AEEResult worker_pool_set_thread_priority(worker_pool_context_t context, unsigned int prio); -WORKERPOOL_API AEEResult worker_pool_get_thread_priority(worker_pool_context_t context, unsigned int * prio); -WORKERPOOL_API AEEResult worker_pool_retrieve_thread_id(worker_pool_context_t context, unsigned int * tids); - -#ifdef __cplusplus -} -#endif - -#endif // #ifndef HTP_WORKER_POOL_H diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 7121193f1c84..5351dcae12db 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -114,10 +114,6 @@ if (GGML_HIP_NO_VMM) add_compile_definitions(GGML_HIP_NO_VMM) endif() -if (GGML_HIP_ROCWMMA_FATTN) - add_compile_definitions(GGML_HIP_ROCWMMA_FATTN) -endif() - if (NOT GGML_HIP_MMQ_MFMA) add_compile_definitions(GGML_HIP_NO_MMQ_MFMA) endif() diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 870f93df0e4b..270c1411a059 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -1834,6 +1834,23 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_col2im_1d(ggml_m return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_snake(ggml_metal_library_t lib, enum ggml_type type) { + GGML_ASSERT(type == GGML_TYPE_F32 || type == GGML_TYPE_F16 || type == GGML_TYPE_BF16); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_snake_%s", ggml_type_name(type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + } + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_2d(ggml_metal_library_t lib, const ggml_tensor * op) { assert(op->op == GGML_OP_CONV_TRANSPOSE_2D); diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 9d4aca121595..b36fa8110b57 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -151,6 +151,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_im2col struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_col2im_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_snake (ggml_metal_library_t lib, enum ggml_type type); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d_dw (ggml_metal_library_t lib, const struct ggml_tensor * op, bool tiled); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_3d (ggml_metal_library_t lib, const struct ggml_tensor * op); diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 80e47f2c2f89..4edd77c6f267 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -1218,8 +1218,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0); case GGML_OP_PAD_REFLECT_1D: case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_LEAKY_RELU: return op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_LEAKY_RELU: + return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; case GGML_OP_ARGSORT: case GGML_OP_TOP_K: case GGML_OP_ARANGE: diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index a45f3ac67b28..330278d003df 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -616,6 +616,11 @@ typedef struct { int32_t p0; } ggml_metal_kargs_col2im_1d; +typedef struct { + int32_t T; + int32_t C; +} ggml_metal_kargs_snake; + typedef struct { int32_t IC; int32_t IH; diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 805bc4093ae0..c716f118f6d3 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -3077,7 +3077,58 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { return 1; } +// Snake activation autofuse: mul -> sin -> sqr -> mul -> add +static bool ggml_metal_op_can_fuse_snake(ggml_metal_op_t ctx, int idx) { + static constexpr ggml_op snake_ops[5] = { GGML_OP_MUL, GGML_OP_SIN, GGML_OP_SQR, GGML_OP_MUL, GGML_OP_ADD }; + + if (ctx->node(idx)->op != GGML_OP_MUL || !ctx->can_fuse(idx, snake_ops, 5)) { + return false; + } + + const ggml_tensor * mul0 = ctx->node(idx + 0); + const ggml_tensor * sin_node = ctx->node(idx + 1); + const ggml_tensor * sqr = ctx->node(idx + 2); + const ggml_tensor * mul1 = ctx->node(idx + 3); + const ggml_tensor * add = ctx->node(idx + 4); + + // x carries the full activation shape, a is the broadcast operand + const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1]; + const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0]; + + // mul1 reads sqr and inv_b in either operand order + const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0]; + + // closure check: the trailing add reads the same x as the leading mul + const ggml_tensor * x_in_add = (add->src[0] == mul1) ? add->src[1] : add->src[0]; + + // x is in the supported whitelist and every chain intermediate shares x's type. + // a and inv_b bind as device const float * in the kernel, so they stay F32. + const bool types_ok = + (x->type == GGML_TYPE_F32 || x->type == GGML_TYPE_F16 || x->type == GGML_TYPE_BF16) && + (a->type == GGML_TYPE_F32) && (inv_b->type == GGML_TYPE_F32) && + (mul0->type == x->type) && (sin_node->type == x->type) && + (sqr->type == x->type) && (mul1->type == x->type) && + (add->type == x->type); + // a / inv_b collapse to [1, C, 1, 1], x and add stay 2D + const bool shape_ok = ggml_are_same_shape(a, inv_b) && a->ne[0] == 1 && a->ne[1] == x->ne[1]; + const bool dim_ok = + (x->ne[2] == 1) && (x->ne[3] == 1) && + (add->ne[2] == 1) && (add->ne[3] == 1) && + (a->ne[2] == 1) && (a->ne[3] == 1) && + (inv_b->ne[2] == 1) && (inv_b->ne[3] == 1); + // kernel reads x[idx] and a[c] / inv_b[c] linearly, so every operand is contiguous + const bool contig_ok = + ggml_is_contiguous(x) && ggml_is_contiguous(add) && + ggml_is_contiguous(a) && ggml_is_contiguous(inv_b); + + return types_ok && shape_ok && dim_ok && contig_ok && x_in_add == x; +} + int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) { + if (ctx->use_fusion && ggml_metal_op_can_fuse_snake(ctx, idx)) { + return ggml_metal_op_snake_fused(ctx, idx); + } + ggml_tensor * op = ctx->node(idx); ggml_metal_library_t lib = ctx->lib; @@ -3984,6 +4035,55 @@ int ggml_metal_op_col2im_1d(ggml_metal_op_t ctx, int idx) { return 1; } +// Dispatch the fused snake kernel from the matched mul -> sin -> sqr -> mul -> add chain. +// idx points at the leading mul. The caller has validated the chain. +int ggml_metal_op_snake_fused(ggml_metal_op_t ctx, int idx) { + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + const ggml_tensor * mul0 = ctx->node(idx + 0); + const ggml_tensor * sqr = ctx->node(idx + 2); + const ggml_tensor * mul1 = ctx->node(idx + 3); + ggml_tensor * add = ctx->node(idx + 4); + + const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1]; + const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0]; + const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0]; + + const int T = (int) x->ne[0]; + const int C = (int) x->ne[1]; + const int total = T * C; + + // the encode loop pre-checked the leading mul only, check the rest of the chain + for (int i = 1; i < 5; ++i) { + if (!ggml_metal_op_concurrency_check(ctx, ctx->node(idx + i))) { + ggml_metal_op_concurrency_reset(ctx); + + break; + } + } + + auto pipeline = ggml_metal_library_get_pipeline_snake(lib, x->type); + + ggml_metal_kargs_snake args = { + /*.T =*/ T, + /*.C =*/ C, + }; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(x), 1); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(a), 2); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(inv_b), 3); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(add), 4); + + const int nth = 256; + const int ntg = (total + nth - 1) / nth; + ggml_metal_encoder_dispatch_threadgroups(enc, ntg, 1, 1, nth, 1, 1); + + return 5; +} + int ggml_metal_op_conv_transpose_2d(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index 0bebd836a185..89a6ad82f1c2 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -80,6 +80,7 @@ int ggml_metal_op_conv_3d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_1d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_2d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_col2im_1d (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_snake_fused (ggml_metal_op_t ctx, int idx); int ggml_metal_op_upscale (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pad (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pad_reflect_1d (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 38919ebadff4..969fddfa5b89 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -5406,6 +5406,35 @@ template [[host_name("kernel_col2im_1d_bf16")]] kernel void kernel_col2im_1d +kernel void kernel_snake( + constant ggml_metal_kargs_snake & args, + device const T * x, + device const float * a, + device const float * inv_b, + device T * dst, + uint tgpig [[threadgroup_position_in_grid]], + uint tpitg [[thread_position_in_threadgroup]], + uint ntg [[threads_per_threadgroup]]) { + + const int idx = tgpig * ntg + tpitg; + if (idx >= args.T * args.C) { + return; + } + + const int c = idx / args.T; // x is [T, C], a / inv_b collapse to [1, C] + const float xi = float(x[idx]); + const float si = sin(a[c] * xi); + dst[idx] = T(xi + si * si * inv_b[c]); +} + +template [[host_name("kernel_snake_f32")]] kernel void kernel_snake(constant ggml_metal_kargs_snake &, device const float *, device const float *, device const float *, device float *, uint, uint, uint); +template [[host_name("kernel_snake_f16")]] kernel void kernel_snake(constant ggml_metal_kargs_snake &, device const half *, device const float *, device const float *, device half *, uint, uint, uint); +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_snake_bf16")]] kernel void kernel_snake(constant ggml_metal_kargs_snake &, device const bfloat *, device const float *, device const float *, device bfloat *, uint, uint, uint); +#endif + + typedef void (conv_transpose_2d_t)( constant ggml_metal_kargs_conv_transpose_2d & args, device const float * src0, diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index ff3ad7e34d55..1dc707177106 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -5,6 +5,8 @@ set(TARGET_NAME ggml-opencl) ggml_add_backend_library(${TARGET_NAME} ggml-opencl.cpp + cl-program-cache.cpp + cl-program-cache.h ../../include/ggml-opencl.h) target_link_libraries(${TARGET_NAME} PRIVATE ${OpenCL_LIBRARIES}) target_include_directories(${TARGET_NAME} PRIVATE ${OpenCL_INCLUDE_DIRS}) @@ -211,6 +213,7 @@ set(GGML_OPENCL_KERNELS tanh exp expm1 + abs softplus pad repeat diff --git a/ggml/src/ggml-opencl/cl-program-cache.cpp b/ggml/src/ggml-opencl/cl-program-cache.cpp new file mode 100644 index 000000000000..1a4a281173b2 --- /dev/null +++ b/ggml/src/ggml-opencl/cl-program-cache.cpp @@ -0,0 +1,453 @@ +// Match the version setup ggml-opencl.cpp uses, so any cl.h declarations we +// touch are consistent across this backend's translation units. +#define CL_TARGET_OPENCL_VERSION GGML_OPENCL_TARGET_VERSION +#define CL_USE_DEPRECATED_OPENCL_1_2_APIS + +#include "cl-program-cache.h" + +#include "ggml-impl.h" // GGML_LOG_INFO / WARN + +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) +# ifndef WIN32_LEAN_AND_MEAN +# define WIN32_LEAN_AND_MEAN +# endif +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +# define ggml_getpid() ((int) GetCurrentProcessId()) +#else +# include +# define ggml_getpid() ((int) getpid()) +#endif + +namespace fs = std::filesystem; + +// ---------------------------------------------------------------------------- +// SHA-256 (FIPS 180-4). Self-contained, ~80 lines, public-domain reference. +// Hot path is a few KB of source per kernel ⇒ <1 ms total per process init. +// ---------------------------------------------------------------------------- + +namespace { + +struct sha256_ctx { + uint32_t state[8]; + uint64_t bitlen; + uint8_t buf[64]; + size_t buf_len; +}; + +const uint32_t K256[64] = { + 0x428a2f98,0x71374491,0xb5c0fbcf,0xe9b5dba5,0x3956c25b,0x59f111f1,0x923f82a4,0xab1c5ed5, + 0xd807aa98,0x12835b01,0x243185be,0x550c7dc3,0x72be5d74,0x80deb1fe,0x9bdc06a7,0xc19bf174, + 0xe49b69c1,0xefbe4786,0x0fc19dc6,0x240ca1cc,0x2de92c6f,0x4a7484aa,0x5cb0a9dc,0x76f988da, + 0x983e5152,0xa831c66d,0xb00327c8,0xbf597fc7,0xc6e00bf3,0xd5a79147,0x06ca6351,0x14292967, + 0x27b70a85,0x2e1b2138,0x4d2c6dfc,0x53380d13,0x650a7354,0x766a0abb,0x81c2c92e,0x92722c85, + 0xa2bfe8a1,0xa81a664b,0xc24b8b70,0xc76c51a3,0xd192e819,0xd6990624,0xf40e3585,0x106aa070, + 0x19a4c116,0x1e376c08,0x2748774c,0x34b0bcb5,0x391c0cb3,0x4ed8aa4a,0x5b9cca4f,0x682e6ff3, + 0x748f82ee,0x78a5636f,0x84c87814,0x8cc70208,0x90befffa,0xa4506ceb,0xbef9a3f7,0xc67178f2, +}; + +inline uint32_t rotr32(uint32_t x, unsigned n) { return (x >> n) | (x << (32 - n)); } + +void sha256_compress(uint32_t state[8], const uint8_t block[64]) { + uint32_t w[64]; + for (int i = 0; i < 16; ++i) { + w[i] = ((uint32_t)block[i*4 ] << 24) | + ((uint32_t)block[i*4 + 1] << 16) | + ((uint32_t)block[i*4 + 2] << 8) | + ((uint32_t)block[i*4 + 3] ); + } + for (int i = 16; i < 64; ++i) { + uint32_t s0 = rotr32(w[i-15], 7) ^ rotr32(w[i-15], 18) ^ (w[i-15] >> 3); + uint32_t s1 = rotr32(w[i-2], 17) ^ rotr32(w[i-2], 19) ^ (w[i-2] >> 10); + w[i] = w[i-16] + s0 + w[i-7] + s1; + } + + uint32_t a = state[0],b = state[1],c = state[2],d = state[3],e = state[4],f = state[5],g = state[6],h = state[7]; + + for (int i = 0; i < 64; ++i) { + uint32_t S1 = rotr32(e, 6) ^ rotr32(e, 11) ^ rotr32(e, 25); + uint32_t ch = (e & f) ^ ((~e) & g); + uint32_t t1 = h + S1 + ch + K256[i] + w[i]; + uint32_t S0 = rotr32(a, 2) ^ rotr32(a, 13) ^ rotr32(a, 22); + uint32_t maj = (a & b) ^ (a & c) ^ (b & c); + uint32_t t2 = S0 + maj; + h = g; g = f; f = e; e = d + t1; + d = c; c = b; b = a; a = t1 + t2; + } + state[0]+=a; state[1]+=b; state[2]+=c; state[3]+=d; + state[4]+=e; state[5]+=f; state[6]+=g; state[7]+=h; +} + +void sha256_init(sha256_ctx & c) { + c.state[0]=0x6a09e667; c.state[1]=0xbb67ae85; c.state[2]=0x3c6ef372; c.state[3]=0xa54ff53a; + c.state[4]=0x510e527f; c.state[5]=0x9b05688c; c.state[6]=0x1f83d9ab; c.state[7]=0x5be0cd19; + c.bitlen = 0; + c.buf_len = 0; +} + +void sha256_update(sha256_ctx & c, const void * data, size_t len) { + const uint8_t * p = (const uint8_t *) data; + c.bitlen += (uint64_t) len * 8; + if (c.buf_len > 0) { + size_t n = 64 - c.buf_len; + if (n > len) { n = len; } + memcpy(c.buf + c.buf_len, p, n); + c.buf_len += n; + p += n; + len -= n; + if (c.buf_len == 64) { + sha256_compress(c.state, c.buf); + c.buf_len = 0; + } + } + while (len >= 64) { + sha256_compress(c.state, p); + p += 64; + len -= 64; + } + if (len > 0) { + memcpy(c.buf, p, len); + c.buf_len = len; + } +} + +void sha256_final(sha256_ctx & c, uint8_t out[32]) { + uint64_t bitlen = c.bitlen; + c.buf[c.buf_len++] = 0x80; + if (c.buf_len > 56) { + while (c.buf_len < 64) { c.buf[c.buf_len++] = 0; } + sha256_compress(c.state, c.buf); + c.buf_len = 0; + } + while (c.buf_len < 56) { c.buf[c.buf_len++] = 0; } + for (int i = 7; i >= 0; --i) { c.buf[c.buf_len++] = (uint8_t) (bitlen >> (i * 8)); } + sha256_compress(c.state, c.buf); + for (int i = 0; i < 8; ++i) { + out[i*4 ] = (uint8_t) (c.state[i] >> 24); + out[i*4 + 1] = (uint8_t) (c.state[i] >> 16); + out[i*4 + 2] = (uint8_t) (c.state[i] >> 8); + out[i*4 + 3] = (uint8_t) (c.state[i] ); + } +} + +std::string sha256_hex(const uint8_t digest[32]) { + static const char hex[] = "0123456789abcdef"; + std::string s(64, '0'); + for (int i = 0; i < 32; ++i) { + s[i*2 ] = hex[digest[i] >> 4]; + s[i*2 + 1] = hex[digest[i] & 0xf]; + } + return s; +} + +std::string compute_key(const std::string & key_suffix, + const char * source, + const std::string & compile_opts) { + sha256_ctx c; + sha256_init(c); + + static const uint8_t sep = 0; + sha256_update(c, source, strlen(source)); + sha256_update(c, &sep, 1); + sha256_update(c, compile_opts.data(), compile_opts.size()); + sha256_update(c, &sep, 1); + sha256_update(c, key_suffix.data(), key_suffix.size()); + + uint8_t digest[32]; + sha256_final(c, digest); + return sha256_hex(digest); +} + +bool make_dir_recursive(const std::string & path) { + if (path.empty()) { return false; } + // create_directories() already creates missing parents. It returns false + // (with ec clear) when the directory is already there, so re-check. + const fs::path p = fs::u8path(path); + std::error_code ec; + if (fs::create_directories(p, ec)) { return true; } + std::error_code ec_stat; + return fs::is_directory(p, ec_stat); +} + +std::string default_cache_dir() { +#if defined(_WIN32) + const char * base = std::getenv("LOCALAPPDATA"); + if (!base || !*base) { base = std::getenv("APPDATA"); } + if (!base || !*base) { base = std::getenv("TEMP"); } + if (!base || !*base) { base = "."; } + return std::string(base) + "\\llama.cpp\\cl-cache"; +#elif defined(__APPLE__) + const char * home = std::getenv("HOME"); + if (!home || !*home) { home = "."; } + return std::string(home) + "/Library/Caches/llama.cpp/cl-cache"; +#else + // The throwing overload aborts the process when no usable temp directory + // exists (e.g. Android app contexts with TMPDIR unset); an empty return + // here just disables the cache instead. + std::error_code ec; + const fs::path tmp_path = fs::temp_directory_path(ec); + if (ec || tmp_path.empty()) { return {}; } + return tmp_path.string() + "/llama.cpp/cl-cache"; +#endif +} + +// Query a NUL-terminated string from clGetDeviceInfo / clGetPlatformInfo. +template +std::string query_string(GetInfoFn fn, Object obj, cl_uint name) { + size_t sz = 0; + if (fn(obj, name, 0, nullptr, &sz) != CL_SUCCESS || sz == 0) { + return {}; + } + std::string s(sz, '\0'); + if (fn(obj, name, sz, &s[0], nullptr) != CL_SUCCESS) { + return {}; + } + if (!s.empty() && s.back() == '\0') { + s.pop_back(); + } + return s; +} + +std::string compute_key_suffix(cl_device_id device) { + cl_platform_id platform = nullptr; + clGetDeviceInfo(device, CL_DEVICE_PLATFORM, sizeof(platform), &platform, nullptr); + + std::string s; + s.reserve(512); + s += query_string(clGetDeviceInfo, device, CL_DEVICE_NAME); s.push_back('\0'); + s += query_string(clGetDeviceInfo, device, CL_DRIVER_VERSION); s.push_back('\0'); + s += query_string(clGetDeviceInfo, device, CL_DEVICE_VERSION); s.push_back('\0'); + if (platform) { + s += query_string(clGetPlatformInfo, platform, CL_PLATFORM_VERSION); s.push_back('\0'); + } + s += "fmt=" + std::to_string(CL_PROGRAM_CACHE_FORMAT_VERSION); + return s; +} + +const uint8_t MAGIC[8] = { 'G','G','M','L','C','L','B','C' }; + +bool read_all(const std::string & path, std::vector & out) { + std::ifstream f(fs::u8path(path), std::ios::binary); + if (!f) { return false; } + f.seekg(0, std::ios::end); + std::streamsize sz = f.tellg(); + if (sz < 0) { return false; } + f.seekg(0, std::ios::beg); + out.resize((size_t) sz); + if (sz > 0) { f.read((char *) out.data(), sz); } + return f.good() || f.eof(); +} + +bool write_atomic(const std::string & path, const uint8_t * data, size_t len) { + const fs::path dst = fs::u8path(path); + const fs::path tmp = fs::u8path(path + ".tmp." + std::to_string(ggml_getpid())); + { + std::ofstream f(tmp, std::ios::binary | std::ios::trunc); + if (!f) { return false; } + f.write((const char *) data, (std::streamsize) len); + if (!f.good()) { + std::error_code ec_rm; + fs::remove(tmp, ec_rm); + return false; + } + } + + std::error_code ec; + fs::rename(tmp, dst, ec); + if (ec) { + std::error_code ec_rm; + fs::remove(tmp, ec_rm); + return false; + } + return true; +} + +} // namespace + +static bool cache_debug_enabled() { + static int cached = -1; + if (cached < 0) { + const char * e = std::getenv("GGML_OPENCL_KERNEL_CACHE_DEBUG"); + cached = (e && *e) ? 1 : 0; + } + return cached != 0; +} + +static std::string opts_preview(const std::string & opts, size_t n = 120) { + if (opts.size() <= n) { return opts; } + return opts.substr(0, n) + "..."; +} + +// Running cache tally (diagnostic; plain ints — a benign race in the rare +// multi-threaded lazy-compile case at worst miscounts by one). +static int g_cache_hits = 0, g_cache_misses = 0, g_cache_saves = 0; + +// Debug trace directly to stderr +static void cache_debug_line(const char * kind, const std::string & key, + const char * source, const std::string & opts) { + if (!cache_debug_enabled()) { return; } + fprintf(stderr, "ggml_opencl: cache %-4s [h=%d m=%d s=%d] key=%s src=%zuB opts='%s'\n", + kind, g_cache_hits, g_cache_misses, g_cache_saves, + key.substr(0, 16).c_str(), strlen(source), opts_preview(opts).c_str()); + fflush(stderr); +} + +cl_program_cache_state cl_program_cache_init(cl_device_id device) { + cl_program_cache_state st; + + const char * env = std::getenv("GGML_OPENCL_KERNEL_CACHE_DIR"); + if (env && (!std::strcmp(env, "0") || !std::strcmp(env, "off") || + !std::strcmp(env, "none") || !std::strcmp(env, "disable") || + !std::strcmp(env, "disabled"))) { + if (cache_debug_enabled()) { + fprintf(stderr, "ggml_opencl: kernel cache disabled by GGML_OPENCL_KERNEL_CACHE_DIR=%s\n", env); + fflush(stderr); + } + return st; + } + + std::string dir; + if (!env || !*env || !std::strcmp(env, "1") || !std::strcmp(env, "default")) { + dir = default_cache_dir(); + if (dir.empty()) { + GGML_LOG_INFO("ggml_opencl: kernel cache disabled (no usable default cache directory)\n"); + return st; + } + } else { + dir = env; + } + + if (!make_dir_recursive(dir)) { + GGML_LOG_INFO("ggml_opencl: kernel cache disabled (cannot create directory '%s')\n", dir.c_str()); + return st; + } + + st.dir = dir; + st.key_suffix = compute_key_suffix(device); + GGML_LOG_INFO("ggml_opencl: kernel cache enabled at '%s'\n", st.dir.c_str()); + if (cache_debug_enabled()) { + fprintf(stderr, "ggml_opencl: kernel cache enabled at '%s' " + "(GGML_OPENCL_KERNEL_CACHE_DIR=off to disable)\n", st.dir.c_str()); + fflush(stderr); + } + return st; +} + +cl_program cl_program_cache_try_load( + const cl_program_cache_state & state, + cl_context context, + cl_device_id device, + const char * source, + const std::string & compile_opts) { + + if (state.dir.empty() || !source) { return nullptr; } + + const std::string key = compute_key(state.key_suffix, source, compile_opts); + const std::string path = state.dir + "/" + key + ".clbin"; + + std::vector file; + if (!read_all(path, file)) { + ++g_cache_misses; + cache_debug_line("MISS", key, source, compile_opts); + return nullptr; + } + if (file.size() < 16 || std::memcmp(file.data(), MAGIC, 8) != 0) { return nullptr; } + + uint32_t fmt = + ((uint32_t) file[ 8]) | ((uint32_t) file[ 9] << 8) | + ((uint32_t) file[10] << 16) | ((uint32_t) file[11] << 24); + if (fmt != CL_PROGRAM_CACHE_FORMAT_VERSION) { return nullptr; } + + const size_t hdr_len = 16; + const unsigned char * bin = file.data() + hdr_len; + const size_t bin_len = file.size() - hdr_len; + + cl_int err = CL_SUCCESS; + cl_int bin_err = CL_SUCCESS; + cl_program p = clCreateProgramWithBinary(context, 1, &device, &bin_len, &bin, &bin_err, &err); + if (err != CL_SUCCESS || bin_err != CL_SUCCESS || p == nullptr) { + if (p) { clReleaseProgram(p); } + return nullptr; + } + + err = clBuildProgram(p, 0, nullptr, compile_opts.c_str(), nullptr, nullptr); + if (err != CL_SUCCESS) { + clReleaseProgram(p); + return nullptr; + } + ++g_cache_hits; + cache_debug_line("HIT", key, source, compile_opts); + return p; +} + +void cl_program_cache_try_save( + const cl_program_cache_state & state, + cl_program program, + cl_device_id /*device*/, + const char * source, + const std::string & compile_opts) { + + if (state.dir.empty() || !program || !source) { + return; + } + + cl_uint n_dev = 0; + if (clGetProgramInfo(program, CL_PROGRAM_NUM_DEVICES, sizeof(n_dev), &n_dev, nullptr) != CL_SUCCESS || n_dev == 0) { + return; + } + + std::vector sizes(n_dev); + if (clGetProgramInfo(program, CL_PROGRAM_BINARY_SIZES, sizeof(size_t) * n_dev, sizes.data(), nullptr) != CL_SUCCESS) { + return; + } + if (sizes.empty() || sizes[0] == 0) { + return; + } + + std::vector> binaries(n_dev); + std::vector bin_ptrs(n_dev); + for (cl_uint i = 0; i < n_dev; ++i) { + binaries[i].resize(sizes[i]); + bin_ptrs[i] = binaries[i].data(); + } + if (clGetProgramInfo(program, CL_PROGRAM_BINARIES, sizeof(unsigned char *) * n_dev, bin_ptrs.data(), nullptr) != CL_SUCCESS) { + return; + } + + // We only care about the first device's binary — that's the one we'd + // re-load with on a future cache hit. Multi-device contexts aren't a + // pattern this backend uses today. + const std::vector & bin = binaries[0]; + + std::vector file; + file.reserve(16 + bin.size()); + file.insert(file.end(), MAGIC, MAGIC + 8); + uint32_t fmt = CL_PROGRAM_CACHE_FORMAT_VERSION; + file.push_back((uint8_t) (fmt & 0xff)); + file.push_back((uint8_t) ((fmt >> 8) & 0xff)); + file.push_back((uint8_t) ((fmt >> 16) & 0xff)); + file.push_back((uint8_t) ((fmt >> 24) & 0xff)); + file.push_back(0); file.push_back(0); file.push_back(0); file.push_back(0); // reserved + file.insert(file.end(), bin.begin(), bin.end()); + + const std::string key = compute_key(state.key_suffix, source, compile_opts); + const std::string path = state.dir + "/" + key + ".clbin"; + if (!write_atomic(path, file.data(), file.size())) { + GGML_LOG_INFO("ggml_opencl: kernel cache: failed to write '%s'\n", path.c_str()); + } else { + ++g_cache_saves; + cache_debug_line("SAVE", key, source, compile_opts); + } +} diff --git a/ggml/src/ggml-opencl/cl-program-cache.h b/ggml/src/ggml-opencl/cl-program-cache.h new file mode 100644 index 000000000000..49aa2d1e82f5 --- /dev/null +++ b/ggml/src/ggml-opencl/cl-program-cache.h @@ -0,0 +1,75 @@ +// On-disk cache for OpenCL cl_program binaries. Lets a fresh process skip the +// expensive clBuildProgram-from-source step when a binary for the exact same +// (source, compile options, device, driver, platform) was previously saved. +// +// Activation: default on via GGML_OPENCL_KERNEL_CACHE_DIR: +// unset / empty / "1" / "default" : platform default cache dir +// (%LOCALAPPDATA%\llama.cpp\cl-cache, +// ~/Library/Caches/llama.cpp/cl-cache, +// /llama.cpp/cl-cache elsewhere) +// "0" / "off" / "none" / "disable(d)" : disabled (all functions no-op) +// any other value : used verbatim as the cache path +// If the chosen directory cannot be created/used, the cache silently disables +// itself for the process and falls back to source compile. +// GGML_OPENCL_KERNEL_CACHE_DEBUG=1 prints a HIT/MISS/SAVE trace (with a running +// tally) straight to stderr — visible even in tools that filter INFO/WARN logs; +// redirect stderr to record it. +// +// Cache key (SHA-256 hex): +// sha256(source_bytes || '\x00' || +// compile_opts || '\x00' || +// CL_DEVICE_NAME || '\x00' || +// CL_DRIVER_VERSION || '\x00' || +// CL_PLATFORM_VERSION || '\x00' || +// CL_PROGRAM_CACHE_FORMAT_VERSION) +// +// The key fully captures everything that can affect the produced binary, +// without needing the host source revision (a kernel source change shows up +// in source_bytes; a compile-option change shows up in compile_opts). +// +// File layout per cache entry: /.clbin +// bytes [0..7] : magic "GGMLCLBC" +// bytes [8..11] : uint32_t format version (CL_PROGRAM_CACHE_FORMAT_VERSION) +// bytes [12..15] : uint32_t reserved (0) +// bytes [16..] : raw cl_program binary as returned by +// clGetProgramInfo(CL_PROGRAM_BINARIES) +// +// Concurrency: writes go to .tmp. then atomic rename. On race, +// last-writer-wins. No locks. + +#pragma once + +#include +#include + +// Bumped manually if host-side OpenCL API usage changes in a way that +// affects compile semantics but does not show up in source_bytes / +// compile_opts (e.g. switching from clCreateProgramWithSource to +// clCompileProgram + clLinkProgram, or changing how multiple sources +// are concatenated). Most commits — including kernel changes — do NOT +// require bumping this; the source bytes already capture those. +#define CL_PROGRAM_CACHE_FORMAT_VERSION 1u + +struct cl_program_cache_state { + // Empty string means cache is disabled. + std::string dir; + // Concatenated device/driver/platform identity + cache format version, + // computed once at init and folded into every key. + std::string key_suffix; +}; + +cl_program_cache_state cl_program_cache_init(cl_device_id device); + +cl_program cl_program_cache_try_load( + const cl_program_cache_state & state, + cl_context context, + cl_device_id device, + const char * source, + const std::string & compile_opts); + +void cl_program_cache_try_save( + const cl_program_cache_state & state, + cl_program program, + cl_device_id device, + const char * source, + const std::string & compile_opts); diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index b14ea8133b87..a05d18ee30af 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -13,6 +13,8 @@ #include "ggml-backend-impl.h" #include "ggml.h" +#include "cl-program-cache.h" + #ifdef GGML_OPENCL_USE_ADRENO_BIN_KERNELS #include "libdl.h" #ifdef _WIN32 @@ -114,6 +116,7 @@ enum GPU_FAMILY { enum ADRENO_GPU_GEN { ADRENO_UNKNOWN, + A6X, A7X, A8X, X1E, @@ -122,6 +125,7 @@ enum ADRENO_GPU_GEN { enum ADRENO_CL_COMPILER_TYPE { E031, + E17, DX, }; @@ -243,14 +247,29 @@ static ggml_cl_version get_opencl_c_version(ggml_cl_version platform_version, cl } static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) { + if (strstr(device_name, "610") || strstr(device_name, "612") || + strstr(device_name, "613") || strstr(device_name, "615") || + strstr(device_name, "616") || strstr(device_name, "618") || + strstr(device_name, "619") || strstr(device_name, "620") || + strstr(device_name, "630") || strstr(device_name, "640") || + strstr(device_name, "642") || strstr(device_name, "643") || + strstr(device_name, "644") || strstr(device_name, "650") || + strstr(device_name, "660") || strstr(device_name, "663") || + strstr(device_name, "680") || strstr(device_name, "685") || + strstr(device_name, "690")) { + return ADRENO_GPU_GEN::A6X; + } + if (strstr(device_name, "730") || strstr(device_name, "740") || strstr(device_name, "750")) { return ADRENO_GPU_GEN::A7X; } - if (strstr(device_name, "830") || - strstr(device_name, "840")) { + if (strstr(device_name, "810") || + strstr(device_name, "830") || + strstr(device_name, "840") || + strstr(device_name, "850")) { return ADRENO_GPU_GEN::A8X; } @@ -274,6 +293,17 @@ static ggml_cl_compiler_version get_adreno_cl_compiler_version(const char *drive size_t compiler_minor_offset = 8; size_t compiler_patch_offset = 11; + if (compiler_ver_pos == std::string::npos) { + compiler_ver_pos = driver_ver_str.find("E17"); + if (compiler_ver_pos != std::string::npos) { + type = ADRENO_CL_COMPILER_TYPE::E17; + compiler_ver_len = 12; + compiler_major_offset = 4; + compiler_minor_offset = 7; + compiler_patch_offset = 10; + } + } + if (compiler_ver_pos == std::string::npos) { compiler_ver_pos = driver_ver_str.find("DX"); if (compiler_ver_pos == std::string::npos) { @@ -282,6 +312,8 @@ static ggml_cl_compiler_version get_adreno_cl_compiler_version(const char *drive type = ADRENO_CL_COMPILER_TYPE::DX; compiler_ver_len = 11; compiler_major_offset = 3; + compiler_minor_offset = 6; + compiler_patch_offset = 9; } std::string compiler_ver_str = driver_ver_str.substr(compiler_ver_pos, compiler_ver_len); @@ -564,6 +596,10 @@ struct ggml_backend_opencl_context { cl_context context; cl_command_queue queue; + // On-disk compiled-program cache (see GGML_OPENCL_KERNEL_CACHE_DIR). + cl_program_cache_state program_cache; + bool program_cache_initialized = false; + // prealloc buffers for transposing weights and activations ggml_cl_buffer prealloc_quant_trans; ggml_cl_buffer prealloc_scales_trans; @@ -815,6 +851,8 @@ struct ggml_backend_opencl_context { cl_kernel kernel_exp_f16, kernel_exp_f16_4, kernel_exp_f16_nc; cl_kernel kernel_expm1_f32, kernel_expm1_f32_4, kernel_expm1_f32_nc; cl_kernel kernel_expm1_f16, kernel_expm1_f16_4, kernel_expm1_f16_nc; + cl_kernel kernel_abs_f32, kernel_abs_f32_4, kernel_abs_f32_nc; + cl_kernel kernel_abs_f16, kernel_abs_f16_4, kernel_abs_f16_nc; cl_kernel kernel_softplus_f32, kernel_softplus_f32_4, kernel_softplus_f32_nc; cl_kernel kernel_softplus_f16, kernel_softplus_f16_4, kernel_softplus_f16_nc; cl_kernel kernel_upscale; @@ -844,7 +882,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemm_moe_q8_1_dp4a_q5k = nullptr; // generic dp4a MoE GEMM (MOE_QT=5, q5_K), opt-in cl_kernel kernel_moe_expand_scale_q5_K = nullptr; // q5_K 6-bit s[] -> uniform scale[16]/min[8] cl_kernel kernel_gemv_moe_q5_k_f32_ns, kernel_gemm_moe_q5_k_f32_ns; - cl_kernel kernel_gemv_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns; + cl_kernel kernel_gemv_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns_bin; cl_kernel kernel_gemm_moe_q6_k_q8_1_dp4a = nullptr; // dp4a (int8) q6_K MoE prefill GEMM cl_kernel kernel_gemv_moe_mxfp4_f32, kernel_gemm_moe_mxfp4_f32; cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns_bin; @@ -1160,8 +1198,25 @@ static cl_program build_program_from_source_ex(cl_context ctx, cl_device_id dev, return NULL; } -static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts) { - return build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true); +static cl_program build_program_from_source(ggml_backend_opencl_context * backend_ctx, const char* program_buffer, const std::string &compile_opts) { + cl_context ctx = backend_ctx->context; + cl_device_id dev = backend_ctx->device; + + // Try the on-disk binary cache first. Falls through silently on miss or + // any failure; never blocks the build path. Disabled cache => nullptr. + cl_program p_cached = cl_program_cache_try_load( + backend_ctx->program_cache, ctx, dev, program_buffer, compile_opts); + if (p_cached != nullptr) { + return p_cached; + } + + cl_program p = build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true); + + // Best-effort save of the freshly-built binary (no-op if cache disabled). + if (p != nullptr) { + cl_program_cache_try_save(backend_ctx->program_cache, p, dev, program_buffer, compile_opts); + } + return p; } static cl_program build_program_from_binary(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts, size_t bin_size = 0) { @@ -1209,7 +1264,7 @@ static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("argsort.cl"); #endif backend_ctx->program_argsort_f32_i32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_argsort_f32_i32 = clCreateKernel(backend_ctx->program_argsort_f32_i32, "kernel_argsort_f32_i32", &err), err)); backend_ctx->kernels_loaded_argsort = true; @@ -1259,7 +1314,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("add.cl"); #endif backend_ctx->program_add = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_add = clCreateKernel(backend_ctx->program_add, "kernel_add", &err), err)); CL_CHECK((backend_ctx->kernel_add_row = clCreateKernel(backend_ctx->program_add, "kernel_add_row", &err), err)); @@ -1278,7 +1333,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("add_id.cl"); #endif backend_ctx->program_add_id = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_add_id = clCreateKernel(backend_ctx->program_add_id, "kernel_add_id", &err), err)); GGML_LOG_CONT("."); @@ -1294,7 +1349,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("tri.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_tri = clCreateKernel(prog, "kernel_tri_f32", &err), err)); GGML_LOG_CONT("."); @@ -1312,7 +1367,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("fill.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_fill = clCreateKernel(prog, "kernel_fill_f32", &err), err)); GGML_LOG_CONT("."); @@ -1330,7 +1385,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("clamp.cl"); #endif backend_ctx->program_clamp = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_clamp = clCreateKernel(backend_ctx->program_clamp, "kernel_clamp", &err), err)); GGML_LOG_CONT("."); @@ -1346,7 +1401,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("cpy.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_cpy_f16_f16 = clCreateKernel(prog, "kernel_cpy_f16_f16", &err), err)); CL_CHECK((backend_ctx->kernel_cpy_f16_f32 = clCreateKernel(prog, "kernel_cpy_f16_f32", &err), err)); @@ -1367,7 +1422,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("cvt.cl"); #endif backend_ctx->program_cvt = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_convert_block_q1_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q1_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q1_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q1_0", &err), err)); @@ -1450,7 +1505,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("diag_mask_inf.cl"); #endif backend_ctx->program_diag_mask_inf = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_diag_mask_inf_8 = clCreateKernel(backend_ctx->program_diag_mask_inf, "kernel_diag_mask_inf_8", &err), err)); CL_CHECK((backend_ctx->kernel_diag_mask_inf = clCreateKernel(backend_ctx->program_diag_mask_inf, "kernel_diag_mask_inf", &err), err)); @@ -1467,7 +1522,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("diag.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_diag_f32 = clCreateKernel(prog, "kernel_diag_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1484,7 +1539,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gelu.cl"); #endif backend_ctx->program_gelu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gelu = clCreateKernel(backend_ctx->program_gelu, "kernel_gelu", &err), err)); CL_CHECK((backend_ctx->kernel_gelu_4 = clCreateKernel(backend_ctx->program_gelu, "kernel_gelu_4", &err), err)); @@ -1505,7 +1560,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("glu.cl"); #endif backend_ctx->program_glu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_geglu = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err)); CL_CHECK((backend_ctx->kernel_reglu = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err)); @@ -1531,7 +1586,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("get_rows.cl"); #endif backend_ctx->program_get_rows = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_get_rows_f32 = clCreateKernel(backend_ctx->program_get_rows, "kernel_get_rows_f32", &err), err)); CL_CHECK((backend_ctx->kernel_get_rows_f16 = clCreateKernel(backend_ctx->program_get_rows, "kernel_get_rows_f16", &err), err)); @@ -1549,7 +1604,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("solve_tri.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_solve_tri_f32 = clCreateKernel(prog, "kernel_solve_tri_f32", &err), err)); GGML_LOG_CONT("."); @@ -1566,7 +1621,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("im2col_f32.cl"); #endif backend_ctx->program_im2col_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_im2col_f32 = clCreateKernel(backend_ctx->program_im2col_f32, "kernel_im2col_f32", &err), err)); GGML_LOG_CONT("."); @@ -1582,7 +1637,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("im2col_f16.cl"); #endif backend_ctx->program_im2col_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_im2col_f16 = clCreateKernel(backend_ctx->program_im2col_f16, "kernel_im2col_f16", &err), err)); GGML_LOG_CONT("."); @@ -1598,7 +1653,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32, "kernel_mul_mat_q4_0_f32", &err), err)); GGML_LOG_CONT("."); @@ -1614,7 +1669,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_v.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_v = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_v = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_v, "kernel_mul_mat_q4_0_f32_v", &err), err)); GGML_LOG_CONT("."); @@ -1630,7 +1685,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_8x_flat.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_8x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_8x_flat, "kernel_mul_mat_q4_0_f32_8x_flat", &err), err)); GGML_LOG_CONT("."); @@ -1641,6 +1696,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { // those compiler versions since it is anyway not used for Adreno. if (backend_ctx->gpu_family != ADRENO || backend_ctx->adreno_cl_compiler_version.newer_than_or_same(E031, 38, 11, 0) || + backend_ctx->adreno_cl_compiler_version.type == E17 || backend_ctx->adreno_cl_compiler_version.type == DX) { #ifdef GGML_OPENCL_EMBED_KERNELS const std::string kernel_src { @@ -1650,7 +1706,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_1d_8x_flat.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_1d_8x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_1d_8x_flat, "kernel_mul_mat_q4_0_f32_1d_8x_flat", &err), err)); GGML_LOG_CONT("."); @@ -1670,7 +1726,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_1d_16x_flat.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_1d_16x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_16x_flat = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_1d_16x_flat, "kernel_mul_mat_q4_0_f32_1d_16x_flat", &err), err)); GGML_LOG_CONT("."); @@ -1686,7 +1742,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_1_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1703,7 +1759,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_1_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1720,7 +1776,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_k_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1737,7 +1793,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_k_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1754,7 +1810,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_0_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_0_f32 = clCreateKernel(prog, "kernel_mul_mv_q5_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1771,7 +1827,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_0_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_0_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q5_0_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1788,7 +1844,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_1_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q5_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1805,7 +1861,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_1_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q5_1_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1822,7 +1878,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_k_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_K_f32 = clCreateKernel(prog, "kernel_mul_mv_q5_K_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1839,7 +1895,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_k_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q5_K_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1855,7 +1911,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q6_k_f32.cl"); #endif backend_ctx->program_mul_mv_q6_K = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32 = clCreateKernel(backend_ctx->program_mul_mv_q6_K, "kernel_mul_mv_q6_K_f32", &err), err)); GGML_LOG_CONT("."); @@ -1871,7 +1927,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q6_k_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q6_K_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1888,7 +1944,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q8_0_f32.cl"); #endif backend_ctx->program_mul_mv_q8_0_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q8_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_q8_0_f32, "kernel_mul_mv_q8_0_f32", &err), err)); GGML_LOG_CONT("."); @@ -1904,7 +1960,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q8_0_f32_flat.cl"); #endif backend_ctx->program_mul_mv_q8_0_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q8_0_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_q8_0_f32_flat, "kernel_mul_mv_q8_0_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -1920,7 +1976,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q1_0_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q1_0_f32 = clCreateKernel(prog, "kernel_mul_mv_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1937,7 +1993,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q1_0_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q1_0_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q1_0_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1954,7 +2010,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_iq4_nl_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_iq4_nl_f32 = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1971,7 +2027,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_iq4_nl_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_iq4_nl_f32_flat = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1988,7 +2044,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_mxfp4_f32.cl"); #endif backend_ctx->program_mul_mv_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_mxfp4_f32 = clCreateKernel(backend_ctx->program_mul_mv_mxfp4_f32, "kernel_mul_mv_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -2004,7 +2060,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_mxfp4_f32_flat.cl"); #endif backend_ctx->program_mul_mv_mxfp4_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_mxfp4_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_mxfp4_f32_flat, "kernel_mul_mv_mxfp4_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -2020,7 +2076,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f16.cl"); #endif backend_ctx->program_mul_mv_f16_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f16 = clCreateKernel(backend_ctx->program_mul_mv_f16_f16, "kernel_mul_mat_f16_f16", &err), err)); GGML_LOG_CONT("."); @@ -2036,7 +2092,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f32_1row.cl"); #endif backend_ctx->program_mul_mv_f16_f32_1row = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_1row = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_1row, "kernel_mul_mat_f16_f32_1row", &err), err)); GGML_LOG_CONT("."); @@ -2052,7 +2108,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f32_l4.cl"); #endif backend_ctx->program_mul_mv_f16_f32_l4 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4 = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4", &err), err)); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4_dr = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4_dr", &err), err)); @@ -2118,7 +2174,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f32.cl"); #endif backend_ctx->program_mul_mv_f16_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32 = clCreateKernel(backend_ctx->program_mul_mv_f16_f32, "kernel_mul_mat_f16_f32", &err), err)); GGML_LOG_CONT("."); @@ -2134,7 +2190,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f32_f32.cl"); #endif backend_ctx->program_mul_mv_f32_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f32_f32 = clCreateKernel(backend_ctx->program_mul_mv_f32_f32, "kernel_mul_mat_f32_f32", &err), err)); GGML_LOG_CONT("."); @@ -2150,7 +2206,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mat_f16_f32.cl"); #endif backend_ctx->program_mul_mat_f16_f32_tiled = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_tiled = clCreateKernel(backend_ctx->program_mul_mat_f16_f32_tiled, "mul_mat_f16_f32", &err), err)); GGML_LOG_CONT("."); @@ -2167,7 +2223,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_xmem_f16_f32_os8.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_adreno_xmem_pack_src_f32 = clCreateKernel(prog, "adreno_xmem_pack_src_f32", &err), err)); @@ -2192,7 +2248,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_f32_f32_l4_lm.cl"); #endif backend_ctx->program_mul_mm_f32_f32_l4_lm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f32_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f32_f32_l4_lm, "kernel_mul_mm_f32_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2208,7 +2264,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_f16_f32_l4_lm.cl"); #endif backend_ctx->program_mul_mm_f16_f32_l4_lm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_l4_lm, "kernel_mul_mm_f16_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2224,7 +2280,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q4_0_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_0_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2240,7 +2296,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q4_1_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_1_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2256,7 +2312,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q5_0_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q5_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q5_0_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2272,7 +2328,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q5_1_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q5_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q5_1_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2288,7 +2344,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q8_0_f32_l4_lm.cl"); #endif backend_ctx->program_mul_mm_q8_0_f32_l4_lm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q8_0_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_q8_0_f32_l4_lm, "kernel_mul_mm_q8_0_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2304,7 +2360,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q1_0_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q1_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q1_0_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2321,7 +2377,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_iq4_nl_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_iq4_nl_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_iq4_nl_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2338,7 +2394,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q4_k_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q4_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_k_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2355,7 +2411,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q6_k_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q6_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q6_k_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2372,7 +2428,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q5_k_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q5_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q5_k_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2389,9 +2445,9 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_f16_f32_kq_kqv.cl"); #endif backend_ctx->program_mul_mm_f16_f32_kqv = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts+" -DKQV "); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts+" -DKQV "); backend_ctx->program_mul_mm_f16_f32_kq = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_kqv = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_kqv, "mul_mm_f16_f32_kqv", &err), err)); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_kq = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_kq, "mul_mm_f16_f32_kq", &err), err)); @@ -2408,7 +2464,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul.cl"); #endif backend_ctx->program_mul = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul = clCreateKernel(backend_ctx->program_mul, "kernel_mul", &err), err)); CL_CHECK((backend_ctx->kernel_mul_row = clCreateKernel(backend_ctx->program_mul, "kernel_mul_row", &err), err)); @@ -2427,7 +2483,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("norm.cl"); #endif backend_ctx->program_norm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_norm = clCreateKernel(backend_ctx->program_norm, "kernel_norm", &err), err)); CL_CHECK((backend_ctx->kernel_norm_mul_add = clCreateKernel(backend_ctx->program_norm, "kernel_norm_mul_add", &err), err)); @@ -2444,7 +2500,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("relu.cl"); #endif backend_ctx->program_relu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_relu = clCreateKernel(backend_ctx->program_relu, "kernel_relu", &err), err)); GGML_LOG_CONT("."); @@ -2460,7 +2516,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("rms_norm.cl"); #endif backend_ctx->program_rms_norm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_rms_norm = clCreateKernel(backend_ctx->program_rms_norm, "kernel_rms_norm", &err), err)); CL_CHECK((backend_ctx->kernel_rms_norm_mul = clCreateKernel(backend_ctx->program_rms_norm, "kernel_rms_norm_mul", &err), err)); @@ -2477,7 +2533,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("l2_norm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_l2_norm_f32 = clCreateKernel(prog, "kernel_l2_norm_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2494,7 +2550,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("rope.cl"); #endif backend_ctx->program_rope = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_rope_norm_f32 = clCreateKernel(backend_ctx->program_rope, "kernel_rope_norm_f32", &err), err)); CL_CHECK((backend_ctx->kernel_rope_norm_f16 = clCreateKernel(backend_ctx->program_rope, "kernel_rope_norm_f16", &err), err)); @@ -2517,7 +2573,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("scale.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_scale_f32 = clCreateKernel(prog, "kernel_scale_f32", &err), err)); CL_CHECK((backend_ctx->kernel_scale_f32_4 = clCreateKernel(prog, "kernel_scale_f32_4", &err), err)); @@ -2535,7 +2591,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("silu.cl"); #endif backend_ctx->program_silu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_silu = clCreateKernel(backend_ctx->program_silu, "kernel_silu", &err), err)); CL_CHECK((backend_ctx->kernel_silu_4 = clCreateKernel(backend_ctx->program_silu, "kernel_silu_4", &err), err)); @@ -2552,7 +2608,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_f32.cl"); #endif backend_ctx->program_softmax_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max = clCreateKernel(backend_ctx->program_softmax_f32, "kernel_soft_max", &err), err)); GGML_LOG_CONT("."); @@ -2568,7 +2624,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_f16.cl"); #endif backend_ctx->program_softmax_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max_f16 = clCreateKernel(backend_ctx->program_softmax_f16, "kernel_soft_max_f16", &err), err)); GGML_LOG_CONT("."); @@ -2584,7 +2640,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_4_f32.cl"); #endif backend_ctx->program_softmax_4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max_4 = clCreateKernel(backend_ctx->program_softmax_4_f32, "kernel_soft_max_4", &err), err)); GGML_LOG_CONT("."); @@ -2600,7 +2656,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_4_f16.cl"); #endif backend_ctx->program_softmax_4_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max_4_f16 = clCreateKernel(backend_ctx->program_softmax_4_f16, "kernel_soft_max_4_f16", &err), err)); GGML_LOG_CONT("."); @@ -2619,7 +2675,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { " -cl-mad-enable -cl-finite-math-only "; backend_ctx->program_div = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_div = clCreateKernel(backend_ctx->program_div, "kernel_div", &err), err)); CL_CHECK((backend_ctx->kernel_div_row = clCreateKernel(backend_ctx->program_div, "kernel_div_row", &err), err)); @@ -2638,7 +2694,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sqr.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sqr_cont_f32 = clCreateKernel(prog, "kernel_sqr_cont_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sqr_cont_f32_4 = clCreateKernel(prog, "kernel_sqr_cont_f32_4", &err), err)); @@ -2659,7 +2715,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sqrt.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sqrt_cont_f32 = clCreateKernel(prog, "kernel_sqrt_cont_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sqrt_cont_f32_4 = clCreateKernel(prog, "kernel_sqrt_cont_f32_4", &err), err)); @@ -2680,7 +2736,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mean.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mean_f32 = clCreateKernel(prog, "kernel_mean_f32", &err), err)); CL_CHECK((backend_ctx->kernel_mean_f32_4 = clCreateKernel(prog, "kernel_mean_f32_4", &err), err)); @@ -2699,7 +2755,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sub.cl"); #endif backend_ctx->program_sub = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sub = clCreateKernel(backend_ctx->program_sub, "kernel_sub", &err), err)); CL_CHECK((backend_ctx->kernel_sub_row = clCreateKernel(backend_ctx->program_sub, "kernel_sub_row", &err), err)); @@ -2718,7 +2774,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sum_rows.cl"); #endif backend_ctx->program_sum_rows_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sum_rows_f32 = clCreateKernel(backend_ctx->program_sum_rows_f32, "kernel_sum_rows_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sum_rows_f32_4 = clCreateKernel(backend_ctx->program_sum_rows_f32, "kernel_sum_rows_f32_4", &err), err)); @@ -2735,7 +2791,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("cumsum.cl"); #endif cl_program prog; - prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_cumsum_blk = clCreateKernel(prog, "kernel_cumsum_blk", &err), err)); CL_CHECK((backend_ctx->kernel_cumsum_add = clCreateKernel(prog, "kernel_cumsum_add", &err), err)); @@ -2753,7 +2809,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sigmoid.cl"); #endif backend_ctx->program_sigmoid = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sigmoid_f32 = clCreateKernel(backend_ctx->program_sigmoid, "kernel_sigmoid_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sigmoid_f16 = clCreateKernel(backend_ctx->program_sigmoid, "kernel_sigmoid_f16", &err), err)); @@ -2770,7 +2826,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("group_norm.cl"); #endif backend_ctx->program_group_norm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_group_norm = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm", &err), err)); CL_CHECK((backend_ctx->kernel_group_norm_mul_add = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm_mul_add", &err), err)); @@ -2787,7 +2843,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("repeat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_repeat_f32 = clCreateKernel(prog, "kernel_repeat_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -2804,7 +2860,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_pad = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_pad = clCreateKernel(backend_ctx->program_pad, "kernel_pad", &err), err)); GGML_LOG_CONT("."); } else { @@ -2824,7 +2880,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("tanh.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_tanh_f32 = clCreateKernel(prog, "kernel_tanh_f32", &err), err)); CL_CHECK((backend_ctx->kernel_tanh_f32_4 = clCreateKernel(prog, "kernel_tanh_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_tanh_f32_nc = clCreateKernel(prog, "kernel_tanh_f32_nc", &err), err)); @@ -2845,7 +2901,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("neg.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_neg_f32 = clCreateKernel(prog, "kernel_neg_f32", &err), err)); CL_CHECK((backend_ctx->kernel_neg_f32_4 = clCreateKernel(prog, "kernel_neg_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_neg_f32_nc = clCreateKernel(prog, "kernel_neg_f32_nc", &err), err)); @@ -2866,7 +2922,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("exp.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_exp_f32 = clCreateKernel(prog, "kernel_exp_f32", &err), err)); CL_CHECK((backend_ctx->kernel_exp_f32_4 = clCreateKernel(prog, "kernel_exp_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_exp_f32_nc = clCreateKernel(prog, "kernel_exp_f32_nc", &err), err)); @@ -2887,7 +2943,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("expm1.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_expm1_f32 = clCreateKernel(prog, "kernel_expm1_f32", &err), err)); CL_CHECK((backend_ctx->kernel_expm1_f32_4 = clCreateKernel(prog, "kernel_expm1_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_expm1_f32_nc = clCreateKernel(prog, "kernel_expm1_f32_nc", &err), err)); @@ -2898,6 +2954,27 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // abs + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "abs.cl.h" + }; +#else + const std::string kernel_src = read_file("abs.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_abs_f32 = clCreateKernel(prog, "kernel_abs_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f32_4 = clCreateKernel(prog, "kernel_abs_f32_4", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f32_nc = clCreateKernel(prog, "kernel_abs_f32_nc", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f16 = clCreateKernel(prog, "kernel_abs_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f16_4 = clCreateKernel(prog, "kernel_abs_f16_4", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f16_nc = clCreateKernel(prog, "kernel_abs_f16_nc", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // softplus { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -2908,7 +2985,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softplus.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_softplus_f32 = clCreateKernel(prog, "kernel_softplus_f32", &err), err)); CL_CHECK((backend_ctx->kernel_softplus_f32_4 = clCreateKernel(prog, "kernel_softplus_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_softplus_f32_nc = clCreateKernel(prog, "kernel_softplus_f32_nc", &err), err)); @@ -2930,7 +3007,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_upscale = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_upscale = clCreateKernel(backend_ctx->program_upscale, "kernel_upscale", &err), err)); if (backend_ctx->program_upscale) { cl_int err_bilinear; @@ -2961,7 +3038,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("concat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_concat_f32 = clCreateKernel(prog, "kernel_concat_f32", &err), err)); CL_CHECK((backend_ctx->kernel_concat_f32_pack = clCreateKernel(prog, "kernel_concat_f32_pack", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2980,7 +3057,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_tsembd = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_timestep_embedding = clCreateKernel(backend_ctx->program_tsembd, "kernel_timestep_embedding", &err), err)); GGML_LOG_CONT("."); } else { @@ -3000,7 +3077,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("set_rows.cl"); #endif backend_ctx->program_set_rows = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_set_rows_f32_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i64", &err), err)); CL_CHECK((backend_ctx->kernel_set_rows_f32_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i32", &err), err)); @@ -3032,11 +3109,11 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_conv_2d_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), (std::string(compile_opts) + " -DUSE_FP16=1").c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), (std::string(compile_opts) + " -DUSE_FP16=1").c_str()); CL_CHECK((backend_ctx->kernel_conv_2d_f16 = clCreateKernel(backend_ctx->program_conv_2d_f16, "kernel_conv_2d", &err), err)); GGML_LOG_CONT("."); backend_ctx->program_conv_2d_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_conv_2d_f32 = clCreateKernel(backend_ctx->program_conv_2d_f32, "kernel_conv_2d", &err), err)); GGML_LOG_CONT("."); } else { @@ -3048,7 +3125,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { } if (!kernel_src_f16_f32.empty()) { backend_ctx->program_conv_2d_f16_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f16_f32.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src_f16_f32.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_conv_2d_f16_f32 = clCreateKernel(backend_ctx->program_conv_2d_f16_f32, "kernel_conv_2d", &err), err)); GGML_LOG_CONT("."); } else { @@ -3068,7 +3145,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("ssm_conv.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_ssm_conv_f32_f32 = clCreateKernel(prog, "kernel_ssm_conv_f32_f32", &err), err)); CL_CHECK((backend_ctx->kernel_ssm_conv_f32_f32_4 = clCreateKernel(prog, "kernel_ssm_conv_f32_f32_4", &err), err)); @@ -3144,8 +3221,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { // Please remember to implement code to handle it. opts += " -DSUBGROUPS_PER_WG=" + std::to_string(spw); - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), opts); CL_CHECK((backend_ctx->kernel_gated_delta_net_f32[si][kda][tgpp] = clCreateKernel(prog, "kernel_gated_delta_net", &err), err)); @@ -3166,7 +3242,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_combine.cl"); #endif cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_moe_combine_f32 = clCreateKernel(prog, "kernel_moe_combine_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3183,7 +3259,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_q4_0_f32_8x_flat.cl"); #endif backend_ctx->program_mul_mv_id_q4_0_f32_8x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_id_q4_0_f32_8x_flat, "kernel_mul_mv_id_q4_0_f32_8x_flat", &err), err)); GGML_LOG_CONT("."); @@ -3199,7 +3275,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_q8_0_f32.cl"); #endif backend_ctx->program_mul_mv_id_q8_0_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_q8_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_id_q8_0_f32, "kernel_mul_mv_id_q8_0_f32", &err), err)); GGML_LOG_CONT("."); @@ -3215,7 +3291,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_q8_0_f32_flat.cl"); #endif backend_ctx->program_mul_mv_id_q8_0_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_q8_0_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_id_q8_0_f32_flat, "kernel_mul_mv_id_q8_0_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -3231,7 +3307,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_mxfp4_f32.cl"); #endif backend_ctx->program_mul_mv_id_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_mxfp4_f32 = clCreateKernel(backend_ctx->program_mul_mv_id_mxfp4_f32, "kernel_mul_mv_id_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -3247,7 +3323,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_mxfp4_f32_flat.cl"); #endif backend_ctx->program_mul_mv_id_mxfp4_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_mxfp4_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_id_mxfp4_f32_flat, "kernel_mul_mv_id_mxfp4_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -3265,7 +3341,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("transpose.cl"); #endif backend_ctx->program_transpose = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_16", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32", &err), err)); @@ -3286,7 +3362,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q1_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3307,8 +3383,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q1_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3333,8 +3408,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q4_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3362,8 +3436,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv = read_file("gemv_noshuffle_q4_0_f32_spec.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3379,8 +3452,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST "; } - prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_11008 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3396,8 +3468,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST "; } - prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_11008_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3414,8 +3485,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST "; } - prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_32000_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3430,7 +3500,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src_CL_gemm = read_file("gemm_noshuffle_q4_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_CL_gemm.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemm.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3445,7 +3515,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_1_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3467,8 +3537,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_q4_1_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3484,7 +3553,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3499,7 +3568,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_0_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3521,8 +3590,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemv_noshuffle_q5_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3537,7 +3605,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_1_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3558,8 +3626,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemv_noshuffle_q5_1_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3574,7 +3641,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_iq4_nl_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_iq4_nl_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3589,7 +3656,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_iq4_nl_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_iq4_nl_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_iq4_nl_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3604,7 +3671,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_0_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3626,8 +3693,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_iq4_nl_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_iq4_nl_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3643,7 +3709,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q8_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3685,8 +3751,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q8_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q8_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3702,7 +3767,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_k_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3721,9 +3786,9 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { // X1 (1152 B/WG -> few resident WGs); TILESIZE_N=8 (288 B) lifts occupancy on // X1, byte-identical. X2E keeps 32. Env override wins. int q4k_dp4a_ts = (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) ? 8 : 32; - if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) q4k_dp4a_ts = atoi(e); + if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) { q4k_dp4a_ts = atoi(e); } std::string dp4a_opts = compile_opts + " -DTILESIZE_N=" + std::to_string(q4k_dp4a_ts); - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), dp4a_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), dp4a_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3739,7 +3804,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q8_0_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3755,7 +3820,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_k_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3770,7 +3835,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q6_k_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3785,7 +3850,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("quant_a_q8_1.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_quant_a_q8_1 = clCreateKernel(prog, "kernel_quant_a_q8_1", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3807,8 +3872,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_q4_k_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3828,7 +3892,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemv_moe_q4_1_f32_ns.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_1_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3844,7 +3908,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_moe_q4_1_f32_ns.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_1_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3879,7 +3943,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_mxfp4_f32.cl"); #endif backend_ctx->program_gemv_moe_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemv_moe_mxfp4_f32, "kernel_gemv_moe_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -3895,7 +3959,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_mxfp4_f32.cl"); #endif backend_ctx->program_gemm_moe_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemm_moe_mxfp4_f32, "kernel_gemm_moe_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -3911,7 +3975,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q4_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_0_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3928,7 +3992,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3963,7 +4027,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q8_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q8_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3980,7 +4044,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q5_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q5_0_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3997,7 +4061,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q5_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q5_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4014,7 +4078,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q5_1_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q5_1_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4031,7 +4095,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q5_1_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q5_1_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4048,7 +4112,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q4_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_k_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_k_f32_ns", &err), err)); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_k_f32_ns_wimg = clCreateKernel(prog, "kernel_gemv_moe_q4_k_f32_ns_wimg", &err), err)); @@ -4066,7 +4130,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_k_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4101,7 +4165,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_k_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_q4_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4118,7 +4182,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_mxfp4_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4135,7 +4199,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_0_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_q4_0_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4153,19 +4217,19 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif const std::string opts80 = CL_moe_compile_opts + " -DMOE_QT=80"; cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts80.c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), opts80.c_str()); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_1_dp4a_q80 = clCreateKernel(prog, "kernel_gemm_moe_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); const std::string opts50 = CL_moe_compile_opts + " -DMOE_QT=50"; cl_program prog50 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts50.c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), opts50.c_str()); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_1_dp4a_q50 = clCreateKernel(prog50, "kernel_gemm_moe_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog50)); const std::string opts5 = CL_moe_compile_opts + " -DMOE_QT=5"; cl_program prog5 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts5.c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), opts5.c_str()); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_1_dp4a_q5k = clCreateKernel(prog5, "kernel_gemm_moe_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog5)); GGML_LOG_CONT("."); @@ -4181,7 +4245,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_reorder_quant_a_q8_1.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_moe_reorder_quant_a_q8_1 = clCreateKernel(prog, "kernel_moe_reorder_quant_a_q8_1", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4198,7 +4262,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q5_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q5_k_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4215,7 +4279,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q5_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q5_k_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4232,7 +4296,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q6_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q6_k_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q6_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4249,13 +4313,31 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q6_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q6_k_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q6_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } + // gemm_moe_q6_k_f32_ns_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q6_k_f32_ns_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q6_k_f32_ns_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } + } + // gemm_moe_q6_k_q8_1_dp4a (dp4a q6_K MoE prefill GEMM) if (backend_ctx->has_integer_dot) { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -4266,7 +4348,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q6_k_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q6_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_q6_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4283,7 +4365,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_mxfp4_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_mxfp4_f32_ns", &err), err)); CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32_ns_wimg = clCreateKernel(prog, "kernel_gemv_moe_mxfp4_f32_ns_wimg", &err), err)); @@ -4301,7 +4383,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_mxfp4_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4336,7 +4418,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_reorder_b.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_moe_reorder_b = clCreateKernel(prog, "kernel_moe_reorder_b", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4353,7 +4435,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_sort_by_expert.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_moe_histogram = clCreateKernel(prog, "kernel_moe_histogram", &err), err)); CL_CHECK((backend_ctx->kernel_moe_scan = clCreateKernel(prog, "kernel_moe_scan", &err), err)); @@ -4380,7 +4462,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { } cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q6_K_f32", &err), err)); GGML_LOG_CONT("."); @@ -4396,7 +4478,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_noshuffle_q6_k_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_K_f32", &err), err)); GGML_LOG_CONT("."); @@ -4418,8 +4500,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_q5_k_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4435,7 +4516,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_k_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -5958,7 +6039,8 @@ static void transpose_2d( cl_kernel kernel, cl_mem src, cl_mem dst, size_t size, cl_int stride, cl_int rows, - bool blocking = true + bool blocking = true, + bool auto_local = false // let driver pick local size for non-uniform workgroups ) { static ggml_cl_buffer buf; @@ -5984,7 +6066,7 @@ static void transpose_2d( size_t local_size[3] = {64, 1, 1}; size_t global_size[3] = {(size_t)stride, (size_t)rows, 1};; CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL, - global_size, local_size, 0, NULL, NULL)); + global_size, auto_local ? NULL : local_size, 0, NULL, NULL)); if (blocking) { CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, trans, dst, 0, 0, size, 0, NULL, &evt)); @@ -6001,10 +6083,11 @@ static void transpose_2d_as_8b( ggml_backend_opencl_context * backend_ctx, cl_mem src, cl_mem dst, size_t size, cl_int stride, cl_int rows, - bool blocking = true + bool blocking = true, + bool auto_local = false ) { transpose_2d(backend_ctx, backend_ctx->kernel_transpose_8_buf, - src, dst, size, stride, rows, blocking); + src, dst, size, stride, rows, blocking, auto_local); } static void transpose_2d_as_16b( @@ -6932,8 +7015,31 @@ inline bool use_adreno_kernels(const ggml_backend_opencl_context *backend_ctx, c return threashold_ok; } +static bool adreno_e17_compiler_quirks(const ggml_backend_opencl_context *backend_ctx) { + if (!backend_ctx || backend_ctx->gpu_family != GPU_FAMILY::ADRENO || + backend_ctx->adreno_cl_compiler_version.type != ADRENO_CL_COMPILER_TYPE::E17) { + return false; + } + const char * env = getenv("GGML_OPENCL_ADRENO_E17_QUIRKS"); + return !(env && env[0] == '0'); +} + inline bool use_adreno_moe_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) { - GGML_UNUSED(backend_ctx); + // The moe weight repack kernels *_trans4_ns alias a private ushort8 through a uchar*. + // Certain compilers (found with some A7x and A6x) miscompiles this, corrupting the weights. + // So, exclude A6x and A7x from using Adreno MoE kernels for now. + // The quants that have a general mul_mat_id kernel fallback to the general version; the + // rest fallback to CPU. + if (backend_ctx && (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A6X || + backend_ctx->adreno_gen == ADRENO_GPU_GEN::A7X || + backend_ctx->adreno_gen == ADRENO_GPU_GEN::ADRENO_UNKNOWN)) { + return false; + } + + if (adreno_e17_compiler_quirks(backend_ctx)) { + return false; + } + int ne01 = tensor->ne[1]; return (((strstr(tensor->name, "ffn") != NULL) && (strstr(tensor->name, "exps") != NULL)) || (strstr(tensor->name, "as") != NULL)) && (ne01 % 32 == 0); } @@ -7099,6 +7205,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return op->src[0]->type == GGML_TYPE_F32; case GGML_UNARY_OP_EXPM1: return op->src[0]->type == GGML_TYPE_F32; + case GGML_UNARY_OP_ABS: + return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; case GGML_UNARY_OP_SOFTPLUS: return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; default: @@ -7266,6 +7374,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te case GGML_OP_MEAN: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_FLASH_ATTN_EXT: { + // The E17 compilers segfault while building FA kernels, skip E17 for now + if (adreno_e17_compiler_quirks(backend_ctx)) { + return false; + } const ggml_tensor * q = op->src[0]; const ggml_tensor * k = op->src[1]; const ggml_tensor * v = op->src[2]; @@ -7319,6 +7431,14 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return false; } + // Some compilers for A7x (Adreno 740, compiler E031.41) crashes when + // building FA kernels with mixed or quant types (f32_f16, f32_q8_0, f32_q4_0) + // Here we skip all A7x for these kernels to avoid crash + if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A7X && + (is_f32_f16 || is_f32_q8_0 || is_f32_q4_0)) { + return false; + } + if (dk == 512) { if (backend_ctx->gpu_family == INTEL) { return false; @@ -8963,6 +9083,9 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M); transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/256, M); transpose_2d_as_16b(backend_ctx, extra->dm, extra->dm, size_dm, K/256, M); + + // Transpose s as uchar + transpose_2d_as_8b(backend_ctx, extra->s, extra->s, size_s, K/256*12, M, true, true); } #endif // GGML_OPENCL_USE_ADRENO_KERNELS return; @@ -10131,23 +10254,27 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2; size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t); size_t size_dm = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t); + size_t size_s = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*12; static ggml_cl_buffer buf_trans_q; static ggml_cl_buffer buf_trans_d; static ggml_cl_buffer buf_trans_dm; + static ggml_cl_buffer buf_trans_s; buf_trans_q.allocate(backend_ctx->context, size_q); buf_trans_d.allocate(backend_ctx->context, size_d); buf_trans_dm.allocate(backend_ctx->context, size_dm); + buf_trans_s.allocate(backend_ctx->context, size_s); - // Transpose q, d, dm back + // Transpose q, d, dm, s back transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4); transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/256); transpose_2d_as_16b(backend_ctx, extra->dm, buf_trans_dm.buffer, size_dm, M, K/256); + transpose_2d_as_8b (backend_ctx, extra->s, buf_trans_s.buffer, size_s, M, K/256*12, true, true); cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K_noshuffle; CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer)); - CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_s.buffer)); CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_trans_d.buffer)); CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_trans_dm.buffer)); CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device)); @@ -10518,6 +10645,11 @@ static const char * ggml_backend_opencl_buffer_type_get_name(ggml_backend_buffer static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) { ggml_backend_opencl_context *backend_ctx = ggml_cl_init(buffer_type->device); + + if (!backend_ctx->program_cache_initialized) { + backend_ctx->program_cache = cl_program_cache_init(backend_ctx->device); + backend_ctx->program_cache_initialized = true; + } load_cl_kernels(backend_ctx); // clCreateBuffer returns -61 for size 0 @@ -12640,7 +12772,7 @@ static void ggml_opencl_op_rms_norm_fused(ggml_backend_t backend, ggml_tensor * ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; cl_ulong offset0 = extra0->offset + src0->view_offs; - cl_ulong offset1 = extra1->offset + src0->view_offs; + cl_ulong offset1 = extra1->offset + src1->view_offs; cl_ulong offsetd = extrad->offset + dst->view_offs; ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; @@ -13321,6 +13453,102 @@ static void ggml_cl_expm1(ggml_backend_t backend, const ggml_tensor * src0, cons } } +static void ggml_cl_abs(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + UNUSED(src1); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + const int ne00 = src0->ne[0]; + const int ne01 = src0->ne[1]; + const int ne02 = src0->ne[2]; + const int ne03 = src0->ne[3]; + + const cl_ulong nb00 = src0->nb[0]; + const cl_ulong nb01 = src0->nb[1]; + const cl_ulong nb02 = src0->nb[2]; + const cl_ulong nb03 = src0->nb[3]; + + const cl_ulong nb0 = dst->nb[0]; + const cl_ulong nb1 = dst->nb[1]; + const cl_ulong nb2 = dst->nb[2]; + const cl_ulong nb3 = dst->nb[3]; + + cl_kernel kernel; + + if (ggml_is_contiguous(src0)) { + // Handle contiguous input + int n = ggml_nelements(dst); + if (n % 4 == 0) { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_abs_f32_4; + } else { + kernel = backend_ctx->kernel_abs_f16_4; + } + n /= 4; + } else { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_abs_f32; + } else { + kernel = backend_ctx->kernel_abs_f16; + } + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + + size_t global_work_size[] = {(size_t)n, 1, 1}; + size_t local_work_size[] = {64, 1, 1}; + + size_t * local_work_size_ptr = local_work_size; + if (n % 64 != 0 && !backend_ctx->non_uniform_workgroups) { + local_work_size_ptr = nullptr; + } + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst); + } else { + // Handle non-contiguous input + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_abs_f32_nc; + } else { + kernel = backend_ctx->kernel_abs_f16_nc; + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb00)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb3)); + + int nth = 64; + + size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } +} + static void ggml_cl_softplus(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -14544,7 +14772,11 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co // Flash-Decoding K-split decision. Resolved here, before the prefill // prepass, because KV-pad and blk prepass are pure overhead when FD fires. - const int is_causal = (mask == NULL && n_q > 1 && n_q == n_kv); + // Do not infer causality from tensor shapes: a NULL mask means full + // (bidirectional) attention, e.g. ViT encoders, where n_q == n_kv as well. + // Causal attention in llama.cpp always comes with an explicit KQ mask. + // Inferring is_causal here corrupted mmproj output on OpenCL (see #23800). + const int is_causal = 0; const int fd_max_n_q = (d_head_q <= FD_MAX_DK_MULTI) ? FD_MAX_N_Q_MULTI : 1; cl_kernel fd_k_split = NULL; bool use_fd_mq = false; @@ -16871,9 +17103,6 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t cl_ulong offset1 = extra1->offset + src1->view_offs; cl_ulong offsetd = extrad->offset + dst->view_offs; - GGML_ASSERT(src1->view_offs == 0); - GGML_ASSERT(dst->view_offs == 0); - const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; @@ -16934,9 +17163,9 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_img)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q8_0->d)); CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &b_img)); - CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &extra1->offset)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1)); CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device)); - CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &extrad->offset)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd)); CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00)); CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01)); CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02)); @@ -18331,6 +18560,26 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co GGML_ASSERT(ne00 == ne10); +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + // adreno GEMM/GEMV kernels do not support broadcast, assuming ne2 and ne3 are 1 for src1 + // so we handle broadcast here + if ((ne12 > 1 || ne13 > 1) && ne02 == 1 && ne03 == 1 && + src0t != GGML_TYPE_F16 && src0t != GGML_TYPE_F32) { + for (int i13 = 0; i13 < ne13; ++i13) { + for (int i12 = 0; i12 < ne12; ++i12) { + ggml_tensor s1 = *src1; + s1.ne[2] = 1; s1.ne[3] = 1; + s1.view_offs = src1->view_offs + (size_t)i12*nb12 + (size_t)i13*nb13; + ggml_tensor d = *dst; + d.ne[2] = 1; d.ne[3] = 1; + d.view_offs = dst->view_offs + (size_t)i12*nb2 + (size_t)i13*nb3; + ggml_cl_mul_mat(backend, src0, &s1, &d); + } + } + return; + } +#endif + int nth0 = 32; int nth1 = 1; int nrows = 1; @@ -22095,8 +22344,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, bool use_moe_dp4a = q5kmdp4a_on && backend_ctx->kernel_gemm_moe_q8_1_dp4a_q5k != nullptr && extra0_q5_K->scale != nullptr; - // bin kernel takes precedence - use_moe_dp4a = use_moe_dp4a && backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin == nullptr; + // dot prod has to be available + use_moe_dp4a = backend_ctx->has_integer_dot && use_moe_dp4a; if (use_moe_dp4a) { const size_t tok_slots = (size_t)max_post_router_tile * n_tile_size; @@ -22314,6 +22563,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, } else { // for gemm kernel = backend_ctx->kernel_gemm_moe_q6_k_f32_ns; + if (backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin) { + kernel = backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin; + } // Reorder router if called from test-backend-ops or when new router is generated. // Otherwise reuse the reordered result from previous mul_mat_id call. @@ -22334,6 +22586,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, || backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E); // dot prod has to be available use_moe_dp4a = backend_ctx->has_integer_dot && use_moe_dp4a; + // bin kernel takes precedence + use_moe_dp4a = use_moe_dp4a && backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin == nullptr; cl_buffer_region region; region.origin = 0; @@ -22370,6 +22624,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, CL_CHECK(status); cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}}; + if (backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin) { + // bin kernel uses slightly different image format + image_format_buf_src1 = {CL_R, CL_FLOAT}; + image_desc_buf_src1.image_width = static_cast(ne00 * max_post_router_tile * n_tile_size); + } image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); CL_CHECK(status); @@ -24342,6 +24601,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_expm1; break; + case GGML_UNARY_OP_ABS: + if (!any_on_device) { + return false; + } + func = ggml_cl_abs; + break; case GGML_UNARY_OP_SOFTPLUS: if (!any_on_device) { return false; diff --git a/ggml/src/ggml-opencl/kernels/abs.cl b/ggml/src/ggml-opencl/kernels/abs.cl new file mode 100644 index 000000000000..96e952c2842a --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/abs.cl @@ -0,0 +1,113 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +//------------------------------------------------------------------------------ +// abs +//------------------------------------------------------------------------------ + +kernel void kernel_abs_f32( + global const float * src0, + ulong offset0, + global float * dst, + ulong offsetd +) { + src0 = (global float*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f32_4( + global const float4 * src0, + ulong offset0, + global float4 * dst, + ulong offsetd +) { + src0 = (global float4*)((global char*)src0 + offset0); + dst = (global float4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f16( + global const half * src0, + ulong offset0, + global half * dst, + ulong offsetd +) { + src0 = (global half*)((global char*)src0 + offset0); + dst = (global half*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f16_4( + global const half4 * src0, + ulong offset0, + global half4 * dst, + ulong offsetd +) { + src0 = (global half4*)((global char*)src0 + offset0); + dst = (global half4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f32_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const float * x = (global const float *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = fabs(*x); + } +} + +kernel void kernel_abs_f16_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const half * x = (global const half *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global half * y = (global half *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = fabs(*x); + } +} diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl index 1cc0cc8c3439..6e43ee81e73b 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl @@ -30,6 +30,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif #define ACC_TYPE float diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl index de09a1eaae37..95d215971e00 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl @@ -10,6 +10,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif // Flash attention: Q=f32, K=q4_0, V=q4_0. diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl index 46bc4bc9d94e..7e89ed0bd8f1 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl @@ -10,6 +10,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif // Flash attention: Q=f32, K=q8_0, V=q8_0. diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl index 834050a4f9ac..10c8855c1ee3 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl @@ -274,8 +274,9 @@ kernel void kernel_gemm_moe_mxfp4_f32_ns( shared_b[b_local_offset.y] = bx8_f16.hi; // Dequantization - reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * s; - reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * s; + // Cast the e8m0 scale to half to satisfy E17 compilers + reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * (half)s; + reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * (half)s; sub_group_barrier(CLK_LOCAL_MEM_FENCE); @@ -304,8 +305,9 @@ kernel void kernel_gemm_moe_mxfp4_f32_ns( shared_b[b_local_offset.y] = bx8_f16.hi; // Dequantization - reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * s; - reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * s; + // Cast the e8m0 scale to half to satisfy E17 compilers + reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * (half)s; + reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * (half)s; sub_group_barrier(CLK_LOCAL_MEM_FENCE); diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl index 95d0638134e1..97fdc8e18c82 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl @@ -37,15 +37,17 @@ static inline float e8m0_to_fp32(uchar x) { // One token's dp4a dot (8 uints = 32 K elems) + mxfp4 block-scale epilogue. // blk_scale already carries the 0.5 factor (== 0.5 * 2^e). #define MOE_MXFP4_DP4A_T(t) do { \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ int raw = 0; \ - raw = dot_acc_sat_4x8packed_ss_int(qw[0], sh_qa[t][0], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[1], sh_qa[t][1], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[2], sh_qa[t][2], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[3], sh_qa[t][3], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[4], sh_qa[t][4], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[5], sh_qa[t][5], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[6], sh_qa[t][6], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[7], sh_qa[t][7], raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[0], a0.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[1], a0.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[2], a0.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[3], a0.s3, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[4], a1.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[5], a1.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[6], a1.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[7], a1.s3, raw); \ acc[t] += blk_scale * (float)sh_d[t] * (float)raw; \ } while (0) @@ -133,11 +135,13 @@ kernel void kernel_gemm_moe_mxfp4_q8_1_dp4a( qw[6] = mxfp4_pack((ushort)(r3)); qw[7] = mxfp4_pack((ushort)(r3 >> 16)); // cooperatively stage the n_real-token x 32-K int8 activations - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * num_blocks + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl index 86ff943c51b2..502472049a9c 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl @@ -17,15 +17,17 @@ // One token's dp4a dot (8 uints = 32 K elems) + q4_0 scale/zero-point epilogue. #define MOE_Q40_DP4A_T(t) do { \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ int raw = 0; \ - raw = dot_acc_sat_4x8packed_ss_int(qw[0], sh_qa[t][0], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[1], sh_qa[t][1], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[2], sh_qa[t][2], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[3], sh_qa[t][3], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[4], sh_qa[t][4], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[5], sh_qa[t][5], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[6], sh_qa[t][6], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[7], sh_qa[t][7], raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[0], a0.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[1], a0.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[2], a0.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[3], a0.s3, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[4], a1.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[5], a1.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[6], a1.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[7], a1.s3, raw); \ acc[t] += d_val * ((float)sh_d[t] * (float)raw - 8.0f * (float)sh_s[t]); \ } while (0) @@ -112,11 +114,13 @@ kernel void kernel_gemm_moe_q4_0_q8_1_dp4a( qw[6] = EXP4(r3); qw[7] = EXP4(r3 >> 16); // cooperatively stage the n_real-token x 32-K int8 activations - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * num_blocks + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl index 540897544082..9d968f32ed5a 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl @@ -37,16 +37,21 @@ inline void get_scale_min_k4( (((uint)((u) & 0xF000u)) << 12) ) // One token's dp4a dot (8 uints = 32 K elems) + q4_K scale/min epilogue into acc[t]. +// The 8 activation uints are read as two 128-bit uint4 loads staged to private (Adreno +// wants 128-bit local reads, and a __local operand fed straight to the dp4a builtin is +// slower and can miscompile). #define MOE_Q4K_DP4A_T(t) do { \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ int raw = 0; \ - raw = dot_acc_sat_4x8packed_ss_int(qw[0], sh_qa[t][0], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[1], sh_qa[t][1], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[2], sh_qa[t][2], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[3], sh_qa[t][3], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[4], sh_qa[t][4], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[5], sh_qa[t][5], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[6], sh_qa[t][6], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[7], sh_qa[t][7], raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[0], a0.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[1], a0.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[2], a0.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[3], a0.s3, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[4], a1.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[5], a1.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[6], a1.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[7], a1.s3, raw); \ acc[t] += scale * (float)sh_d[t] * (float)raw - minv * (float)sh_s[t]; \ } while (0) @@ -145,12 +150,14 @@ kernel void kernel_gemm_moe_q4_k_q8_1_dp4a( qw[4] = EXP4(r2); qw[5] = EXP4(r2 >> 16); qw[6] = EXP4(r3); qw[7] = EXP4(r3 >> 16); - // --- cooperatively stage the n_real-token x 32-K int8 activations to LDS --- - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // cooperatively stage the n_real-token x 32-K int8 activations to lm + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * ne00_b + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl index 35e63dcab05f..4ffe9f8e66c6 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl @@ -41,8 +41,10 @@ inline int dp4a_q6(uint qw0, uint qw1, uint qw2, uint qw3, // One token's q6_K dp4a dot (two halves, per-16 scales) + epilogue into acc[t]. #define MOE_Q6K_DP4A_T(t) do { \ - const int raw1 = dp4a_q6(qw[0], qw[1], qw[2], qw[3], sh_qa[t][0], sh_qa[t][1], sh_qa[t][2], sh_qa[t][3]); \ - const int raw2 = dp4a_q6(qw[4], qw[5], qw[6], qw[7], sh_qa[t][4], sh_qa[t][5], sh_qa[t][6], sh_qa[t][7]); \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ + const int raw1 = dp4a_q6(qw[0], qw[1], qw[2], qw[3], a0.s0, a0.s1, a0.s2, a0.s3); \ + const int raw2 = dp4a_q6(qw[4], qw[5], qw[6], qw[7], a1.s0, a1.s1, a1.s2, a1.s3); \ const float a_d = (float)sh_d[t]; \ acc[t] += scale0 * a_d * (float)raw1 + scale1 * a_d * (float)raw2; \ } while (0) @@ -144,11 +146,13 @@ kernel void kernel_gemm_moe_q6_k_q8_1_dp4a( qw[6] = SIGN6(EXP4(r3) | EXP2((qh2 >> 16) & 0xFFu)); qw[7] = SIGN6(EXP4(r3 >> 16) | EXP2((qh2 >> 24) & 0xFFu)); - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * ne00_b + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl index 39bf5d83211c..d0b191e18363 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl @@ -102,8 +102,10 @@ inline int dp4a4(uint w0,uint w1,uint w2,uint w3,uint a0,uint a1,uint a2,uint a3 // One token's two-half dp4a + uniform scale/min epilogue into acc[t]. #define MOE_DP4A_T(t) do { \ - const int raw1 = dp4a4(qw[0],qw[1],qw[2],qw[3], sh_qa[t][0],sh_qa[t][1],sh_qa[t][2],sh_qa[t][3]); \ - const int raw2 = dp4a4(qw[4],qw[5],qw[6],qw[7], sh_qa[t][4],sh_qa[t][5],sh_qa[t][6],sh_qa[t][7]); \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ + const int raw1 = dp4a4(qw[0],qw[1],qw[2],qw[3], a0.s0,a0.s1,a0.s2,a0.s3); \ + const int raw2 = dp4a4(qw[4],qw[5],qw[6],qw[7], a1.s0,a1.s1,a1.s2,a1.s3); \ const float a_d = (float)sh_d[t]; \ acc[t] += sc0*a_d*(float)raw1 + sc1*a_d*(float)raw2 - mn*(float)sh_s[t]; \ } while (0) @@ -178,10 +180,13 @@ kernel void kernel_gemm_moe_q8_1_dp4a( LOAD_QW(step, sub) - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3, u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * ne00_b + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl index 99fd1fd7bf1e..22b4e9114628 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl @@ -8,9 +8,11 @@ #define QK_K 256 #define K_SCALE_SIZE 12 +// scales are transposed: consecutive codes of a row are `stride` apart inline void get_scale_min_k4( int j, global const uchar * q, + int stride, uchar * d, uchar * m, uchar mask_d6, @@ -18,11 +20,11 @@ inline void get_scale_min_k4( uchar mask_hi2 ) { if (j < 4) { - *d = q[j] & mask_d6; - *m = q[j+4] & mask_d6; + *d = q[j*stride] & mask_d6; + *m = q[(j+4)*stride] & mask_d6; } else { - *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2); - *m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2); + *d = (q[(j+4)*stride] & mask_d4) | ((q[(j-4)*stride] & mask_hi2) >> 2); + *m = ((q[(j+4)*stride] >> 4) & mask_d4) | ((q[j*stride] & mask_hi2) >> 2); } } @@ -55,7 +57,6 @@ kernel void kernel_gemm_noshuffle_q4_k_f32( half8 B; half4 dequantized_weights; - int num_blocks_K = k / QK_K; global const ushort * weight_ptr = src0_q + gx_2; global const half * d_ptr = src0_d + gx_2; @@ -68,16 +69,16 @@ kernel void kernel_gemm_noshuffle_q4_k_f32( half4 d = vload4(0, d_ptr + sb_idx * m); half4 dm = vload4(0, dm_ptr + sb_idx * m); - global const uchar * sc0 = src0_s + (gx_2+0) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; - global const uchar * sc1 = src0_s + (gx_2+1) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; - global const uchar * sc2 = src0_s + (gx_2+2) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; - global const uchar * sc3 = src0_s + (gx_2+3) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + global const uchar * sc0 = src0_s + sb_idx * K_SCALE_SIZE * m + (gx_2+0); + global const uchar * sc1 = sc0 + 1; + global const uchar * sc2 = sc0 + 2; + global const uchar * sc3 = sc0 + 3; uchar sv0, mn0, sv1, mn1, sv2, mn2, sv3, mn3; - get_scale_min_k4(sub_idx, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(sub_idx, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(sub_idx, sc2, &sv2, &mn2, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(sub_idx, sc3, &sv3, &mn3, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc0, m, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc1, m, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc2, m, &sv2, &mn2, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc3, m, &sv3, &mn3, mask_d6, mask_d4, mask_hi2); half4 scale = convert_half4(convert_float4(d) * convert_float4((uchar4)(sv0, sv1, sv2, sv3))); half4 mval = convert_half4(convert_float4(dm) * convert_float4((uchar4)(mn0, mn1, mn2, mn3))); diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl index 86561801499e..a3b39b6aa984 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl @@ -10,9 +10,11 @@ #define QK_K 256 #define K_SCALE_SIZE 12 +// scales are transposed: consecutive codes of a row are `stride` apart inline void get_scale_min_k4( int j, global const uchar * q, + uint stride, uchar * d, uchar * m, uchar mask_d6, @@ -20,11 +22,11 @@ inline void get_scale_min_k4( uchar mask_hi2 ) { if (j < 4) { - *d = q[j] & mask_d6; - *m = q[j+4] & mask_d6; + *d = q[j*stride] & mask_d6; + *m = q[(j+4)*stride] & mask_d6; } else { - *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2); - *m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2); + *d = (q[(j+4)*stride] & mask_d4) | ((q[(j-4)*stride] & mask_hi2) >> 2); + *m = ((q[(j+4)*stride] >> 4) & mask_d4) | ((q[j*stride] & mask_hi2) >> 2); } } @@ -79,7 +81,6 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a( const bool row_valid = row < (uint)m; const uint rrow = row_valid ? row : 0; // clamp OOB rows; their writes are masked - const uint num_superblocks = (uint)k / QK_K; const uint k_u = (uint)k >> 2; // K in uint (int8x4) units const uint k_b = (uint)k >> 5; // blocks-of-32 along K @@ -101,9 +102,9 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a( // weight scale/min for this WI's row, this subblock const float dd = (float)src0_d [rrow + sb_idx * m]; const float dmm = (float)src0_dm[rrow + sb_idx * m]; - global const uchar * sc = src0_s + rrow * num_superblocks * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + global const uchar * sc = src0_s + sb_idx * K_SCALE_SIZE * (uint)m + rrow; uchar sv, mn; - get_scale_min_k4(sub_idx, sc, &sv, &mn, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc, (uint)m, &sv, &mn, mask_d6, mask_d4, mask_hi2); const float scale = dd * (float)sv; const float minv = dmm * (float)mn; @@ -202,7 +203,6 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg( const uint k_u = (uint)k >> 2; // K in uint (int8x4) units const uint k_b = (uint)k >> 5; // blocks-of-32 along K - const uint num_superblocks = (uint)k / QK_K; __local uint sh_qa[TILESIZE_N][8]; __local half sh_d[TILESIZE_N]; @@ -220,9 +220,9 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg( const float dd = (float)src0_d [rrow + sb_idx * m]; const float dmm = (float)src0_dm[rrow + sb_idx * m]; - global const uchar * sc = src0_s + rrow * num_superblocks * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + global const uchar * sc = src0_s + sb_idx * K_SCALE_SIZE * (uint)m + rrow; uchar sv, mn; - get_scale_min_k4(sub_idx, sc, &sv, &mn, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc, (uint)m, &sv, &mn, mask_d6, mask_d4, mask_hi2); const float scale = dd * (float)sv; const float minv = dmm * (float)mn; diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl index 2eb20e2f7625..c1829fc38208 100644 --- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl +++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl @@ -11,9 +11,11 @@ #define NSUBGROUPS 4 #define SUBGROUP_SIZE 64 +// scales are transposed: consecutive codes of a row are `stride` apart inline void get_scale_min_k4( int j, global const uchar * q, + uint stride, uchar * d, uchar * m, uchar mask_d6, @@ -21,11 +23,11 @@ inline void get_scale_min_k4( uchar mask_hi2 ) { if (j < 4) { - *d = q[j] & mask_d6; - *m = q[j+4] & mask_d6; + *d = q[j*stride] & mask_d6; + *m = q[(j+4)*stride] & mask_d6; } else { - *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2); - *m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2); + *d = (q[(j+4)*stride] & mask_d4) | ((q[(j-4)*stride] & mask_hi2) >> 2); + *m = ((q[(j+4)*stride] >> 4) & mask_d4) | ((q[j*stride] & mask_hi2) >> 2); } } @@ -232,7 +234,6 @@ kernel void kernel_gemv_noshuffle_q4_k_f32( uint LINE_STRIDE_A = M / 2; uint BLOCK_STRIDE_A = NSUBGROUPS * M; - uint scales_per_row = (K / QK_K) * 12; private uint4 regA; private half2 regS; @@ -248,12 +249,12 @@ kernel void kernel_gemv_noshuffle_q4_k_f32( half2 d = src0_d[gid + sb * LINE_STRIDE_A]; half2 dm = src0_m[gid + sb * LINE_STRIDE_A]; - global const uchar * sc0 = src0_s + 2 * gid * scales_per_row + sb * 12; - global const uchar * sc1 = src0_s + (2 * gid + 1) * scales_per_row + sb * 12; + global const uchar * sc0 = src0_s + sb * 12 * M + 2 * gid; + global const uchar * sc1 = sc0 + 1; uchar sv0, mn0, sv1, mn1; - get_scale_min_k4(j, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(j, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(j, sc0, M, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(j, sc1, M, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); regS = convert_half2(convert_float2(d) * convert_float2((uchar2)(sv0, sv1))); regM = convert_half2(convert_float2(dm) * convert_float2((uchar2)(mn0, mn1))); diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl b/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl index da2e14ae993a..97148d370fbd 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl @@ -24,6 +24,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif // Assumes row size (ne00) is a multiple of 4 diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl index 71ab9898213f..4c3d5f00c75b 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl @@ -1,3 +1,5 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + #ifdef cl_intel_required_subgroup_size #pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable #define INTEL_GPU 1 diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl index d92fb9689042..70391866ca6c 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl @@ -153,18 +153,27 @@ kernel void kernel_mul_mv_q4_K_f32_flat( global ushort * q2 = q1 + 32; - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1.s0 += yl[i+0] * (q1[i/2] & 0x000F); - acc1.s1 += yl[i+1] * (q1[i/2] & 0x0F00); - acc1.s2 += yl[i+8] * (q1[i/2] & 0x00F0); - acc1.s3 += yl[i+9] * (q1[i/2] & 0xF000); - acc2.s0 += yh[i+0] * (q2[i/2] & 0x000F); - acc2.s1 += yh[i+1] * (q2[i/2] & 0x0F00); - acc2.s2 += yh[i+8] * (q2[i/2] & 0x00F0); - acc2.s3 += yh[i+9] * (q2[i/2] & 0xF000); - } + // Load the 4 q1 / 4 q2 quant ushorts as 2 uints each. 16-bit integer ops are + // disproportionately slow on the A7X (E031.41) compiler; keeping the dequant + // operands in 32-bit registers avoids the ushort path. q1/q2 are 4-byte aligned + // (ib*128 + (32*iq+8*ir) bytes; q1 += blk*128 bytes/row). Math is unchanged: + // w & 0x0F00 on the low/high halves equals the original ushort mask value. + global uint * q1u = (global uint *)q1; + global uint * q2u = (global uint *)q2; + uint a0 = q1u[0], a1 = q1u[1]; + uint b0 = q2u[0], b1 = q2u[1]; + uint w0 = a0 & 0xFFFF, w1 = a0 >> 16, w2 = a1 & 0xFFFF, w3 = a1 >> 16; + uint v0 = b0 & 0xFFFF, v1 = b0 >> 16, v2 = b1 & 0xFFFF, v3 = b1 >> 16; + + float4 acc1, acc2; + acc1.s0 = yl[0]*(w0&0x000F) + yl[ 2]*(w1&0x000F) + yl[ 4]*(w2&0x000F) + yl[ 6]*(w3&0x000F); + acc1.s1 = yl[1]*(w0&0x0F00) + yl[ 3]*(w1&0x0F00) + yl[ 5]*(w2&0x0F00) + yl[ 7]*(w3&0x0F00); + acc1.s2 = yl[8]*(w0&0x00F0) + yl[10]*(w1&0x00F0) + yl[12]*(w2&0x00F0) + yl[14]*(w3&0x00F0); + acc1.s3 = yl[9]*(w0&0xF000) + yl[11]*(w1&0xF000) + yl[13]*(w2&0xF000) + yl[15]*(w3&0xF000); + acc2.s0 = yh[0]*(v0&0x000F) + yh[ 2]*(v1&0x000F) + yh[ 4]*(v2&0x000F) + yh[ 6]*(v3&0x000F); + acc2.s1 = yh[1]*(v0&0x0F00) + yh[ 3]*(v1&0x0F00) + yh[ 5]*(v2&0x0F00) + yh[ 7]*(v3&0x0F00); + acc2.s2 = yh[8]*(v0&0x00F0) + yh[10]*(v1&0x00F0) + yh[12]*(v2&0x00F0) + yh[14]*(v3&0x00F0); + acc2.s3 = yh[9]*(v0&0xF000) + yh[11]*(v1&0xF000) + yh[13]*(v2&0xF000) + yh[15]*(v3&0xF000); float dall = *d; float dmin = *dm; diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl index e353a72be703..6020364b5c35 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl @@ -159,18 +159,59 @@ kernel void kernel_mul_mv_q5_K_f32_flat( global ushort * q2 = q1 + 32; - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1.s0 += yl[i+0] * ((q1[i/2] & 0x000F) + (qh[i+0] & u1_lo ? 16.f : 0.f)); - acc1.s1 += yl[i+1] * ((q1[i/2] & 0x0F00) + (qh[i+1] & u1_lo ? 16.f*256.f : 0.f)); - acc1.s2 += yl[i+8] * ((q1[i/2] & 0x00F0) + (qh[i+0] & u2_lo ? 16.f*16.f : 0.f)); - acc1.s3 += yl[i+9] * ((q1[i/2] & 0xF000) + (qh[i+1] & u2_lo ? 16.f*4096.f: 0.f)); - acc2.s0 += yh[i+0] * ((q2[i/2] & 0x000F) + (qh[i+0] & u1_hi ? 16.f : 0.f)); - acc2.s1 += yh[i+1] * ((q2[i/2] & 0x0F00) + (qh[i+1] & u1_hi ? 16.f*256.f : 0.f)); - acc2.s2 += yh[i+8] * ((q2[i/2] & 0x00F0) + (qh[i+0] & u2_hi ? 16.f*16.f : 0.f)); - acc2.s3 += yh[i+9] * ((q2[i/2] & 0xF000) + (qh[i+1] & u2_hi ? 16.f*4096.f: 0.f)); - } + // Load the 4 q1 / 4 q2 quant ushorts as 2 uints each. 16-bit integer ops are + // disproportionately slow on the A7X (E031.41) compiler; keeping the dequant + // operands in 32-bit registers avoids the ushort path (same fix as q4_K flat). + // q1/q2 are 4-byte aligned; w & 0x0F00 on the low/high half of a uint equals the + // original ushort mask value, so the math is unchanged. The qh high-bit term is + // byte-indexed (qh[0..7]) and left as-is. + global uint * q1u = (global uint *)q1; + global uint * q2u = (global uint *)q2; + uint a0 = q1u[0], a1 = q1u[1], b0 = q2u[0], b1 = q2u[1]; + uint w0 = a0 & 0xFFFF, w1 = a0 >> 16, w2 = a1 & 0xFFFF, w3 = a1 >> 16; + uint v0 = b0 & 0xFFFF, v1 = b0 >> 16, v2 = b1 & 0xFFFF, v3 = b1 >> 16; + + float4 acc1, acc2; + acc1.s0 = + yl[0]*((w0&0x000F)+(qh[0]&u1_lo?16.f:0.f)) + + yl[2]*((w1&0x000F)+(qh[2]&u1_lo?16.f:0.f)) + + yl[4]*((w2&0x000F)+(qh[4]&u1_lo?16.f:0.f)) + + yl[6]*((w3&0x000F)+(qh[6]&u1_lo?16.f:0.f)); + acc1.s1 = + yl[1]*((w0&0x0F00)+(qh[1]&u1_lo?16.f*256.f:0.f)) + + yl[3]*((w1&0x0F00)+(qh[3]&u1_lo?16.f*256.f:0.f)) + + yl[5]*((w2&0x0F00)+(qh[5]&u1_lo?16.f*256.f:0.f)) + + yl[7]*((w3&0x0F00)+(qh[7]&u1_lo?16.f*256.f:0.f)); + acc1.s2 = + yl[ 8]*((w0&0x00F0)+(qh[0]&u2_lo?16.f*16.f:0.f)) + + yl[10]*((w1&0x00F0)+(qh[2]&u2_lo?16.f*16.f:0.f)) + + yl[12]*((w2&0x00F0)+(qh[4]&u2_lo?16.f*16.f:0.f)) + + yl[14]*((w3&0x00F0)+(qh[6]&u2_lo?16.f*16.f:0.f)); + acc1.s3 = + yl[ 9]*((w0&0xF000)+(qh[1]&u2_lo?16.f*4096.f:0.f)) + + yl[11]*((w1&0xF000)+(qh[3]&u2_lo?16.f*4096.f:0.f)) + + yl[13]*((w2&0xF000)+(qh[5]&u2_lo?16.f*4096.f:0.f)) + + yl[15]*((w3&0xF000)+(qh[7]&u2_lo?16.f*4096.f:0.f)); + acc2.s0 = + yh[0]*((v0&0x000F)+(qh[0]&u1_hi?16.f:0.f)) + + yh[2]*((v1&0x000F)+(qh[2]&u1_hi?16.f:0.f)) + + yh[4]*((v2&0x000F)+(qh[4]&u1_hi?16.f:0.f)) + + yh[6]*((v3&0x000F)+(qh[6]&u1_hi?16.f:0.f)); + acc2.s1 = + yh[1]*((v0&0x0F00)+(qh[1]&u1_hi?16.f*256.f:0.f)) + + yh[3]*((v1&0x0F00)+(qh[3]&u1_hi?16.f*256.f:0.f)) + + yh[5]*((v2&0x0F00)+(qh[5]&u1_hi?16.f*256.f:0.f)) + + yh[7]*((v3&0x0F00)+(qh[7]&u1_hi?16.f*256.f:0.f)); + acc2.s2 = + yh[ 8]*((v0&0x00F0)+(qh[0]&u2_hi?16.f*16.f:0.f)) + + yh[10]*((v1&0x00F0)+(qh[2]&u2_hi?16.f*16.f:0.f)) + + yh[12]*((v2&0x00F0)+(qh[4]&u2_hi?16.f*16.f:0.f)) + + yh[14]*((v3&0x00F0)+(qh[6]&u2_hi?16.f*16.f:0.f)); + acc2.s3 = + yh[ 9]*((v0&0xF000)+(qh[1]&u2_hi?16.f*4096.f:0.f)) + + yh[11]*((v1&0xF000)+(qh[3]&u2_hi?16.f*4096.f:0.f)) + + yh[13]*((v2&0xF000)+(qh[5]&u2_hi?16.f*4096.f:0.f)) + + yh[15]*((v3&0xF000)+(qh[7]&u2_hi?16.f*4096.f:0.f)); float dall = *d; float dmin = *dm; diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp index 659dbd4b5acb..0e7501fefe38 100644 --- a/ggml/src/ggml-openvino/ggml-openvino.cpp +++ b/ggml/src/ggml-openvino/ggml-openvino.cpp @@ -1378,3 +1378,5 @@ GGML_BACKEND_API ggml_backend_reg_t ggml_backend_openvino_reg(void) { return ® } + +GGML_BACKEND_DL_IMPL(ggml_backend_openvino_reg) diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index 1c17d20df12b..a8d9c0d804bf 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -199,9 +199,20 @@ if (GGML_SYCL_DEVICE_ARCH) -fsycl-targets=spir64_gen "SHELL:-Xsycl-target-backend=spir64_gen \"-device ${GGML_SYCL_DEVICE_ARCH}\"" ) + + # Pass through parallel job (process) count for parallelising the + # `llvm-foreach -- ocloc` invocation for compiling AOT device images. + include(ProcessorCount) + ProcessorCount(_ggml_sycl_nproc) + if (_ggml_sycl_nproc LESS 1) + set(_ggml_sycl_nproc 1) + endif() + set(GGML_SYCL_MAX_PARALLEL_LINK_JOBS ${_ggml_sycl_nproc} CACHE STRING + "Parallel ocloc jobs for spir64_gen AOT device-image lowering") target_link_options( ggml-sycl PRIVATE -fsycl-targets=spir64_gen "SHELL:-Xsycl-target-backend=spir64_gen \"-device ${GGML_SYCL_DEVICE_ARCH}\"" + -fsycl-max-parallel-link-jobs=${GGML_SYCL_MAX_PARALLEL_LINK_JOBS} ) endif() diff --git a/ggml/src/ggml-sycl/conv2d-dw.cpp b/ggml/src/ggml-sycl/conv2d-dw.cpp index 0a52b79174c4..8755a4c95f95 100644 --- a/ggml/src/ggml-sycl/conv2d-dw.cpp +++ b/ggml/src/ggml-sycl/conv2d-dw.cpp @@ -71,8 +71,8 @@ struct dw_cwhn_layout { } }; -template -static void conv2d_dw_kernel(const float * input, const float * kernel, float * output, +template +static void conv2d_dw_kernel(const float * input, const KernelT * kernel, float * output, const conv2d_dw_params p, const sycl::nd_item<3> & item_ct1) { const int global_idx = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2); @@ -93,15 +93,15 @@ static void conv2d_dw_kernel(const float * input, const float * kernel, float * for (int kx = bounds.x_min; kx < bounds.x_max; ++kx) { const int in_x = dw_calculate_input_coord(out_x, kx, p.stride_x, p.dilation_x, p.padding_x); acc += input[Layout::input_index(n, c, in_y, in_x, p)] * - kernel[Layout::kernel_index(c, ky, kx, p)]; + static_cast(kernel[Layout::kernel_index(c, ky, kx, p)]); } } output[Layout::output_index(n, c, out_y, out_x, p)] = acc; } -template -static void conv2d_dw_sycl(const float * x_d, const float * w_d, float * y_d, +template +static void conv2d_dw_sycl(const float * x_d, const KernelT * w_d, float * y_d, const conv2d_dw_params p, const queue_ptr & stream) { const int total = p.batches * p.channels * p.out_h * p.out_w; const int num_blocks = (total + SYCL_CONV2D_DW_BLOCK_SIZE - 1) / SYCL_CONV2D_DW_BLOCK_SIZE; @@ -109,7 +109,7 @@ static void conv2d_dw_sycl(const float * x_d, const float * w_d, float * y_d, const sycl::range<3> block_nums(1, 1, num_blocks); stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - conv2d_dw_kernel(x_d, w_d, y_d, p, item_ct1); + conv2d_dw_kernel(x_d, w_d, y_d, p, item_ct1); }); } @@ -119,9 +119,9 @@ void ggml_sycl_op_conv2d_dw(ggml_backend_sycl_context & ctx, ggml_tensor * dst) const ggml_tensor * kernel = dst->src[0]; const ggml_tensor * input = dst->src[1]; - GGML_ASSERT(kernel->type == GGML_TYPE_F32 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + GGML_ASSERT((kernel->type == GGML_TYPE_F32 || kernel->type == GGML_TYPE_F16) && + input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - const float * w_d = (const float *) kernel->data; const float * x_d = (const float *) input->data; float * y_d = (float *) dst->data; @@ -148,11 +148,23 @@ void ggml_sycl_op_conv2d_dw(ggml_backend_sycl_context & ctx, ggml_tensor * dst) const queue_ptr stream = ctx.stream(); - if (ggml_is_contiguous(input)) { - conv2d_dw_sycl(x_d, w_d, y_d, params, stream); - } else if (ggml_is_contiguous_channels(input)) { - conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + if (kernel->type == GGML_TYPE_F16) { + const sycl::half * w_d = (const sycl::half *) kernel->data; + if (ggml_is_contiguous(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else if (ggml_is_contiguous_channels(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else { + GGML_ABORT("Unsupported memory layout for conv2d_dw"); + } } else { - GGML_ABORT("Unsupported memory layout for conv2d_dw"); + const float * w_d = (const float *) kernel->data; + if (ggml_is_contiguous(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else if (ggml_is_contiguous_channels(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else { + GGML_ABORT("Unsupported memory layout for conv2d_dw"); + } } } diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index 7b66c73b0cf9..3db55319fe4a 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -19,6 +19,7 @@ typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); typedef void (*dequantize_kernel_t_reorder)(const void *d, const int64_t ib, const void *qs, const int iqs, dfloat2 &v); +typedef void (*dequantize_kernel_f32_t)(const void * vx, const int64_t ib, const int iqs, float & v0, float & v1); #if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m); @@ -85,6 +86,21 @@ static __dpct_inline__ void dequantize_q1_0_reorder(const void *d_ptr, const int v.y() = (2 * bit_1 - 1) * d; } +static __dpct_inline__ void dequantize_q1_0(const void *vx, const int64_t ib, + const int iqs, dfloat2 &v) { + const block_q1_0 * x = (const block_q1_0 *) vx; + const dfloat d = x[ib].d; + + const int bit_index_0 = iqs + 0; + const int bit_index_1 = iqs + 1; + + const int bit_0 = (x[ib].qs[bit_index_0 / 8] >> (bit_index_0 % 8)) & 1; + const int bit_1 = (x[ib].qs[bit_index_1 / 8] >> (bit_index_1 % 8)) & 1; + + v.x() = (2 * bit_0 - 1) * d; + v.y() = (2 * bit_1 - 1) * d; +} + static __dpct_inline__ void dequantize_q4_1(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { const block_q4_1 * x = (const block_q4_1 *) vx; @@ -140,6 +156,39 @@ static __dpct_inline__ void dequantize_q4_K(const void *vx, const int64_t ib, #endif } +static __dpct_inline__ void dequantize_q4_K_f32(const void *vx, const int64_t ib, + const int iqs, float &v0, float &v1) { +#if QK_K == 256 + const block_q4_K * x = (const block_q4_K *) vx; + const sycl::half2 dm = x[ib].dm; + const float dall = dm[0]; + const float dmin = dm[1]; + + auto dequantize_one = [&](const int idx) -> float { + const int il = idx / 64; + const int in = idx % 64; + const int is = 2 * il + (in >= 32 ? 1 : 0); + const int qsi = 32 * il + (in & 31); + + uint8_t sc; + uint8_t m; + get_scale_min_k4(is, x[ib].scales, sc, m); + + const float d = dall * sc; + const float mn = dmin * m; + const uint8_t q = x[ib].qs[qsi]; + const uint8_t qv = (in >= 32) ? (q >> 4) : (q & 0xF); + + return d * qv - mn; + }; + + v0 = dequantize_one(iqs + 0); + v1 = dequantize_one(iqs + 1); +#else + GGML_ABORT("Q4_K dequantize not supported for QK_K != 256"); +#endif +} + static __dpct_inline__ void dequantize_q2_K(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { #if QK_K == 256 @@ -159,7 +208,7 @@ static __dpct_inline__ void dequantize_q2_K(const void *vx, const int64_t ib, const float d = dall * (sc & 0xF); const float m = dmin * (sc >> 4); - return sycl::fma((dfloat) ((q >> (2 * g)) & 3), (dfloat) d, (dfloat) (-m)); + return (dfloat) d * (dfloat) ((q >> (2 * g)) & 3) - (dfloat) m; }; v.x() = dequantize_one(iqs + 0); @@ -169,6 +218,35 @@ static __dpct_inline__ void dequantize_q2_K(const void *vx, const int64_t ib, #endif } +static __dpct_inline__ void dequantize_q2_K_f32(const void *vx, const int64_t ib, + const int iqs, float &v0, float &v1) { +#if QK_K == 256 + const block_q2_K * x = (const block_q2_K *) vx; + const float dall = x[ib].dm[0]; + const float dmin = x[ib].dm[1]; + + auto dequantize_one = [&](const int idx) -> float { + const int n = idx / 128; + const int r = idx % 128; + const int g = r / 32; + const int l = r % 32; + const int is = 8 * n + l / 16; + + const uint8_t q = x[ib].qs[32 * n + l]; + const uint8_t sc = x[ib].scales[is + 2 * g]; + const float d = dall * (sc & 0xF); + const float m = dmin * (sc >> 4); + + return d * ((q >> (2 * g)) & 3) - m; + }; + + v0 = dequantize_one(iqs + 0); + v1 = dequantize_one(iqs + 1); +#else + GGML_ABORT("Q2_K dequantize not supported for QK_K != 256"); +#endif +} + static __dpct_inline__ void dequantize_q3_K(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { #if QK_K == 256 @@ -242,6 +320,42 @@ static __dpct_inline__ void dequantize_q5_K(const void *vx, const int64_t ib, #endif } +static __dpct_inline__ void dequantize_q5_K_f32(const void *vx, const int64_t ib, + const int iqs, float &v0, float &v1) { +#if QK_K == 256 + const block_q5_K * x = (const block_q5_K *) vx; + const float dall = x[ib].dm[0]; + const float dmin = x[ib].dm[1]; + + auto dequantize_one = [&](const int idx) -> float { + const int il = idx / 64; + const int in = idx % 64; + const int is = 2 * il + (in >= 32 ? 1 : 0); + const int ir = (in & 31) / 2; + const int iq = in & 1; + + const uint8_t q = x[ib].qs[32 * il + 2 * ir + iq]; + const uint8_t h = x[ib].qh[2 * ir + iq]; + const uint8_t qv = (in >= 32) ? (q >> 4) : (q & 0xF); + + uint8_t sc; + uint8_t m; + get_scale_min_k4(is, x[ib].scales, sc, m); + + const float d = dall * sc; + const float mn = dmin * m; + const uint8_t hm = 1 << (2 * il + (in >= 32 ? 1 : 0)); + + return (qv + ((h & hm) ? 16 : 0)) * d - mn; + }; + + v0 = dequantize_one(iqs + 0); + v1 = dequantize_one(iqs + 1); +#else + GGML_ABORT("Q5_K dequantize not supported for QK_K != 256"); +#endif +} + static __dpct_inline__ void dequantize_q6_K(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { #if QK_K == 256 @@ -296,21 +410,6 @@ static __dpct_inline__ void dequantize_mxfp4(const void *vx, const int64_t ib, v.y() = d * kvalues_mxfp4[q >> 4] * 0.5f; } -static __dpct_inline__ void dequantize_q1_0(const void *vx, const int64_t ib, - const int iqs, dfloat2 &v) { - const block_q1_0 * x = (const block_q1_0 *) vx; - const dfloat d = x[ib].d; - - const int bit_index_0 = iqs + 0; - const int bit_index_1 = iqs + 1; - - const int bit_0 = (x[ib].qs[bit_index_0 / 8] >> (bit_index_0 % 8)) & 1; - const int bit_1 = (x[ib].qs[bit_index_1 / 8] >> (bit_index_1 % 8)) & 1; - - v.x() = (2 * bit_0 - 1) * d; - v.y() = (2 * bit_1 - 1) * d; -} - static __dpct_inline__ void dequantize_nvfp4(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { const block_nvfp4 & xb = ((const block_nvfp4 *) vx)[ib]; diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp index fa62975d3ff5..ee7cd2d48d5e 100644 --- a/ggml/src/ggml-sycl/dmmv.cpp +++ b/ggml/src/ggml-sycl/dmmv.cpp @@ -266,7 +266,7 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -387,7 +387,7 @@ static void dequantize_mul_mat_vec_q2_k_reorder(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -483,7 +483,7 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -595,7 +595,7 @@ static void dequantize_mul_mat_vec_q3_k_reorder(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -693,7 +693,7 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -841,7 +841,7 @@ static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -994,10 +994,12 @@ static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx, static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, const float *__restrict__ yy, float *__restrict__ dst, - const int ncols, + const int ncols, int nrows, const sycl::nd_item<3> &item_ct1) { - const int row = item_ct1.get_group(2); + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1126,7 +1128,9 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx, const int ncols, int nrows, const sycl::nd_item<3> &item_ct1) { - const int row = item_ct1.get_group(2); + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1148,19 +1152,13 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx, const int tid = item_ct1.get_local_id(2) / 2; // 0...15 const int ix = item_ct1.get_local_id(2) % 2; - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 + const int il_base = tid/4; // 0...3 + const int ir = tid - 4*il_base;// 0...3 const int n = 2; - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; + const int in = il_base%2; const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; uint16_t aux[4]; const uint8_t * sc = (const uint8_t *)aux; @@ -1171,52 +1169,60 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx, for (int i = ix; i < num_blocks_per_row; i += 2) { const int bi = ib0 + i; - const uint8_t * ql1 = qs_base + bi * (QK_K / 2) + q_offset; const uint8_t * qh = qh_base + bi * (QK_K / 8) + l0; - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; - const sycl::half2 dm_val = dm_base[bi]; const float dall = dm_val[0]; const float dmin = dm_val[1]; - const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE); - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - sycl::float4 sum = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - const uint16_t * q1 = (const uint16_t *)ql1; - const uint16_t * q2 = q1 + 32; - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[8] & 0x0f0f; - q16[2] = (q1[0] >> 4) & 0x0f0f; - q16[3] = (q1[8] >> 4) & 0x0f0f; - q16[4] = q2[0] & 0x0f0f; - q16[5] = q2[8] & 0x0f0f; - q16[6] = (q2[0] >> 4) & 0x0f0f; - q16[7] = (q2[8] >> 4) & 0x0f0f; - for (int l = 0; l < n; ++l) { - sum.x() += - y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) + - y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0)); - sum.y() += - y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) + - y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0)); - sum.z() += - y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) + - y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0)); - sum.w() += - y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) + - y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; + for (int im = 0; im < 2; ++im) { + const int q_offset = 32*im + l0; + const int y_offset = 64*im + l0; + + const uint8_t hm1 = 1 << (2*im); + const uint8_t hm2 = hm1 << 4; + + const uint8_t * ql1 = qs_base + bi * (QK_K / 2) + q_offset; + const float * y1 = yy + i*QK_K + y_offset; + const float * y2 = y1 + 128; + + const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE); + aux[0] = a[im+0] & kmask1; + aux[1] = a[im+2] & kmask1; + aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); + aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); + + sycl::float4 sum = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + const uint16_t * q1 = (const uint16_t *)ql1; + const uint16_t * q2 = q1 + 32; + q16[0] = q1[0] & 0x0f0f; + q16[1] = q1[8] & 0x0f0f; + q16[2] = (q1[0] >> 4) & 0x0f0f; + q16[3] = (q1[8] >> 4) & 0x0f0f; + q16[4] = q2[0] & 0x0f0f; + q16[5] = q2[8] & 0x0f0f; + q16[6] = (q2[0] >> 4) & 0x0f0f; + q16[7] = (q2[8] >> 4) & 0x0f0f; + for (int l = 0; l < n; ++l) { + sum.x() += + y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) + + y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0)); + sum.y() += + y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) + + y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0)); + sum.z() += + y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) + + y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0)); + sum.w() += + y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) + + y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0)); + smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] + + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; + } + tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] + + sum.w() * sc[5]) - + dmin * smin; } - tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] + - sum.w() * sc[5]) - - dmin * smin; } #else // The reordered Q5_K layout is only produced for QK_K == 256. @@ -1241,7 +1247,7 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1358,7 +1364,7 @@ static void dequantize_mul_mat_vec_q6_k_reorder(const void * __restrict__ vx, co const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1831,11 +1837,14 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, const int nrows, dpct::queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); - const sycl::range<3> block_dims(1, 1, WARP_SIZE); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, ny, WARP_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), + sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1); + dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, nrows, item_ct1); }); } diff --git a/ggml/src/ggml-sycl/getrows.cpp b/ggml/src/ggml-sycl/getrows.cpp index 298f247f84e0..2113f3563398 100644 --- a/ggml/src/ggml-sycl/getrows.cpp +++ b/ggml/src/ggml-sycl/getrows.cpp @@ -60,6 +60,50 @@ static void k_get_rows( dst_row[iybs + iqs + y_offset] = v.y(); } +template +static void k_get_rows_f32( + const void * src0, const int32_t * src1, dst_t * dst, + int64_t ne00, + int64_t ne12, + size_t s1, size_t s2, size_t s3, + size_t nb01, size_t nb02, size_t nb03, + size_t s10, size_t s11, size_t s12, + const sycl::nd_item<3> &item_ct1) { + + const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) + + item_ct1.get_local_id(2)) * + 2; + const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) / + ne12; + const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) % + ne12; + + if (i00 >= ne00) { + return; + } + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03; + + const int ib = i00/qk; + const int iqs = (i00%qk)/qr; + const int iybs = i00 - i00%qk; + const int y_offset = qr == 1 ? 1 : qk/2; + + float v0; + float v1; + dequantize_kernel(src0_row, ib, iqs, v0, v1); + + dst_row[iybs + iqs + 0] = (dst_t) v0; + dst_row[iybs + iqs + y_offset] = (dst_t) v1; +} + template static void k_get_rows_float( const src0_t * src0, const int32_t * src1, dst_t * dst, @@ -129,6 +173,39 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr GGML_UNUSED(ctx); } +template +static void get_rows_sycl_f32(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const void *src0_dd, + const int32_t *src1_dd, float *dst_dd, + queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); + const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE); + const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); + + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); + + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + + GGML_ASSERT(ne00 % 2 == 0); + + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_get_rows_f32( + src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, + s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); + }); + + GGML_UNUSED(dst); + GGML_UNUSED(ctx); +} + template static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, @@ -244,7 +321,7 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q2_K: - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + get_rows_sycl_f32(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q3_K: @@ -260,7 +337,7 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q4_K: - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + get_rows_sycl_f32(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q5_0: @@ -272,7 +349,7 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q5_K: - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + get_rows_sycl_f32(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q6_K: diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 1704da07eadb..5dcf4503bbee 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -156,6 +156,24 @@ typedef struct VkPhysicalDeviceShaderFloat8FeaturesEXT { } VkPhysicalDeviceShaderFloat8FeaturesEXT; #endif +#ifndef VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME +#define VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME "VK_KHR_internally_synchronized_queues" +#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_INTERNALLY_SYNCHRONIZED_QUEUES_FEATURES_KHR ((VkStructureType)1000504000) +#define VK_DEVICE_QUEUE_CREATE_INTERNALLY_SYNCHRONIZED_BIT_KHR ((VkDeviceQueueCreateFlagBits)0x00000004) + +// Compile-time constant guaranteed; no runtime initialization overhead +static constexpr vk::DeviceQueueCreateFlagBits eInternallySynchronizedKHR = + static_cast(0x00000004); + +typedef struct VkPhysicalDeviceInternallySynchronizedQueuesFeaturesKHR { + VkStructureType sType; + void* pNext; + VkBool32 internallySynchronizedQueues; +} VkPhysicalDeviceInternallySynchronizedQueuesFeaturesKHR; +#else +static constexpr vk::DeviceQueueCreateFlagBits eInternallySynchronizedKHR = vk::DeviceQueueCreateFlagBits::eInternallySynchronizedKHR; +#endif + #define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1)) #define CEIL_DIV(M, N) (((M) / (N)) + (((M) % (N)) != 0)) static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; } @@ -285,27 +303,41 @@ struct vk_command_pool { }; // Prevent simultaneous submissions to the same queue. -// This could be per vk_queue if we stopped having two vk_queue structures -// sharing the same vk::Queue. -static std::mutex queue_mutex; +struct vk_queue_handle { + vk::Queue queue; + virtual void submit(vk::ArrayProxy submits, vk::Fence fence) = 0; + virtual void lock() {} // no-op by default (internally synchronized case) + virtual void unlock() {} + virtual ~vk_queue_handle() = default; +}; + +struct vk_queue_handle_synchronized : vk_queue_handle { + std::mutex mutex; + void submit(vk::ArrayProxy submits, vk::Fence fence) override { + std::lock_guard guard(mutex); + queue.submit(submits, fence); + } + void lock() override { mutex.lock(); } + void unlock() override { mutex.unlock(); } +}; + +struct vk_queue_handle_unsynchronized : vk_queue_handle { + void submit(vk::ArrayProxy submits, vk::Fence fence) override { + // Driver guarantees internal synchronization via VK_KHR_internally_synchronized_queues + queue.submit(submits, fence); + } + // lock()/unlock() inherited no-ops +}; struct vk_queue { uint32_t queue_family_index; - vk::Queue queue; + std::shared_ptr handle; vk_command_pool cmd_pool; vk::PipelineStageFlags stage_flags; bool transfer_only; - - // copy everything except the cmd_pool - void copyFrom(vk_queue &other) { - queue_family_index = other.queue_family_index; - queue = other.queue; - stage_flags = other.stage_flags; - transfer_only = other.transfer_only; - } }; static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft); @@ -712,11 +744,12 @@ struct vk_device_struct { uint32_t vendor_id; vk::DriverId driver_id; vk_device_architecture architecture; - vk_queue compute_queue; - vk_queue transfer_queue; + std::unique_ptr compute_queue; + std::unique_ptr transfer_queue; bool single_queue; bool support_async; bool async_use_transfer_queue; + bool has_internally_synchronized_queues = false; uint32_t subgroup_size; uint32_t subgroup_size_log2; uint32_t shader_core_count; @@ -961,6 +994,7 @@ struct vk_device_struct { vk_pipeline pipeline_col2im_1d_f32; vk_pipeline pipeline_col2im_1d_f16; vk_pipeline pipeline_col2im_1d_bf16; + vk_pipeline pipeline_out_prod_f32; vk_pipeline pipeline_snake_f32; vk_pipeline pipeline_snake_f16; vk_pipeline pipeline_snake_bf16; @@ -1018,8 +1052,13 @@ struct vk_device_struct { ggml_vk_destroy_buffer(sync_staging); - compute_queue.cmd_pool.destroy(device); - transfer_queue.cmd_pool.destroy(device); + if (compute_queue) compute_queue->cmd_pool.destroy(device); + if (transfer_queue) transfer_queue->cmd_pool.destroy(device); + + // Explicitly clear to ensure queues drop their shared_ptrs to handles + // before the Vulkan logical device instance is destroyed + compute_queue.reset(); + transfer_queue.reset(); for (auto& pipeline : all_pipelines) { if (pipeline.expired()) { @@ -2908,8 +2947,7 @@ static vk_command_buffer* ggml_vk_create_cmd_buffer(vk_device& device, vk_comman static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { if (ctx->seqs.empty()) { if (fence) { - std::lock_guard guard(queue_mutex); - ctx->p->q->queue.submit({}, fence); + ctx->p->q->handle->submit({}, fence); } return; } @@ -2978,8 +3016,7 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { } } - std::lock_guard guard(queue_mutex); - ctx->p->q->queue.submit(submit_infos, fence); + ctx->p->q->handle->submit(submit_infos, fence); ctx->seqs.clear(); } @@ -3030,18 +3067,44 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector ggml_vk_create_queue(vk_device& device, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) { VK_LOG_DEBUG("ggml_vk_create_queue()"); std::lock_guard guard(device->mutex); - q.queue_family_index = queue_family_index; - q.transfer_only = transfer_only; + auto q = std::make_unique(); + q->queue_family_index = queue_family_index; + q->transfer_only = transfer_only; - q.cmd_pool.init(device, &q); + std::shared_ptr h; + vk::DeviceQueueInfo2 queue_info2{}; + queue_info2.queueFamilyIndex = queue_family_index; + queue_info2.queueIndex = queue_index; - q.queue = device->device.getQueue(queue_family_index, queue_index); + if (device->has_internally_synchronized_queues) { + h = std::make_shared(); + queue_info2.flags = eInternallySynchronizedKHR; + } else { + h = std::make_shared(); + } + + h->queue = device->device.getQueue2(queue_info2); + q->handle = h; - q.stage_flags = stage_flags; + q->cmd_pool.init(device, q.get()); + + q->stage_flags = stage_flags; + return q; +} + +static std::unique_ptr ggml_vk_create_aliased_queue(vk_device& device, const std::unique_ptr& source) { + std::lock_guard guard(device->mutex); + auto q = std::make_unique(); + q->handle = source->handle; + q->queue_family_index = source->queue_family_index; + q->stage_flags = source->stage_flags; + q->transfer_only = source->transfer_only; + q->cmd_pool.init(device, q.get()); + return q; } static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p) { @@ -3106,11 +3169,11 @@ static void ggml_vk_queue_command_pools_cleanup(vk_device& device) { // Arbitrary frequency to cleanup/reuse command buffers static constexpr uint32_t cleanup_frequency = 10; - if (device->compute_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { - ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool); + if (device->compute_queue->cmd_pool.buffers_in_use() >= cleanup_frequency) { + ggml_vk_command_pool_cleanup(device, device->compute_queue->cmd_pool); } - if (device->transfer_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { - ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool); + if (device->transfer_queue->cmd_pool.buffers_in_use() >= cleanup_frequency) { + ggml_vk_command_pool_cleanup(device, device->transfer_queue->cmd_pool); } } @@ -3734,6 +3797,7 @@ static bool ggml_vk_matmul_int_shmem_support(const vk_device& device, const std: uint32_t block_a_size = 0; switch (src0_type) { + case GGML_TYPE_Q2_0: block_a_size = std430_size({{32, 4}, {fp_size, fp_align}}); break; // qs[8] + dm case GGML_TYPE_Q4_0: block_a_size = std430_size({{16, 4}, {fp_size, fp_align}}); break; // qs[16/4] + dm case GGML_TYPE_Q4_1: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[16/4] + dm(vec2) case GGML_TYPE_Q5_0: block_a_size = std430_size({{16, 4}, {4, 4}, {fp_size, fp_align}}); break; // qs[16/4] + qh + dm @@ -4338,6 +4402,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { } #endif CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q1_0], matmul_q1_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_0], matmul_q2_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0], matmul_q4_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1], matmul_q4_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0], matmul_q5_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) @@ -4377,6 +4442,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { } #endif CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) @@ -4447,6 +4513,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #endif CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_0], matmul_q2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); @@ -4491,6 +4558,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #endif CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); @@ -4580,6 +4648,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_0], matmul_q2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); @@ -4604,6 +4673,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_0], matmul_q2_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0], matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1], matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0], matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); @@ -4626,6 +4696,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); @@ -4650,6 +4721,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); @@ -4671,6 +4743,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_q2_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); @@ -4695,6 +4768,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_0], matmul_id_q2_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); @@ -4747,6 +4821,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0].f32acc, matmul_q1_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_0].f32acc, matmul_q2_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); @@ -4772,6 +4847,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_0].f32acc, matmul_q2_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); @@ -4793,6 +4869,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_subgroup_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0].f32acc, matmul_id_subgroup_q2_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_subgroup_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_subgroup_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_subgroup_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); @@ -4821,6 +4898,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0].f32acc, matmul_id_q2_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); @@ -4923,6 +5001,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32", arr_dmmv_f16_f32_f32_len[reduc], arr_dmmv_f16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q1_0][i], "mul_mat_vec_q1_0_f32_f32", arr_dmmv_q1_0_f32_f32_len[reduc], arr_dmmv_q1_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_0][i], "mul_mat_vec_q2_0_f32_f32", arr_dmmv_q2_0_f32_f32_len[reduc], arr_dmmv_q2_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -4949,6 +5028,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32", arr_dmmv_f16_f16_f32_len[reduc], arr_dmmv_f16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q1_0][i], "mul_mat_vec_q1_0_f16_f32", arr_dmmv_q1_0_f16_f32_len[reduc], arr_dmmv_q1_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_0][i], "mul_mat_vec_q2_0_f16_f32", arr_dmmv_q2_0_f16_f32_len[reduc], arr_dmmv_q2_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -4976,6 +5056,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q2_0][i], "mul_mat_vec_q2_0_q8_1_f32", arr_dmmv_q2_0_q8_1_f32_len[reduc], arr_dmmv_q2_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); @@ -5001,6 +5082,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", arr_dmmv_id_f16_f32_f32_len[reduc], arr_dmmv_id_f16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", arr_dmmv_id_bf16_f32_f32_len[reduc], arr_dmmv_id_bf16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q1_0], "mul_mat_vec_id_q1_0_f32", arr_dmmv_id_q1_0_f32_f32_len[reduc], arr_dmmv_id_q1_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q2_0], "mul_mat_vec_id_q2_0_f32", arr_dmmv_id_q2_0_f32_f32_len[reduc], arr_dmmv_id_q2_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", arr_dmmv_id_q4_0_f32_f32_len[reduc], arr_dmmv_id_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", arr_dmmv_id_q4_1_f32_f32_len[reduc], arr_dmmv_id_q4_1_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", arr_dmmv_id_q5_0_f32_f32_len[reduc], arr_dmmv_id_q5_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); @@ -5028,6 +5110,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q2_0], "mul_mat_vec_id_q2_0_q8_1_f32", arr_dmmv_id_q2_0_q8_1_f32_len[reduc], arr_dmmv_id_q2_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_q8_1_f32", arr_dmmv_id_q4_0_q8_1_f32_len[reduc], arr_dmmv_id_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_q8_1_f32", arr_dmmv_id_q4_1_q8_1_f32_len[reduc], arr_dmmv_id_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_q8_1_f32", arr_dmmv_id_q5_0_q8_1_f32_len[reduc], arr_dmmv_id_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); @@ -5060,6 +5143,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { // dequant shaders ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q1_0], "dequant_q1_0", dequant_q1_0_len, dequant_q1_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 8, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_0], "dequant_q2_0", dequant_q2_0_len, dequant_q2_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); @@ -5087,6 +5171,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_BF16], "get_rows_bf16", get_rows_bf16_len, get_rows_bf16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q1_0], "get_rows_q1_0", get_rows_q1_0_len, get_rows_q1_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q2_0], "get_rows_q2_0", get_rows_q2_0_len, get_rows_q2_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); @@ -5114,6 +5199,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_BF16], "get_rows_bf16_f32", get_rows_bf16_f32_len, get_rows_bf16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q1_0], "get_rows_q1_0_f32", get_rows_q1_0_f32_len, get_rows_q1_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q2_0], "get_rows_q2_0_f32", get_rows_q2_0_f32_len, get_rows_q2_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); @@ -5198,6 +5284,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_16, "cpy_transpose_16", cpy_transpose_16_len, cpy_transpose_16_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q1_0], "cpy_f32_q1_0", cpy_f32_q1_0_len, cpy_f32_q1_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q2_0], "cpy_f32_q2_0", cpy_f32_q2_0_len, cpy_f32_q2_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_len, cpy_f32_q4_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_len, cpy_f32_q4_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0], "cpy_f32_q5_0", cpy_f32_q5_0_len, cpy_f32_q5_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); @@ -5210,6 +5297,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_F16], "set_rows_" #src "_f16" #itype, set_rows_ ## src ## _f16 ## itype ## _len, set_rows_ ## src ## _f16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_BF16], "set_rows_" #src "_bf16" #itype, set_rows_ ## src ## _bf16 ## itype ## _len, set_rows_ ## src ## _bf16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q1_0], "set_rows_" #src "_q1_0" #itype, set_rows_ ## src ## _q1_0 ## itype ## _len, set_rows_ ## src ## _q1_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q2_0], "set_rows_" #src "_q2_0" #itype, set_rows_ ## src ## _q2_0 ## itype ## _len, set_rows_ ## src ## _q2_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q4_0], "set_rows_" #src "_q4_0" #itype, set_rows_ ## src ## _q4_0 ## itype ## _len, set_rows_ ## src ## _q4_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q4_1], "set_rows_" #src "_q4_1" #itype, set_rows_ ## src ## _q4_1 ## itype ## _len, set_rows_ ## src ## _q4_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q5_0], "set_rows_" #src "_q5_0" #itype, set_rows_ ## src ## _q5_0 ## itype ## _len, set_rows_ ## src ## _q5_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ @@ -5225,6 +5313,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q1_0], "cpy_q1_0_f32", cpy_q1_0_f32_len, cpy_q1_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q1_0), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q2_0], "cpy_q2_0_f32", cpy_q2_0_f32_len, cpy_q2_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q2_0), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_0], "cpy_q4_0_f32", cpy_q4_0_f32_len, cpy_q4_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_1], "cpy_q4_1_f32", cpy_q4_1_f32_len, cpy_q4_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_0], "cpy_q5_0_f32", cpy_q5_0_f32_len, cpy_q5_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_0), 1, 1}, {}, 1); @@ -5479,6 +5568,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_f16, "col2im_1d_f16", col2im_1d_f16_len, col2im_1d_f16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_bf16, "col2im_1d_bf16", col2im_1d_bf16_len, col2im_1d_bf16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_out_prod_f32, "out_prod_f32", out_prod_f32_len, out_prod_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_snake_f32, "snake_f32", snake_f32_len, snake_f32_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_snake_f16, "snake_f16", snake_f16_len, snake_f16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_snake_bf16, "snake_bf16", snake_bf16_len, snake_bf16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); @@ -5857,6 +5948,7 @@ static vk_device ggml_vk_get_device(size_t idx) { bool coopmat2_support = false; bool coopmat2_decode_vector_support = false; bool pipeline_executable_properties_support = false; + bool internally_sync_support = false; device->coopmat_support = false; device->integer_dot_product = false; device->shader_64b_indexing = false; @@ -5928,6 +6020,8 @@ static vk_device ggml_vk_get_device(size_t idx) { } else if (strcmp("VK_EXT_shader_64bit_indexing", properties.extensionName) == 0) { device->shader_64b_indexing = true; #endif + } else if (strcmp(VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME, properties.extensionName) == 0) { + internally_sync_support = true; } } @@ -6114,14 +6208,6 @@ static vk_device ggml_vk_get_device(size_t idx) { device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; std::vector device_queue_create_infos; - if (compute_queue_family_index != transfer_queue_family_index) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!device->single_queue) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); - } else { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - } vk::DeviceCreateInfo device_create_info{}; std::vector device_extensions; vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures(); @@ -6143,6 +6229,17 @@ static vk_device ggml_vk_get_device(size_t idx) { last_struct = (VkBaseOutStructure *)&vk12_features; + VkPhysicalDeviceInternallySynchronizedQueuesFeaturesKHR internally_synchronized_queues_features{}; + internally_synchronized_queues_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_INTERNALLY_SYNCHRONIZED_QUEUES_FEATURES_KHR; + internally_synchronized_queues_features.pNext = nullptr; + internally_synchronized_queues_features.internallySynchronizedQueues = VK_FALSE; + + if (internally_sync_support) { + last_struct->pNext = (VkBaseOutStructure *)&internally_synchronized_queues_features; + last_struct = (VkBaseOutStructure *)&internally_synchronized_queues_features; + device_extensions.push_back(VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME); + } + VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features; pl_robustness_features.pNext = nullptr; pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT; @@ -6281,6 +6378,23 @@ static vk_device ggml_vk_get_device(size_t idx) { vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2); + device->has_internally_synchronized_queues = internally_synchronized_queues_features.internallySynchronizedQueues; + + // Build queue create infos only after querying whether internally synchronized queues are enabled. + // getQueue2() later uses the same flag, so creation/retrieval must stay consistent. + vk::DeviceQueueCreateFlags queue_flags = device->has_internally_synchronized_queues ? + eInternallySynchronizedKHR : + vk::DeviceQueueCreateFlags(); + + if (compute_queue_family_index != transfer_queue_family_index) { + device_queue_create_infos.push_back({queue_flags, compute_queue_family_index, 1, priorities}); + device_queue_create_infos.push_back({queue_flags, transfer_queue_family_index, 1, priorities + 1}); + } else if(!device->single_queue) { + device_queue_create_infos.push_back({queue_flags, compute_queue_family_index, 2, priorities}); + } else { + device_queue_create_infos.push_back({queue_flags, compute_queue_family_index, 1, priorities}); + } + device->pipeline_executable_properties_support = pipeline_executable_properties_support; device->fp16 = device->fp16 && vk12_features.shaderFloat16; @@ -6563,7 +6677,7 @@ static vk_device ggml_vk_get_device(size_t idx) { device->device = device->physical_device.createDevice(device_create_info); // Queues - ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); + device->compute_queue = ggml_vk_create_queue(device, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); // Shaders // Disable matmul tile sizes early if performance low or not supported @@ -6665,13 +6779,11 @@ static vk_device ggml_vk_get_device(size_t idx) { if (!device->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); + device->transfer_queue = ggml_vk_create_queue(device, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); device->async_use_transfer_queue = prefers_transfer_queue || (getenv("GGML_VK_ASYNC_USE_TRANSFER_QUEUE") != nullptr); } else { - // TODO: Use pointer or reference to avoid copy - device->transfer_queue.copyFrom(device->compute_queue); - device->transfer_queue.cmd_pool.init(device, &device->transfer_queue); + device->transfer_queue = ggml_vk_create_aliased_queue(device, device->compute_queue); device->async_use_transfer_queue = false; } @@ -7234,7 +7346,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->fence = ctx->device->device.createFence({}); ctx->almost_ready_fence = ctx->device->device.createFence({}); - ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue); + ctx->compute_cmd_pool.init(ctx->device, ctx->device->compute_queue.get()); if (ctx->device->async_use_transfer_queue) { vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; @@ -7242,7 +7354,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->transfer_semaphore.s = ctx->device->device.createSemaphore(ci); ctx->transfer_semaphore.value = 0; - ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue); + ctx->transfer_cmd_pool.init(ctx->device, ctx->device->transfer_queue.get()); } if (vk_perf_logger_enabled) { @@ -7262,6 +7374,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type switch (type) { case GGML_TYPE_F32: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7335,6 +7448,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte switch (src0_type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7378,6 +7492,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * if (b_type == GGML_TYPE_Q8_1) { switch (a_type) { + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7402,6 +7517,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7494,6 +7610,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co switch (src0_type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7540,6 +7657,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context if (b_type == GGML_TYPE_Q8_1) { switch (a_type) { + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7564,6 +7682,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7826,6 +7945,20 @@ static vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx) { return result; } +static vk_context ggml_vk_get_transfer_ctx(ggml_backend_vk_context * ctx) { + vk_context result; + if (!ctx->transfer_ctx.expired()) { + result = ctx->transfer_ctx.lock(); + } else { + result = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); + + ctx->transfer_ctx = result; + ggml_vk_ctx_begin(ctx->device, result); + } + + return result; +} + // Submit any pending transfer queue work and signal the transfer semaphore. // The next compute context created via ggml_vk_get_compute_ctx will wait on this semaphore. // Returns true if work was submitted. @@ -8076,7 +8209,7 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * } else { std::lock_guard guard(dst->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(dst->device, subctx); bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, dpitch, width, height, true); GGML_ASSERT(ret); @@ -8191,7 +8324,7 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); std::lock_guard guard(src->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue->cmd_pool); ggml_vk_ctx_begin(src->device, subctx); subctx->s->buffer->buf.pipelineBarrier( vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer, @@ -8217,7 +8350,7 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si } else { std::lock_guard guard(src->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(src->device, subctx); bool ret = ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, spitch, dpitch, width, height, true); GGML_ASSERT(ret); @@ -8254,7 +8387,7 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr std::lock_guard guard(src->device->mutex); VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")"); // Copy within the device - vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(src->device, subctx); ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); ggml_vk_ctx_end(subctx); @@ -8297,7 +8430,7 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz } std::lock_guard guard(dst->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(dst->device, subctx); subctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c); ggml_vk_ctx_end(subctx); @@ -8586,6 +8719,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const if (src->type == GGML_TYPE_F32) { switch (to) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -8601,6 +8735,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const if (to == GGML_TYPE_F32) { switch (src->type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -9027,7 +9162,7 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_ // Quantization overhead is not worth it for small k switch (device->vendor_id) { case VK_VENDOR_ID_NVIDIA: - if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) { + if (src0_type == GGML_TYPE_Q2_0 || src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) { return true; } @@ -9055,7 +9190,7 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_ } case VK_VENDOR_ID_INTEL: if (device->architecture == vk_device_architecture::INTEL_XE2) { - if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) { + if (src0_type == GGML_TYPE_Q2_0 || src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) { return true; } } @@ -10745,6 +10880,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_add_id_f32; } return nullptr; + case GGML_OP_OUT_PROD: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_out_prod_f32; + } + return nullptr; case GGML_OP_CONCAT: { if (src0->type != src1->type || src0->type != dst->type) { return nullptr; @@ -11701,6 +11841,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co case GGML_OP_DIV: case GGML_OP_MUL: case GGML_OP_ADD1: + case GGML_OP_OUT_PROD: case GGML_OP_ARANGE: case GGML_OP_FILL: case GGML_OP_SCALE: @@ -12014,6 +12155,24 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const }); } +static void ggml_vk_out_prod(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_OUT_PROD, { + (uint32_t)ggml_nelements(dst), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], + (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], + (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], + (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + static void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); @@ -14801,6 +14960,9 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr ggml_vk_add(ctx, compute_ctx, src0, src1, node); } break; + case GGML_OP_OUT_PROD: + ggml_vk_out_prod(ctx, compute_ctx, src0, src1, node); + break; case GGML_OP_SUB: ggml_vk_sub(ctx, compute_ctx, src0, src1, node); @@ -15603,13 +15765,7 @@ static void ggml_backend_vk_set_tensor_2d_async(ggml_backend_t backend, ggml_ten vk_context cpy_ctx; if (ctx->device->async_use_transfer_queue) { - if (ctx->transfer_ctx.expired()) { - cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); - ctx->transfer_ctx = cpy_ctx; - ggml_vk_ctx_begin(ctx->device, cpy_ctx); - } else { - cpy_ctx = ctx->transfer_ctx.lock(); - } + cpy_ctx = ggml_vk_get_transfer_ctx(ctx); } else { cpy_ctx = ggml_vk_get_compute_ctx(ctx); } @@ -15755,13 +15911,7 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_ba vk_context cpy_ctx; if (ctx->device->async_use_transfer_queue) { - if (ctx->transfer_ctx.expired()) { - cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); - ctx->transfer_ctx = cpy_ctx; - ggml_vk_ctx_begin(ctx->device, cpy_ctx); - } else { - cpy_ctx = ctx->transfer_ctx.lock(); - } + cpy_ctx = ggml_vk_get_transfer_ctx(ctx); } else { cpy_ctx = ggml_vk_get_compute_ctx(ctx); } @@ -15808,19 +15958,17 @@ static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) { 1, &ctx->transfer_semaphore.value, 0, nullptr, }; - vk::PipelineStageFlags stage = ctx->device->transfer_queue.stage_flags; + vk::PipelineStageFlags stage = ctx->device->transfer_queue->stage_flags; vk::SubmitInfo si{ 1, &ctx->transfer_semaphore.s, &stage, 0, nullptr, 0, nullptr, }; si.setPNext(&tl_info); - std::lock_guard guard(queue_mutex); - ctx->device->compute_queue.queue.submit({ si }, ctx->fence); + ctx->device->compute_queue->handle->submit({ si }, ctx->fence); ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value; } else { - std::lock_guard guard(queue_mutex); - ctx->device->compute_queue.queue.submit({}, ctx->fence); + ctx->device->compute_queue->handle->submit({}, ctx->fence); } ggml_vk_wait_for_fence(ctx); ctx->submit_pending = false; @@ -16382,7 +16530,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg vk::DebugUtilsLabelEXT dul = {}; dul.pLabelName = "ggml_backend_vk_graph_compute"; dul.color = std::array{1.0f, 1.0f, 1.0f, 1.0f}; - vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast(&dul)); + + std::lock_guard guard(*ctx->device->compute_queue->handle); + vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue->handle->queue, reinterpret_cast(&dul)); } ctx->prealloc_size_add_rms_partials_offset = 0; @@ -17093,6 +17243,11 @@ static void ggml_backend_vk_event_wait(ggml_backend_t backend, ggml_backend_even if (vkev->has_event) { // Wait for latest event ggml_vk_wait_events(compute_ctx, { vkev->event }); + + if (ctx->device->async_use_transfer_queue) { + vk_context transfer_ctx = ggml_vk_get_transfer_ctx(ctx); + transfer_ctx->s->wait_semaphores.push_back(vkev->tl_semaphore); + } } } @@ -17392,6 +17547,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17497,6 +17653,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17537,6 +17694,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17561,6 +17719,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17577,6 +17736,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17655,6 +17815,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_OPT_STEP_ADAMW: case GGML_OP_OPT_STEP_SGD: return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_OUT_PROD: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32 + && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32 + && op->type == GGML_TYPE_F32; case GGML_OP_LOG: case GGML_OP_TRI: case GGML_OP_DIAG: diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp index c92798f8e2b9..776e9b8a5572 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp @@ -208,6 +208,36 @@ void quantize(uint dst_idx, uint src_idx) } #endif +#if defined(DATA_A_Q2_0) +uint quantize_q2_0(float x) +{ + const int q = int(x >= 0.0f ? floor(x + 0.5f) : ceil(x - 0.5f)) + 1; + return uint(clamp(q, 0, 3)); +} + +void quantize(uint dst_idx, uint src_idx) +{ + float amax = 0.0f; + + [[unroll]] for (int j = 0; j < QUANT_K_Q2_0; ++j) { + amax = max(amax, abs(float(data_s[src_idx + j]))); + } + + const float d = amax; + const float id = d != 0.0f ? 1.0f / d : 0.0f; + + data_q[dst_idx].d = float16_t(d); + + [[unroll]] for (int j = 0; j < QUANT_K_Q2_0 / 4; ++j) { + const uint q0 = quantize_q2_0(float(data_s[src_idx + 4*j ]) * id); + const uint q1 = quantize_q2_0(float(data_s[src_idx + 4*j + 1]) * id); + const uint q2 = quantize_q2_0(float(data_s[src_idx + 4*j + 2]) * id); + const uint q3 = quantize_q2_0(float(data_s[src_idx + 4*j + 3]) * id); + data_q[dst_idx].qs[j] = uint8_t(q0 | (q1 << 2u) | (q2 << 4u) | (q3 << 6u)); + } +} +#endif + #if defined(DATA_A_IQ4_NL) uint best_index(float x) { if (x <= kvalues_iq4nl[0]) return 0; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl index ac702332bab9..d902ff3a67bd 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl @@ -143,6 +143,17 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) { } #endif +#if defined(DATA_A_Q2_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint bits = uint(data_a[a_offset + ib].qs[iqs / 4u]) >> (2u * (iqs % 4u)); + return vec2(bits & 3u, (bits >> 2u) & 3u) - 1.0f; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint bits = uint(data_a[a_offset + ib].qs[iqs / 4u]); + return vec4(bits & 3u, (bits >> 2u) & 3u, (bits >> 4u) & 3u, bits >> 6u) - 1.0f; +} +#endif + #if defined(DATA_A_IQ1_S) vec2 dequantize(uint ib, uint iqs, uint a_offset) { const uint ib32 = iqs / 32; @@ -547,7 +558,7 @@ vec2 get_dm(uint ib, uint a_offset) { } #endif -#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) +#if defined(DATA_A_Q2_0) || defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) vec2 get_dm(uint ib, uint a_offset) { return vec2(float(data_a[a_offset + ib].d), 0); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl index 6da4034657c6..6bf2cb0e08ed 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl @@ -46,6 +46,26 @@ f16vec4 dequantFuncQ1_0_v(const in decodeBufQ1_0 bl, const in uint blockCoords[2 (qs_nib & 8u) != 0u ? d : md); } +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ2_0 { + block_q2_0 block; +}; + +float16_t dequantFuncQ2_0(const in decodeBufQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint bits = uint(bl.block.qs[idx >> 2]) >> (2u * (idx & 3u)); + return (float16_t(bits & 3u) - float16_t(1.0)) * d; +} + +f16vec4 dequantFuncQ2_0_v(const in decodeBufQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint bits = uint(bl.block.qs[idx >> 2]); + return f16vec4((vec4(bits & 3u, (bits >> 2u) & 3u, (bits >> 4u) & 3u, bits >> 6u) - 1.0f) * float(d)); +} + layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ4_0 { block_q4_0_packed16 block; }; @@ -1330,6 +1350,9 @@ f16vec4 dequantFuncNVFP4_v(const in decodeBufNVFP4 bl, const in uint blockCoords #if defined(DATA_A_Q1_0) #define dequantFuncA dequantFuncQ1_0 #define dequantFuncA_v dequantFuncQ1_0_v +#elif defined(DATA_A_Q2_0) +#define dequantFuncA dequantFuncQ2_0 +#define dequantFuncA_v dequantFuncQ2_0_v #elif defined(DATA_A_Q4_0) #define dequantFuncA dequantFuncQ4_0 #define dequantFuncA_v dequantFuncQ4_0_v diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_0.comp new file mode 100644 index 000000000000..0294e6eeea0f --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_0.comp @@ -0,0 +1,29 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q2_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid / 4; + const uint ir = tid % 4; + const uint ib = 4*i + ir; + if (ib >= p.nel / QUANT_K_Q2_0) { + return; + } + + const uint b_idx = 256*i + QUANT_K_Q2_0*ir + 4*il; + const uint bits = uint(data_a[ib].qs[il]); + const float d = float(data_a[ib].d); + + data_b[b_idx ] = D_TYPE(d * (float(bits & 3u) - 1.0f)); + data_b[b_idx + 1] = D_TYPE(d * (float((bits >> 2u) & 3u) - 1.0f)); + data_b[b_idx + 2] = D_TYPE(d * (float((bits >> 4u) & 3u) - 1.0f)); + data_b[b_idx + 3] = D_TYPE(d * (float(bits >> 6u) - 1.0f)); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp index 7bbee577fb74..18d441ead40e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp @@ -11,10 +11,10 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) -#define K_PER_ITER 8 -#elif defined(DATA_A_QUANT_K) +#if defined(DATA_A_Q2_0) || defined(DATA_A_QUANT_K) #define K_PER_ITER 16 +#elif defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) +#define K_PER_ITER 8 #elif defined(DATA_A_IQ1_S) || defined(DATA_A_IQ1_M) #define K_PER_ITER 32 #else diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl index 73cf9c799554..a5403ac82121 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl @@ -4,7 +4,11 @@ #include "types.glsl" -#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) +#if defined(DATA_A_Q2_0) +FLOAT_TYPE get_dm(uint ib) { + return FLOAT_TYPE(data_a[ib / 2].d); +} +#elif defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) FLOAT_TYPE get_dm(uint ib) { return FLOAT_TYPE(data_a[ib].d); } @@ -30,6 +34,27 @@ FLOAT_TYPEV2 get_dm(uint ib) { #endif // Each iqs value maps to a 32-bit integer +#if defined(DATA_A_Q2_0) +uint unpack_q2_0(uint bits) { + // Move bit pairs [1:0], [3:2], [5:4], [7:6] to [1:0], [9:8], [17:16], [25:24]. + bits &= 0xffu; + bits = (bits | (bits << 12u)) & 0x000f000fu; + return (bits | (bits << 6u)) & 0x03030303u; +} + +i32vec4 repack4(uint ib, uint iqs) { + const uint qs_idx = (ib & 1u) * 4u + iqs * 2u; + const uint bits = pack32(u16vec2(data_a_packed16[ib / 2].qs[qs_idx], + data_a_packed16[ib / 2].qs[qs_idx + 1])); + return i32vec4(unpack_q2_0(bits), unpack_q2_0(bits >> 8u), + unpack_q2_0(bits >> 16u), unpack_q2_0(bits >> 24u)); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(da * (float(q_sum) * dsb.x - dsb.y / float(sum_divisor))); +} +#endif + #if defined(DATA_A_Q4_0) // 2-byte loads for Q4_0 blocks (18 bytes) i32vec2 repack(uint ib, uint iqs) { @@ -132,7 +157,19 @@ FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const i } #endif -#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) +#if defined(DATA_A_Q2_0) +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; + const i32vec4 qs_a = repack4(ib_a, iqs); + q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]); + q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]); + q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]); + q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]); + + // 16 quants per call => divide sums by 32/16 = 2 + return mul_q8_1(q_sum, get_dm(ib_a), cache_b_ds, 2); +} +#elif defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { int32_t q_sum = 0; #if QUANT_R == 2 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl index 17c436a2f826..31dfefec8f94 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl @@ -151,6 +151,18 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin buf_a[buf_idx + 1] = FLOAT_TYPEV2((bits & 0x04u) != 0u ? d : -d, (bits & 0x08u) != 0u ? d : -d); buf_a[buf_idx + 2] = FLOAT_TYPEV2((bits & 0x10u) != 0u ? d : -d, (bits & 0x20u) != 0u ? d : -d); buf_a[buf_idx + 3] = FLOAT_TYPEV2((bits & 0x40u) != 0u ? d : -d, (bits & 0x80u) != 0u ? d : -d); +#elif defined(DATA_A_Q2_0) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 16; + const uint iqs = idx & 0xfu; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib].d); + const uint bits = uint(data_a[ib].qs[iqs]); + + buf_a[buf_idx ] = d * (FLOAT_TYPEV2(bits & 3u, (bits >> 2u) & 3u) - FLOAT_TYPEV2(1.0f)); + buf_a[buf_idx + 1] = d * (FLOAT_TYPEV2((bits >> 4u) & 3u, bits >> 6u) - FLOAT_TYPEV2(1.0f)); #elif defined(DATA_A_Q2_K) const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl index 59931b04b941..24da4f715f83 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl @@ -6,6 +6,40 @@ // Each iqs value maps to a 32-bit integer +#if defined(DATA_A_Q2_0) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint block_idx = ib / 2; + const uint byte_idx = (ib & 1u) * 8u + iqs; + const uint bits = uint(data_a[block_idx].qs[byte_idx]); + buf_a[buf_ib].qs[iqs] = pack32(i8vec4( + int8_t(bits & 3u), + int8_t((bits >> 2u) & 3u), + int8_t((bits >> 4u) & 3u), + int8_t(bits >> 6u))); + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPE(data_a[block_idx].d); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + + [[unroll]] for (uint iqs = 0; iqs < 8; ++iqs) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + [[unroll]] for (uint iqs = 0; iqs < 8; ++iqs) { + q_sum += dotPacked4x8EXT(cache_a[ib_a].qs[iqs], cache_b.qs[iqs]); + } + + return ACC_TYPE(float(cache_a[ib_a].dm) * (float(q_sum) * float(cache_b.ds.x) - float(cache_b.ds.y))); +} +#endif + #if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1) // 2-byte loads for Q4_0 blocks (18 bytes) // 4-byte loads for Q4_1 blocks (20 bytes) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl index 79c933f40cf2..2b7adcb6c2fc 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl @@ -13,6 +13,12 @@ struct block_a_cache { uint32_t qs[16/4]; FLOAT_TYPE dm; }; +#elif defined(DATA_A_Q2_0) +#define QUANT_R_MMQ 1 +struct block_a_cache { + int32_t qs[8]; + FLOAT_TYPE dm; +}; #elif defined(DATA_A_Q4_1) #define QUANT_R_MMQ 2 struct block_a_cache { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/out_prod.comp b/ggml/src/ggml-vulkan/vulkan-shaders/out_prod.comp new file mode 100644 index 000000000000..1973169960b4 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/out_prod.comp @@ -0,0 +1,59 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; + uint misalign_offsets; + float param1; float param2; int param3; +} p; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) readonly buffer B {float data_b[];}; +layout (binding = 2) writeonly buffer D {float data_d[];}; + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; } +uint get_doffset() { return p.misalign_offsets & 0xFF; } + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + if (idx >= p.ne) { + return; + } + + uint tmp = idx; + uint i0 = tmp % p.ne20; tmp /= p.ne20; + uint i1 = tmp % p.ne21; tmp /= p.ne21; + uint i2 = tmp % p.ne22; tmp /= p.ne22; + uint i3 = tmp; + + uint a_i0 = i0 % p.ne00; + uint a_i2 = i2 / (p.ne22 / p.ne02); + uint a_i3 = i3 / (p.ne23 / p.ne03); + + uint b_i0 = i1 % p.ne10; + uint b_i2 = i2; + uint b_i3 = i3; + + float sum = 0.0f; + uint K = p.ne01; + for (uint k = 0; k < K; k++) { + uint aoff = get_aoffset() + a_i3*p.nb03 + a_i2*p.nb02 + k*p.nb01 + a_i0*p.nb00; + uint boff = get_boffset() + b_i3*p.nb13 + b_i2*p.nb12 + k*p.nb11 + b_i0*p.nb10; + sum += data_a[aoff] * data_b[boff]; + } + + uint doff = get_doffset() + i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20; + data_d[doff] = sum; +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl index 010ef84253c3..9616a26c7b39 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl @@ -211,6 +211,30 @@ struct block_q1_0 #define A_TYPE block_q1_0 #endif +#define QUANT_K_Q2_0 64 +#define QUANT_R_Q2_0 1 + +struct block_q2_0 +{ + float16_t d; + uint8_t qs[QUANT_K_Q2_0 / 4]; +}; + +struct block_q2_0_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_Q2_0 / 8]; +}; + +#if defined(DATA_A_Q2_0) +#define QUANT_K QUANT_K_Q2_0 +#define QUANT_R QUANT_R_Q2_0 +#define QUANT_AUXF 1 +#define A_TYPE block_q2_0 +#define A_TYPE_PACKED16 block_q2_0_packed16 +#define DATA_A_QUANT_LEGACY +#endif + #define QUANT_K_Q8_1 32 #define QUANT_R_Q8_1 1 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 240e1d1b397b..58d347bc547d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -50,6 +50,7 @@ const std::vector type_names = { "f32", "f16", "q1_0", + "q2_0", "q4_0", "q4_1", "q5_0", @@ -232,7 +233,7 @@ bool is_quantized_type(const std::string& type_name) { } bool is_legacy_quant(const std::string& type_name) { - return type_name == "q4_0" || type_name == "q4_1" || type_name == "q5_0" || type_name == "q5_1" || type_name == "q8_0"; + return type_name == "q2_0" || type_name == "q4_0" || type_name == "q4_1" || type_name == "q5_0" || type_name == "q5_1" || type_name == "q8_0"; } bool is_k_quant(const std::string& type_name) { @@ -583,7 +584,7 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c std::string load_vec_quant = "2"; if ((tname == "q1_0") || (tname == "q4_0") || (tname == "q4_1") || (tname == "q5_1") || (tname == "iq1_s") || (tname == "iq1_m") || (tname == "iq2_xxs") || (tname == "iq2_xs") || (tname == "iq2_s")) load_vec_quant = "8"; - else if ((tname == "q5_0") || (tname == "q8_0") || (tname == "q2_k") || (tname == "q4_k") || (tname == "q5_k") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_xs") || (tname == "iq4_nl") || (tname == "mxfp4") || (tname == "nvfp4")) + else if ((tname == "q2_0") || (tname == "q5_0") || (tname == "q8_0") || (tname == "q2_k") || (tname == "q4_k") || (tname == "q5_k") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_xs") || (tname == "iq4_nl") || (tname == "mxfp4") || (tname == "nvfp4")) load_vec_quant = "4"; if (tname == "bf16") { @@ -823,13 +824,13 @@ void process_shaders() { string_to_spv("cpy_transpose_16", "copy_transpose.comp", {{"A_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); string_to_spv("cpy_transpose_32", "copy_transpose.comp", {{"A_TYPE", "uint"}, {"D_TYPE", "uint"}}); - for (std::string t : {"q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { + for (std::string t : {"q1_0", "q2_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { string_to_spv("cpy_f32_" + t, "copy_to_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"S_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); string_to_spv("cpy_" + t + "_f32", "copy_from_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); } for (auto src : {std::pair{"f32", "float"}, std::pair{"f16", "float16_t"}}) { - for (std::string dst : {"f32", "f16", "bf16", "q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { + for (std::string dst : {"f32", "f16", "bf16", "q1_0", "q2_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { string_to_spv("set_rows_" + std::string(src.first) + "_" + dst + "_i32", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(dst), "1"}, {"B_TYPE", "uint"}, {"B_SIZE", "32"}, {"S_TYPE", src.second}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); string_to_spv("set_rows_" + std::string(src.first) + "_" + dst + "_i64", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(dst), "1"}, {"B_TYPE", "uvec2"}, {"B_SIZE", "64"}, {"S_TYPE", src.second}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); } @@ -1036,6 +1037,8 @@ void process_shaders() { } } + string_to_spv("out_prod_f32", "out_prod.comp", {}); + string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); string_to_spv("conv_transpose_1d_f32", "conv_transpose_1d.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp index d7692363a1de..bed9265b8ab7 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp @@ -355,6 +355,30 @@ struct ggml_webgpu_conv2d_pipeline_key_hash { } }; +// Same type fields as conv2d plus the input layout (WHCN vs CWHN). +struct ggml_webgpu_conv2d_dw_pipeline_key { + ggml_type weight_type; + ggml_type input_type; + ggml_type output_type; + bool whcn; + + bool operator==(const ggml_webgpu_conv2d_dw_pipeline_key & other) const { + return weight_type == other.weight_type && input_type == other.input_type && output_type == other.output_type && + whcn == other.whcn; + } +}; + +struct ggml_webgpu_conv2d_dw_pipeline_key_hash { + size_t operator()(const ggml_webgpu_conv2d_dw_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.weight_type); + ggml_webgpu_hash_combine(seed, key.input_type); + ggml_webgpu_hash_combine(seed, key.output_type); + ggml_webgpu_hash_combine(seed, key.whcn); + return seed; + } +}; + /** Im2Col **/ struct ggml_webgpu_im2col_pipeline_key { ggml_type input_type; @@ -1210,6 +1234,8 @@ class ggml_webgpu_shader_lib { soft_max_pipelines; std::unordered_map conv2d_pipelines; + std::unordered_map + conv2d_dw_pipelines; std::unordered_map im2col_pipelines; @@ -3172,6 +3198,50 @@ class ggml_webgpu_shader_lib { return conv2d_pipelines[key]; } + // whcn selects the input layout: contiguous WHCN vs contiguous-channels CWHN + webgpu_pipeline get_conv2d_dw_pipeline(const ggml_webgpu_shader_lib_context & context, bool whcn) { + ggml_webgpu_conv2d_dw_pipeline_key key = {}; + key.weight_type = context.src0->type; + key.input_type = context.src1->type; + key.output_type = context.dst->type; + key.whcn = whcn; + + auto it = conv2d_dw_pipelines.find(key); + if (it != conv2d_dw_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = whcn ? "conv_2d_dw_whcn" : "conv_2d_dw_cwhn"; + + auto push_type_defines = [&](const char * prefix, ggml_type type) { + std::string s_prefix = prefix; + if (type == GGML_TYPE_F32) { + defines.push_back(s_prefix + "_F32"); + } else if (type == GGML_TYPE_F16) { + defines.push_back(s_prefix + "_F16"); + } else { + GGML_ABORT("Unsupported type for CONV_2D_DW shader"); + } + }; + + push_type_defines("WEIGHT", key.weight_type); + push_type_defines("INPUT", key.input_type); + push_type_defines("OUTPUT", key.output_type); + if (whcn) { + defines.push_back("WHCN"); + } + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_conv2d_dw, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + conv2d_dw_pipelines[key] = pipeline; + return conv2d_dw_pipelines[key]; + } + webgpu_pipeline get_im2col_pipeline(const ggml_webgpu_shader_lib_context & context) { ggml_webgpu_im2col_pipeline_key key = {}; key.input_type = context.src1->type; diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 29025e9ba4e3..75286ec7313c 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -978,6 +978,67 @@ static webgpu_encoded_op ggml_webgpu_conv_2d(webgpu_context & ctx, return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); } +// Same param/binding layout as conv_2d; the shader differs +static webgpu_encoded_op ggml_webgpu_conv_2d_dw(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + const int32_t s0 = ggml_get_op_params_i32(dst, 0); + const int32_t s1 = ggml_get_op_params_i32(dst, 1); + const int32_t p0 = ggml_get_op_params_i32(dst, 2); + const int32_t p1 = ggml_get_op_params_i32(dst, 3); + const int32_t d0 = ggml_get_op_params_i32(dst, 4); + const int32_t d1 = ggml_get_op_params_i32(dst, 5); + + // Scalar params matching conv2d_dw.wgsl (weight src0 [KW,KH,1,C], input src1, output dst). + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + (uint32_t) ggml_nelements(dst), + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) src1->ne[0], + (uint32_t) src1->ne[1], + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + + (uint32_t) s0, + (uint32_t) s1, + (uint32_t) p0, + (uint32_t) p1, + (uint32_t) d0, + (uint32_t) d1, + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + // Input layout: contiguous -> WHCN, contiguous-channels -> CWHN + const bool whcn = ggml_is_contiguous(src1); + webgpu_pipeline pipeline = ctx->shader_lib->get_conv2d_dw_pipeline(shader_lib_ctx, whcn); + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t wg_x; + uint32_t wg_y; + uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + static webgpu_encoded_op ggml_webgpu_im2col(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, @@ -3164,6 +3225,8 @@ static std::optional ggml_webgpu_encode(webgpu_context ctx, return ggml_webgpu_sum_rows(ctx, src0, node); case GGML_OP_CONV_2D: return ggml_webgpu_conv_2d(ctx, src0, src1, node); + case GGML_OP_CONV_2D_DW: + return ggml_webgpu_conv_2d_dw(ctx, src0, src1, node); case GGML_OP_IM2COL: return ggml_webgpu_im2col(ctx, src0, src1, node); case GGML_OP_UPSCALE: @@ -4349,6 +4412,12 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); break; + case GGML_OP_CONV_2D_DW: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) && + (ggml_is_contiguous(src1) || ggml_is_contiguous_channels(src1)); + break; case GGML_OP_IM2COL: supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl new file mode 100644 index 000000000000..42d6f027cab6 --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl @@ -0,0 +1,137 @@ +#include "common_decls.tmpl" +enable f16; + +// Ported from the Vulkan backend's conv2d_dw.comp. Two variants (based on WHCN) +// selected by the input (src1) layout: contiguous -> WHCN, else CWHN. +// weight (src0) is [KW,KH,1,C]; output matches the input layout. + +@group(0) @binding(0) +#if defined(WEIGHT_F32) +var weights: array; +#elif defined(WEIGHT_F16) +var weights: array; +#endif + +@group(0) @binding(1) +#if defined(INPUT_F32) +var input: array; +#elif defined(INPUT_F16) +var input: array; +#endif + +@group(0) @binding(2) +#if defined(OUTPUT_F32) +var output: array; +#elif defined(OUTPUT_F16) +var output: array; +#endif + +struct Params { + offset_w: u32, + offset_i: u32, + offset_o: u32, + + ne: u32, + channels: u32, + batches: u32, + dst_w: u32, dst_h: u32, + src_w: u32, src_h: u32, + knl_w: u32, knl_h: u32, + + stride_x: i32, stride_y: i32, + pad_x: i32, pad_y: i32, + dilation_x: i32, dilation_y: i32, +}; + +@group(0) @binding(3) +var params: Params; + +fn load_weight(idx: u32) -> f32 { + #if defined(WEIGHT_F32) + return weights[idx]; + #elif defined(WEIGHT_F16) + return f32(weights[idx]); + #endif +} +fn load_input(idx: u32) -> f32 { + #if defined(INPUT_F32) + return input[idx]; + #elif defined(INPUT_F16) + return f32(input[idx]); + #endif +} +fn store_output(idx: u32, val: f32) { + #if defined(OUTPUT_F32) + output[idx] = val; + #elif defined(OUTPUT_F16) + output[idx] = f16(val); + #endif +} + +#if defined(WHCN) +// Input/output/kernel contiguous in [W, H, C, N] order (kernel [KW,KH,C]). +fn conv_2d_dw(idx: u32) -> f32 { + let i0 = idx / params.dst_w; + let dst_x = idx - i0 * params.dst_w; + let i1 = i0 / params.dst_h; + let dst_y = i0 - i1 * params.dst_h; + let n = i1 / params.channels; + let c = i1 - n * params.channels; + + let src_i = params.offset_i + n * params.channels * params.src_h * params.src_w + + c * params.src_h * params.src_w; + let knl_i = params.offset_w + c * params.knl_h * params.knl_w; + + var sum: f32 = 0.0; + for (var ky: u32 = 0u; ky < params.knl_h; ky += 1u) { + let src_y = i32(dst_y) * params.stride_y + i32(ky) * params.dilation_y - params.pad_y; + if (src_y < 0 || src_y >= i32(params.src_h)) { continue; } + for (var kx: u32 = 0u; kx < params.knl_w; kx += 1u) { + let src_x = i32(dst_x) * params.stride_x + i32(kx) * params.dilation_x - params.pad_x; + if (src_x < 0 || src_x >= i32(params.src_w)) { continue; } + let v = load_input(src_i + u32(src_y) * params.src_w + u32(src_x)); + let k = load_weight(knl_i + ky * params.knl_w + kx); + sum += v * k; + } + } + return sum; +} +#else +// Channels contiguous (CWHN): channel is the innermost axis. +fn conv_2d_dw(idx: u32) -> f32 { + let i0 = idx / params.channels; + let c = idx - i0 * params.channels; + let i1 = i0 / params.dst_w; + let dst_x = i0 - i1 * params.dst_w; + let n = i1 / params.dst_h; + let dst_y = i1 - n * params.dst_h; + + let src_i = params.offset_i + n * params.channels * params.src_h * params.src_w; + let src_row = params.src_w * params.channels; + let knl_row = params.knl_w * params.channels; + + var sum: f32 = 0.0; + for (var ky: u32 = 0u; ky < params.knl_h; ky += 1u) { + let src_y = i32(dst_y) * params.stride_y + i32(ky) * params.dilation_y - params.pad_y; + if (src_y < 0 || src_y >= i32(params.src_h)) { continue; } + for (var kx: u32 = 0u; kx < params.knl_w; kx += 1u) { + let src_x = i32(dst_x) * params.stride_x + i32(kx) * params.dilation_x - params.pad_x; + if (src_x < 0 || src_x >= i32(params.src_w)) { continue; } + let v = load_input(src_i + u32(src_y) * src_row + u32(src_x) * params.channels + c); + let k = load_weight(params.offset_w + ky * knl_row + kx * params.channels + c); + sum += v * k; + } + } + return sum; +} +#endif + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3 +) { + let idx = gid.x + (num_wg.x * u32(WG_SIZE)) * gid.y; + if (idx >= params.ne) { return; } + store_output(params.offset_o + idx, conv_2d_dw(idx)); +} diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 086b6ab08400..a7d1fe7d94be 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1080,6 +1080,9 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "SOLVE_TRI", "GATED_DELTA_NET", "LIGHTNING_INDEXER", + "DSV4_HC_COMB", + "DSV4_HC_PRE", + "DSV4_HC_POST", "UNARY", @@ -1097,7 +1100,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "GLU", }; -static_assert(GGML_OP_COUNT == 98, "GGML_OP_COUNT != 98"); +static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT != 101"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1192,6 +1195,9 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "A X = B, A triangular, solve X", "gated_delta_net(q, k, v, g, beta, s)", "lightning_indexer(q, k, weights, mask)", + "dsv4_hc_comb(mixes, scale, base)", + "dsv4_hc_pre(x, weights)", + "dsv4_hc_post(x, residual, post, comb)", "unary(x)", @@ -1209,7 +1215,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "glu(x)", }; -static_assert(GGML_OP_COUNT == 98, "GGML_OP_COUNT != 98"); +static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT != 101"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -5437,6 +5443,7 @@ struct ggml_tensor * ggml_flash_attn_ext( return result; } + void ggml_flash_attn_ext_set_prec( struct ggml_tensor * a, enum ggml_prec prec) { @@ -6337,6 +6344,132 @@ struct ggml_tensor * ggml_lightning_indexer( return result; } +// ggml_dsv4_hc_comb + +struct ggml_tensor * ggml_dsv4_hc_comb( + struct ggml_context * ctx, + struct ggml_tensor * mixes, + struct ggml_tensor * scale, + struct ggml_tensor * base, + float eps, + int32_t n_iter) { + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(n_iter > 0); + + const int64_t hc_mix_dim = mixes->ne[0]; + const int64_t n_tokens = mixes->ne[1]; + + int64_t hc = 0; + for (int64_t i = 1; i*i + 2*i <= hc_mix_dim; ++i) { + if ((2 + i)*i == hc_mix_dim) { + hc = i; + break; + } + } + + GGML_ASSERT(hc > 0); + GGML_ASSERT(hc == 4); + GGML_ASSERT(mixes->ne[2] == 1); + GGML_ASSERT(mixes->ne[3] == 1); + GGML_ASSERT(scale->ne[0] >= 3); + GGML_ASSERT(scale->ne[1] == 1); + GGML_ASSERT(scale->ne[2] == 1); + GGML_ASSERT(scale->ne[3] == 1); + GGML_ASSERT(base->ne[0] == hc_mix_dim); + GGML_ASSERT(base->ne[1] == 1); + GGML_ASSERT(base->ne[2] == 1); + GGML_ASSERT(base->ne[3] == 1); + + struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hc, hc, n_tokens); + + ggml_set_op_params_f32(result, 0, eps); + ggml_set_op_params_i32(result, 1, n_iter); + + result->op = GGML_OP_DSV4_HC_COMB; + result->src[0] = mixes; + result->src[1] = scale; + result->src[2] = base; + + return result; +} + +// ggml_dsv4_hc_pre + +struct ggml_tensor * ggml_dsv4_hc_pre( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * weights) { + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t hc = x->ne[1]; + const int64_t n_tokens = x->ne[2]; + + GGML_ASSERT(hc > 0); + GGML_ASSERT(x->ne[3] == 1); + GGML_ASSERT(weights->ne[0] == hc); + GGML_ASSERT(weights->ne[1] == n_tokens); + GGML_ASSERT(weights->ne[2] == 1); + GGML_ASSERT(weights->ne[3] == 1); + + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); + + result->op = GGML_OP_DSV4_HC_PRE; + result->src[0] = x; + result->src[1] = weights; + + return result; +} + +// ggml_dsv4_hc_post + +struct ggml_tensor * ggml_dsv4_hc_post( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * residual, + struct ggml_tensor * post, + struct ggml_tensor * comb) { + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t n_tokens = x->ne[1]; + const int64_t hc = residual->ne[1]; + + GGML_ASSERT(hc > 0); + GGML_ASSERT(x->ne[2] == 1); + GGML_ASSERT(x->ne[3] == 1); + + GGML_ASSERT(residual->ne[0] == n_embd); + GGML_ASSERT(residual->ne[2] == n_tokens); + GGML_ASSERT(residual->ne[3] == 1); + + GGML_ASSERT(post->ne[0] == hc); + GGML_ASSERT(post->ne[1] == n_tokens); + GGML_ASSERT(post->ne[2] == 1); + GGML_ASSERT(post->ne[3] == 1); + + GGML_ASSERT(comb->ne[0] == hc); + GGML_ASSERT(comb->ne[1] == hc); + GGML_ASSERT(comb->ne[2] == n_tokens); + GGML_ASSERT(comb->ne[3] == 1); + + struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, hc, n_tokens); + + result->op = GGML_OP_DSV4_HC_POST; + result->src[0] = x; + result->src[1] = residual; + result->src[2] = post; + result->src[3] = comb; + + return result; +} + //////////////////////////////////////////////////////////////////////////////// struct ggml_hash_set ggml_hash_set_new(size_t size) { diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp index 7920b8100b61..9f9e4fe5d104 100644 --- a/ggml/src/gguf.cpp +++ b/ggml/src/gguf.cpp @@ -1424,7 +1424,7 @@ void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const vo struct gguf_writer_base { size_t written_bytes {0u}; - ~gguf_writer_base(void) = default; + virtual ~gguf_writer_base(void) = default; // we bet on devirtualization virtual void write(int8_t val) = 0; diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 63ac2ed1f5cf..3a1d0bdc8c5e 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -126,6 +126,9 @@ class LLM: EXPERTS_PER_GROUP = "{arch}.experts_per_group" MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers" MOE_LATENT_SIZE = "{arch}.moe_latent_size" + SITU_BETA = "{arch}.situ_beta" + SITU_LINEAR_BETA = "{arch}.situ_linear_beta" + ATTN_RES_BLOCK_SIZE = "{arch}.attn_res_block_size" NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers" NUM_DEEPSTACK_LAYERS = "{arch}.n_deepstack_layers" DEEPSTACK_MAPPING = "{arch}.deepstack_mapping" @@ -145,6 +148,8 @@ class LLM: TOKEN_SHIFT_COUNT = "{arch}.token_shift_count" INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step" FULL_ATTENTION_INTERVAL = "{arch}.full_attention_interval" + NUM_LOOPS = "{arch}.num_loops" + SKIP_LOOP_FINAL_NORM = "{arch}.skip_loop_final_norm" HASH_LAYER_COUNT = "{arch}.hash_layer_count" ACTIVATION_SPARSITY_SCALE = "{arch}.activation_sparsity_scale" ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx" @@ -200,6 +205,9 @@ class Indexer: HEAD_COUNT = "{arch}.attention.indexer.head_count" KEY_LENGTH = "{arch}.attention.indexer.key_length" TOP_K = "{arch}.attention.indexer.top_k" + BLOCK_SIZE = "{arch}.attention.indexer.block_size" # MSA + LOCAL_BLOCKS = "{arch}.attention.indexer.local_blocks" # MSA + TYPES = "{arch}.attention.indexer.types" class HyperConnection: COUNT = "{arch}.hyper_connection.count" @@ -238,7 +246,8 @@ class SSM: DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms" class KDA: - HEAD_DIM = "{arch}.kda.head_dim" + HEAD_DIM = "{arch}.kda.head_dim" + GATE_LOWER_BOUND = "{arch}.kda.gate_lower_bound" class WKV: HEAD_SIZE = "{arch}.wkv.head_size" @@ -347,6 +356,7 @@ class ClipVision: class Attention: HEAD_COUNT = "clip.vision.attention.head_count" HEAD_COUNT_KV = "clip.vision.attention.head_count_kv" # used by mimovl (GQA) + HEAD_DIM = "clip.vision.attention.head_dim" # kimi-k3: qkv width != n_embd LAYERNORM_EPS = "clip.vision.attention.layer_norm_epsilon" class Projector: @@ -507,6 +517,7 @@ class MODEL_ARCH(IntEnum): DOTS1 = auto() ARCEE = auto() AFMOE = auto() + LAGUNA = auto() ERNIE4_5 = auto() ERNIE4_5_MOE = auto() HUNYUAN_MOE = auto() @@ -526,6 +537,7 @@ class MODEL_ARCH(IntEnum): APERTUS = auto() COGVLM = auto() MINIMAXM2 = auto() + MINIMAXM3 = auto() RND1 = auto() PANGU_EMBED = auto() MISTRAL3 = auto() @@ -538,8 +550,10 @@ class MODEL_ARCH(IntEnum): LLAMA_EMBED = auto() MAINCODER = auto() KIMI_LINEAR = auto() + KIMI_K3 = auto() TALKIE = auto() MELLUM = auto() + NANBEIGE = auto() class VISION_PROJECTOR_TYPE(IntEnum): @@ -612,6 +626,13 @@ class MODEL_TENSOR(IntEnum): FFN_GATE_TID2EID = auto() MOE_LATENT_DOWN = auto() # nemotron 3 super MOE_LATENT_UP = auto() # nemotron 3 super + MOE_LATENT_NORM = auto() # kimi k3 + ATTN_RES_NORM = auto() # kimi k3 + ATTN_RES_PROJ = auto() # kimi k3 + FFN_RES_NORM = auto() # kimi k3 + FFN_RES_PROJ = auto() # kimi k3 + OUTPUT_RES_NORM = auto() # kimi k3 + OUTPUT_RES_PROJ = auto() # kimi k3 ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() @@ -772,6 +793,9 @@ class MODEL_TENSOR(IntEnum): INDEXER_PROJ = auto() INDEXER_ATTN_K = auto() INDEXER_ATTN_Q_B = auto() + INDEXER_Q_PROJ = auto() + INDEXER_K_PROJ = auto() + INDEXER_Q_NORM = auto() INDEXER_COMPRESSOR_WKV = auto() INDEXER_COMPRESSOR_WGATE = auto() INDEXER_COMPRESSOR_APE = auto() @@ -849,6 +873,8 @@ class MODEL_TENSOR(IntEnum): V_MM_UP = auto() # cogvlm V_MM_DOWN = auto() # cogvlm V_MM_GATE = auto() # cogvlm + V_MM_MERGER_FC1 = auto() # minimax-m3 (patch-merge MLP) + V_MM_MERGER_FC2 = auto() # minimax-m3 (patch-merge MLP) V_TOK_BOI = auto() # cogvlm V_TOK_EOI = auto() # cogvlm V_TOK_IMG_BEGIN = auto() # hunyuanvl @@ -1088,6 +1114,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.DOTS1: "dots1", MODEL_ARCH.ARCEE: "arcee", MODEL_ARCH.AFMOE: "afmoe", + MODEL_ARCH.LAGUNA: "laguna", MODEL_ARCH.ERNIE4_5: "ernie4_5", MODEL_ARCH.ERNIE4_5_MOE: "ernie4_5-moe", MODEL_ARCH.FALCON_H1: "falcon-h1", @@ -1107,6 +1134,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.GROVEMOE: "grovemoe", MODEL_ARCH.APERTUS: "apertus", MODEL_ARCH.MINIMAXM2: "minimax-m2", + MODEL_ARCH.MINIMAXM3: "minimax-m3", MODEL_ARCH.COGVLM: "cogvlm", MODEL_ARCH.RND1: "rnd1", MODEL_ARCH.PANGU_EMBED: "pangu-embedded", @@ -1120,8 +1148,10 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.LLAMA_EMBED: "llama-embed", MODEL_ARCH.MAINCODER: "maincoder", MODEL_ARCH.KIMI_LINEAR: "kimi-linear", + MODEL_ARCH.KIMI_K3: "kimi-k3", MODEL_ARCH.TALKIE: "talkie", MODEL_ARCH.MELLUM: "mellum", + MODEL_ARCH.NANBEIGE: "nanbeige", } VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { @@ -1194,6 +1224,13 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_GATE_TID2EID: "blk.{bid}.ffn_gate_tid2eid", MODEL_TENSOR.MOE_LATENT_DOWN: "blk.{bid}.ffn_latent_down", # nemotron 3 super MODEL_TENSOR.MOE_LATENT_UP: "blk.{bid}.ffn_latent_up", # nemotron 3 super + MODEL_TENSOR.MOE_LATENT_NORM: "blk.{bid}.ffn_latent_norm", # kimi k3 + MODEL_TENSOR.ATTN_RES_NORM: "blk.{bid}.attn_res_norm", # kimi k3 + MODEL_TENSOR.ATTN_RES_PROJ: "blk.{bid}.attn_res_proj", # kimi k3 + MODEL_TENSOR.FFN_RES_NORM: "blk.{bid}.ffn_res_norm", # kimi k3 + MODEL_TENSOR.FFN_RES_PROJ: "blk.{bid}.ffn_res_proj", # kimi k3 + MODEL_TENSOR.OUTPUT_RES_NORM: "output_res_norm", # kimi k3 + MODEL_TENSOR.OUTPUT_RES_PROJ: "output_res_proj", # kimi k3 MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", MODEL_TENSOR.LAYER_OUT_SCALE: "blk.{bid}.layer_output_scale", MODEL_TENSOR.PER_LAYER_TOKEN_EMBD: "per_layer_token_embd", # gemma3n @@ -1352,6 +1389,9 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.INDEXER_PROJ: "blk.{bid}.indexer.proj", MODEL_TENSOR.INDEXER_ATTN_K: "blk.{bid}.indexer.attn_k", MODEL_TENSOR.INDEXER_ATTN_Q_B: "blk.{bid}.indexer.attn_q_b", + MODEL_TENSOR.INDEXER_Q_PROJ: "blk.{bid}.indexer.q_proj", + MODEL_TENSOR.INDEXER_K_PROJ: "blk.{bid}.indexer.k_proj", + MODEL_TENSOR.INDEXER_Q_NORM: "blk.{bid}.indexer.q_norm", MODEL_TENSOR.INDEXER_COMPRESSOR_WKV: "blk.{bid}.indexer_compressor_kv", MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE: "blk.{bid}.indexer_compressor_gate", MODEL_TENSOR.INDEXER_COMPRESSOR_APE: "blk.{bid}.indexer_compressor_ape", @@ -1428,6 +1468,8 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.V_MM_UP: "mm.up", MODEL_TENSOR.V_MM_DOWN: "mm.down", MODEL_TENSOR.V_MM_GATE: "mm.gate", + MODEL_TENSOR.V_MM_MERGER_FC1: "mm.merger.fc1", + MODEL_TENSOR.V_MM_MERGER_FC2: "mm.merger.fc2", MODEL_TENSOR.V_TOK_BOI: "v.boi", MODEL_TENSOR.V_TOK_EOI: "v.eoi", MODEL_TENSOR.V_MM_PRE_NORM: "mm.pre_norm", @@ -1624,6 +1666,8 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.V_RESMPL_QUERY, MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK, MODEL_TENSOR.V_MM_PATCH_MERGER, + MODEL_TENSOR.V_MM_MERGER_FC1, + MODEL_TENSOR.V_MM_MERGER_FC2, MODEL_TENSOR.V_DS_NORM, MODEL_TENSOR.V_DS_FC1, MODEL_TENSOR.V_DS_FC2, @@ -3823,6 +3867,31 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_POST_NORM, MODEL_TENSOR.FFN_EXP_PROBS_B, ], + MODEL_ARCH.LAGUNA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + ], MODEL_ARCH.ERNIE4_5: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -4135,6 +4204,34 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_EXP_PROBS_B, ], + MODEL_ARCH.MINIMAXM3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.INDEXER_Q_PROJ, + MODEL_TENSOR.INDEXER_K_PROJ, + MODEL_TENSOR.INDEXER_Q_NORM, + MODEL_TENSOR.INDEXER_K_NORM, + ], MODEL_ARCH.COGVLM: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -4362,6 +4459,55 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.KIMI_K3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_RES_NORM, + MODEL_TENSOR.OUTPUT_RES_PROJ, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_RES_NORM, + MODEL_TENSOR.ATTN_RES_PROJ, + MODEL_TENSOR.FFN_RES_NORM, + MODEL_TENSOR.FFN_RES_PROJ, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.MOE_LATENT_DOWN, + MODEL_TENSOR.MOE_LATENT_NORM, + MODEL_TENSOR.MOE_LATENT_UP, + MODEL_TENSOR.SSM_CONV1D_Q, + MODEL_TENSOR.SSM_CONV1D_K, + MODEL_TENSOR.SSM_CONV1D_V, + MODEL_TENSOR.SSM_F_A, + MODEL_TENSOR.SSM_F_B, + MODEL_TENSOR.SSM_BETA, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], MODEL_ARCH.KIMI_LINEAR: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -4433,7 +4579,22 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], - # TODO + MODEL_ARCH.NANBEIGE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], } # tensors that will not be serialized @@ -4500,6 +4661,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.NANBEIGE: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # @@ -4693,6 +4858,7 @@ class VisionProjectorType: KIMIVL = "kimivl" PADDLEOCR = "paddleocr" KIMIK25 = "kimik25" + KIMIK3 = "kimik3" LIGHTONOCR = "lightonocr" COGVLM = "cogvlm" JANUS_PRO = "janus_pro" @@ -4705,6 +4871,7 @@ class VisionProjectorType: YOUTUVL = "youtuvl" NEMOTRON_V2_VL = "nemotron_v2_vl" HUNYUANVL = "hunyuanvl" + MINIMAXM3 = "minimax_m3" MINICPMV4_6 = "minicpmv4_6" GRANITE_SPEECH = "granite_speech" # audio MIMOVL = "mimovl" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 1e277f0687c5..fab8edf52095 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -793,6 +793,16 @@ def add_indexer_key_length(self, length: int) -> None: def add_indexer_top_k(self, top_k: int) -> None: self.add_uint32(Keys.Attention.Indexer.TOP_K.format(arch=self.arch), top_k) + def add_indexer_block_size(self, block_size: int) -> None: + self.add_uint32(Keys.Attention.Indexer.BLOCK_SIZE.format(arch=self.arch), block_size) + + def add_indexer_local_blocks(self, local_blocks: int) -> None: + self.add_uint32(Keys.Attention.Indexer.LOCAL_BLOCKS.format(arch=self.arch), local_blocks) + + def add_indexer_types(self, value: Sequence[bool]) -> None: + key = Keys.Attention.Indexer.TYPES.format(arch=self.arch) + self.add_array(key, value) + def add_max_alibi_bias(self, bias: float) -> None: self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) @@ -871,6 +881,15 @@ def add_moe_every_n_layers(self, value: int) -> None: def add_moe_latent_size(self, value: int) -> None: self.add_uint32(Keys.LLM.MOE_LATENT_SIZE.format(arch=self.arch), value) + def add_situ_beta(self, value: float) -> None: + self.add_float32(Keys.LLM.SITU_BETA.format(arch=self.arch), value) + + def add_situ_linear_beta(self, value: float) -> None: + self.add_float32(Keys.LLM.SITU_LINEAR_BETA.format(arch=self.arch), value) + + def add_attn_res_block_size(self, value: int) -> None: + self.add_uint32(Keys.LLM.ATTN_RES_BLOCK_SIZE.format(arch=self.arch), value) + def add_nextn_predict_layers(self, count: int) -> None: self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count) @@ -898,6 +917,12 @@ def add_wkv_head_size(self, size: int) -> None: def add_token_shift_count(self, count: int) -> None: self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count) + def add_num_loops(self, count: int) -> None: + self.add_uint32(Keys.LLM.NUM_LOOPS.format(arch=self.arch), count) + + def add_skip_loop_final_norm(self, value: bool) -> None: + self.add_bool(Keys.LLM.SKIP_LOOP_FINAL_NORM.format(arch=self.arch), value) + def add_interleave_moe_layer_step(self, value: int) -> None: self.add_uint32(Keys.LLM.INTERLEAVE_MOE_LAYER_STEP.format(arch=self.arch), value) @@ -1068,6 +1093,9 @@ def add_ssm_dt_b_c_rms(self, value: bool) -> None: def add_kda_head_dim(self, value: int) -> None: self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value) + def add_kda_gate_lower_bound(self, value: float) -> None: + self.add_float32(Keys.KDA.GATE_LOWER_BOUND.format(arch=self.arch), value) + def add_tokenizer_model(self, model: str) -> None: self.add_string(Keys.Tokenizer.MODEL, model) @@ -1207,6 +1235,9 @@ def add_vision_head_count(self, value: int) -> None: def add_vision_head_count_kv(self, value: int) -> None: self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT_KV, value) + def add_vision_head_dim(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Attention.HEAD_DIM, value) + def add_vision_attention_layernorm_eps(self, value: float) -> None: self.add_float32(Keys.ClipVision.Attention.LAYERNORM_EPS, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 9efb36f8a447..b8957c9d7cc1 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -118,6 +118,14 @@ class TensorNameMap: "model.norm", # cogvlm ), + MODEL_TENSOR.OUTPUT_RES_NORM: ( + "model.output_attn_res_norm", # kimi k3 + ), + + MODEL_TENSOR.OUTPUT_RES_PROJ: ( + "model.output_attn_res_proj", # kimi k3 + ), + # Rope frequencies MODEL_TENSOR.ROPE_FREQS: ( "rope.freqs", # llama-pth @@ -479,6 +487,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.e_score_correction", # exaone-moe "model.layers.{bid}.block_sparse_moe.gate.e_score_correction", # kimi "model.layers.{bid}.moe.router_bias", # step3.5 expert selection bias + "model.layers.{bid}.mlp.experts.e_score_correction", # laguna ), # Feed-forward up @@ -610,10 +619,32 @@ class TensorNameMap: MODEL_TENSOR.MOE_LATENT_DOWN: ( "backbone.layers.{bid}.mixer.fc1_latent_proj", # nemotron 3 super + "model.layers.{bid}.block_sparse_moe.routed_expert_down_proj", # kimi k3 ), MODEL_TENSOR.MOE_LATENT_UP: ( "backbone.layers.{bid}.mixer.fc2_latent_proj", # nemotron 3 super + "model.layers.{bid}.block_sparse_moe.routed_expert_up_proj", # kimi k3 + ), + + MODEL_TENSOR.MOE_LATENT_NORM: ( + "model.layers.{bid}.block_sparse_moe.routed_expert_norm", # kimi k3 + ), + + MODEL_TENSOR.ATTN_RES_NORM: ( + "model.layers.{bid}.self_attention_res_norm", # kimi k3 + ), + + MODEL_TENSOR.ATTN_RES_PROJ: ( + "model.layers.{bid}.self_attention_res_proj", # kimi k3 + ), + + MODEL_TENSOR.FFN_RES_NORM: ( + "model.layers.{bid}.mlp_res_norm", # kimi k3 + ), + + MODEL_TENSOR.FFN_RES_PROJ: ( + "model.layers.{bid}.mlp_res_proj", # kimi k3 ), # Feed-forward down @@ -1263,7 +1294,8 @@ class TensorNameMap: ), MODEL_TENSOR.INDEXER_K_NORM: ( - "model.layers.{bid}.self_attn.indexer.k_norm", # DSA + "model.layers.{bid}.self_attn.indexer.k_norm", # DSA + "model.layers.{bid}.self_attn.index_k_norm", # MSA ), MODEL_TENSOR.INDEXER_PROJ: ( @@ -1278,6 +1310,18 @@ class TensorNameMap: "model.layers.{bid}.self_attn.indexer.wq_b", # DSA ), + MODEL_TENSOR.INDEXER_Q_PROJ: ( + "model.layers.{bid}.self_attn.index_q_proj", # MSA + ), + + MODEL_TENSOR.INDEXER_K_PROJ: ( + "model.layers.{bid}.self_attn.index_k_proj", # MSA + ), + + MODEL_TENSOR.INDEXER_Q_NORM: ( + "model.layers.{bid}.self_attn.index_q_norm", # MSA + ), + ############################################################################ # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg MODEL_TENSOR.ENC_OUTPUT_NORM: ( @@ -1746,6 +1790,7 @@ class TensorNameMap: "visual.merger.post_projection_norm", # glm4v "vision_tower.post_trunk_norm", # dots.ocr "vit.perceive.after_rms", # HunyuanVL + "mm_projector.post_norm", # Kimi-K3 (patchmergerv2 post-norm) ), MODEL_TENSOR.V_MM_INP_PROJ: ( @@ -1824,6 +1869,14 @@ class TensorNameMap: "visual.downsample", # glm4v ), + MODEL_TENSOR.V_MM_MERGER_FC1: ( + "patch_merge_mlp.linear_1", # minimax-m3 + ), + + MODEL_TENSOR.V_MM_MERGER_FC2: ( + "patch_merge_mlp.linear_2", # minimax-m3 + ), + MODEL_TENSOR.V_DS_NORM: ( "model.visual.deepstack_merger_list.{bid}.norm", # deepstack in qwen3vl ), diff --git a/include/llama.h b/include/llama.h index a311ac202357..3c6d22be8999 100644 --- a/include/llama.h +++ b/include/llama.h @@ -202,6 +202,17 @@ extern "C" { LLAMA_SPLIT_MODE_TENSOR = 3, }; + enum llama_load_mode { + LLAMA_LOAD_MODE_NONE = 0, // no special loading mode + LLAMA_LOAD_MODE_MMAP = 1, // memory map the model + LLAMA_LOAD_MODE_MLOCK = 2, // force system to keep model in RAM rather than swapping or compressing + LLAMA_LOAD_MODE_MMAP_MLOCK = 3, // mmap + force system to keep model in RAM rather than swapping or compressing + LLAMA_LOAD_MODE_DIRECT_IO = 4, // use direct I/O if available + }; + + LLAMA_API const char * llama_load_mode_name(enum llama_load_mode load_mode); + LLAMA_API enum llama_load_mode llama_load_mode_from_str(const char * str); + enum llama_context_type { LLAMA_CONTEXT_TYPE_DEFAULT = 0, LLAMA_CONTEXT_TYPE_MTP = 1, @@ -301,6 +312,7 @@ extern "C" { int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers enum llama_split_mode split_mode; // how to split the model across multiple GPUs + enum llama_load_mode load_mode; // how to load the model // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE int32_t main_gpu; @@ -321,9 +333,6 @@ extern "C" { // Keep the booleans together to avoid misalignment during copy-by-value. bool vocab_only; // only load the vocabulary, no weights - bool use_mmap; // use mmap if possible - bool use_direct_io; // use direct io, takes precedence over use_mmap when supported - bool use_mlock; // force system to keep model in RAM bool check_tensors; // validate model tensor data bool use_extra_bufts; // use extra buffer types (used for weight repacking) bool no_host; // bypass host buffer allowing extra buffers to be used diff --git a/models/templates/Kimi-K3.jinja b/models/templates/Kimi-K3.jinja new file mode 100644 index 000000000000..67e51a6af4b3 --- /dev/null +++ b/models/templates/Kimi-K3.jinja @@ -0,0 +1,325 @@ +{%- macro escape_attr(value) -%} +{{- value|string|replace('&', '&')|replace('"', '"') -}} +{%- endmacro -%} + +{%- macro open_tag(tag, attrs=[]) -%} +{{- '<|open|>' + tag -}} +{%- for attr in attrs -%} +{{- ' ' + attr[0] + '="' -}}{{- escape_attr(attr[1]) -}}{{- '"' -}} +{%- endfor -%} +{{- '<|sep|>' -}} +{%- endmacro -%} + +{%- macro close_tag(tag) -%} +{{- '<|close|>' + tag + '<|sep|>' -}} +{%- endmacro -%} + +{%- macro next_image(state) -%} +{%- if image_prompts is defined and image_prompts is not none -%} + {%- if state.image_index >= image_prompts|length -%} + {{- raise_exception('More image placeholders than image prompts.') -}} + {%- endif -%} + {{- image_prompts[state.image_index] -}} + {%- set state.image_index = state.image_index + 1 -%} +{%- else -%} + {{- '<|kimi_image_placeholder|>' -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_text(text, state) -%} +{%- set text = text|string -%} +{%- if image_prompts is defined and image_prompts is not none and '<|kimi_image_placeholder|>' in text -%} + {%- set parts = text.split('<|kimi_image_placeholder|>') -%} + {%- for part in parts -%} + {{- part -}} + {%- if not loop.last -%}{{- next_image(state) -}}{%- endif -%} + {%- endfor -%} +{%- else -%} + {{- text -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_content(content, state) -%} +{%- if content is string -%} + {{- render_text(content, state) -}} +{%- elif content is not none and content is defined -%} + {%- for part in content -%} + {%- if part.type in ['image', 'image_url'] -%} + {{- next_image(state) -}} + {%- else -%} + {{- render_text(part.text, state) -}} + {%- endif -%} + {%- endfor -%} +{%- endif -%} +{%- endmacro -%} + +{%- macro internal_system_message(message_type, body) -%} +{{- open_tag('message', [('role', 'system'), ('type', message_type)]) -}} +{{- body|trim -}} +{{- close_tag('message') -}} +{{- '<|end_of_msg|>' -}} +{%- endmacro -%} + +{%- macro json_sorted(value) -%} +{#- Minja は tojson(sort_keys=true) を実装していない。K3 参照実装は + deep_sort_dict() の後に compact JSON 化するため、dictsort と再帰マクロで + 同じバイト列を作る。配列の順序は保持し、mapping の各階層だけソートする。 -#} +{%- if value is mapping -%} +{{- '{' -}} +{%- for key, item in value|dictsort -%} +{%- if not loop.first -%}{{- ',' -}}{%- endif -%} +{{- key|tojson(ensure_ascii=false) -}}{{- ':' -}}{{- json_sorted(item) -}} +{%- endfor -%} +{{- '}' -}} +{%- elif value is string or value is number or value is boolean or value is none -%} +{{- value|tojson(ensure_ascii=false) -}} +{%- else -%} +{{- '[' -}} +{%- for item in value -%} +{%- if not loop.first -%}{{- ',' -}}{%- endif -%} +{{- json_sorted(item) -}} +{%- endfor -%} +{{- ']' -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_tool_declare(tool_list, dynamic=false) -%} +{{- open_tag('message', [('role', 'system'), ('type', 'tool-declare')]) -}} +{%- if dynamic -%} +{{- '## New Tools Available\nThe system dynamically extends the toolset via lazy-loading.\nYou have access to all existing and extended tools.\nHere are the specs for the extended tools.\n\n```json\n' -}} +{%- else -%} +{{- '# Tools\nHere are the available tools, described in JSONSchema.\n\n```json\n' -}} +{%- endif -%} +{{- json_sorted(tool_list) -}} +{{- '\n```' -}} +{{- close_tag('message') -}} +{{- '<|end_of_msg|>' -}} +{%- endmacro -%} + +{%- macro xtml_type(value) -%} +{%- if value is boolean -%}boolean +{%- elif value is none -%}null +{%- elif value is number -%}number +{%- elif value is string -%}string +{%- elif value is mapping -%}object +{%- else -%}array +{%- endif -%} +{%- endmacro -%} + +{%- macro xtml_value(value) -%} +{%- if value is string -%} +{{- value -}} +{%- else -%} +{{- value|tojson(ensure_ascii=false) -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_assistant(message, state) -%} +{%- if thinking -%} + {%- set reasoning_content = message.get('reasoning_content') or message.get('reasoning') -%} + {{- open_tag('think') -}} + {%- if reasoning_content is not none and reasoning_content|string|trim -%} + {{- render_text(reasoning_content, state) -}} + {%- endif -%} + {{- close_tag('think') -}} +{%- endif -%} +{{- open_tag('response') -}} +{{- render_content(message.get('content'), state) -}} +{{- close_tag('response') -}} +{%- set tool_calls = message.get('tool_calls') -%} +{%- if tool_calls -%} + {{- open_tag('tools') -}} + {%- for tool_call in tool_calls -%} + {%- if tool_call is not mapping -%} + {{- raise_exception('Kimi K3 tool calls must be mappings.') -}} + {%- endif -%} + {%- set fn = tool_call.function if tool_call.function is defined and tool_call.function is mapping else tool_call -%} + {%- if fn.get('name') is none -%} + {{- raise_exception('Kimi K3 tool calls require a function name.') -}} + {%- endif -%} + {{- open_tag('call', [('tool', fn.name), ('index', loop.index)]) -}} + {%- set arguments = fn.get('arguments', {}) -%} + {%- set json_block = fn.get('_xtml_json_block') -%} + {%- if json_block is not none -%} + {{- open_tag('json', [('type', 'object')]) -}} + {{- render_text(json_block, state) -}} + {{- close_tag('json') -}} + {%- elif arguments is mapping -%} + {%- for key, value in arguments.items() -%} + {{- open_tag('argument', [('key', key), ('type', xtml_type(value))]) -}} + {{- render_text(xtml_value(value), state) -}} + {{- close_tag('argument') -}} + {%- endfor -%} + {%- elif arguments is string and arguments|trim -%} + {{- open_tag('json', [('type', 'object')]) -}} + {{- render_text(arguments, state) -}} + {{- close_tag('json') -}} + {%- elif arguments is not none and arguments is not string -%} + {{- raise_exception('Kimi K3 tool call arguments must be a mapping or a JSON object string.') -}} + {%- endif -%} + {{- close_tag('call') -}} + {%- endfor -%} + {{- close_tag('tools') -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_tool_message(message, state, resolved_name=none) -%} +{%- set state.tool_index = state.tool_index + 1 -%} +{%- if resolved_name is not none -%} + {%- set tool_name = resolved_name -%} +{%- elif 'tool' in message -%} + {%- set tool_name = message.get('tool') -%} +{%- else -%} + {%- set tool_name = message.get('name') -%} +{%- endif -%} +{%- if tool_name is none and state.tool_calls is not none and state.tool_index <= state.tool_calls|length -%} + {%- set fallback_call = state.tool_calls[state.tool_index - 1] -%} + {%- set fallback_fn = fallback_call.function if fallback_call.function is defined and fallback_call.function is mapping else fallback_call -%} + {%- set tool_name = fallback_fn.name -%} +{%- endif -%} +{%- if tool_name is none -%} + {{- raise_exception('Kimi K3 tool messages need a resolvable tool name: carry `tool`/`name`, or match a preceding assistant tool_call by order.') -}} +{%- endif -%} +{{- open_tag('message', [('role', 'tool'), ('tool', tool_name), ('index', state.tool_index)]) -}} +{{- render_content(message.get('content'), state) -}} +{{- close_tag('message') -}} +{{- '<|end_of_msg|>' -}} +{%- endmacro -%} + +{%- if thinking is undefined -%} + {%- set thinking = true -%} +{%- endif -%} +{%- if thinking_effort is undefined -%} + {%- set thinking_effort = 'max' -%} +{%- endif -%} +{%- if thinking and thinking_effort is not none and thinking_effort not in ['low', 'high', 'max'] -%} + {{- raise_exception('Unsupported thinking_effort=' + thinking_effort|string + '; supported values are low, high, and max.') -}} +{%- endif -%} + +{%- set state = namespace(image_index=0, tool_calls=none, tool_index=0, response_schema=none) -%} + +{%- if tools is defined and tools -%} + {{- render_tool_declare(tools) -}} +{%- endif -%} + +{%- if thinking and thinking_effort in ['low', 'high', 'max'] -%} + {{- internal_system_message( + 'thinking-effort', + '`thinking_effort` guides on how much to think in your thinking channel (not including the response channel), supported values include `low`, `medium`, `high`, and `max`.\nNow the system is invoked with `thinking_effort=' + thinking_effort|string + '`.' + ) -}} +{%- endif -%} + +{%- for message in messages -%} + {%- if message is mapping -%} + {%- if 'role' not in message -%} + {{- raise_exception('Kimi K3 messages require a role.') -}} + {%- elif message.role == 'user' -%} + {%- set attrs = [('role', 'user')] -%} + {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%} + {{- open_tag('message', attrs) -}} + {{- render_content(message.get('content'), state) -}} + {{- close_tag('message') -}} + {{- '<|end_of_msg|>' -}} + {%- elif message.role == 'system' and message.get('tools') -%} + {{- render_tool_declare(message.tools, dynamic=true) -}} + {%- elif message.role == 'system' -%} + {%- set attrs = [('role', 'system')] -%} + {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%} + {{- open_tag('message', attrs) -}} + {{- render_content(message.get('content'), state) -}} + {{- close_tag('message') -}} + {{- '<|end_of_msg|>' -}} + {%- elif message.role == 'assistant' -%} + {%- set state.tool_calls = message.get('tool_calls') -%} + {%- set state.tool_index = 0 -%} + {%- set attrs = [('role', 'assistant')] -%} + {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%} + {{- open_tag('message', attrs) -}} + {{- render_assistant(message, state) -}} + {{- close_tag('message') -}} + {{- '<|end_of_msg|>' -}} + {%- elif message.role == 'tool' and (loop.first or messages[loop.index0 - 1].role != 'tool') -%} + {%- set run = namespace(tool_messages=[], resolved_count=0) -%} + {%- for candidate in messages[loop.index0:] -%} + {%- if candidate is not mapping or candidate.role != 'tool' -%}{%- break -%}{%- endif -%} + {%- set run.tool_messages = run.tool_messages + [candidate] -%} + {%- set call_id = candidate.get('tool_call_id', candidate.get('id')) -%} + {%- set match = namespace(found=false) -%} + {%- if call_id is not none and state.tool_calls is not none -%} + {%- for tool_call in state.tool_calls -%} + {%- if not match.found and tool_call is mapping and tool_call.get('id') is not none and tool_call.get('id')|string == call_id|string -%} + {%- set match.found = true -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- if match.found -%}{%- set run.resolved_count = run.resolved_count + 1 -%}{%- endif -%} + {%- endfor -%} + {%- if run.tool_messages|length > 0 and run.resolved_count == run.tool_messages|length -%} + {%- set emitted = namespace(ids=[]) -%} + {%- for tool_call in state.tool_calls -%} + {%- if tool_call is mapping and tool_call.get('id') is not none and tool_call.get('id')|string not in emitted.ids -%} + {%- set emitted.ids = emitted.ids + [tool_call.get('id')|string] -%} + {%- set fn = tool_call.function if tool_call.function is defined and tool_call.function is mapping else tool_call -%} + {%- for tool_message in run.tool_messages -%} + {%- set result_id = tool_message.get('tool_call_id', tool_message.get('id')) -%} + {%- if result_id is not none and result_id|string == tool_call.get('id')|string -%} + {{- render_tool_message(tool_message, state, fn.get('name')) -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {%- for tool_message in run.tool_messages -%} + {{- render_tool_message(tool_message, state) -}} + {%- endfor -%} + {%- endif -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%} + +{%- if tool_choice is defined and tool_choice == 'required' -%} + {{- internal_system_message('tool-choice', 'The system is invoked with `tool_choice=required`.\nYou MUST call tools in the next message.') -}} +{%- elif tool_choice is defined and tool_choice == 'none' -%} + {{- internal_system_message('tool-choice', 'The system is invoked with `tool_choice=none`.\nYou MUST NOT call any tools in the next message.') -}} +{%- endif -%} + +{%- if response_schema is defined -%} + {%- set state.response_schema = response_schema -%} +{%- elif response_format is defined and response_format is mapping and response_format.get('json_schema') is not none -%} + {%- set schema_wrapper = response_format.get('json_schema') -%} + {%- if schema_wrapper is mapping and 'schema' in schema_wrapper -%} + {%- set state.response_schema = schema_wrapper.get('schema') -%} + {%- elif schema_wrapper is mapping and 'json_schema' in schema_wrapper -%} + {%- set state.response_schema = schema_wrapper.get('json_schema') -%} + {%- else -%} + {%- set state.response_schema = schema_wrapper -%} + {%- endif -%} +{%- endif -%} + +{%- set response_format_type = none -%} +{%- if response_format is defined and response_format is mapping -%} + {%- set response_format_type = response_format.get('type') -%} +{%- elif response_format is defined -%} + {%- set response_format_type = response_format -%} +{%- endif -%} +{%- if response_format_type == 'json_object' -%} + {{- internal_system_message( + 'response-format', + 'The system is invoked with `response_format=json_object`.\nYour response must be raw JSON data without markdown code blocks (```json) or any additional formatting.' + ) -}} +{%- elif response_format_type == 'json_schema' -%} + {{- internal_system_message( + 'response-format', + 'The system is invoked with `response_format=json_schema`.\nYour response must be raw JSON data without markdown code blocks (```json) or any additional formatting.\nThe JSON data must match the following schema:\n```json\n' + json_sorted(state.response_schema) + '\n```' + ) -}} +{%- endif -%} + +{%- if add_generation_prompt -%} + {{- open_tag('message', [('role', 'assistant')]) -}} + {{- open_tag('think' if thinking else 'response') -}} +{%- endif -%} + +{%- if image_prompts is defined and image_prompts is not none and state.image_index != image_prompts|length -%} + {{- raise_exception('image prompt count ' + image_prompts|length|string + ' != consumed placeholder count ' + state.image_index|string) -}} +{%- endif -%} + diff --git a/models/templates/deepseek-ai-DeepSeek-V4.jinja b/models/templates/deepseek-ai-DeepSeek-V4.jinja index f19f787b1b7e..ad1ff8ce23aa 100644 --- a/models/templates/deepseek-ai-DeepSeek-V4.jinja +++ b/models/templates/deepseek-ai-DeepSeek-V4.jinja @@ -8,12 +8,15 @@ {%- set thinking = false -%} {%- endif -%} {%- endif -%} +{%- if not drop_thinking is defined -%} + {%- set drop_thinking = false -%} +{%- endif -%} {%- set dsml_token = '｜DSML｜' -%} {%- set thinking_start_token = '' -%} {%- set thinking_end_token = '' -%} {%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE\n...\n\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n\n\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%} {%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%} -{%- set ns = namespace(system_prompt='', is_first_sp=true) -%} +{%- set ns = namespace(system_prompt='', is_first_sp=true, has_tool_calls=false) -%} {%- for message in messages -%} {%- if message['role'] == 'system' -%} {%- if ns.is_first_sp -%} @@ -46,6 +49,11 @@ {%- endif -%} {%- endfor -%} {%- set state = namespace(in_user=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.has_tool_calls = true -%} + {%- endif -%} +{%- endfor -%} {%- for message in messages -%} {%- if message['role'] == 'user' or message['role'] == 'developer' -%} {%- if state.in_user -%} @@ -67,7 +75,8 @@ {%- set state.in_user = false -%} {{- '<｜Assistant｜>' -}} {%- set is_after_last_user = loop.index0 > last_user_idx.value -%} - {%- if is_after_last_user and thinking -%} + {%- set retain_reasoning = (not drop_thinking) or (is_after_last_user or ns.has_tool_calls) -%} + {%- if retain_reasoning and thinking -%} {{- thinking_start_token -}} {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%} {{- message['reasoning_content'] -}} diff --git a/models/templates/poolside-Laguna-S-2.1.jinja b/models/templates/poolside-Laguna-S-2.1.jinja new file mode 100644 index 000000000000..75c5f4cec0d4 --- /dev/null +++ b/models/templates/poolside-Laguna-S-2.1.jinja @@ -0,0 +1,93 @@ +{#- Iteration on laguna_glm_thinking_v8/chat_template.jinja -#} +{#- No formatting instructions -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{#- A caller-supplied system message with empty content opts out of the default below, producing no block — used to train without a system message. -#} +{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} + {%- set messages = messages[1:] -%} +{%- endif -%} + +{%- set has_sys = system_message and system_message.strip() -%} +{%- if has_sys or tools or enable_thinking -%} + {{- "" -}} + + {%- if has_sys -%} + {{- system_message.rstrip() -}} + {%- if tools -%}{{- "\n\n" -}}{%- endif -%} + {%- endif -%} + + {%- if tools -%} + {{- "### Tools\n\n" -}} + {{- "You may call functions to assist with the user query.\n" -}} + {{- "All available function signatures are listed below:\n" -}} + {{- "\n" -}} + {%- for tool in tools -%} + {{- (tool | tojson) ~ "\n" -}} + {%- endfor -%} + {{- "" -}} + {%- endif -%} + + {{- "\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "" + content + "\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "" -}} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content -#} + {%- set reasoning_content = '' -%} + {%- if message.reasoning is string -%} + {%- set reasoning_content = message.reasoning -%} + {%- elif message.reasoning_content is string -%} + {%- set reasoning_content = message.reasoning_content -%} + {%- endif -%} + {#- Display reasoning content for all messages if enable_thinking -#} + {%- if enable_thinking -%} + {{- '' + reasoning_content + '' -}} + {%- else -%} + {{- '' -}} + {%- endif -%} + {#- Display main content (trailing newline only when no tool_calls follow) -#} + {%- if content -%} + {{- content -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name -}} + {%- set _args = function_data.arguments -%} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "" -}} + {{- "" -}}{{- v | tojson(ensure_ascii=False) if v is not string else v -}}{{- "" -}} + {%- endfor -%} + {{- "" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "" + content + "\n" -}} + {%- elif message.role == "system" -%} + {#- Render additional system messages (the first one, if any, is handled separately in the header and was sliced off above) -#} + {{- "" + content + "\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if enable_thinking -%} + {{- '' -}} + {%- else -%} + {{- '' -}} + {%- endif -%} +{%- endif -%} \ No newline at end of file diff --git a/models/templates/poolside-Laguna-XS-2.1.jinja b/models/templates/poolside-Laguna-XS-2.1.jinja new file mode 100644 index 000000000000..d45f23f7038a --- /dev/null +++ b/models/templates/poolside-Laguna-XS-2.1.jinja @@ -0,0 +1,132 @@ +{#- Copied from laguna_glm_thinking_v4/chat_template.jinja -#} +{#- Removes prefix that references token, and replaces message.reasoning_content reference with message.reasoning -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{%- set system_message = "" -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} +{%- endif -%} + +{%- if (system_message and system_message.strip()) or tools -%} + {{- "\n" -}} + + {%- if system_message and system_message.strip() -%} + {{- "\n" -}} + {{- system_message.rstrip() -}} + {%- endif -%} + + {%- if tools -%} + {{- "\n\n### Tools\n\n" -}} + {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n" + ~ "All available function signatures are listed below:\n" + ~ "\n") -%} + {%- for tool in tools -%} + {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%} + {%- endfor -%} + {%- if enable_thinking -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "Wrap your thinking in '', '' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '' and '' tags, like here:\n" ~ + " your thoughts here \n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- else -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "For each function call, return an unescaped XML-like object " ~ + "with function name and arguments within '' and '' tags, like here:\n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- endif -%} + {{- tool_string -}} + {%- endif -%} + + {{- "\n\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "\n" -}} + {%- if render_assistant_messages_raw -%} + {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#} + {#- The generation prompt is when enable_thinking, otherwise. -#} + {#- Only prepend if content doesn't already start with it. -#} + {%- if enable_thinking -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- else -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- endif -%} + {{- content -}} + {#- Append closing tag if content doesn't already end with it. -#} + {%- if not content.endswith('\n') and not content.endswith('') -%} + {{- '\n' -}} + {%- endif -%} + {{- "\n" -}} + {%- else -%} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from tags -#} + {%- set reasoning_content = '' %} + {%- if message.reasoning is string %} + {%- set reasoning_content = message.reasoning %} + {%- elif message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- endif %} + {#- Always strip tags from content if present to avoid duplication -#} + {%- if '' in content %} + {%- if not reasoning_content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- endif %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {#- Display reasoning content for all messages -#} + {%- if reasoning_content -%} + {{- '\n' + reasoning_content.strip() + '\n\n' -}} + {%- else -%} + {{- '\n' -}} + {%- endif -%} + {#- Display main content -#} + {%- if content.strip() -%} + {{- content.strip() ~ "\n" -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name }} + {% set _args = function_data.arguments %} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "\n" -}} + {{- ""}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "\n" -}} + {%- endfor -%} + {{- "\n" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endif -%} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "system" and loop.index0 != 0 -%} + {#- Render additional system messages (skip the first one which is handled separately in the header) -#} + {{- "\n" + content + "\n\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "\n" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if not enable_thinking %} + {{- '' -}} + {%- else %} + {{- '' -}} + {%- endif %} +{%- endif -%} diff --git a/models/templates/poolside-Laguna-XS.2.jinja b/models/templates/poolside-Laguna-XS.2.jinja new file mode 100644 index 000000000000..4baa3fded6d2 --- /dev/null +++ b/models/templates/poolside-Laguna-XS.2.jinja @@ -0,0 +1,132 @@ +{#- Iteration on laguna_glm_thinking_v5/chat_template.jinja -#} +{#- Adds a default system message (used when no system message is provided in `messages`). -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} +{%- endif -%} + +{%- if (system_message and system_message.strip()) or tools -%} + {{- "\n" -}} + + {%- if system_message and system_message.strip() -%} + {{- "\n" -}} + {{- system_message.rstrip() -}} + {%- endif -%} + + {%- if tools -%} + {{- "\n\n### Tools\n\n" -}} + {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n" + ~ "All available function signatures are listed below:\n" + ~ "\n") -%} + {%- for tool in tools -%} + {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%} + {%- endfor -%} + {%- if enable_thinking -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "Wrap your thinking in '', '' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '' and '' tags, like here:\n" ~ + " your thoughts here \n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- else -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "For each function call, return an unescaped XML-like object " ~ + "with function name and arguments within '' and '' tags, like here:\n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- endif -%} + {{- tool_string -}} + {%- endif -%} + + {{- "\n\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "\n" -}} + {%- if render_assistant_messages_raw -%} + {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#} + {#- The generation prompt is when enable_thinking, otherwise. -#} + {#- Only prepend if content doesn't already start with it. -#} + {%- if enable_thinking -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- else -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- endif -%} + {{- content -}} + {#- Append closing tag if content doesn't already end with it. -#} + {%- if not content.endswith('\n') and not content.endswith('') -%} + {{- '\n' -}} + {%- endif -%} + {{- "\n" -}} + {%- else -%} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from tags -#} + {%- set reasoning_content = '' %} + {%- if message.reasoning is string %} + {%- set reasoning_content = message.reasoning %} + {%- elif message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- endif %} + {#- Always strip tags from content if present to avoid duplication -#} + {%- if '' in content %} + {%- if not reasoning_content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- endif %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {#- Display reasoning content for all messages -#} + {%- if reasoning_content -%} + {{- '\n' + reasoning_content.strip() + '\n\n' -}} + {%- else -%} + {{- '\n' -}} + {%- endif -%} + {#- Display main content -#} + {%- if content.strip() -%} + {{- content.strip() ~ "\n" -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name }} + {% set _args = function_data.arguments %} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "\n" -}} + {{- ""}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "\n" -}} + {%- endfor -%} + {{- "\n" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endif -%} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "system" and loop.index0 != 0 -%} + {#- Render additional system messages (skip the first one which is handled separately in the header) -#} + {{- "\n" + content + "\n\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "\n" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if not enable_thinking %} + {{- '' -}} + {%- else %} + {{- '' -}} + {%- endif %} +{%- endif -%} diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 5a6cc7dbb134..e5f26b5a41ff 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -28,7 +28,7 @@ "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", - "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", + "load_mode", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", "n_cpu_moe", "fit_target", "fit_min_ctx" ] @@ -38,7 +38,7 @@ "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "TEXT", "INTEGER", "INTEGER", "TEXT", "TEXT", "INTEGER", "TEXT", "INTEGER", "INTEGER", "INTEGER", "TEXT", "TEXT", - "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", + "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "TEXT", "INTEGER", "INTEGER", "REAL", "REAL", "INTEGER", "INTEGER", "INTEGER" ] @@ -63,7 +63,7 @@ LLAMA_BENCH_KEY_PROPERTIES = [ "cpu_info", "gpu_info", "backends", "n_gpu_layers", "n_cpu_moe", "tensor_buft_overrides", "model_filename", "model_type", "n_batch", "n_ubatch", "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", - "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth", + "load_mode", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth", "fit_target", "fit_min_ctx" ] @@ -73,7 +73,7 @@ ] # Properties that are boolean and are converted to Yes/No for the table: -LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "use_mmap", "no_kv_offload", "flash_attn"] +LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "no_kv_offload", "flash_attn"] TEST_BACKEND_OPS_BOOL_PROPERTIES = ["supported", "passed"] # Header names for the table (llama-bench): @@ -82,7 +82,7 @@ "tensor_buft_overrides": "Tensor overrides", "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]", "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", "embeddings": "Embeddings", "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", - "use_mmap": "Use mmap", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split", + "load_mode": "Load mode", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split", "flash_attn": "FlashAttention", } diff --git a/scripts/snapdragon/ggml-hexagon-profile.py b/scripts/snapdragon/ggml-hexagon-profile.py index 0f9240ddc62d..97a3acd26c26 100755 --- a/scripts/snapdragon/ggml-hexagon-profile.py +++ b/scripts/snapdragon/ggml-hexagon-profile.py @@ -6,6 +6,7 @@ import argparse import statistics import logging +import bisect from typing import Any, Dict, List, Optional from collections import defaultdict @@ -30,7 +31,7 @@ ) trace_pattern = re.compile( - r"trace-op\s+(?P[A-Z_0-9+]+):\s+thread\s+(?P\d+)\s+event\s+(?P[A-Z_0-9\-]+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" + r"trace-evt\s+(?P[A-Z_0-9\-]+):\s+thread\s+(?P\d+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" ) logger = logging.getLogger("ggml-hexagon-profile") @@ -50,9 +51,13 @@ def normalize_event_name(evt_type): class CycleUnwrapper: - def __init__(self): - self.last_raw = None - self.high_part = 0 + def __init__(self, initial_val=None): + if initial_val is not None: + self.last_raw = initial_val & 0xFFFFFFFF + self.high_part = initial_val & 0xFFFFFFFF00000000 + else: + self.last_raw = None + self.high_part = 0 def unwrap(self, raw): if self.last_raw is None: @@ -78,10 +83,12 @@ def parse_log(file_path, pmu_index=None): sys.exit(1) all_ops: List[Dict[str, Any]] = [] + all_traces: List[Dict[str, Any]] = [] current_op: Optional[Dict[str, Any]] = None timestamp_pattern = re.compile(r"^(?P\d+)\.(?P\d+)\.(?P\d+)\.(?P\d+)\s+[A-Z]\s+") - unwrapper = CycleUnwrapper() + unwrapper = None + trace_unwrapper = None for line in f: ts_match = timestamp_pattern.match(line) @@ -100,6 +107,7 @@ def parse_log(file_path, pmu_index=None): if not prefix_match: continue + names = parts[1] if len(parts) == 7: dims, types, timings = parts[2], parts[3], parts[6] elif len(parts) == 6: @@ -120,6 +128,7 @@ def parse_log(file_path, pmu_index=None): op_match = op_pattern.search(line) if op_match: op_name = op_match.group('op_name') + names = "" dims = op_match.group('dims').strip() types = op_match.group('types').strip() else: @@ -136,24 +145,31 @@ def parse_log(file_path, pmu_index=None): except (ValueError, IndexError): pmu_val = None - evt_raw = op_match.group('evt') if 'evt' in op_match.groupdict() else None evt_val = None - if evt_raw: + evt_val = None + if types.startswith("evt-cnt "): try: - evt_val = [int(x.strip()) for x in evt_raw.split(',')] + evt_val = [int(x.strip()) for x in types[8:].split(',')] except ValueError: evt_val = None cycles_start_raw = op_match.group('start') unwrapped_cycles_start = None - if cycles_start_raw: - unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) + if op_name == "OPBATCH": + if cycles_start_raw: + unwrapped_cycles_start = int(cycles_start_raw) + unwrapper = CycleUnwrapper(unwrapped_cycles_start) + trace_unwrapper = CycleUnwrapper(unwrapped_cycles_start) + else: + if cycles_start_raw and unwrapper is not None: + unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) idx = line.find("profile-op ") op_text = line[idx + 11:].strip() if idx != -1 else line.strip() current_op = { 'name': op_name, + 'names': names, 'dims': dims, 'types': types, 'op_text': op_text, @@ -170,110 +186,239 @@ def parse_log(file_path, pmu_index=None): continue trace_match = trace_pattern.search(line) - if trace_match and current_op: - if trace_match.group('op_name') == current_op['name']: - raw_cyc = int(trace_match.group('cycles')) - current_op['trace_events'].append({ - 'thread': int(trace_match.group('thread')), - 'event': trace_match.group('event'), - 'info': int(trace_match.group('info')), - 'cycles': raw_cyc, - 'unwrapped_cycles': unwrapper.unwrap(raw_cyc), - 'state': trace_match.group('state') - }) + if trace_match: + raw_cyc = int(trace_match.group('cycles')) + unwrapped_cyc = None + if trace_unwrapper is not None: + unwrapped_cyc = trace_unwrapper.unwrap(raw_cyc) + all_traces.append({ + 'thread': int(trace_match.group('thread')), + 'event': trace_match.group('event'), + 'info': int(trace_match.group('info')), + 'cycles': raw_cyc, + 'unwrapped_cycles': unwrapped_cyc, + 'state': trace_match.group('state') + }) f.close() + + # Assign start/end cycles to all ops + for op in all_ops: + op['start_cycles'] = op['unwrapped_cycles_start'] + op['end_cycles'] = op['start_cycles'] + op['cycles'] if op['start_cycles'] is not None else None + + # Filter ops with valid start_cycles + valid_ops = [op for op in all_ops if op['start_cycles'] is not None and op['end_cycles'] is not None] + + # Separate OPBATCH ops from other ops + opbatch_ops = [op for op in valid_ops if op['name'] == "OPBATCH"] + other_ops = [op for op in valid_ops if op['name'] != "OPBATCH"] + + # Sort them by start_cycles to enable binary search + opbatch_ops.sort(key=lambda op: op['start_cycles']) + other_ops.sort(key=lambda op: op['start_cycles']) + + opbatch_starts = [op['start_cycles'] for op in opbatch_ops] + other_starts = [op['start_cycles'] for op in other_ops] + + # Map trace events to any operator whose cycles contain them + for e in all_traces: + cyc = e['unwrapped_cycles'] + if cyc is None: + continue + + # Map to OPBATCH + idx = bisect.bisect_right(opbatch_starts, cyc) - 1 + if idx >= 0: + op = opbatch_ops[idx] + if op['start_cycles'] <= cyc <= op['end_cycles']: + op['trace_events'].append(e) + + # Map to other ops + idx = bisect.bisect_right(other_starts, cyc) - 1 + if idx >= 0: + op = other_ops[idx] + if op['start_cycles'] <= cyc <= op['end_cycles']: + op['trace_events'].append(e) + return all_ops -def print_ascii_timeline(op_name, dims, types, usec, cycles, events, evt_val=None): - evt_str = "" - if evt_val: - evt_str = " - evt [" + ",".join(str(x) for x in evt_val) + "]" +def print_bubbles_timeline(op): + op_name = op['name'] + dims = op['dims'] + types = op['types'] + usec = op['usec'] + cycles = op['cycles'] + events = op['trace_events'] logger.info("=" * 100) - logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles{evt_str}") + logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles") logger.info("=" * 100) - events = sorted(events, key=lambda e: e['cycles']) if not events: logger.info(" No trace events recorded.") return - min_cycles = events[0]['cycles'] - - logger.info("Cycles %-30s" % "EventDetails" + " ".join(f"T{i:<2}" for i in range(10)) + " HMX") - logger.info("-" * 100) + # Identify start and end cycles for this operator + op_start = op['start_cycles'] + op_end = op['end_cycles'] + if op_start is None or op_end is None: + logger.info(" Cannot analyze bubbles: missing start/end cycle counts.") + return - thread_stacks = [[] for _ in range(11)] + batch_duration = op_end - op_start + if batch_duration <= 0: + logger.info(" Cannot analyze bubbles: batch duration is 0.") + return + # Group events by (thread, track_type) + tracks = defaultdict(list) for e in events: t = e['thread'] - if t < 0 or t > 10: - continue + is_dma = (normalize_event_name(e['event']) == 'DMA') + track_type = 'dma' if is_dma else 'compute' + tracks[(t, track_type)].append(e) - if e['cycles'] >= min_cycles: - rel_cycles = e['cycles'] - min_cycles - else: - rel_cycles = (e['cycles'] + 0x100000000) - min_cycles + active_threads = sorted(list(set(t for (t, track_type) in tracks.keys()))) + if not active_threads: + logger.info(" No active threads in trace.") + return - state = e['state'] - evt_type = e['event'] - - # Determine char representing the event - norm_evt = normalize_event_name(evt_type) - char = '?' - if norm_evt == 'V-COMP': - char = 'V' - elif norm_evt == 'M-COMP': - char = 'H' - elif norm_evt == 'A-QUANT': - char = 'Q' - elif norm_evt == 'A-PREP': - char = 'A' - elif norm_evt == 'Q-PREP': - char = 'q' - elif norm_evt == 'K-PREP': - char = 'k' - elif norm_evt == 'V-PREP': - char = 'v' - elif norm_evt == 'W-DEQUANT': - char = 'D' - elif norm_evt == 'O-PROC': - char = 'O' - elif norm_evt == 'W-PREP': - char = 'P' - elif norm_evt == 'DMA': - char = 'M' + bubble_threshold = 10000 # 10k cycles - if state == 'start': - thread_stacks[t].append(char) - elif state == 'stop': - if thread_stacks[t]: - if thread_stacks[t][-1] == char: - thread_stacks[t].pop() - elif char in thread_stacks[t]: - thread_stacks[t].remove(char) - else: - thread_stacks[t].pop() + thread_stats = {} + for t in active_threads: + thread_stats[t] = { + 'compute_idle_cycles': batch_duration, + 'compute_idle_pct': 100.0, + 'compute_bubbles': [], - cols = [] - for i in range(11): - if thread_stacks[i]: - cols.append(f"[{thread_stacks[i][-1]}]") - else: - cols.append(" | ") + 'dma_idle_cycles': batch_duration, + 'dma_idle_pct': 100.0, + 'dma_bubbles': [] + } + + total_compute_idle_pct = 0.0 + total_dma_idle_pct = 0.0 + + for t in active_threads: + for track_type in ['compute', 'dma']: + key = (t, track_type) + track_events = tracks.get(key, []) - evt_desc = f"T{t}: {evt_type} {state} ({e['info']})" - logger.info(f"{rel_cycles:10d} %-30s" % evt_desc + " ".join(cols[:10]) + " " + cols[10]) + if not track_events: + gaps = [(op_start, op_end)] + idle_cycles = batch_duration + else: + track_events = sorted(track_events, key=lambda e: e.get('unwrapped_cycles') or e['cycles']) + + active_intervals = [] + active_count = 0 + curr_start = None + + for e in track_events: + cyc = e.get('unwrapped_cycles') or e['cycles'] + cyc = max(op_start, min(op_end, cyc)) + state = e['state'] + + if state == 'start': + if active_count == 0: + curr_start = cyc + active_count += 1 + elif state == 'stop': + if active_count > 0: + active_count -= 1 + if active_count == 0: + active_intervals.append((curr_start, cyc)) + else: + active_intervals.append((op_start, cyc)) + + if active_count > 0 and curr_start is not None: + active_intervals.append((curr_start, op_end)) + + # Merge intervals + active_intervals.sort(key=lambda x: x[0]) + merged_intervals = [] + for start, end in active_intervals: + if not merged_intervals: + merged_intervals.append([start, end]) + else: + last_start, last_end = merged_intervals[-1] + if start <= last_end: + merged_intervals[-1][1] = max(last_end, end) + else: + merged_intervals.append([start, end]) + + # Calculate gaps + gaps = [] + curr_time = op_start + for start, end in merged_intervals: + if start > curr_time: + gaps.append((curr_time, start)) + curr_time = max(curr_time, end) + if curr_time < op_end: + gaps.append((curr_time, op_end)) + + idle_cycles = sum(end - start for start, end in gaps) + + idle_pct = (idle_cycles / batch_duration) * 100.0 + + bubbles = [] + for start, end in gaps: + dur = end - start + if dur >= bubble_threshold: + bubbles.append((start, end, dur)) + + if track_type == 'compute': + thread_stats[t]['compute_idle_cycles'] = idle_cycles + thread_stats[t]['compute_idle_pct'] = idle_pct + thread_stats[t]['compute_bubbles'] = bubbles + total_compute_idle_pct += idle_pct + else: + thread_stats[t]['dma_idle_cycles'] = idle_cycles + thread_stats[t]['dma_idle_pct'] = idle_pct + thread_stats[t]['dma_bubbles'] = bubbles + total_dma_idle_pct += idle_pct + + avg_compute_idle = total_compute_idle_pct / len(active_threads) + avg_dma_idle = total_dma_idle_pct / len(active_threads) + + logger.info(" Combined Idle Statistics:") + logger.info(f" Active Threads : {', '.join(str(t) for t in active_threads)}") + logger.info(f" Avg Thread Compute IDLE : {avg_compute_idle:.1f}%") + logger.info(f" Avg Thread DMA IDLE : {avg_dma_idle:.1f}%") logger.info("-" * 100) + logger.info(" Per-Thread Idle Analysis:") + for t in active_threads: + stats = thread_stats[t] + thread_name = f"Thread {t:<2} (HVX)" if t != 10 else "Thread 10 (HMX)" + logger.info(f" {thread_name} -> Compute Idle: {stats['compute_idle_pct']:.1f}% | DMA Idle: {stats['dma_idle_pct']:.1f}%") + + all_bubbles = [] + for t in active_threads: + stats = thread_stats[t] + for start, end, dur in stats['compute_bubbles']: + pct = (dur / batch_duration) * 100.0 + all_bubbles.append((dur, f"Thread {t} Compute: bubble of {dur} cycles ({pct:.1f}%) at {start - op_start} to {end - op_start}")) + for start, end, dur in stats['dma_bubbles']: + pct = (dur / batch_duration) * 100.0 + all_bubbles.append((dur, f"Thread {t} DMA : bubble of {dur} cycles ({pct:.1f}%) at {start - op_start} to {end - op_start}")) + + if all_bubbles: + logger.info("-" * 100) + logger.info(f" Significant Bubbles (>= {bubble_threshold} cycles):") + all_bubbles.sort(key=lambda x: x[0], reverse=True) + for dur, desc in all_bubbles[:15]: + logger.info(f" {desc}") + else: + logger.info("-" * 100) + logger.info(f" No significant bubbles detected (all idle gaps < {bubble_threshold} cycles).") + -def print_ascii_summary(op_name, dims, types, usec, cycles, events, evt_val=None): - evt_str = "" - if evt_val: - evt_str = " - evt [" + ",".join(str(x) for x in evt_val) + "]" +def print_ascii_summary(op_name, dims, types, usec, cycles, events): logger.info("=" * 100) - logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles{evt_str}") + logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles") logger.info("=" * 100) events = sorted(events, key=lambda e: e['cycles']) @@ -415,8 +560,8 @@ def main(): parser.add_argument("--pmu-index", type=int) parser.add_argument("--pmu-name", type=str) parser.add_argument("--width", action='append', default=['dims:40'], help="Override column width, e.g. --width dims:50") - parser.add_argument("--timeline", type=str, nargs='?', const='summary', choices=["summary", "diagram"], - help="Output ASCII art event summary or timing diagram (default: summary)") + parser.add_argument("--timeline", type=str, nargs='?', const='summary', choices=["summary", "bubbles"], + help="Output ASCII art event summary or thread idle bubble analysis (default: summary)") parser.add_argument("--filter", type=str, help="Regex filter matching against the original profile-op line") group = parser.add_mutually_exclusive_group() @@ -457,16 +602,11 @@ def main(): ops = ops[-args.tail:] if args.timeline: - logger.info(f"\n# ASCII Timing {args.timeline.capitalize()}\n") - printed_cnt = 0 for op in ops: if args.timeline == "summary": - print_ascii_summary(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events'], op.get('evt_val')) - elif args.timeline == "diagram": - print_ascii_timeline(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events'], op.get('evt_val')) - printed_cnt += 1 - if printed_cnt >= args.top: - break + print_ascii_summary(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events']) + elif args.timeline == "bubbles": + print_bubbles_timeline(op) else: generate_report(ops, args.top, overrides, args.sort, pmu_name=final_pmu_name) diff --git a/scripts/snapdragon/ggml-hexagon-trace.py b/scripts/snapdragon/ggml-hexagon-trace.py index 37f137a9e758..4755adfa1339 100755 --- a/scripts/snapdragon/ggml-hexagon-trace.py +++ b/scripts/snapdragon/ggml-hexagon-trace.py @@ -6,6 +6,7 @@ import argparse import statistics import logging +import bisect from typing import Any, Dict, List, Optional from collections import defaultdict @@ -16,11 +17,11 @@ ) trace_pattern = re.compile( - r"trace-op\s+(?P[A-Z_0-9+]+):\s+thread\s+(?P\d+)\s+event\s+(?P[A-Z_0-9\-]+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" + r"trace-evt\s+(?P[A-Z_0-9\-]+):\s+thread\s+(?P\d+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" ) -def normalize_event_name(evt_type): +def normalize_event_name(evt_type, info=0): if evt_type == "HVX_COMP": return "V-COMP" if evt_type == "HMX_COMP": @@ -32,9 +33,13 @@ def normalize_event_name(evt_type): class CycleUnwrapper: - def __init__(self): - self.last_raw = None - self.high_part = 0 + def __init__(self, initial_val=None): + if initial_val is not None: + self.last_raw = initial_val & 0xFFFFFFFF + self.high_part = initial_val & 0xFFFFFFFF00000000 + else: + self.last_raw = None + self.high_part = 0 def unwrap(self, raw): if self.last_raw is None: @@ -60,8 +65,10 @@ def parse_log(file_path): sys.exit(1) all_ops: List[Dict[str, Any]] = [] + all_traces: List[Dict[str, Any]] = [] current_op: Optional[Dict[str, Any]] = None - unwrapper = CycleUnwrapper() + unwrapper = None + trace_unwrapper = None line_idx = 0 for line in f: @@ -73,6 +80,7 @@ def parse_log(file_path): if not prefix_match: continue + names = parts[1] if len(parts) == 7: dims, types, strides, params, timings = parts[2], parts[3], parts[4], parts[5], parts[6] elif len(parts) == 6: @@ -93,6 +101,7 @@ def parse_log(file_path): op_match = op_pattern.search(line) if op_match: op_name = op_match.group('op_name') + names = "" dims = op_match.group('dims').strip() if op_match.group('dims') else '' types = op_match.group('types').strip() if op_match.group('types') else '' strides = op_match.group('strides').strip() if op_match.group('strides') else '' @@ -103,18 +112,30 @@ def parse_log(file_path): if op_match: cycles_start_raw = op_match.group('start') unwrapped_cycles_start = None - if cycles_start_raw: - unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) + if op_name == "OPBATCH": + if cycles_start_raw: + unwrapped_cycles_start = int(cycles_start_raw) + unwrapper = CycleUnwrapper(unwrapped_cycles_start) + trace_unwrapper = CycleUnwrapper(unwrapped_cycles_start) + else: + if cycles_start_raw and unwrapper is not None: + unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) idx = line.find("profile-op ") op_text = line[idx + 11:].strip() if idx != -1 else line.strip() + evt_str = None + if types.startswith("evt-cnt "): + evt_str = types[8:].strip() + current_op = { 'name': op_name, + 'names': names, 'dims': dims, 'types': types, 'strides': strides, 'params': params, + 'evt': evt_str, 'op_text': op_text, 'usec': int(op_match.group('usec')), 'cycles': int(op_match.group('cycles')), @@ -127,20 +148,22 @@ def parse_log(file_path): continue trace_match = trace_pattern.search(line) - if trace_match and current_op: - if trace_match.group('op_name') == current_op['name']: - raw_cyc = int(trace_match.group('cycles')) - current_op['trace_events'].append({ - 'thread': int(trace_match.group('thread')), - 'event': trace_match.group('event'), - 'info': int(trace_match.group('info')), - 'cycles': raw_cyc, - 'unwrapped_cycles': unwrapper.unwrap(raw_cyc), - 'state': trace_match.group('state') - }) + if trace_match: + raw_cyc = int(trace_match.group('cycles')) + unwrapped_cyc = None + if trace_unwrapper is not None: + unwrapped_cyc = trace_unwrapper.unwrap(raw_cyc) + all_traces.append({ + 'thread': int(trace_match.group('thread')), + 'event': trace_match.group('event'), + 'info': int(trace_match.group('info')), + 'cycles': raw_cyc, + 'unwrapped_cycles': unwrapped_cyc, + 'state': trace_match.group('state') + }) f.close() - return all_ops + return all_ops, all_traces # --- Simple protobuf encoder --- @@ -246,7 +269,7 @@ def write_trace_packet_to_file(f, packet_bytes): # --- End Protobuf Encoder --- -def generate_perfetto_trace(filtered_ops, output_path): +def generate_perfetto_trace(filtered_ops, trace_events, output_path): if not filtered_ops: logger.warning("No operators found after filtering.") return @@ -269,14 +292,12 @@ def generate_perfetto_trace(filtered_ops, output_path): # Process events completed_events = [] - for op in filtered_ops: - events = op['trace_events'] - if not events: - continue - events = sorted(events, key=lambda e: e['unwrapped_cycles']) + if trace_events: + trace_events = sorted(trace_events, key=lambda e: e['unwrapped_cycles']) + one_usec_cycles = max(avg_freq_mhz, 1.0) active_starts = {} - for e in events: + for e in trace_events: t = e['thread'] evt = e['event'] info = e['info'] @@ -285,6 +306,17 @@ def generate_perfetto_trace(filtered_ops, output_path): key = (t, evt, info) if state == 'start': + # Handle missing stop (start followed by another start) + if key in active_starts: + prev_start = active_starts[key] + completed_events.append({ + 'thread': t, + 'event': evt, + 'info': info, + 'start_cyc': prev_start, + 'end_cyc': prev_start + one_usec_cycles, + 'missing_stop': True, + }) active_starts[key] = cyc elif state == 'stop': if key in active_starts: @@ -296,9 +328,30 @@ def generate_perfetto_trace(filtered_ops, output_path): 'info': info, 'start_cyc': start_cyc, 'end_cyc': cyc, - 'op_name': op['name'] + }) + else: + # Handle missing start (stop without start) + completed_events.append({ + 'thread': t, + 'event': evt, + 'info': info, + 'start_cyc': cyc - one_usec_cycles, + 'end_cyc': cyc, + 'missing_start': True, }) + # Clear remaining unmatched starts + for key, start_cyc in active_starts.items(): + t, evt, info = key + completed_events.append({ + 'thread': t, + 'event': evt, + 'info': info, + 'start_cyc': start_cyc, + 'end_cyc': start_cyc + one_usec_cycles, + 'missing_stop': True, + }) + completed_events.sort(key=lambda e: e['start_cyc']) # Convert event times to microseconds and apply clamp rounded to 1ns resolution (3 decimals) @@ -316,7 +369,7 @@ def generate_perfetto_trace(filtered_ops, output_path): ts = e['ts_ns'] dur = e['dur_ns'] - norm_evt = normalize_event_name(evt) + norm_evt = normalize_event_name(evt, e['info']) if norm_evt == "DMA": track_key = (t, "DMA") elif t == 10: @@ -343,7 +396,7 @@ def generate_perfetto_trace(filtered_ops, output_path): evt = e['event'] slot = e['slot'] - norm_evt = normalize_event_name(evt) + norm_evt = normalize_event_name(evt, e['info']) if norm_evt == "DMA": track_evt = "DMA" evt_id = 1 @@ -421,18 +474,26 @@ def generate_perfetto_trace(filtered_ops, output_path): for op in filtered_ops: op_start_ns = int(round(((op['start_cycles'] - global_min_cyc) / avg_freq_mhz) * 1000)) op_dur_ns = int(round((op['cycles'] / avg_freq_mhz) * 1000)) - if op_start_ns < last_op_end_ns: - op_start_ns = last_op_end_ns - clamped_dur = max(op_dur_ns, 100) # Clamp to 100ns (0.1us) + if op['name'] != "OPBATCH": + if op_start_ns < last_op_end_ns: + op_start_ns = last_op_end_ns + clamped_dur = max(op_dur_ns, 100) # Clamp to 100ns (0.1us) + last_op_end_ns = op_start_ns + clamped_dur + else: + clamped_dur = max(op_dur_ns, 100) # Debug annotations for Ops debug_annots = [] if 'line_num' in op: debug_annots.append(make_debug_annotation("line", int_val=op['line_num'])) - if 'strides' in op and op['strides']: + if 'names' in op and op['names'] and op['names'] != '----': + debug_annots.append(make_debug_annotation("names", string_val=op['names'])) + if 'strides' in op and op['strides'] and op['strides'] != '----': debug_annots.append(make_debug_annotation("strides", string_val=op['strides'])) if 'params' in op and op['params'] and op['params'] != '----': debug_annots.append(make_debug_annotation("params", string_val=op['params'])) + if 'evt' in op and op['evt']: + debug_annots.append(make_debug_annotation("evt", string_val=op['evt'])) # Slice Begin evt_begin = make_track_event(1, 2, name=f"{op['name']} ({op['dims']})", category="operator", debug_annotations=debug_annots) @@ -444,15 +505,21 @@ def generate_perfetto_trace(filtered_ops, output_path): packet_end = make_trace_packet(op_start_ns + clamped_dur, track_event=evt_end) write_trace_packet_to_file(f, packet_end) - last_op_end_ns = op_start_ns + clamped_dur - # Emit Thread Trace Events for e in completed_events: - norm_name = normalize_event_name(e['event']) + norm_name = normalize_event_name(e['event'], e['info']) name = f"DMA {e['info']}" if norm_name == "DMA" else norm_name + if e.get('missing_start') or e.get('missing_stop'): + name += "!" + + debug_annots = [] + if e.get('missing_start'): + debug_annots.append(make_debug_annotation("missing_start", string_val="true")) + if e.get('missing_stop'): + debug_annots.append(make_debug_annotation("missing_stop", string_val="true")) # Slice Begin - evt_begin = make_track_event(1, e['uuid'], name=name, category="trace") + evt_begin = make_track_event(1, e['uuid'], name=name, category="trace", debug_annotations=debug_annots if debug_annots else None) packet_begin = make_trace_packet(e['ts_ns'], track_event=evt_begin) write_trace_packet_to_file(f, packet_begin) @@ -477,7 +544,7 @@ def main(): args = parser.parse_args() logging.basicConfig(level=logging.INFO, format='%(message)s') - ops = parse_log(args.logfile) + ops, traces = parse_log(args.logfile) if args.filter: try: @@ -492,7 +559,30 @@ def main(): elif args.tail is not None: ops = ops[-args.tail:] - generate_perfetto_trace(ops, args.output) + if args.filter or args.head is not None or args.tail is not None: + valid_ranges = [] + for op in ops: + start_cyc = op['unwrapped_cycles_start'] + end_cyc = start_cyc + op['cycles'] if start_cyc is not None else None + if start_cyc is not None and end_cyc is not None: + valid_ranges.append((start_cyc, end_cyc)) + + valid_ranges.sort(key=lambda r: r[0]) + range_starts = [r[0] for r in valid_ranges] + + filtered_traces = [] + for e in traces: + cyc = e['unwrapped_cycles'] + if cyc is None: + continue + idx = bisect.bisect_right(range_starts, cyc) - 1 + if idx >= 0: + start, end = valid_ranges[idx] + if start <= cyc <= end: + filtered_traces.append(e) + traces = filtered_traces + + generate_perfetto_trace(ops, traces, args.output) if __name__ == "__main__": diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index e9f6ed2e5c1c..24e27e6f026e 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -eaa0a74fa768bb72da623a61d9da3d436053ea91 +9be313313c8ecb9488911bd64550190e3ed80f38 diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py index 7bab415a8311..679e557a3215 100755 --- a/scripts/sync_vendor.py +++ b/scripts/sync_vendor.py @@ -5,7 +5,7 @@ import sys import subprocess -HTTPLIB_VERSION = "refs/tags/v0.50.1" +HTTPLIB_VERSION = "refs/tags/v0.51.0" vendor = { "https://github.com/nlohmann/json/releases/latest/download/json.hpp": "vendor/nlohmann/json.hpp", @@ -21,7 +21,7 @@ f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/split.py": "split.py", f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/LICENSE": "vendor/cpp-httplib/LICENSE", - "https://raw.githubusercontent.com/sheredom/subprocess.h/b49c56e9fe214488493021017bf3954b91c7c1f5/subprocess.h": "vendor/sheredom/subprocess.h", + "https://raw.githubusercontent.com/sheredom/subprocess.h/8671cee1fc09f11a70ce3782a0ee13177c3aa387/subprocess.h": "vendor/sheredom/subprocess.h", } for url, filename in vendor.items(): diff --git a/skills/add-new-model/SKILL.md b/skills/add-new-model/SKILL.md new file mode 100644 index 000000000000..f76d1abfd768 --- /dev/null +++ b/skills/add-new-model/SKILL.md @@ -0,0 +1,99 @@ +--- +name: add-new-model +description: Guided workflow for adding a new model architecture to llama.cpp. Use when the user wants to add/port a new model architecture. +--- + +# Add a new model architecture to llama.cpp + +This skill walks a contributor through adding a new model architecture. AI-generated code is permitted in this project, so you may write full implementations for the steps below rather than only pointing at patterns - but follow `AGENTS.md`'s AI usage policy throughout: + +- The contributor is 100% responsible for every line, however it was produced. They must be able to explain and defend any part of it to a reviewer. Check in with them as you go (don't silently generate everything and hand over a finished diff) so they actually absorb what was written. +- Before writing code, make sure the contributor owns the design choices for this architecture (which reference model to follow, how non-standard bits like RoPE variants or MoE routing should be handled) - AI accelerates a design the contributor has already made, it doesn't make the design for them. +- Disclosure is mandatory: any AI-meaningful contribution must be disclosed per the PR template. Remind the contributor of this before they open the PR. +- Never write the PR description, commit message, GitHub issue/discussion post, or reviewer replies - those must come from the contributor. If asked to commit on their behalf, use `Assisted-by:` (never `Co-authored-by:`) and only after explicit confirmation. +- If the requested change looks large or introduces a new pattern not covered here, pause and tell the user this kind of change is likely to need prior discussion with maintainers before a PR. +- Keep the PR self-contained. If the work would require a lot of unconventional changes outside the new model file(s) (e.g. touching shared graph-building code, the sampler, or core APIs in ways other models don't), STOP and tell the contributor to open a discussion/issue first - invasive or excessive changes get closed without full review. +- Do not bundle unrelated work into this PR - see Step 4 and Step 5 below for the specifics on multimodal and chat-template/parsing work. +- Never hack around RoPE with a custom sin/cos implementation. Several past PRs tried this and were closed. If the existing `ggml_rope_ext` (see Step 2's RoPE tips) genuinely cannot express what this model needs, the contributor should open an issue to discuss it with maintainers first - not send a PR with a custom RoPE implementation. + +Before starting, read `CONTRIBUTING.md`, `AGENTS.md` and `docs/development/HOWTO-add-model.md` if they are not already in context. Also run `git log --oneline -- src/models` and look at at least 3 recent PRs that added a model (their merge commits/diffs) - this shows current convention more reliably than the docs, which can lag behind. + +## Step 0 - Scope and dedup check + +Ask the contributor: +1. Which model (HF repo id or name)? Is it text-only or does it have a multimodal (vision/audio) encoder? +2. Do they already have the HF `config.json`/weights available locally? +3. Have they checked for an existing PR/issue on this model? Suggest `gh search issues ""` and `gh search prs ""` in the `ggml-org/llama.cpp` repo. If an existing PR covers it, the contributor should comment there and collaborate rather than open a duplicate (per CONTRIBUTING.md's AI Usage Policy). +4. What existing supported architecture is this model closest to (e.g. "Llama-like with sliding window", "MoE like DBRX", "BERT-style encoder")? + +If the contributor doesn't know the closest reference architecture, you may grep `conversion/*.py` and `src/models/*.cpp` for architectures with a similar config shape (layer count, head count, MoE expert count, norm placement) and suggest 1-2 candidates - but let the contributor confirm the choice rather than picking one yourself; this choice is a design decision they need to own. + +Do not proceed to Step 1 until the contributor has answered these and named a reference architecture. + +## Step 1 - Convert the model to GGUF + +Follow HOWTO-add-model.md section 1 for the actual touch points (conversion class registration, `constants.py`, `tensor_mapping.py`, etc.) - don't re-derive them here, read them from the doc. + +Skill-specific addition: for each touch point, show the contributor the equivalent code in the reference architecture they named in Step 0 before writing the new version, and check that they understand what's different about their model (e.g. non-standard tensor shapes, extra hparams) rather than just copying the pattern silently. + +## Step 2 - Define the architecture in llama.cpp + +Follow HOWTO-add-model.md section 2 for the actual touch points (`llm_arch` enum, `LLM_ARCH_NAMES`, hparam loading, RoPE type case, etc.), including its "Tips and tricks" section for `ggml_rope_ext` gotchas. + +Skill-specific addition: never hack around RoPE with a custom sin/cos implementation - see the RoPE rule above. + +## Step 3 - Build the GGML graph + +Follow HOWTO-add-model.md section 3 for the actual touch points (`src/models/.cpp` struct, `llama_model_mapping` registration, etc.). + +Skill-specific addition: before writing `src/models/.cpp`, read at least 10 other files under `src/models/` (pick a mix, not just the one reference architecture) to confirm the struct layout, naming, and style you're about to write actually matches current convention - the pattern drifts over time and the HOWTO doc can lag behind it. + +## Step 4 - Optional: multimodal encoder + +Only do this if the contributor flagged a vision/audio encoder in Step 0. Follow HOWTO-add-model.md section 4 and `docs/multimodal.md` for the actual touch points (`MmprojModel` subclass, `clip.cpp`, `mtmd.cpp`, encoder graph in `tools/mtmd/models`, etc.). + +Skill-specific addition, and read this carefully: **whether the multimodal encoder can be bundled into the same PR as the base text-model support depends on how conventional the change is.** It's OK to bundle it if the encoder support is conventional - i.e. no new infra or logic is needed, it's just a new cgraph reusing existing preprocessing/projector machinery (e.g. siglip/pixtral/qwen with just a new projector). If it requires anything beyond that - a new preprocessor, non-standard projector logic, or changes to shared `libmtmd` infra/logic - STOP, tell the contributor this is non-conventional, and have them land the text model first with the encoder as a dedicated follow-up PR. Do not let this decision pass silently - call it out explicitly to the contributor before writing any `clip.cpp`/`mtmd.cpp` code. + +## Step 5 - Optional: chat template / parsing support + +Only do this if the model needs a new built-in chat template (`src/llama-chat.cpp`) or a new output parser (see `docs/development/parsing.md` and `docs/autoparser.md`). If either is needed beyond what a user-supplied Jinja template already covers, treat it as its own dedicated follow-up PR, not part of the base model-support PR - call this out explicitly to the contributor rather than silently bundling it in. + +## Common pitfalls (from past PR reviews) + +These recur often enough in review comments on past add-model PRs that they're worth checking proactively, not just waiting for a reviewer to catch them: + +- Don't validate the same hparam/config assumption in both the Python conversion script and the C++ load path - pick one layer to own the check, duplicating it just adds maintenance surface. +- Optional hparams that are genuinely absent from some configs (e.g. a shared-expert count) should be read with an explicit optional/fallback accessor, not assumed present. +- Hparams that are actually load-bearing (the model produces wrong output or crashes without them, e.g. `sliding_window_pattern`, norm-eps) must hard-error if missing, not silently fall back to a default. +- Don't bake a default chat template into the C++ binary - inject it into the GGUF at conversion time instead, since one `llm_arch` can be reused by multiple fine-tunes with different templates, and a baked-in C++ default fails silently for those. +- Before writing a dedicated tool-call/output parser, check whether the existing autoparser already handles the template (`llama-debug-template-parser ` shows what it detects). +- Marking a custom EOS/closing-tag token as `eot` at conversion time isn't always sufficient - in long/agentic generations a model can emit the closing sequence as literal text instead of the token, so generation never stops on EOG and raw text leaks past the parser. Verify this case, not just the token path. +- If reusing or aliasing an existing pre-tokenizer for convenience, justify and test that choice explicitly - silent reuse is an easy source of subtle tokenizer bugs. +- Watch for excessive graph splits caused by building per-layer view/index tensors inside the layer loop - hoist tensors that don't vary per layer out of the loop (relevant if you hit `GGML_SCHED_MAX_SPLIT_INPUTS`). +- A custom KQ mask fed into flash attention must match FA's expected dtype - cast it to F16 before passing it to `build_attn_mha` when FA is enabled. +- When padding a custom KV-cache size to an alignment (e.g. `GGML_PAD(..., 256)`), apply the padding after all other size adjustments, not before - otherwise later logic can un-align it again. +- For non-standard cache/SWA (sliding-window-attention) semantics, override the dedicated hook (e.g. `llama_model_n_swa()`) rather than mutating hparams to fake the behavior - hparams may be read elsewhere for unrelated purposes. +- Don't ship unfinished or unverified speculative-decoding (e.g. MTP) scaffolding in the base model PR - if it hasn't actually been confirmed to work, pull it out and land it as its own follow-up. +- Conversion code should call into the base class's existing hparam logic (e.g. `super().set_gguf_parameters()`) rather than re-deriving it - large blocks of code that duplicate what `TextModel`/`MmprojModel` already provide will get flagged as redundant. +- Do constant tensor modifications (e.g. `norm(1 + weight)`) and permutations/chunking at conversion time, not in the graph - see HOWTO-add-model.md's "Prefer conversion-time tensor modifications" tip (Gemma 3 folds its `1 +` into the weights, Qwen3-Next permutes in `modify_tensors`). Doing these at runtime in the graph is very likely to be rejected as over-complicated; if you genuinely can't do it at conversion time, open a discussion first explaining why rather than implementing it in the graph. + - Exception: a plain `weight * scale` with a constant scale is usually better applied at inference time instead of being folded into the weight at conversion. The scale conceptually applies to the activation, not the weight, so folding it in can hurt numerical stability, and it shifts the weight's value range in a way that can make quantization worse. + +## Validation checklist + +Reference: `examples/model-conversion/README.md`. + +1. Convert to GGUF, then inspect/run both the original and converted tensors. +2. Run logits verification (original vs converted). If this model is a new version of an already-supported family, verify the *previous* version still passes logits verification first - numerical differences may be pre-existing, not caused by the new work. The tools to perform full logits validation are available in `examples/model-conversion`. +3. Quantize (including QAT variants if relevant) and re-verify. +4. Run perplexity evaluation (simple and full). +5. Sanity-check across `tools/cli`, `tools/completion`, `tools/imatrix`, `tools/quantize`, and `tools/server`. +6. CPU backend first; other backends (CUDA, Metal, ...) can be separate follow-up PRs per `CONTRIBUTING.md`. +7. Re-review every changed file against the coding/naming guidelines in `AGENTS.md` (and `CONTRIBUTING.md`'s "Coding guidelines"/"Naming guidelines" sections) - this is a separate pass from functional testing and is just as important: no forced line-wrapping, no unicode punctuation, minimal/non-redundant comments, `snake_case` naming (`kebab-case` for file names), matching indentation/brace style, etc. + +## Before opening a PR + +- Run the `code-review` skill on the diff first - it catches the convention and scope issues reviewers flag most often, and it's recommended to do this locally before pushing the PR. +- Confirm the contributor can explain every changed line to a reviewer and is prepared to be asked about any of it - this is required regardless of how much of the code was AI-generated. +- Confirm they did a comprehensive manual review of the full diff, not just a skim. +- Fill in the AI-disclosure section of `.github/pull_request_template.md` describing how AI was used (do not omit or understate this). +- Do not write the PR description, commit message, GitHub issue/discussion text, or any reviewer replies yourself - the contributor writes these. diff --git a/skills/code-review/SKILL.md b/skills/code-review/SKILL.md new file mode 100644 index 000000000000..ba76c481150e --- /dev/null +++ b/skills/code-review/SKILL.md @@ -0,0 +1,143 @@ +--- +name: code-review +description: Review llama.cpp changes against project conventions and common reviewer pitfalls before a PR. Use when the user wants to review a diff, branch, or PR. +--- + +# Review llama.cpp changes + +This skill reviews changes against llama.cpp's conventions and the pitfalls that reviewers flag most often, so the contributor can fix them before a maintainer has to. It has two modes: + +- **Self-review (default):** review the contributor's own local changes (uncommitted work, or a branch vs `master`) as a pre-PR pass. Ask which if it's ambiguous; default to `git diff master...HEAD` plus any uncommitted changes. +- **Read-only review of a PR/file:** if the user points at a PR number or specific files (including code they didn't write), review those and report findings. + +In both modes the output is **private review notes for the user to read and act on** - it is never something to post. This is a hard rule from `AGENTS.md`: an agent must NEVER write, or help write, a PR comment, a review comment, or a reply to a reviewer, by any means including `gh`. Do not offer to. If the user asks you to post the notes, refuse and point them at that rule. Present findings in the conversation only. + +Before starting, read `AGENTS.md` and `CONTRIBUTING.md` if not already in context - the "Coding guidelines", "Naming guidelines", and AI usage sections are the baseline this review enforces. For a diff that adds a new model architecture, also read `docs/development/HOWTO-add-model.md` and consider the dedicated `add-new-model` skill. + +## Step 0 - Scope the diff and pick the checklists + +Identify what actually changed and which area checklists below apply. Run `git diff --stat` (or `gh pr view --json files` for PR mode) and bucket the touched paths: + +- `conversion/`, `gguf-py/`, `src/models/`, `src/llama-arch.*` -> **New model / architecture** +- `ggml/` (any backend, op, or `ggml.h`) -> **ggml / backend** +- `include/llama.h` and other public headers -> **Public API** +- `tools/server/` -> **Server** +- anything else, plus all of the above -> **General** (always runs) + +Always run the **Scope and quick-reject gate**, the **Security review**, and the **General** checklist. Run each area checklist whose paths were touched. Additionally, if the diff introduces a new component, subsystem, or piece of infrastructure (a new file/class/module, a new abstraction, or hand-rolled machinery), run the **Approach and design** review. Tell the user which checklists you're running and why. + +## Scope and quick-reject gate (always) + +These are the patterns that get PRs closed without a full review. Check them first - a finding here is more important than any code nit, because it can mean the change shouldn't be a PR in its current form at all. + +- Is there a prior issue/discussion for this? Features are supposed to start as an issue, not a PR (`CONTRIBUTING.md`). If this is a nontrivial feature with no linked issue, flag it and suggest opening one first. +- Is it a duplicate of existing/in-flight work? Suggest `gh search prs` / `gh search issues` for the feature. Many closed PRs were duplicates of something already queued. +- Is it self-contained and single-purpose? Multiple unrelated changes/optimizations bundled together get sent back to be split. Flag unrelated changes and suggest separate PRs. +- Does it touch multiple ggml backends at once? Initial support should be CPU-only, other backends as follow-ups (`CONTRIBUTING.md`). Flag CUDA/Metal/Vulkan/etc. changes bundled into a feature's first PR. +- Does it add a new `ggml_type` / quantization type? That carries a disproportionate maintenance burden and needs the full justification package (GGUF sample upload, perplexity vs FP16/BF16 and similar sizes, KL-divergence data, CPU perf numbers). Absent that, it will be rejected regardless of code quality. +- Is it invasive - new subsystem, core-API reshaping, changes to shared graph/sampler code that other models don't need? Flag it and suggest a discussion with maintainers before investing further. +- Is it niche/vendor-specific in a way that adds a maintenance burden nobody will own long-term? Flag the maintenance-ownership question. +- Is the change semantically correct, or a plausible-looking "fix" that misunderstands the code? Sanity-check the actual behavior, not just that it compiles. +- AI-disclosure: if AI meaningfully contributed, is the PR template's disclosure section filled in? Remind the user. Never suggest writing the PR description or commit message for them. + +## Security review (mandatory) + +Mandatory on every review; any finding here is **blocking**. Rule of thumb: GGUF metadata, tensor shapes, tokenizer/grammar input, and all server/RPC fields are attacker-controlled - bound them before use. + +- **Sizes/counts from tensor dims:** validate before allocating. Products like `ne[i]*nb[i]`/nbytes can overflow on crafted dims into an undersized alloc then heap overflow. Overflow checks must run BEFORE the arithmetic they guard - padding/alignment macros wrap to 0 near `SIZE_MAX`, so a guard after the pad passes. +- **GGUF strings/arrays:** cap declared lengths and element counts before using them to size a loop or buffer; validate element type and length before casting an array to a pointer or reading fixed indices (`[i+1]`, `[0..2]`). +- **File-supplied counts indexing fixed arrays:** bound any count (e.g. layer/block count into a `LLAMA_MAX_*` array) before indexing; watch checks that only fire when an optional key is present. +- **Bounds comparisons:** flag narrowing casts (`size_t`->`int32_t`) and signed/unsigned mixing that can bypass a length check and copy past a buffer. +- **Parsed/derived indices:** range-check `stoi`/`atoi` results and catch parse throws; never use a default or derived token id (EOS/BOS/...) as an index without a bounds check. +- **Reused/reserved buffers:** recheck bounds after a buffer is shrunk or reused; watch `reserve()` then index-by-assumed-size, and header fields read before their length is checked. +- **Server JSON ints:** clamp client-supplied integers (token/discard counts, offsets) to non-negative and an upper bound before they reach index/pointer arithmetic. +- **RPC-deserialized fields:** treat every field (type/buffer/data/ne/nb/op_params) as hostile - validate before use. Null/zero buffers skipping validation, attacker data pointers, out-of-range type indices, and negative strides sign-extending past a corner-only assert all give arbitrary read/write. +- **Lifetime/UAF:** flag stored raw pointers to caller/temporary storage, cached pointers to buffers a later free releases, async ops whose source may drop before completion, and structures not invalidated on free/realloc. Null-check conditionally-built or "not required" tensors before dereferencing. + +## Approach and design (when a new component/infra is introduced) + +Run this whenever the diff adds a new component, subsystem, or piece of infrastructure. Reviews too often stop at "does it work" - a diff can be correct and still be the wrong approach, and a messy design costs more long-term than a bug. Evaluate the *approach*, not just the behavior; raising a cleaner one is a high-value finding, not a nit. If you see a better design, describe it concretely rather than just calling the current one bad. + +- **Simpler approach upstream:** the biggest win is often a different data model or design that removes whole subsystems, not tweaks to the code as written. Complexity must be justified by the problem, not by the first thing that worked. +- **Reuse over reinvention:** grep for an existing helper, library, object, or mechanism before adding a new one. Reimplementing what the codebase already has reintroduces solved bugs and adds maintenance surface. +- **Clear ownership/lifetime:** prefer RAII and obvious ownership over manual liveness flags, hand-tracked pointers, and "is it still alive?" checks - manual lifetime tracking is a recurring source of subtle bugs. +- **Right-sized machinery:** flag redundant, overkill, or heavier-than-needed primitives and abstractions; use the minimum the design actually needs. +- **Right structure and fit:** a new type should earn its place (split it if it serves two roles); follow existing patterns, idioms, and naming, and avoid constructs the project shuns. +- **Root cause vs symptom:** fixes layered on fixes signal a design to correct, not guard around. + +## New model / architecture + +See the `add-new-model` skill and `docs/development/HOWTO-add-model.md` for the full workflow; this is the review-time subset that reviewers most often catch: + +- Don't branch on `model.arch` when the real dependency is a config/capability value - gate on the hparam/capability, not the architecture enum. +- If the model is a close variant of an existing arch, is the delta justified? Prefer reusing or subclassing the existing arch/model class over duplicating it. A near-duplicate class or `src/models/.cpp` will be asked to merge with its sibling. +- New tensor names go through `tensor_mapping.py`, not ad-hoc name matching. +- For QKV, split the *activation* with `ggml_view`, not the *weight* tensor; rely on ggml broadcasting instead of manually duplicating tensors. +- New graph inputs are declared at the top of the graph-build function, not inline where first used. +- Hparams that the model can't run correctly without must be mandatory (hard-error if missing), not read with a silent default fallback. Only genuinely-optional-across-configs values get a fallback accessor. +- New/optional weight tensors (scales, etc.) must route through `build_lora_mm` and the existing helpers, matching convention - don't leave raw matmuls copied from another arch. +- Don't hack RoPE with a custom sin/cos implementation. If `ggml_rope_ext` genuinely can't express it, that's an issue for discussion, not a PR. +- Test the quantized-KV path (`-ctk`/`-ctv q8_0`), not just default f16 - new speculative/attention features silently break there. +- Preserve existing explanatory comments about model-specific quirks when copying code; note the provenance ("copied from X, with Y added"). +- Remove dead code/branches left over from adapting a reference implementation. + +## ggml / backend + +- `supports_op` (and any dispatch/gating condition) must be scoped exactly to the cases being changed - a condition meant for a few quant types must not silently disable or enable everything else. +- No hardcoded warp/lane size - use `ggml_cuda_get_physical_warp_size()` (32 on CUDA, 64 on HIP/ROCm) and the portable helpers. +- Strip leftover debug/profiling/logging code before review. +- New or changed op? Update `docs/ops.md` and the relevant `docs/ops/*.csv` for the touched backend. +- New op or operator change needs corresponding `test-backend-ops` cases, and (per `CONTRIBUTING.md`) consistency across at least two backends. +- New kernels are expected to come with concrete perf data (throughput across realistic tensor shapes), not just correctness. +- Don't have a backend mutate the cgraph as a shortcut - that's an unresolved architectural question, not something to slip in. +- Expect this to need two maintainer approvals; that's normal for `ggml/` changes, not a sign something is wrong. +- For CUDA: Avoid excessively templating kernels, only add this where it shows visible performance gain. + +## Public API (`include/llama.h`) + +Public API changes carry a higher bar than internal ones (`CONTRIBUTING.md`). Review for: + +- Justification: why doesn't an existing mechanism (e.g. `cb_eval`, existing batch/sampler knobs) suffice? If it does, the change likely shouldn't add public surface. This is the single most common reason these PRs are rejected. +- Experimental or stop-gap surface belongs in a side header (`llama-ext.h`), not in `llama.h`. +- Keep it minimal and general: prefer one general call over several narrow convenience wrappers; make new calls forward-compatible (e.g. mixed-modality batches) rather than assuming today's shape. +- The C API is the first-class, stable, ABI-defining surface - don't propose a parallel C++ API as a replacement. `llama-cpp.h` stays a thin convenience layer. +- Types and naming: sized integer types (`int32_t`, `size_t` for sizes/offsets); `snake_case`; `_` = `__`; enum values upper-case and prefixed with the enum name; `_t` suffix for opaque types. Avoid gratuitous signature/ABI changes to existing exported functions. +- Every new API needs a working example/tool exercising it in the same PR - reviewers find real bugs by requiring it to be wired into `server`, `embedding`, `perplexity`, etc. + +## Server (`tools/server/`) + +- Is the feature within server's defined scope? Check `tools/server/README-dev.md` - out-of-scope features get declined. +- Security: don't trust client-supplied headers (e.g. `X-Forwarded-For`) or add footguns; things like IP allowlisting belong at a reverse proxy unless there's a trusted-proxy design. +- Wire new behavior into the existing request/response and checkpoint paths correctly; watch for resource leaks across requests. + +## Multimodal (`tools/mtmd/`) + +- Tensor names must be prefixed by `v.`, `a.`, `mm.` or `a.mm.` (legacy naming doesn't follow this convention - this is expected, but new code should follow it). +- Do not use explicit sin/cos for RoPE; use `ggml_rope_ext` instead, see `HOWTO-add-model.md`. If it can't express the needed behavior, that's a design discussion, not a PR. +- New GGML ops must not be introduced in the same PR, you must push it as a separate PR. +- In most cases, `build_vit` should be enough to build the transformer graph for vision models. Do not add a loop to build the transformer graph manually, unless you have a very good reason to do so. If you do, please explain why in the PR description. +- If you need a dedicated preprocessor, there is a high chance that it can be a derived class from one of the existing preprocessors. Check carefully before adding a new preprocessor class. +- If the model need a new public API in `mtmd.h`, open a discussion first. + +## General (always) + +Enforce the `AGENTS.md` / `CONTRIBUTING.md` coding and naming guidelines on every changed line - this is a distinct pass from checking that the code works, and matters just as much for review speed: + +- ASCII only in code and comments - no emdash, unicode arrows, `x`, `...` used as unicode; use `-`, `->`, `x`, `...` ASCII equivalents. +- Comments are concise and explain non-obvious *why*, not *what*. Flag verbose comments, comments that restate the code, comments that reference the current task/PR, and comments hard-wrapped to a fixed column width. +- Do not force-wrap prose/comments to a fixed character count or split a sentence across lines. +- `snake_case` names; `kebab-case` (lowercase-with-dashes) file names for C/C++, `.h` headers; Python files lowercase-with-underscores. Naming optimizes for longest common prefix (`number_small`, not `small_number`). +- 4-space indentation, brackets on the same line, `void * ptr`, `int & a`, no trailing whitespace; match the surrounding style. +- Reuse existing infrastructure over introducing new components; no new third-party dependencies, extra headers, or files unless clearly justified. +- Keep it simple: a simpler change doing 90% is often preferable to a complex one doing 100%. Flag unnecessary templates/fancy STL; basic `for` loops are fine here. +- Every added line should be something the contributor can explain and defend to a reviewer without AI help - flag anything that looks copied-in without understanding. + +## Reporting + +Group findings by severity so the user knows what actually blocks a merge: + +1. **Blocking** - quick-reject/scope issues and correctness bugs; these can sink the PR regardless of everything else. +2. **Will slow the review** - convention/naming/comment violations, missing tests/docs/perf data, missing API justification or example. +3. **Nits** - minor style, optional cleanups. + +For each finding, point to the file and line and say concretely what to change and why. Do not rewrite the whole diff unprompted; let the contributor make the fixes so they own and understand them. And do not draft any PR text, commit message, or reviewer reply - that is the contributor's to write. diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 72968607db80..3cc6933e55f4 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -108,6 +108,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_DOTS1, "dots1" }, { LLM_ARCH_ARCEE, "arcee" }, { LLM_ARCH_AFMOE, "afmoe" }, + { LLM_ARCH_LAGUNA, "laguna" }, { LLM_ARCH_ERNIE4_5, "ernie4_5" }, { LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" }, { LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" }, @@ -126,6 +127,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_GROVEMOE, "grovemoe" }, { LLM_ARCH_APERTUS, "apertus" }, { LLM_ARCH_MINIMAX_M2, "minimax-m2" }, + { LLM_ARCH_MINIMAX_M3, "minimax-m3" }, { LLM_ARCH_COGVLM, "cogvlm" }, { LLM_ARCH_RND1, "rnd1" }, { LLM_ARCH_PANGU_EMBED, "pangu-embedded" }, @@ -139,8 +141,10 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA_EMBED, "llama-embed" }, { LLM_ARCH_MAINCODER, "maincoder" }, { LLM_ARCH_KIMI_LINEAR, "kimi-linear" }, + { LLM_ARCH_KIMI_K3, "kimi-k3" }, { LLM_ARCH_TALKIE, "talkie" }, { LLM_ARCH_MELLUM, "mellum" }, + { LLM_ARCH_NANBEIGE, "nanbeige" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -219,6 +223,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" }, { LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" }, { LLM_KV_FULL_ATTENTION_INTERVAL, "%s.full_attention_interval" }, + { LLM_KV_NUM_LOOPS, "%s.num_loops" }, + { LLM_KV_SKIP_LOOP_FINAL_NORM, "%s.skip_loop_final_norm" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -252,6 +258,9 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" }, { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" }, { LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" }, + { LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, "%s.attention.indexer.block_size" }, + { LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, "%s.attention.indexer.local_blocks" }, + { LLM_KV_ATTENTION_INDEXER_TYPES, "%s.attention.indexer.types" }, { LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, "%s.attention.output_group_count" }, { LLM_KV_ATTENTION_OUTPUT_LORA_RANK, "%s.attention.output_lora_rank" }, { LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, "%s.attention.compress_rope_freq_base" }, @@ -294,7 +303,12 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_SSM_GROUP_COUNT, "%s.ssm.group_count" }, { LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" }, - { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" }, + { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" }, + { LLM_KV_KDA_GATE_LOWER_BOUND, "%s.kda.gate_lower_bound" }, + + { LLM_KV_SITU_BETA, "%s.situ_beta" }, + { LLM_KV_SITU_LINEAR_BETA, "%s.situ_linear_beta" }, + { LLM_KV_ATTN_RES_BLOCK_SIZE, "%s.attn_res_block_size" }, { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" }, @@ -407,6 +421,13 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" }, { LLM_TENSOR_FFN_LATENT_DOWN, "blk.%d.ffn_latent_down" }, { LLM_TENSOR_FFN_LATENT_UP, "blk.%d.ffn_latent_up" }, + { LLM_TENSOR_FFN_LATENT_NORM, "blk.%d.ffn_latent_norm" }, + { LLM_TENSOR_ATTN_RES_NORM, "blk.%d.attn_res_norm" }, + { LLM_TENSOR_ATTN_RES_PROJ, "blk.%d.attn_res_proj" }, + { LLM_TENSOR_FFN_RES_NORM, "blk.%d.ffn_res_norm" }, + { LLM_TENSOR_FFN_RES_PROJ, "blk.%d.ffn_res_proj" }, + { LLM_TENSOR_OUTPUT_RES_NORM, "output_res_norm" }, + { LLM_TENSOR_OUTPUT_RES_PROJ, "output_res_proj" }, { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, @@ -595,6 +616,9 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_INDEXER_PROJ, "blk.%d.indexer.proj" }, { LLM_TENSOR_INDEXER_ATTN_K, "blk.%d.indexer.attn_k" }, { LLM_TENSOR_INDEXER_ATTN_Q_B, "blk.%d.indexer.attn_q_b" }, + { LLM_TENSOR_INDEXER_Q_PROJ, "blk.%d.indexer.q_proj" }, + { LLM_TENSOR_INDEXER_K_PROJ, "blk.%d.indexer.k_proj" }, + { LLM_TENSOR_INDEXER_Q_NORM, "blk.%d.indexer.q_norm" }, { LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "blk.%d.indexer_compressor_kv" }, { LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "blk.%d.indexer_compressor_gate" }, { LLM_TENSOR_INDEXER_COMPRESSOR_APE, "blk.%d.indexer_compressor_ape" }, @@ -665,7 +689,7 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_HC_FFN_SCALE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_ATTN_COMPRESSOR_WKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_ATTN_COMPRESSOR_WGATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, - {LLM_TENSOR_ATTN_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_ATTN_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, {LLM_TENSOR_ATTN_COMPRESSOR_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_ATTN_K_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_ATTN_V_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, @@ -830,9 +854,12 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_INDEXER_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_INDEXER_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_INDEXER_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_INDEXER_Q_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_INDEXER_K_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_INDEXER_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_INDEXER_COMPRESSOR_WKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, - {LLM_TENSOR_INDEXER_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_INDEXER_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, {LLM_TENSOR_INDEXER_COMPRESSOR_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_FFN_GATE_TID2EID, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, {LLM_TENSOR_NEXTN_PROJ_PRE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, @@ -850,6 +877,14 @@ static const std::map LLM_TENSOR_INFOS = { // latent projections feed ggml_mul_mat, the buft probe must use MUL_MAT to keep them on GPU {LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + // Kimi K3 + {LLM_TENSOR_FFN_LATENT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_RES_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_RES_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_RES_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_RES_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_OUTPUT_RES_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_OUTPUT_RES_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, {LLM_TENSOR_MASKED_EMBD_CENTROIDS, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}}, {LLM_TENSOR_MASKED_EMBD_ORDERING, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}}, // eagle3 @@ -944,6 +979,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) { case LLM_ARCH_NEMOTRON_H_MOE: case LLM_ARCH_QWEN3NEXT: case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_KIMI_K3: case LLM_ARCH_QWEN35: case LLM_ARCH_QWEN35MOE: return true; @@ -999,8 +1035,10 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) { case LLM_ARCH_LFM2: case LLM_ARCH_LFM2MOE: case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_MINIMAX_M3: case LLM_ARCH_MISTRAL4: case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_KIMI_K3: return false; default: return true; diff --git a/src/llama-arch.h b/src/llama-arch.h index b74d53af4a32..15cdce597cbd 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -113,6 +113,7 @@ enum llm_arch { LLM_ARCH_DOTS1, LLM_ARCH_ARCEE, LLM_ARCH_AFMOE, + LLM_ARCH_LAGUNA, LLM_ARCH_ERNIE4_5, LLM_ARCH_ERNIE4_5_MOE, LLM_ARCH_HUNYUAN_MOE, @@ -142,10 +143,13 @@ enum llm_arch { LLM_ARCH_LLAMA_EMBED, LLM_ARCH_MAINCODER, LLM_ARCH_KIMI_LINEAR, + LLM_ARCH_KIMI_K3, LLM_ARCH_TALKIE, LLM_ARCH_MELLUM, LLM_ARCH_EAGLE3, + LLM_ARCH_MINIMAX_M3, LLM_ARCH_DFLASH, + LLM_ARCH_NANBEIGE, LLM_ARCH_UNKNOWN, }; @@ -224,6 +228,8 @@ enum llm_kv { LLM_KV_TOKEN_SHIFT_COUNT, LLM_KV_INTERLEAVE_MOE_LAYER_STEP, LLM_KV_FULL_ATTENTION_INTERVAL, + LLM_KV_NUM_LOOPS, + LLM_KV_SKIP_LOOP_FINAL_NORM, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, @@ -257,6 +263,9 @@ enum llm_kv { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, LLM_KV_ATTENTION_INDEXER_TOP_K, + LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, + LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, + LLM_KV_ATTENTION_INDEXER_TYPES, LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, LLM_KV_ATTENTION_OUTPUT_LORA_RANK, LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, @@ -300,6 +309,11 @@ enum llm_kv { LLM_KV_SSM_DT_B_C_RMS, LLM_KV_KDA_HEAD_DIM, + LLM_KV_KDA_GATE_LOWER_BOUND, + + LLM_KV_SITU_BETA, + LLM_KV_SITU_LINEAR_BETA, + LLM_KV_ATTN_RES_BLOCK_SIZE, LLM_KV_WKV_HEAD_SIZE, @@ -428,6 +442,13 @@ enum llm_tensor { LLM_TENSOR_FFN_EXP_PROBS_B, LLM_TENSOR_FFN_LATENT_DOWN, LLM_TENSOR_FFN_LATENT_UP, + LLM_TENSOR_FFN_LATENT_NORM, + LLM_TENSOR_ATTN_RES_NORM, + LLM_TENSOR_ATTN_RES_PROJ, + LLM_TENSOR_FFN_RES_NORM, + LLM_TENSOR_FFN_RES_PROJ, + LLM_TENSOR_OUTPUT_RES_NORM, + LLM_TENSOR_OUTPUT_RES_PROJ, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, @@ -595,6 +616,9 @@ enum llm_tensor { LLM_TENSOR_INDEXER_PROJ, LLM_TENSOR_INDEXER_ATTN_K, LLM_TENSOR_INDEXER_ATTN_Q_B, + LLM_TENSOR_INDEXER_Q_PROJ, + LLM_TENSOR_INDEXER_K_PROJ, + LLM_TENSOR_INDEXER_Q_NORM, LLM_TENSOR_INDEXER_COMPRESSOR_WKV, LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, LLM_TENSOR_INDEXER_COMPRESSOR_APE, diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 3a469bc90bd9..9c7cf59deb86 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -61,6 +61,24 @@ static const llm_fused_op_probe llm_fused_op_lid_probe = { /*.n_tokens_per_seq =*/ 1, }; +static const llm_fused_op_probe llm_fused_op_dsv4_hc_pre_probe = { + /*.op =*/ LLM_FUSED_OP_DSV4_HC_PRE, + /*.name =*/ "fused DeepSeek V4 HC pre", + /*.n_tokens_per_seq =*/ 1, +}; + +static const llm_fused_op_probe llm_fused_op_dsv4_hc_comb_probe = { + /*.op =*/ LLM_FUSED_OP_DSV4_HC_COMB, + /*.name =*/ "fused DeepSeek V4 HC comb", + /*.n_tokens_per_seq =*/ 1, +}; + +static const llm_fused_op_probe llm_fused_op_dsv4_hc_post_probe = { + /*.op =*/ LLM_FUSED_OP_DSV4_HC_POST, + /*.name =*/ "fused DeepSeek V4 HC post", + /*.n_tokens_per_seq =*/ 1, +}; + llama_context::llama_context( const llama_model & model, llama_context_params params) : @@ -235,6 +253,11 @@ llama_context::llama_context( cparams.fused_lid = true; cparams.auto_flid = true; + cparams.fused_dsv4_hc_pre = true; + cparams.fused_dsv4_hc_comb = true; + cparams.fused_dsv4_hc_post = true; + cparams.auto_fhc = true; + // with causal attention, the batch size is limited by the context size cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; @@ -537,6 +560,14 @@ void llama_context::resolve_fused_ops(const llama_memory_context_i * mctx, uint3 resolve(llm_fused_op_lid_probe, cparams.fused_lid); cparams.auto_flid = false; } + + if (cparams.auto_fhc) { + LLAMA_LOG_INFO("%s: resolving fused DeepSeek V4 HC support:\n", func); + resolve(llm_fused_op_dsv4_hc_pre_probe, cparams.fused_dsv4_hc_pre); + resolve(llm_fused_op_dsv4_hc_comb_probe, cparams.fused_dsv4_hc_comb); + resolve(llm_fused_op_dsv4_hc_post_probe, cparams.fused_dsv4_hc_post); + cparams.auto_fhc = false; + } } void llama_context::sched_reserve() { @@ -2303,11 +2334,17 @@ void llama_context::output_reorder() { // uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const { + if (model.arch == LLM_ARCH_KIMI_K3) { + // the n_tokens*40 budget below is exhausted at ubatch 3840 + return std::max(n_tokens * 160, 64u * model.n_tensors()); + } if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE || - model.arch == LLM_ARCH_DEEPSEEK4) { + model.arch == LLM_ARCH_DEEPSEEK4 || + model.arch == LLM_ARCH_NANBEIGE || + model.arch == LLM_ARCH_MINIMAX_M3) { return std::max(n_tokens * 40, 32u * model.n_tensors()); } uint32_t res = std::max(1024u, 8u*model.n_tensors()); @@ -2441,11 +2478,12 @@ llm_graph_cb llama_context::graph_get_cb() const { ggml_set_name(cur, name); } - // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends + // - norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends + // - force the last op of the layer on the specified backend to avoid running it on the backend of the next layer due to scheduling // FIXME: fix in ggml_backend_sched const bool full_offload = model.n_gpu_layers() > model.hparams.n_layer_all; if (ubatch.n_tokens < 32 || full_offload) { - if (il != -1 && strcmp(name, "norm") == 0) { + if (il != -1 && (strcmp(name, "norm") == 0 || strcmp(name, "l_last") == 0)) { const auto & dev_layer = model.dev_layer(il); for (const auto & backend : backends) { if (ggml_backend_get_device(backend.get()) == dev_layer) { diff --git a/src/llama-cparams.h b/src/llama-cparams.h index 58520caa3651..5018170ed85e 100644 --- a/src/llama-cparams.h +++ b/src/llama-cparams.h @@ -43,6 +43,10 @@ struct llama_cparams { bool auto_fgdn; bool fused_lid; // use fused lightning indexer bool auto_flid; + bool fused_dsv4_hc_pre; + bool fused_dsv4_hc_comb; + bool fused_dsv4_hc_post; + bool auto_fhc; bool no_perf; bool warmup; // TODO: remove [TAG_LLAMA_GRAPH_NO_WARMUP] bool op_offload; diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp index badcbfd0fbb6..363644464bad 100644 --- a/src/llama-grammar.cpp +++ b/src/llama-grammar.cpp @@ -1139,6 +1139,18 @@ struct llama_grammar * llama_grammar_init_impl( vec_rules[i].push_back({LLAMA_GRETYPE_END, 0}); } + // Validate that all rule references point to valid rules + for (size_t i = 0; i < n_rules; i++) { + for (const auto & elem : vec_rules[i]) { + if (elem.type == LLAMA_GRETYPE_RULE_REF) { + if (elem.value >= n_rules || vec_rules[elem.value].empty()) { + LLAMA_LOG_ERROR("invalid grammar: rule %zu references undefined rule %u\n", i, elem.value); + return nullptr; + } + } + } + } + // Check for left recursion std::vector rules_visited(n_rules); std::vector rules_in_progress(n_rules); diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index c8ecb0a2854c..3fb5889fcac6 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -1674,6 +1674,26 @@ ggml_tensor * llm_graph_context::build_ffn( cur = ggml_silu(ctx0, cur); cb(cur, "ffn_silu", il); } break; + case LLM_FFN_SITU: + { + // Kimi K3 SiTU-GLU: [beta*tanh(gate/beta)*sigmoid(gate)] * [linear_beta*tanh(up/linear_beta)] + const float beta = hparams.situ_beta; + const float lbeta = hparams.situ_linear_beta; + GGML_ASSERT(beta > 0.0f && lbeta > 0.0f); + + ggml_tensor * gate_act = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, cur, 1.0f/beta)), beta); + gate_act = ggml_mul(ctx0, gate_act, ggml_sigmoid(ctx0, cur)); + cb(gate_act, "ffn_situ", il); + + if (gate && type_gate == LLM_FFN_PAR) { + ggml_tensor * up_cap = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, tmp, 1.0f/lbeta)), lbeta); + cur = ggml_mul(ctx0, gate_act, up_cap); + cb(cur, "ffn_situ_glu", il); + type_gate = LLM_FFN_SEQ; + } else { + cur = gate_act; + } + } break; case LLM_FFN_GELU: if (gate && type_gate == LLM_FFN_PAR) { cur = ggml_geglu_split(ctx0, cur, tmp); @@ -1709,6 +1729,17 @@ ggml_tensor * llm_graph_context::build_ffn( cur = ggml_swiglu(ctx0, cur); cb(cur, "ffn_swiglu", il); } break; + case LLM_FFN_SWIGLU_OAI_MOE: + if (gate && type_gate == LLM_FFN_PAR) { + // same alpha/limit constants as gpt-oss + const float alpha = 1.702f; + const float limit = 7.0f; + cur = ggml_swiglu_oai(ctx0, cur, tmp, alpha, limit); + cb(cur, "ffn_swiglu_oai", il); + type_gate = LLM_FFN_SEQ; + } else { + GGML_ABORT("LLM_FFN_SWIGLU_OAI_MOE requires a parallel gate"); + } break; case LLM_FFN_GEGLU: { cur = ggml_geglu(ctx0, cur); @@ -2074,6 +2105,22 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cur = ggml_swiglu_oai(ctx0, cur, up, alpha, limit); cb(cur, "ffn_moe_swiglu_oai", il); } break; + case LLM_FFN_SITU: + { + // Kimi K3 SiTU-GLU: [beta*tanh(gate/beta)*sigmoid(gate)] * [linear_beta*tanh(up/linear_beta)] + const float beta = hparams.situ_beta; + const float lbeta = hparams.situ_linear_beta; + GGML_ASSERT(beta > 0.0f && lbeta > 0.0f); + GGML_ASSERT(has_gate && "SiTU without gate branch not implemented"); + + ggml_tensor * gate_act = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, cur, 1.0f/beta)), beta); + gate_act = ggml_mul(ctx0, gate_act, ggml_sigmoid(ctx0, cur)); + cb(gate_act, "ffn_moe_situ", il); + + ggml_tensor * up_cap = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, up, 1.0f/lbeta)), lbeta); + cur = ggml_mul(ctx0, gate_act, up_cap); + cb(cur, "ffn_moe_situ_glu", il); + } break; case LLM_FFN_RELU: if (has_gate) { cur = ggml_reglu_split(ctx0, cur, up); @@ -2668,7 +2715,7 @@ ggml_tensor * llm_graph_context::build_attn( ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il)); } - const auto & kq_mask = inp->get_kq_mask(); + ggml_tensor * kq_mask = inp->get_kq_mask(); ggml_tensor * q = q_cur; ggml_tensor * k = mctx_cur->get_k(ctx0, il); diff --git a/src/llama-graph.h b/src/llama-graph.h index c84cb6a452b1..8b4800f847fc 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -43,6 +43,9 @@ enum llm_fused_op { LLM_FUSED_OP_GDN_AR, LLM_FUSED_OP_GDN_CH, LLM_FUSED_OP_LIGHTNING_INDEXER, + LLM_FUSED_OP_DSV4_HC_PRE, + LLM_FUSED_OP_DSV4_HC_COMB, + LLM_FUSED_OP_DSV4_HC_POST, }; enum llm_ffn_op_type : int { @@ -55,6 +58,7 @@ enum llm_ffn_op_type : int { LLM_FFN_GEGLU, LLM_FFN_REGLU, LLM_FFN_SWIGLU_OAI_MOE, + LLM_FFN_SITU, // kimi k3: soft-capped SiLU gate + soft-capped up branch }; enum llm_ffn_gate_type { diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 9d0683d2fec4..50af97f358c3 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -180,6 +180,16 @@ uint32_t llama_hparams::n_embd_v_gqa_max() const { return val; } +uint32_t llama_hparams::n_embd_k_idx(uint32_t il) const { + if (!indexer_kv || indexer_head_size == 0) { + return 0; // arch without a MSA indexer + } + if (il < n_layer_dense_lead) { + return 0; // leading dense layers carry no indexer + } + return indexer_head_size; // 128 +} + uint32_t llama_hparams::n_embd_r() const { if (wkv_head_size != 0) { // for RWKV models @@ -248,6 +258,14 @@ bool llama_hparams::is_mla() const { return n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0; } +bool llama_hparams::is_indexer_full(uint32_t il) const { + if (il < n_layer()) { + return is_indexer_full_impl[il]; + } + + GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer()); +} + uint32_t llama_hparams::n_embd_head_k_mla() const { return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k(); } diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 8be5f28f39e6..a44a3636418e 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -7,7 +7,7 @@ // bump if necessary #define LLAMA_MAX_LAYERS 512 -#define LLAMA_MAX_EXPERTS 512 // Qwen3 Next +#define LLAMA_MAX_EXPERTS 1024 // Kimi K3 has 896 experts enum llama_expert_gating_func_type { LLAMA_EXPERT_GATING_FUNC_TYPE_NONE = 0, @@ -162,6 +162,12 @@ struct llama_hparams { // for Kimi Linear KDA uint32_t n_embd_head_kda = 0; + // for Kimi K3 + float kda_gate_lower_bound = 0.0f; // safe-gate lower bound (K3: -5.0) + float situ_beta = 0.0f; // SiTU gate soft-cap (K3: 4.0) + float situ_linear_beta = 0.0f; // SiTU up-branch soft-cap (K3: 25.0) + uint32_t attn_res_block_size = 0; // AttnRes snapshot interval (K3: 12) + bool ssm_dt_b_c_rms = false; float f_clamp_kqv = 0.0f; @@ -226,6 +232,15 @@ struct llama_hparams { uint32_t indexer_n_head = 0; uint32_t indexer_head_size = 0; uint32_t indexer_top_k = 0; + // MSA + uint32_t indexer_block_size = 0; + uint32_t indexer_local_blocks = 0; + // MSA stores its indexer keys in the main KV cache (k_idx tensors); + bool indexer_kv = false; + + // Indexer is "full" (1) or "shared" (0) + // Shared indexers reuse top-k from previous full layer + std::array is_indexer_full_impl; // DeepSeek-V4 uint32_t dsv4_o_group_count = 0; @@ -302,6 +317,8 @@ struct llama_hparams { bool is_swa(uint32_t il) const; + bool is_indexer_full(uint32_t il) const; + void set_recr_pattern(uint32_t n_pattern, bool dense_first = false); // whether or not the given layer is recurrent (for hybrid models) @@ -344,6 +361,9 @@ struct llama_hparams { uint32_t n_embd_k_gqa_max() const; uint32_t n_embd_v_gqa_max() const; + // dimension of the single-head MSA indexer key stream + uint32_t n_embd_k_idx(uint32_t il = 0) const; + // dimension of the rolling state embeddings // corresponds to Mamba's conv_states size or RWKV's token_shift states size uint32_t n_embd_r() const; diff --git a/src/llama-kv-cache-dsv4.cpp b/src/llama-kv-cache-dsv4.cpp index 7cb6cc18dac3..069da45f4ea3 100644 --- a/src/llama-kv-cache-dsv4.cpp +++ b/src/llama-kv-cache-dsv4.cpp @@ -22,7 +22,7 @@ static constexpr uint32_t DSV4_STATE_MAGIC = 0x34565344; // DSV4 static constexpr uint32_t DSV4_STATE_VERSION = 1; static constexpr uint32_t DSV4_STATE_MODE_FULL = 0; static constexpr uint32_t DSV4_STATE_MODE_PARTIAL = 1; -static constexpr uint32_t DSV4_K_CACHE_STATE_VER = 1; +static constexpr uint32_t DSV4_K_CACHE_STATE_VER = 2; static constexpr uint32_t DSV4_COMP_STATE_VER = 1; static uint32_t dsv4_comp_size(uint32_t kv_size, uint32_t ratio) { @@ -38,6 +38,16 @@ static void dsv4_clear_tensor_stream(ggml_tensor * tensor, uint32_t stream) { ggml_backend_tensor_memset(tensor, 0, stream*stream_size, stream_size); } +static uint32_t dsv4_state_n_used_k_rows(llama_pos pos_max, uint32_t ratio, uint32_t kv_size) { + if (pos_max < 0) { + return 0; + } + + const uint64_t n_rows = ((uint64_t) pos_max + 1)/ratio; + + return (uint32_t) std::min(kv_size, n_rows); +} + static int64_t dsv4_stream_offset(uint32_t n_stream, llama_seq_id seq_id, uint32_t size) { if (n_stream <= 1) { return 0; @@ -239,6 +249,7 @@ static void dsv4_state_dst_stream_range( static void dsv4_state_write_tensor_streams( llama_io_write_i & io, ggml_tensor * tensor, + uint32_t tensor_rows, uint32_t n_rows, uint32_t s0, uint32_t ns) { @@ -247,20 +258,31 @@ static void dsv4_state_write_tensor_streams( const uint64_t rows = n_rows; const uint64_t row_size = ggml_row_size(tensor->type, tensor->ne[0]); + if (n_rows > tensor_rows) { + throw std::runtime_error("DSV4 state tensor row count exceeds storage"); + } + io.write(&type_i, sizeof(type_i)); io.write(&ne0, sizeof(ne0)); io.write(&rows, sizeof(rows)); io.write(&row_size, sizeof(row_size)); - const size_t offset = (size_t) s0*n_rows*row_size; - const size_t size = (size_t) ns*n_rows*row_size; + const size_t stream_stride = (size_t) tensor_rows*row_size; + const size_t size = (size_t) n_rows*row_size; + if (size == 0) { + return; + } - io.write_tensor(tensor, offset, size); + for (uint32_t s = 0; s < ns; ++s) { + const size_t offset = (size_t) (s0 + s)*stream_stride; + io.write_tensor(tensor, offset, size); + } } static void dsv4_state_read_tensor_streams( llama_io_read_i & io, ggml_tensor * tensor, + uint32_t tensor_rows, uint32_t n_rows, uint32_t s0, uint32_t ns) { @@ -282,18 +304,28 @@ static void dsv4_state_read_tensor_streams( if (type_i != type_i_ref || ne0 != ne0_ref || rows != rows_ref || row_size != row_size_ref) { throw std::runtime_error("DSV4 state tensor metadata mismatch"); } + if (n_rows > tensor_rows) { + throw std::runtime_error("DSV4 state tensor row count exceeds storage"); + } - const size_t offset = (size_t) s0*n_rows*row_size; - const size_t size = (size_t) ns*n_rows*row_size; + const size_t stream_stride = (size_t) tensor_rows*row_size; + const size_t size = (size_t) n_rows*row_size; + if (size == 0) { + return; + } - io.read_tensor(tensor, offset, size); + for (uint32_t s = 0; s < ns; ++s) { + const size_t offset = (size_t) (s0 + s)*stream_stride; + io.read_tensor(tensor, offset, size); + } } static void dsv4_state_write_k_cache( llama_io_write_i & io, const llama_kv_cache * kv, llama_seq_id seq_id, - llama_state_seq_flags flags) { + llama_state_seq_flags flags, + uint32_t n_rows) { GGML_UNUSED(flags); uint32_t s0; @@ -305,14 +337,18 @@ static void dsv4_state_write_k_cache( const auto layer_ids = kv->get_layer_ids(); const uint32_t n_layer = layer_ids.size(); + if (n_rows > kv_size) { + throw std::runtime_error("DSV4 K-cache state row count exceeds cache size"); + } + io.write(&version, sizeof(version)); - io.write(&kv_size, sizeof(kv_size)); + io.write(&n_rows, sizeof(n_rows)); io.write(&ns, sizeof(ns)); io.write(&n_layer, sizeof(n_layer)); for (uint32_t il : layer_ids) { io.write(&il, sizeof(il)); - dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, s0, ns); + dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, n_rows, s0, ns); } } @@ -324,19 +360,26 @@ static void dsv4_state_read_k_cache( GGML_UNUSED(flags); uint32_t version; - uint32_t kv_size_ref; + uint32_t n_rows_ref; uint32_t ns; uint32_t n_layer_ref; io.read(&version, sizeof(version)); - io.read(&kv_size_ref, sizeof(kv_size_ref)); + io.read(&n_rows_ref, sizeof(n_rows_ref)); io.read(&ns, sizeof(ns)); io.read(&n_layer_ref, sizeof(n_layer_ref)); - if (version != DSV4_K_CACHE_STATE_VER) { + if (version != 1 && version != DSV4_K_CACHE_STATE_VER) { throw std::runtime_error("DSV4 K-cache state version mismatch"); } - if (kv_size_ref != kv->get_size()) { + + const uint32_t kv_size = kv->get_size(); + if (version == 1 && n_rows_ref != kv_size) { + LLAMA_LOG_INFO("kv size ref %d kv %d\n", n_rows_ref, kv_size); + throw std::runtime_error("DSV4 K-cache state size mismatch"); + } + if (n_rows_ref > kv_size) { + LLAMA_LOG_INFO("kv rows ref %d kv %d\n", n_rows_ref, kv_size); throw std::runtime_error("DSV4 K-cache state size mismatch"); } @@ -355,7 +398,7 @@ static void dsv4_state_read_k_cache( throw std::runtime_error("DSV4 K-cache layer id mismatch"); } - dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv->get_size(), s0, ns); + dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv_size, n_rows_ref, s0, ns); } } @@ -882,8 +925,8 @@ void llama_dsv4_comp_state::state_write(llama_io_write_i & io, llama_seq_id seq_ for (const auto & layer : layers) { io.write(&layer.il, sizeof(layer.il)); - dsv4_state_write_tensor_streams(io, layer.kv, state_size, s0, ns); - dsv4_state_write_tensor_streams(io, layer.score, state_size, s0, ns); + dsv4_state_write_tensor_streams(io, layer.kv, state_size, state_size, s0, ns); + dsv4_state_write_tensor_streams(io, layer.score, state_size, state_size, s0, ns); } } @@ -924,8 +967,8 @@ void llama_dsv4_comp_state::state_read(llama_io_read_i & io, llama_seq_id seq_id throw std::runtime_error("DSV4 compressor state layer id mismatch"); } - dsv4_state_read_tensor_streams(io, layer.kv, state_size, s0, ns); - dsv4_state_read_tensor_streams(io, layer.score, state_size, s0, ns); + dsv4_state_read_tensor_streams(io, layer.kv, state_size, state_size, s0, ns); + dsv4_state_read_tensor_streams(io, layer.score, state_size, state_size, s0, ns); } } @@ -1328,9 +1371,19 @@ void llama_kv_cache_dsv4::state_write(llama_io_write_i & io, llama_seq_id seq_id kv_raw->state_write(io, seq_id, flags); if (!partial_only) { - dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags); - dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags); - dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags); + const llama_pos pos_max = seq_id >= 0 ? kv_raw->seq_pos_max(seq_id) : -1; + + //FIXME : note that we conflate token positions with rows, which is not true for multi-modal case. + const uint32_t n_rows_csa = seq_id >= 0 ? + dsv4_state_n_used_k_rows(pos_max, DSV4_CSA_RATIO, kv_csa->get_size()) : kv_csa->get_size(); + const uint32_t n_rows_hca = seq_id >= 0 ? + dsv4_state_n_used_k_rows(pos_max, DSV4_HCA_RATIO, kv_hca->get_size()) : kv_hca->get_size(); + const uint32_t n_rows_lid = seq_id >= 0 ? + dsv4_state_n_used_k_rows(pos_max, DSV4_CSA_RATIO, kv_lid->get_size()) : kv_lid->get_size(); + + dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags, n_rows_csa); + dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags, n_rows_hca); + dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags, n_rows_lid); } csa_state->state_write(io, seq_id, flags); @@ -1366,6 +1419,10 @@ void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id, kv_raw->state_read(io, seq_id, flags); if (!partial_only) { + kv_csa->clear(true); + kv_hca->clear(true); + kv_lid->clear(true); + dsv4_state_read_k_cache(io, kv_csa.get(), seq_id, flags); dsv4_state_read_k_cache(io, kv_hca.get(), seq_id, flags); dsv4_state_read_k_cache(io, kv_lid.get(), seq_id, flags); diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index e70583e64152..44cb1668dacf 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -112,7 +112,7 @@ llama_kv_cache::llama_kv_cache( auto it = ctx_map.find(buft); if (it == ctx_map.end()) { ggml_init_params params = { - /*.mem_size =*/ size_t(2u*(1 + n_stream)*n_layer*ggml_tensor_overhead()), + /*.mem_size =*/ size_t(3u*(1 + n_stream)*n_layer*ggml_tensor_overhead()), //Reserve tensor metadata for up to 3 tensors per layer (K, V, and optional K_idx), plus one view per tensor per stream. /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; @@ -242,9 +242,25 @@ llama_kv_cache::llama_kv_cache( v_stream.push_back(has_v ? ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2]) : nullptr); } + const uint32_t n_embd_k_idx = hparams.n_embd_k_idx(il); + ggml_tensor * k_idx = n_embd_k_idx > 0 + ? ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_k_idx, kv_size, n_stream) + : nullptr; + if (k_idx) { + ggml_format_name(k_idx, "cache_k_idx_l%d", il); + msa_strict_slots = (n_stream == n_seq_max); + } + + std::vector k_idx_stream; + for (uint32_t s = 0; s < n_stream; ++s) { + k_idx_stream.push_back(k_idx + ? ggml_view_2d(ctx, k_idx, n_embd_k_idx, kv_size, k_idx->nb[1], s*k_idx->nb[2]) + : nullptr); + } + map_layer_ids[il] = layers.size(); - layers.push_back({ il, k, v, k_stream, v_stream, }); + layers.push_back({ il, k, v, k_idx, k_stream, v_stream, k_idx_stream }); } if (reuse) { @@ -293,13 +309,24 @@ llama_kv_cache::llama_kv_cache( } { - const size_t memory_size_k = size_k_bytes(); - const size_t memory_size_v = size_v_bytes(); + const size_t memory_size_k = size_k_bytes(); + const size_t memory_size_v = size_v_bytes(); + const size_t memory_size_k_idx = size_k_idx_bytes(); + const size_t memory_size_total = memory_size_k + memory_size_v + memory_size_k_idx; + + constexpr float mib = 1024.0f * 1024.0f; + + const std::string k_log = format(", K (%s): %7.2f MiB", ggml_type_name(type_k), (float) memory_size_k / mib); + const std::string v_log = format(", V (%s): %7.2f MiB", ggml_type_name(type_v), (float) memory_size_v / mib); + + std::string k_idx_log; + if (memory_size_k_idx > 0) { + k_idx_log = format(", K_idx (%s): %7.2f MiB", ggml_type_name(GGML_TYPE_F32), (float) memory_size_k_idx / mib); + } - LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u/%u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, - (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max, n_stream, - ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), - ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); + LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u/%u seqs)%s%s%s\n", __func__, + (float) memory_size_total / mib, kv_size, (int) layers.size(), n_seq_max, n_stream, + k_log.c_str(), v_log.c_str(), k_idx_log.c_str()); } // TODO: refactor [TAG_KV_CACHE_SHARE_CELLS] @@ -323,7 +350,7 @@ llama_kv_cache::llama_kv_cache( hparams.n_embd_head_k() % 64 == 0; // always create Hadamard rotation tensors for DeepSeek lightning indexers - if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4) && + if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 || model.arch == LLM_ARCH_GLM_DSA) && hparams.n_embd_head_k_full == hparams.indexer_head_size) { attn_rot_k = true; } @@ -392,6 +419,39 @@ bool llama_kv_cache::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { p1 = std::numeric_limits::max(); } + // empty range - nothing to remove + if (p0 >= p1) { + return true; + } + + // MSA anchors block selection to absolute cache slots (slot == position). Tail trim and full removal preserve this invariant, but removing a prefix + // or middle range would free slots while later cells survive, desynchronizing the indexer cache. Reject such removals before modifying the cache. + if (msa_strict_slots) { + for (llama_seq_id sid = 0; sid < (llama_seq_id) seq_to_stream.size(); ++sid) { + if (seq_id >= 0 && sid != seq_id) { + continue; + } + + const auto & cells = v_cells[seq_to_stream[sid]]; + + const llama_pos pmin = cells.seq_pos_min(sid); + const llama_pos pmax = cells.seq_pos_max(sid); + + if (pmin < 0) { + continue; // empty sequence + } + + const bool overlaps = p0 <= pmax && p1 > pmin; // the range removes something + const bool leaves_tail = p1 <= pmax; // cells beyond the range survive + + if (overlaps && leaves_tail) { + LLAMA_LOG_WARN("%s: MSA: partial (non-suffix) removal [%d, %d) for seq %d is not supported " + "(block selection is anchored to cache slots) - rejected\n", __func__, p0, p1, sid); + return false; + } + } + } + if (seq_id >= 0) { auto & cells = v_cells[seq_to_stream[seq_id]]; auto & head = v_heads[seq_to_stream[seq_id]]; @@ -846,6 +906,10 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_co if (layer.v_stream[ssrc]) { ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]); } + if (layer.k_idx_stream[ssrc]) { + GGML_ASSERT(layer.k_idx_stream[sdst]); + ggml_backend_tensor_copy(layer.k_idx_stream[ssrc], layer.k_idx_stream[sdst]); + } } } } @@ -994,6 +1058,44 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch, const auto & cells = v_cells[seq_to_stream[seq_id]]; + if (n_tokens > cells.size()) { + LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size()); + return { }; + } + + // MSA block selection assumes slot == logical position (append-only streams). + if (msa_strict_slots) { + for (uint32_t ii = 0; ii < n_tokens; ++ii) { + const llama_pos pos = ubatch.pos[s*n_tokens + ii]; + + if (pos < 0 || (uint64_t) pos >= cells.size()) { + LLAMA_LOG_WARN("%s: MSA: position %d is outside the cache range [0, %u)\n", + __func__, pos, cells.size()); + return { }; + } + + const uint32_t idx = (uint32_t) pos; + + if (!cells.is_empty(idx)) { + LLAMA_LOG_WARN("%s: MSA: required slot %u is already occupied (stream %u)\n", + __func__, idx, seq_to_stream[seq_id]); + return { }; + } + + // strictly increasing positions, rules out duplicates and, for contiguous requests, is tightened to exact adjacency + if (!res.idxs[s].empty() && (cont ? idx != res.idxs[s].back() + 1 + : idx <= res.idxs[s].back())) { + LLAMA_LOG_WARN("%s: MSA: token positions are not %s within the ubatch\n", + __func__, cont ? "contiguous" : "strictly increasing"); + return { }; + } + + res.idxs[s].push_back(idx); + } + + continue; + } + uint32_t head_cur = v_heads[seq_to_stream[seq_id]]; // if we have enough unused cells before the current head -> @@ -1002,11 +1104,6 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch, head_cur = 0; } - if (n_tokens > cells.size()) { - LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size()); - return { }; - } - uint32_t n_tested = 0; // for continuous slots, we test that all tokens in the ubatch fit, starting from the current head @@ -1113,6 +1210,15 @@ void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch & const auto idx = sinfo.idxs[s][ii]; + if (msa_strict_slots && (llama_pos) idx != ubatch.pos[i]) { + LLAMA_LOG_ERROR("%s: MSA slot/position invariant violated: " + "writing pos %d into cell %u (stream %u). The indexer cache " + "would desync and block selection would silently corrupt. " + "This is a bug, please report it with reproduction steps.\n", + __func__, ubatch.pos[i], idx, sinfo.strm[s]); + GGML_ABORT("MSA: slot != pos"); + } + if (!cells.is_empty(idx)) { assert(cells.seq_count(idx) == 1); @@ -1156,7 +1262,8 @@ void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch & LLAMA_LOG_DEBUG("%s: purging positions [%d, %d] of sequence %d from KV cache\n", __func__, cells.seq_pos_min(s), seq_pos_max_rm[s], s); - seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1); + // under MSA strict slots this path should be unreachable, since strict MSA placement never selects occupied cells + GGML_ASSERT(seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1)); } } @@ -1176,6 +1283,12 @@ bool llama_kv_cache::get_can_shift() const { if (hparams.n_pos_per_embd() > 1) { return false; } + // shifting would leave k_idx stale + for (const auto & layer : layers) { + if (layer.k_idx) { + return false; + } + } return true; } @@ -1292,6 +1405,23 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0); } +ggml_tensor * llama_kv_cache::get_k_idx(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const { + const int32_t ikv = map_layer_ids.at(il); + auto * k_idx = layers[ikv].k_idx; + GGML_ASSERT(k_idx); + + const uint64_t kv_size = get_size(); + const int64_t n_idx = k_idx->ne[0]; // 128 + const uint32_t ns = sinfo.s1 - sinfo.s0 + 1; + + return ggml_view_4d(ctx, k_idx, + n_idx, 1, n_kv, ns, + ggml_row_size(k_idx->type, n_idx), // nb1 (single head) + ggml_row_size(k_idx->type, n_idx), // nb2 (per cell) + ggml_row_size(k_idx->type, n_idx*kv_size), // nb3 (per stream) + ggml_row_size(k_idx->type, n_idx*kv_size)*sinfo.s0); +} + ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const { GGML_UNUSED(sinfo); @@ -1393,6 +1523,28 @@ ggml_tensor * llama_kv_cache::build_input_k_idxs(ggml_context * ctx, const llama return k_idxs; } +ggml_tensor * llama_kv_cache::cpy_k_idx(ggml_context * ctx, ggml_tensor * k_idx_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const { + GGML_UNUSED(sinfo); + const int32_t ikv = map_layer_ids.at(il); + ggml_tensor * k_idx = layers[ikv].k_idx; + GGML_ASSERT(k_idx && "cpy_k_idx on a layer with no indexer cache"); + + const int64_t n_embd_head = k_idx_cur->ne[0]; // 128 + const int64_t n_head = k_idx_cur->ne[1]; // 1 + const int64_t n_tokens = k_idx_cur->ne[2]; + const int64_t n_embd_gqa = n_embd_head*n_head; // 128 + + GGML_ASSERT(ggml_row_size(k_idx_cur->type, n_embd_head) == k_idx_cur->nb[1]); + k_idx_cur = ggml_view_2d(ctx, k_idx_cur, n_embd_gqa, n_tokens, k_idx_cur->nb[2], 0); + + const int64_t n_stream = k_idx->ne[2]; + if (n_stream > 1) { + const int64_t kv_size = get_size(); + k_idx = ggml_reshape_2d(ctx, k_idx, n_embd_gqa, kv_size*n_stream); + } + return ggml_set_rows(ctx, k_idx, k_idx_cur, k_idxs); // same k_idxs as the K store +} + ggml_tensor * llama_kv_cache::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const { const uint32_t n_tokens = ubatch.n_tokens; @@ -1827,6 +1979,18 @@ size_t llama_kv_cache::size_v_bytes() const { return size_v_bytes; } +size_t llama_kv_cache::size_k_idx_bytes() const { + size_t size_k_idx_bytes = 0; + + for (const auto & layer : layers) { + if (layer.k_idx) { + size_k_idx_bytes += ggml_nbytes(layer.k_idx); + } + } + + return size_k_idx_bytes; +} + ggml_tensor * llama_kv_cache::build_rope_shift( const llama_cparams & cparams, ggml_context * ctx, @@ -2054,7 +2218,12 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama bool res = true; res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id); - res = res && state_read_data(io, strm, cell_count, sinfo); + + try { + res = res && state_read_data(io, strm, cell_count, sinfo); + } catch (...) { + res = false; + } if (!res) { if (seq_id == -1) { @@ -2134,6 +2303,36 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t } } + if (size_k_idx_bytes() > 0) { + const uint32_t has_k_idx_u32 = 1; + io.write(&has_k_idx_u32, sizeof(has_k_idx_u32)); + + for (const auto & layer : layers) { + const uint32_t layer_has_k_idx = layer.k_idx ? 1 : 0; + io.write(&layer_has_k_idx, sizeof(layer_has_k_idx)); + + if (!layer_has_k_idx) { + continue; + } + + GGML_ASSERT(layer.k_idx_stream[cr.strm]); + + const int32_t k_idx_type_i = (int32_t) layer.k_idx->type; + io.write(&k_idx_type_i, sizeof(k_idx_type_i)); + + const uint64_t k_idx_size_row = ggml_row_size(layer.k_idx->type, layer.k_idx->ne[0]); + io.write(&k_idx_size_row, sizeof(k_idx_size_row)); + + for (const auto & range : cr.data) { + const size_t range_size = range.second - range.first; + const size_t buf_size = range_size * k_idx_size_row; + const size_t offset = range.first * k_idx_size_row; + + io.write_tensor(layer.k_idx_stream[cr.strm], offset, buf_size); + } + } + } + if (!v_trans) { for (const auto & layer : layers) { const uint32_t il = layer.il; @@ -2382,6 +2581,68 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32 } } + if (size_k_idx_bytes() > 0) { + uint32_t has_k_idx_u32 = 0; + io.read(&has_k_idx_u32, sizeof(has_k_idx_u32)); + + if (has_k_idx_u32 != 1) { + LLAMA_LOG_ERROR("%s: missing k_idx data in KV cache state\n", __func__); + return false; + } + + for (const auto & layer : layers) { + uint32_t layer_has_k_idx = 0; + io.read(&layer_has_k_idx, sizeof(layer_has_k_idx)); + + const uint32_t expected_layer_has_k_idx = layer.k_idx ? 1 : 0; + + if (layer_has_k_idx != expected_layer_has_k_idx) { + LLAMA_LOG_ERROR( + "%s: mismatched k_idx state for layer: got %u, expected %u\n", + __func__, layer_has_k_idx, expected_layer_has_k_idx); + return false; + } + + if (!layer_has_k_idx) { + continue; + } + + GGML_ASSERT(layer.k_idx_stream[strm]); + + int32_t k_idx_type_i = -1; + io.read(&k_idx_type_i, sizeof(k_idx_type_i)); + + if (k_idx_type_i != (int32_t) layer.k_idx->type) { + LLAMA_LOG_ERROR( + "%s: mismatched k_idx type: got %d, expected %d\n", + __func__, k_idx_type_i, (int32_t) layer.k_idx->type); + return false; + } + + uint64_t k_idx_size_row = 0; + io.read(&k_idx_size_row, sizeof(k_idx_size_row)); + + const uint64_t expected_k_idx_size_row = ggml_row_size(layer.k_idx->type, layer.k_idx->ne[0]); + + if (k_idx_size_row != expected_k_idx_size_row) { + LLAMA_LOG_ERROR( + "%s: mismatched k_idx row size: got %zu, expected %zu\n", + __func__, (size_t) k_idx_size_row, (size_t) expected_k_idx_size_row); + return false; + } + + if (cell_count) { + if (sinfo.is_contiguous()) { + io.read_tensor(layer.k_idx_stream[strm], sinfo.head() * k_idx_size_row, cell_count * k_idx_size_row); + } else { + for (uint32_t i = 0; i < cell_count; ++i) { + io.read_tensor(layer.k_idx_stream[strm], sinfo.idxs[0][i] * k_idx_size_row, k_idx_size_row); + } + } + } + } + } + if (!this->v_trans) { for (const auto & layer : layers) { const uint32_t il = layer.il; @@ -2583,6 +2844,10 @@ ggml_tensor * llama_kv_cache_context::get_v(ggml_context * ctx, int32_t il) cons return kv->get_v(ctx, il, n_kv, sinfos[i_cur]); } +ggml_tensor * llama_kv_cache_context::get_k_idx(ggml_context * ctx, int32_t il) const { + return kv->get_k_idx(ctx, il, n_kv, sinfos[i_cur]); +} + ggml_tensor * llama_kv_cache_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const { return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]); } @@ -2591,6 +2856,10 @@ ggml_tensor * llama_kv_cache_context::cpy_v(ggml_context * ctx, ggml_tensor * v_ return kv->cpy_v(ctx, v_cur, v_idxs, il, sinfos[i_cur]); } +ggml_tensor * llama_kv_cache_context::cpy_k_idx(ggml_context * ctx, ggml_tensor * k_idx_cur, ggml_tensor * k_idxs, int32_t il) const { + return kv->cpy_k_idx(ctx, k_idx_cur, k_idxs, il, sinfos[i_cur]); +} + ggml_tensor * llama_kv_cache_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const { return kv->build_input_k_idxs(ctx, ubatch); } diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index 531d99dbdec1..d5a92f4405b5 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -173,10 +173,12 @@ class llama_kv_cache : public llama_memory_i { // get views of the current state of the cache ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const; ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const; + ggml_tensor * get_k_idx(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const; // store k_cur and v_cur in the cache based on the provided head location ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const; ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const; + ggml_tensor * cpy_k_idx(ggml_context * ctx, ggml_tensor * k_idx_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const; // // preparation API @@ -228,9 +230,11 @@ class llama_kv_cache : public llama_memory_i { ggml_tensor * k; ggml_tensor * v; + ggml_tensor * k_idx; // MSA single-head indexer keys, F32 std::vector k_stream; std::vector v_stream; + std::vector k_idx_stream; }; bool v_trans = true; // the value tensor is transposed @@ -259,6 +263,9 @@ class llama_kv_cache : public llama_memory_i { // env: LLAMA_KV_CACHE_DEBUG int debug = 0; + // set when a k_idx (indexer) cache exists and the stream layout supports MSA (single seq, or one stream per seq) + bool msa_strict_slots = false; + // this is the SWA type of the cache - not to be confused with the model SWA type const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE; @@ -291,6 +298,7 @@ class llama_kv_cache : public llama_memory_i { size_t size_k_bytes() const; size_t size_v_bytes() const; + size_t size_k_idx_bytes() const; ggml_tensor * build_rope_shift( const llama_cparams & cparams, @@ -370,6 +378,7 @@ class llama_kv_cache_context : public llama_memory_context_i { // get views of the current state of the cache ggml_tensor * get_k(ggml_context * ctx, int32_t il) const; ggml_tensor * get_v(ggml_context * ctx, int32_t il) const; + ggml_tensor * get_k_idx(ggml_context * ctx, int32_t il) const; // store k_cur and v_cur in the cache based on the provided head location // note: the heads in k_cur and v_cur should be laid out contiguously in memory @@ -379,6 +388,7 @@ class llama_kv_cache_context : public llama_memory_context_i { // - v_idxs [n_tokens] or [n_tokens*n_embd_v_gqa] depending if V cache is transposed ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const; ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const; + ggml_tensor * cpy_k_idx(ggml_context * ctx, ggml_tensor * k_idx_cur, ggml_tensor * k_idxs, int32_t il) const; // create destination indices for each head of the current batch for where it would be written in the KV cache // the indices address the global KV cache (not per stream) - this is not relevant for the user of this API, but diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index 3d6c6db876b4..ef82eb976ca7 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -819,7 +819,12 @@ void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_i bool res = true; res = res && state_read_meta(io, cell_count, seq_id); - res = res && state_read_data(io, cell_count); + + try { + res = res && state_read_data(io, cell_count); + } catch (...) { + res = false; + } if (!res) { if (seq_id == -1) { diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 28f8bb7934bb..510586e96c20 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -4,6 +4,7 @@ #include "ggml.h" #include "gguf.h" #include "llama-hparams.h" +#include "llama.h" #include #include @@ -522,8 +523,7 @@ llama_model_loader::llama_model_loader( const std::string & fname, std::vector & splits, FILE * file, - bool use_mmap, - bool use_direct_io, + llama_load_mode load_mode, bool check_tensors, bool no_alloc, const llama_model_kv_override * param_overrides_p, @@ -542,6 +542,9 @@ llama_model_loader::llama_model_loader( tensor_buft_overrides = param_tensor_buft_overrides_p; + this->use_mmap = load_mode == LLAMA_LOAD_MODE_MMAP || load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK; + this->use_direct_io = load_mode == LLAMA_LOAD_MODE_DIRECT_IO; + if (!fname.empty()) { // Load the main GGUF struct ggml_context * ctx = NULL; @@ -562,20 +565,6 @@ llama_model_loader::llama_model_loader( files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io)); contexts.emplace_back(ctx); - if (use_mmap && use_direct_io) { - if (files.back()->has_direct_io()) { - LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__); - use_mmap = false; - } else { - LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__); - use_direct_io = false; - - // reopen file using std::fopen for mmap - files.pop_back(); - files.emplace_back(new llama_file(fname.c_str(), "rb", false)); - } - } - // Save tensors data offset of the main file. // For subsidiary files, `meta` tensor data offset must not be used, // so we build a unified tensors index for weights. @@ -816,13 +805,11 @@ llama_model_loader::llama_model_loader( } } - if (!llama_mmap::SUPPORTED) { + if (this->use_mmap && !llama_mmap::SUPPORTED) { LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__); - use_mmap = false; + this->use_mmap = false; } - this->use_mmap = use_mmap; - this->use_direct_io = use_direct_io; this->check_tensors = check_tensors; this->no_alloc = no_alloc; } diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index c476026d3e51..75a3652d06bd 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -126,8 +126,7 @@ struct llama_model_loader { const std::string & fname, std::vector & splits, // optional, only need if the split does not follow naming scheme FILE * file, - bool use_mmap, - bool use_direct_io, + llama_load_mode load_mode, bool check_tensors, bool no_alloc, const llama_model_kv_override * param_overrides_p, diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index a3928523ba8d..3812c594e795 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -28,6 +28,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) { case LLM_ARCH_MIMO2: case LLM_ARCH_STEP35: case LLM_ARCH_MELLUM: + case LLM_ARCH_LAGUNA: return false; default: return true; @@ -280,6 +281,9 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head); add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size); add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k); + add_kv(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size); + add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks); + add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, true); add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, true); const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index eaf3f35d2d8e..5c24a732c061 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -16,6 +16,7 @@ #include "llama-memory-hybrid-iswa.h" #include "llama-memory-recurrent.h" +#include "llama.h" #include "models/models.h" #include "ggml.h" @@ -84,6 +85,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_stablelm(params); case LLM_ARCH_MELLUM: return new llama_model_mellum(params); + case LLM_ARCH_NANBEIGE: + return new llama_model_nanbeige(params); case LLM_ARCH_QWEN: return new llama_model_qwen(params); case LLM_ARCH_QWEN2: @@ -250,6 +253,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_arcee(params); case LLM_ARCH_AFMOE: return new llama_model_afmoe(params); + case LLM_ARCH_LAGUNA: + return new llama_model_laguna(params); case LLM_ARCH_ERNIE4_5: return new llama_model_ernie4_5(params); case LLM_ARCH_ERNIE4_5_MOE: @@ -282,6 +287,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_apertus(params); case LLM_ARCH_MINIMAX_M2: return new llama_model_minimax_m2(params); + case LLM_ARCH_MINIMAX_M3: + return new llama_model_minimax_m3(params); case LLM_ARCH_COGVLM: return new llama_model_cogvlm(params); case LLM_ARCH_PANGU_EMBED: @@ -302,6 +309,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_mimo2(params); case LLM_ARCH_KIMI_LINEAR: return new llama_model_kimi_linear(params); + case LLM_ARCH_KIMI_K3: + return new llama_model_kimi_k3(params); case LLM_ARCH_STEP35: return new llama_model_step35(params); default: @@ -360,8 +369,10 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str static const std::regex pattern_ssm_conv1d ("blk\\.\\d*\\.ssm_conv1d.weight"); static const std::regex pattern_ssm_out_weight ("blk\\.\\d*\\.ssm_out.weight"); - static const std::regex pattern_ffn_up_gate_weight("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.weight"); - static const std::regex pattern_ffn_up_gate_bias ("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.bias"); + static const std::regex pattern_ffn_up_weight ("blk\\.\\d*\\.ffn_up(_exps)?.weight"); + static const std::regex pattern_ffn_up_bias ("blk\\.\\d*\\.ffn_up(_exps)?.bias"); + static const std::regex pattern_ffn_gate_weight ("blk\\.\\d*\\.ffn_gate(_exps)?.weight"); + static const std::regex pattern_ffn_gate_bias ("blk\\.\\d*\\.ffn_gate(_exps)?.bias"); static const std::regex pattern_ffn_gate_up_weight("blk\\.\\d*\\.ffn_gate_up(_exps)?.weight"); static const std::regex pattern_ffn_down_weight ("blk\\.\\d*\\.ffn_down(_exps)?.weight"); static const std::regex pattern_ffn_down_bias ("blk\\.\\d*\\.ffn_down.bias"); @@ -469,10 +480,10 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str } // FFN - if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight)) { + if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_gate_weight)) { return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ffn_down.weight", "ffn_down_exps.weight"); } - if (std::regex_match(tensor_name, pattern_ffn_up_gate_bias)) { + if (std::regex_match(tensor_name, pattern_ffn_up_bias) || std::regex_match(tensor_name, pattern_ffn_gate_bias)) { return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ffn_down.weight", "ffn_down_exps.weight"); } if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) { @@ -556,6 +567,14 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str GGML_ASSERT(tensor->ne[axis] == n_embd + 2*n_embd_gqa); return {{n_embd, 1}, {n_embd_gqa, 2}}; } + if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_up_bias)) { + const int64_t n_ff = hparams.n_ff(il); + // some models such as Phi 3 have fused up + gate tensors named "up" tensors, which need to be segmented + if (tensor->ne[axis] == 2*n_ff) { + return {{n_ff, 2}}; + } + return {{tensor->ne[axis], 1}}; + } if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) { const int64_t n_ff_exp = hparams.n_ff_exp; GGML_ASSERT(tensor->ne[axis] == 2*n_ff_exp); @@ -632,7 +651,8 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str } // FFN - if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight) || std::regex_match(tensor_name, pattern_ffn_up_gate_bias) || + if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_up_bias) || + std::regex_match(tensor_name, pattern_ffn_gate_weight) || std::regex_match(tensor_name, pattern_ffn_gate_bias) || std::regex_match(tensor_name, pattern_ffn_gate_up_weight) || std::regex_match(tensor_name, pattern_ffn_down_weight)) { const int64_t blck_size_perf = std::lcm(blck_size, 128); GGML_ASSERT(segments.size() == 1); @@ -804,6 +824,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_122B_A10B: return "122B.A10B"; case LLM_TYPE_196B_A11B: return "196B.A11B"; case LLM_TYPE_230B_A10B: return "230B.A10B"; + case LLM_TYPE_428B_A23B: return "428B.A23B"; case LLM_TYPE_235B_A22B: return "235B.A22B"; case LLM_TYPE_300B_A47B: return "300B.A47B"; case LLM_TYPE_310B_A15B: return "310B.A15B"; @@ -811,6 +832,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_397B_A17B: return "397B.A17B"; case LLM_TYPE_685B_A37B: return "685B.A37B"; case LLM_TYPE_744B_A40B: return "744B.A40B"; + case LLM_TYPE_2_8T_A104B: return "2.8T.A104B"; case LLM_TYPE_E2B: return "E2B"; case LLM_TYPE_E4B: return "E4B"; default: return "?B"; @@ -1069,6 +1091,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer_all); + GGML_ASSERT(hparams.n_layer_all > 0 && hparams.n_layer_all <= LLAMA_MAX_LAYERS); ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups, false); @@ -1114,6 +1137,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) { std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0); std::fill(hparams.is_swa_impl.begin(), hparams.is_swa_impl.end(), 0); std::fill(hparams.is_recr_impl.begin(), hparams.is_recr_impl.end(), llm_arch_is_recurrent(ml.get_arch()) ? 1 : 0); + std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 0); std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f); std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f); @@ -1230,7 +1254,7 @@ void llama_model_base::load_vocab(llama_model_loader & ml) { bool llama_model_base::load_tensors(llama_model_loader & ml) { const auto & split_mode = params.split_mode; - const auto & use_mlock = params.use_mlock; + const bool use_mlock = params.load_mode == LLAMA_LOAD_MODE_MLOCK || params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK; const auto & tensor_split = params.tensor_split; const int n_layer_all = hparams.n_layer_all; @@ -1240,8 +1264,8 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) { this->ml = &ml; // to be used by create_tensor() and load_arch_tensors() - LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n", - __func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false"); + LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (load_mode = %s)\n", + __func__, llama_load_mode_name(params.load_mode)); // build a list of buffer types for the CPU and GPU devices pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host); @@ -2050,6 +2074,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, res = nullptr; } break; case LLM_ARCH_DEEPSEEK32: + case LLM_ARCH_GLM_DSA: { res = new llama_kv_cache_dsa( *this, @@ -2305,15 +2330,13 @@ llama_model_params llama_model_default_params() { /*.tensor_buft_overrides =*/ nullptr, /*.n_gpu_layers =*/ -1, /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER, + /*.load_mode =*/ LLAMA_LOAD_MODE_MMAP, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.kv_overrides =*/ nullptr, /*.vocab_only =*/ false, - /*.use_mmap =*/ true, - /*.use_direct_io =*/ false, - /*.use_mlock =*/ false, /*.check_tensors =*/ false, /*.use_extra_bufts =*/ true, /*.no_host =*/ false, @@ -2433,6 +2456,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_NEMOTRON_H: case LLM_ARCH_NEMOTRON_H_MOE: case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_KIMI_K3: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values @@ -2473,6 +2497,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_LLAMA_EMBED: case LLM_ARCH_MAINCODER: case LLM_ARCH_GLM_DSA: + case LLM_ARCH_NANBEIGE: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 @@ -2535,9 +2560,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_GROVEMOE: case LLM_ARCH_APERTUS: case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_MINIMAX_M3: case LLM_ARCH_COGVLM: case LLM_ARCH_PANGU_EMBED: case LLM_ARCH_AFMOE: + case LLM_ARCH_LAGUNA: case LLM_ARCH_QWEN3NEXT: case LLM_ARCH_MIMO2: case LLM_ARCH_STEP35: diff --git a/src/llama-model.h b/src/llama-model.h index 45b054cedf1d..f989b543a920 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -134,6 +134,7 @@ enum llm_type { LLM_TYPE_122B_A10B, // Qwen3.5 LLM_TYPE_196B_A11B, // Step3.5-Flash LLM_TYPE_230B_A10B, // Minimax M2 + LLM_TYPE_428B_A23B, // Minimax M3 LLM_TYPE_235B_A22B, LLM_TYPE_300B_A47B, // Ernie MoE big LLM_TYPE_310B_A15B, // /MiMo-V2-Flash @@ -141,6 +142,7 @@ enum llm_type { LLM_TYPE_397B_A17B, // Qwen3.5 LLM_TYPE_685B_A37B, // DeepSeek V3.2 LLM_TYPE_744B_A40B, // GLM-5 + LLM_TYPE_2_8T_A104B, // Kimi-K3 LLM_TYPE_E2B, LLM_TYPE_E4B, }; @@ -317,6 +319,13 @@ struct llama_layer { // ff MoE latent proj struct ggml_tensor * ffn_latent_down = nullptr; struct ggml_tensor * ffn_latent_up = nullptr; + struct ggml_tensor * ffn_latent_norm = nullptr; // kimi k3 + + // attention residuals (kimi k3) + struct ggml_tensor * attn_res_norm = nullptr; + struct ggml_tensor * attn_res_proj = nullptr; + struct ggml_tensor * ffn_res_norm = nullptr; + struct ggml_tensor * ffn_res_proj = nullptr; // ff shared expert (shexp) struct ggml_tensor * ffn_gate_inp_shexp = nullptr; @@ -515,6 +524,12 @@ struct llama_layer { struct ggml_tensor * indexer_attn_k = nullptr; struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias + // MSA + struct ggml_tensor * index_q_proj = nullptr; + struct ggml_tensor * index_k_proj = nullptr; + struct ggml_tensor * index_q_norm = nullptr; + struct ggml_tensor * index_k_norm = nullptr; + // gemma4 layer output scale, reused for talkie embedding skip scale struct ggml_tensor * out_scale = nullptr; @@ -564,6 +579,10 @@ struct llama_model { struct ggml_tensor * output_b = nullptr; struct ggml_tensor * output_norm_enc = nullptr; + // attention residuals output mixture (kimi k3) + struct ggml_tensor * output_res_norm = nullptr; + struct ggml_tensor * output_res_proj = nullptr; + // NVFP4 per-tensor scale2, input_scale for LM head struct ggml_tensor * output_s = nullptr; diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index aebbc1ffb6f1..7c0bac07d096 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -2,6 +2,7 @@ #include "llama-model.h" #include "llama-model-loader.h" #include "llama-ext.h" +#include "llama.h" #include #include @@ -306,6 +307,9 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param // NOTE: can't use LLM_TN here because the layer number is not known quantize &= name.find("ffn_gate_inp.weight") == std::string::npos; + // do not quantize the i32 token-id -> expert-id routing table (DeepSeek-V4) + quantize &= name.find("ffn_gate_tid2eid.weight") == std::string::npos; + // these are very small (e.g. 4x4) quantize &= name.find("altup") == std::string::npos; quantize &= name.find("laurel") == std::string::npos; @@ -322,6 +326,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param quantize &= name.find("ssm_conv1d") == std::string::npos; quantize &= name.find("shortconv.conv.weight") == std::string::npos; + // do not quantize MiniMax's indexer projection weights, they are tiny + quantize &= name.find("indexer.k_proj.weight") == std::string::npos; + quantize &= name.find("indexer.q_proj.weight") == std::string::npos; + // do not quantize RWKV's small yet 2D weights quantize &= name.find("time_mix_first.weight") == std::string::npos; quantize &= name.find("time_mix_w0.weight") == std::string::npos; @@ -673,7 +681,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod ggml_type new_type = default_type; // get more optimal quantization type based on the tensor shape, layer, etc. - if (!params->pure && ggml_is_quantized(default_type)) { + if (ggml_is_quantized(default_type)) { // if the user provided tensor types - use those bool manual = false; if (!qs.tensor_type_patterns.empty()) { @@ -692,7 +700,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod } // if not manual - use the standard logic for choosing the quantization type based on the selected mixture - if (!manual) { + if (!manual && !params->pure) { new_type = llama_tensor_get_type_impl(qs, new_type, tensor, params->ftype, tm.category); } @@ -873,15 +881,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: // mmap consistently increases speed on Linux, and also increases speed on Windows with // hot cache. It may cause a slowdown on macOS, possibly related to free memory. #if defined(__linux__) || defined(_WIN32) - constexpr bool use_mmap = true; + constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP; #else - constexpr bool use_mmap = false; + constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_NONE; #endif const llama_model_kv_override * kv_overrides = params->kv_overrides; std::vector splits = {}; llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr, - fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); + fname_inp, splits, /*file*/ nullptr, /*load_mode*/ load_mode, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); ml.init_mappings(false); // no prefetching auto mparams = llama_model_default_params(); @@ -1351,6 +1359,7 @@ llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * des model->hparams.n_embd_head_k_full = desc->n_embd_head_k; model->hparams.n_embd_head_v_full = desc->n_embd_head_v; model->hparams.n_layer_all = desc->n_layer; + GGML_ASSERT(desc->n_layer > 0 && desc->n_layer <= LLAMA_MAX_LAYERS); model->hparams.n_expert = desc->n_expert; for (uint32_t i = 0; i < desc->n_layer; i++) { diff --git a/src/llama-sampler.cpp b/src/llama-sampler.cpp index 2370e91a1491..6520e4181e61 100644 --- a/src/llama-sampler.cpp +++ b/src/llama-sampler.cpp @@ -263,6 +263,10 @@ static void llama_log_softmax(float * array, size_t size) { */ static void llama_sampler_temp_impl(llama_token_data_array * cur_p, float temp) { + if (cur_p->size == 0) { + return; + } + if (temp <= 0.0f) { // find the token with the highest logit and set the rest to -inf size_t max_i = 0; diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index fdd447147d43..9164a4dd888d 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -496,6 +496,12 @@ struct llm_tokenizer_bpe : llm_tokenizer { "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\\r\\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }; break; + case LLAMA_VOCAB_PRE_TYPE_LAGUNA: + regex_exprs = { + "[^\\n]+|[\\n]+", + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }; + break; case LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE: regex_exprs = { // original regex from tokenizer.json @@ -1325,6 +1331,9 @@ struct llm_tokenizer_rwkv_session { token_id = node->value; token_length = position + 1; } + if (position + 1 >= text.size()) { + break; + } node = node->traverse(text[++position]); } @@ -2342,6 +2351,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "afmoe") { pre_type = LLAMA_VOCAB_PRE_TYPE_AFMOE; clean_spaces = false; + } else if ( + tokenizer_pre == "laguna") { + pre_type = LLAMA_VOCAB_PRE_TYPE_LAGUNA; + clean_spaces = false; } else if ( tokenizer_pre == "minimax-m2") { pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2; @@ -2796,6 +2809,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "" // gemma4 || t.first == "<|tool_response>" // gemma4 || t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr + || t.first == "[e~[" // minimax-m2/m3 ) { special_eog_ids.insert(t.second); if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { @@ -2855,6 +2869,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { LLAMA_LOG_INFO("%s: printing all EOG tokens:\n", __func__); for (auto tid : special_eog_ids) { + if (tid < 0 || tid >= (llama_token) id_to_token.size()) { + LLAMA_LOG_WARN("%s: EOG token id %d is out of range (vocab size %zu), skipping\n", + __func__, tid, id_to_token.size()); + continue; + } auto & text = id_to_token[tid].text; LLAMA_LOG_INFO("%s: - %d ('%s')\n", __func__, tid, text.c_str()); @@ -2889,6 +2908,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { llama_token s_id = LLAMA_TOKEN_NULL; for (auto tid : special_eog_ids) { + if (tid < 0 || tid >= (llama_token) id_to_token.size()) { + continue; + } const auto & text = id_to_token[tid].text; if (text == "<|tool_response>") { has_tool_response = true; @@ -4018,7 +4040,11 @@ int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string } std::vector llama_vocab::get_bpe_merges() const { - std::vector result(pimpl->bpe_ranks.size()); + int max_rank = -1; + for (const auto & pair : pimpl->bpe_ranks) { + max_rank = std::max(max_rank, pair.second); + } + std::vector result(max_rank + 1); for (const auto & pair : pimpl->bpe_ranks) { result[pair.second] = pair.first.first + " " + pair.first.second; diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 707cd4bac4bf..b7c28926338b 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -64,6 +64,7 @@ enum llama_vocab_pre_type { LLAMA_VOCAB_PRE_TYPE_WHITESPACE = 53, LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54, LLAMA_VOCAB_PRE_TYPE_MELLUM2 = 55, + LLAMA_VOCAB_PRE_TYPE_LAGUNA = 56, }; struct LLM_KV; diff --git a/src/llama.cpp b/src/llama.cpp index 0de6048f2820..d22e4c81a9e3 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -46,6 +46,31 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty GGML_ABORT("fatal error"); } +const char * llama_load_mode_name(enum llama_load_mode load_mode) { + switch (load_mode) { + case LLAMA_LOAD_MODE_NONE: + return "none"; + case LLAMA_LOAD_MODE_MMAP: + return "mmap"; + case LLAMA_LOAD_MODE_MLOCK: + return "mlock"; + case LLAMA_LOAD_MODE_MMAP_MLOCK: + return "mmap+mlock"; + case LLAMA_LOAD_MODE_DIRECT_IO: + return "dio"; + } + GGML_ABORT("fatal error"); +} + +enum llama_load_mode llama_load_mode_from_str(const char * str) { + if (std::strcmp(str, "none") == 0) { return LLAMA_LOAD_MODE_NONE; } + if (std::strcmp(str, "mmap") == 0) { return LLAMA_LOAD_MODE_MMAP; } + if (std::strcmp(str, "mlock") == 0) { return LLAMA_LOAD_MODE_MLOCK; } + if (std::strcmp(str, "mmap+mlock") == 0) { return LLAMA_LOAD_MODE_MMAP_MLOCK; } + if (std::strcmp(str, "dio") == 0) { return LLAMA_LOAD_MODE_DIRECT_IO; } + throw std::invalid_argument(std::string("unknown load mode: ") + str); +} + struct llama_sampler_chain_params llama_sampler_chain_default_params() { struct llama_sampler_chain_params result = { /*.no_perf =*/ true, @@ -279,7 +304,7 @@ static bool llama_prepare_model_devices(const llama_model_params & params, llama static std::pair llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud, const std::string & fname, std::vector & splits, FILE * file, llama_model_params & params) { try { - llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io, + llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.load_mode, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides); ml.print_info(); @@ -412,7 +437,7 @@ struct llama_model * llama_model_init_from_user( GGML_ASSERT(metadata != nullptr); std::string path_model; std::vector splits = {}; - params.use_mmap = false; + params.load_mode = LLAMA_LOAD_MODE_NONE; params.use_extra_bufts = false; return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params); } diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp index e64e43451f7f..2d41dace0b28 100644 --- a/src/models/deepseek4.cpp +++ b/src/models/deepseek4.cpp @@ -197,22 +197,31 @@ static ggml_tensor * dsv4_hc_affine( return x; } -ggml_tensor * llama_model_deepseek4::graph::build_hc_weighted_sum( +ggml_tensor * llama_model_deepseek4::graph::build_hc_pre( ggml_tensor * x, - ggml_tensor * weights) const { + ggml_tensor * weights, + int il) const { + GGML_ASSERT(x->ne[0] == n_embd); + GGML_ASSERT(x->ne[1] == hparams.dsv4_hc_mult); + const int64_t hc = hparams.dsv4_hc_mult; const int64_t nt = x->ne[2]; - ggml_tensor * acc = nullptr; + if (cparams.fused_dsv4_hc_pre && il >= 0) { + ggml_tensor * result = ggml_dsv4_hc_pre(ctx0, x, weights); + res->add_fused_node({LLM_FUSED_OP_DSV4_HC_PRE, result, il}); + return result; + } + + ggml_tensor * result = nullptr; for (int64_t ih = 0; ih < hc; ++ih) { ggml_tensor * xh = ggml_view_2d(ctx0, x, n_embd, nt, x->nb[2], ih*x->nb[1]); ggml_tensor * wh = ggml_view_2d(ctx0, weights, 1, nt, weights->nb[1], ih*weights->nb[0]); - ggml_tensor * cur = ggml_mul(ctx0, xh, wh); - acc = acc ? ggml_add(ctx0, acc, cur) : cur; + result = result ? ggml_add(ctx0, result, cur) : cur; } - return acc; + return result; } ggml_tensor * llama_model_deepseek4::graph::build_hc_sinkhorn( @@ -275,11 +284,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_pre( ggml_tensor * scale_pre = dsv4_view_1d(ctx0, hc_scale, 1, 0); ggml_tensor * scale_post = dsv4_view_1d(ctx0, hc_scale, 1, 1); - ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2); ggml_tensor * base_pre = dsv4_view_1d(ctx0, hc_base, hc, 0); ggml_tensor * base_post = dsv4_view_1d(ctx0, hc_base, hc, hc); - ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc); ggml_tensor * pre = dsv4_view_2d(ctx0, mixes, hc, nt, 0); pre = dsv4_hc_affine(ctx0, pre, scale_pre, base_pre); @@ -293,13 +300,23 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_pre( *post = ggml_scale(ctx0, *post, 2.0f); cb(*post, "hc_post", il); - *comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc); - *comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb); - *comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt); - *comb = build_hc_sinkhorn(*comb, il); + if (cparams.fused_dsv4_hc_comb) { + *comb = ggml_dsv4_hc_comb(ctx0, mixes, hc_scale, hc_base, hparams.dsv4_hc_eps, + (int32_t) hparams.dsv4_hc_sinkhorn_iters); + res->add_fused_node({LLM_FUSED_OP_DSV4_HC_COMB, *comb, il}); + } else { + ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2); + ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc); + + *comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc); + *comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb); + *comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt); + *comb = build_hc_sinkhorn(*comb, il); + } cb(*comb, "hc_comb", il); - return build_hc_weighted_sum(x, pre); + ggml_tensor * result = build_hc_pre(x, pre, il); + return result; } ggml_tensor * llama_model_deepseek4::graph::build_hc_post( @@ -308,7 +325,14 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_post( ggml_tensor * post, ggml_tensor * comb, int il) const { - GGML_UNUSED(il); + GGML_ASSERT(x->ne[0] == n_embd); + GGML_ASSERT(residual->ne[1] == hparams.dsv4_hc_mult); + + if (cparams.fused_dsv4_hc_post) { + ggml_tensor * result = ggml_dsv4_hc_post(ctx0, x, residual, post, comb); + res->add_fused_node({LLM_FUSED_OP_DSV4_HC_POST, result, il}); + return result; + } const int64_t hc = hparams.dsv4_hc_mult; const int64_t nt = x->ne[1]; @@ -320,7 +344,8 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_post( for (int64_t src = 0; src < hc; ++src) { ggml_tensor * res_src = ggml_view_2d(ctx0, residual, n_embd, nt, residual->nb[2], src*residual->nb[1]); - ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2], dst*comb->nb[0] + src*comb->nb[1]); + ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2], + dst*comb->nb[0] + src*comb->nb[1]); cur = ggml_add(ctx0, cur, ggml_mul(ctx0, res_src, comb_src_dst)); } @@ -350,7 +375,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_head( pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps); cb(pre, "hc_head_pre", -1); - return build_hc_weighted_sum(x, pre); + return build_hc_pre(x, pre, -1); } ggml_tensor * llama_model_deepseek4::graph::build_hca_compressed_kv_from_state( @@ -435,27 +460,29 @@ ggml_tensor * llama_model_deepseek4::graph::build_overlap_compressed_kv_from_sta kv_state = dsv4_append_zero_row(ctx0, kv_state, false); score_state = dsv4_append_zero_row(ctx0, score_state, true); - ggml_tensor * prev_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, 0); - ggml_tensor * cur_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, ratio*n_blocks); + const int64_t n_read = ratio*n_blocks; + + ggml_tensor * kv_rows = ggml_get_rows(ctx0, kv_state, state_read_idxs); + ggml_tensor * score_rows = ggml_get_rows(ctx0, score_state, state_read_idxs); - ggml_tensor * kv_prev = ggml_get_rows(ctx0, kv_state, prev_idxs); - kv_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_prev, n_embd_head, ratio*n_blocks, kv_prev->nb[1], 0)); + ggml_tensor * kv_prev = ggml_cont(ctx0, + ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1], 0)); kv_prev = ggml_reshape_3d(ctx0, kv_prev, n_embd_head, ratio, n_blocks); cb(kv_prev, name, il); - ggml_tensor * score_prev = ggml_get_rows(ctx0, score_state, prev_idxs); - score_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, score_prev, n_embd_head, ratio*n_blocks, score_prev->nb[1], 0)); + ggml_tensor * score_prev = ggml_cont(ctx0, + ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1], 0)); score_prev = ggml_reshape_3d(ctx0, score_prev, n_embd_head, ratio, n_blocks); cb(score_prev, name, il); - ggml_tensor * kv_cur = ggml_get_rows(ctx0, kv_state, cur_idxs); - kv_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_cur, n_embd_head, ratio*n_blocks, kv_cur->nb[1], - ggml_row_size(kv_cur->type, n_embd_head))); + ggml_tensor * kv_cur = ggml_cont(ctx0, + ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1], + n_read*kv_rows->nb[1] + ggml_row_size(kv_rows->type, n_embd_head))); kv_cur = ggml_reshape_3d(ctx0, kv_cur, n_embd_head, ratio, n_blocks); - ggml_tensor * score_cur = ggml_get_rows(ctx0, score_state, cur_idxs); - score_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, score_cur, n_embd_head, ratio*n_blocks, score_cur->nb[1], - ggml_row_size(score_cur->type, n_embd_head))); + ggml_tensor * score_cur = ggml_cont(ctx0, + ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1], + n_read*score_rows->nb[1] + ggml_row_size(score_rows->type, n_embd_head))); score_cur = ggml_reshape_3d(ctx0, score_cur, n_embd_head, ratio, n_blocks); ggml_tensor * values = ggml_concat(ctx0, kv_prev, kv_cur, 1); @@ -1106,6 +1133,10 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p &post, &comb, il); cb(cur, "hc_ffn_pre", il); + ggml_build_forward_expand(gf, residual); + ggml_build_forward_expand(gf, post); + ggml_build_forward_expand(gf, comb); + cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il); @@ -1148,7 +1179,7 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p inpL = build_hc_post(cur, residual, post, comb, il); inpL = build_cvec(inpL, il); - cb(inpL, "l_out", il); + cb(inpL, "l_last", il); } if (inp_out_ids) { diff --git a/src/models/dflash.cpp b/src/models/dflash.cpp index a7b4f4435a88..427eed4594e3 100644 --- a/src/models/dflash.cpp +++ b/src/models/dflash.cpp @@ -1,5 +1,6 @@ #include "models.h" +#include "llama-impl.h" #include "llama-kv-cache.h" #include "llama-kv-cache-iswa.h" @@ -164,9 +165,25 @@ llama_model_dflash::graph::graph(const llama_model & model, const llm_gra const auto * kv = is_swa ? inp_attn_iswa->mctx->get_swa() : inp_attn_iswa->mctx->get_base(); ggml_tensor * k_idxs = is_swa ? inp_attn_iswa->get_k_idxs_swa() : inp_attn_iswa->get_k_idxs(); ggml_tensor * v_idxs = is_swa ? inp_attn_iswa->get_v_idxs_swa() : inp_attn_iswa->get_v_idxs(); + // rotate K/V into the cache's rotated space + ggml_tensor * k_rot = is_swa ? inp_attn_iswa->self_k_rot_swa : inp_attn_iswa->self_k_rot; + ggml_tensor * v_rot = is_swa ? inp_attn_iswa->self_v_rot_swa : inp_attn_iswa->self_v_rot; + if (k_rot) { + Kcur = llama_mul_mat_hadamard(ctx0, Kcur, k_rot); + } + if (v_rot) { + Vcur = llama_mul_mat_hadamard(ctx0, Vcur, v_rot); + } ggml_build_forward_expand(gf, kv->cpy_k(ctx0, Kcur, k_idxs, il)); ggml_build_forward_expand(gf, kv->cpy_v(ctx0, Vcur, v_idxs, il)); } else { + // rotate K/V into the cache's rotated space + if (inp_attn->self_k_rot) { + Kcur = llama_mul_mat_hadamard(ctx0, Kcur, inp_attn->self_k_rot); + } + if (inp_attn->self_v_rot) { + Vcur = llama_mul_mat_hadamard(ctx0, Vcur, inp_attn->self_v_rot); + } ggml_build_forward_expand(gf, inp_attn->mctx->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il)); ggml_build_forward_expand(gf, inp_attn->mctx->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il)); } diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index 32fe6def6f3c..df190e1f634b 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -1,5 +1,31 @@ #include "models.h" +#include "llama-kv-cache-dsa.h" + +// https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26 +const std::array GLM_5_2_DEFAULT_INDEXER_TYPES = { + 1, 1, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, +}; + void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -34,10 +60,19 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { // NextN/MTP parameters ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); - GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl"); + GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all"); + + // BC for GLM 5, 5.1 (full indexers) without indexer_types metadata + const bool is_pre_5_2 = hparams.n_ctx_train < 1048576; + if (is_pre_5_2) { + std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 1); + } else { + hparams.is_indexer_full_impl = GLM_5_2_DEFAULT_INDEXER_TYPES; + } + ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false); switch (hparams.n_layer()) { - case 79: type = LLM_TYPE_744B_A40B; break; + case 78: type = LLM_TYPE_744B_A40B; break; default: type = LLM_TYPE_UNKNOWN; } } @@ -150,3 +185,361 @@ std::unique_ptr llama_model_glm_dsa::build_arch_graph(const l return std::make_unique(*this, params); } +llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const bool is_mla = hparams.is_mla(); + GGML_ASSERT(is_mla); + + // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA + const int64_t n_embd_head_k = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v = hparams.n_embd_head_v_mla(); + GGML_UNUSED(n_embd_head_v); + + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; + + const int64_t n_indexer_head = hparams.indexer_n_head; + const int64_t n_embd_indexer_head = hparams.indexer_head_size; + const int64_t n_embd_indexer_head_rope = hparams.n_rot(); + const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope; + const uint32_t n_indexer_top_k = hparams.indexer_top_k; + + const uint32_t kv_lora_rank = hparams.n_lora_kv; + + // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. + // See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation. + // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + + // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor + GGML_ASSERT(ext_factor >= 0.0f); + const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); + + // use the original attn_factor to pre-scale the kq_scale + const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); + + ggml_tensor * cur; + ggml_tensor * inpL; + + // {n_embd, n_tokens} + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + llm_graph_input_attn_k_dsa * inp_attn_dsa = build_attn_inp_k_dsa(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // Difference vs Deepseek 3.2: shared indexer layers reuse the top_k from the previous full indexer layers + // See https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L30 + ggml_tensor * prev_top_k = nullptr; + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self_attention + { + ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); + cb(qr, "qr", il); + + qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + cb(qr, "qr", il); + + ggml_tensor * top_k = nullptr; + + // lightning indexer + if (hparams.is_indexer_full(il)) { + // "full" layer + ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr); + cb(indexer_q, "indexer_q", il); + + // split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens} + ggml_tensor * indexer_q_pe = + ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens, + ggml_row_size(indexer_q->type, n_embd_indexer_head), + ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0); + cb(indexer_q_pe, "indexer_q_pe", il); + + // and {n_embd_indexer_head_nope, n_indexer_head, n_tokens} + ggml_tensor * indexer_q_nope = + ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens, + ggml_row_size(indexer_q->type, n_embd_indexer_head), + ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, + ggml_row_size(indexer_q->type, n_embd_indexer_head_nope)); + cb(indexer_q_nope, "indexer_q_nope", il); + + indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_rot, + LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(indexer_q_pe, "indexer_q_pe", il); + + // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens} + indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0); + cb(indexer_q, "indexer_q", il); + + ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur); + cb(indexer_k, "indexer_k", il); + + indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il); + cb(indexer_k, "indexer_k", il); + + // split into {n_embd_indexer_head_rope, 1, n_tokens} + ggml_tensor * indexer_k_pe = + ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens, + ggml_row_size(indexer_k->type, n_embd_indexer_head), + ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0); + cb(indexer_k_pe, "indexer_k_pe", il); + + // and {n_embd_indexer_head_nope, 1, n_tokens} + ggml_tensor * indexer_k_nope = + ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens, + ggml_row_size(indexer_k->type, n_embd_indexer_head), + ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, + ggml_row_size(indexer_k->type, n_embd_indexer_head_nope)); + cb(indexer_k_nope, "indexer_k_nope", il); + + indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, inp_pos, nullptr, n_rot, + LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(indexer_k_pe, "indexer_k_pe", il); + + // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens} + indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0); + cb(indexer_k, "indexer_k", il); + + // perform Hadamard transform on indexer q and k + indexer_q = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_q); + cb(indexer_q, "indexer_q", il); + indexer_k = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_k); + cb(indexer_k, "indexer_k", il); + + // store indexer keys to KV cache + const auto * mctx_lid = inp_attn_dsa->mctx->get_lid(); + const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid(); + ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il)); + + // prepare indexer weights + ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur); + cb(indexer_weights, "indexer_weights", il); + + // get cached indexer keys + indexer_k = mctx_lid->get_k(ctx0, il); + + // split the batch into streams if needed + const auto n_stream = indexer_k->ne[3]; + indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0); + indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0); + + // pre-scale weights to avoid scaling operations on huge indexer_score tensor + indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head))); + cb(indexer_weights, "indexer_weights", il); + + ggml_tensor * indexer_score = nullptr; + if (cparams.fused_lid) { + indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn_dsa->get_kq_mask_lid()); + cb(indexer_score, "indexer_score", il); + res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il}); + } else { + // calculate indexer kq + indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3); + cb(indexer_q, "indexer_q", il); + indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3); + cb(indexer_k, "indexer_k", il); + + ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q); + cb(indexer_kq, "indexer_kq", il); + + // ReLU requires contiguous tensors + indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3)); + cb(indexer_kq, "indexer_kq", il); + + // apply ReLU + indexer_score = ggml_relu(ctx0, indexer_kq); + cb(indexer_score, "indexer_score", il); + + // multiply scores by indexer weights + indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights); + cb(indexer_score, "indexer_score", il); + + // sum by q n_indexer_head dimension + indexer_score = ggml_sum_rows(ctx0, indexer_score); + cb(indexer_score, "indexer_score", il); + + // permute result to match KQ mask + indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3)); + cb(indexer_score, "indexer_score", il); + + // mask indexer scores + ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid(); + indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask); + cb(indexer_score, "indexer_score", il); + } + + // get indices of top k indexer scores + uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k; + top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k)); + prev_top_k = top_k; + cb(top_k, "top_k", il); + } else { + // "shared" indexer layer - reuse top-k from a previous full layer + GGML_ASSERT(prev_top_k != nullptr && "shared indexer layer must follow a previous full indexer layer"); + top_k = prev_top_k; + cb(top_k, "top_k", il); + } + + ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr); + cb(q, "q", il); + + // split into {n_embd_head_qk_nope, n_head, n_tokens} + ggml_tensor * q_nope = + ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, 0); + cb(q_nope, "q_nope", il); + + // and {n_embd_head_qk_rope, n_head, n_tokens} + ggml_tensor * q_pe = ggml_view_3d( + ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope)); + cb(q_pe, "q_pe", il); + + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); + cb(kv_cmpr_pe, "kv_cmpr_pe", il); + + // split into {kv_lora_rank, n_tokens} + ggml_tensor * kv_cmpr = + ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + cb(kv_cmpr, "kv_cmpr", il); + + // and {n_embd_head_qk_rope, 1, n_tokens} + ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + cb(k_pe, "k_pe", il); + + q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(q_pe, "q_pe", il); + + k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(k_pe, "k_pe", il); + + kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + cb(kv_cmpr, "kv_cmpr", il); + + // MLA attention + { + // {n_embd_head_qk_nope, n_tokens, n_head} + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + cb(q_nope, "q_nope_perm", il); + + // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope); + cb(q_nope_absorbed, "q_nope_absorbed", il); + + // {kv_lora_rank, n_head, n_tokens} + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + cb(q_nope_absorbed, "q_nope_absorbed_perm", il); + + // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens} + // note: rope must go first for in-place context shifting in build_rope_shift() + ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qcur, "Qcur", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + cb(kv_cmpr, "kv_cmpr_reshape", il); + + // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens} + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + cb(Kcur, "Kcur", il); + + // {kv_lora_rank, 1, n_tokens} + ggml_tensor * Vcur = kv_cmpr; + cb(Vcur, "Vcur", il); + + // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) + cur = build_attn(inp_attn_dsa, + model.layers[il].wo, NULL, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il); + } + } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // MoE branch + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, + model.layers[il].ffn_gate_up_exps, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); + cb(moe_out, "ffn_moe_out", il); + + // FFN shared expert + { + ggml_tensor * ffn_shexp = + build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, + model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s, + model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + } + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/kimi-k3.cpp b/src/models/kimi-k3.cpp new file mode 100644 index 000000000000..c0ed5a614295 --- /dev/null +++ b/src/models/kimi-k3.cpp @@ -0,0 +1,576 @@ +#include "models.h" +#include "llama-memory-recurrent.h" + +// Kimi K3: hybrid KDA + gated-MLA (NoPE) with Attention Residuals (AttnRes), +// Stable LatentMoE and SiTU-GLU activation. +// +// Reference: moonshotai/Kimi-K3 modeling_kimi_linear.py (HF), sglang kimi_k3.py +// +// Deltas vs Kimi Linear (LLM_ARCH_KIMI_LINEAR): +// - KDA safe gate: g_log = gate_lower_bound * sigmoid(exp(A_log) * (f_b(f_a(x)) + dt_bias)) +// (ssm_a stores exp(A_log) > 0, sliced to n_head at conversion) +// - KDA output gate is full-rank: g2 = g_proj(x) (wqkv_gate), not low-rank g_a/g_b +// - MLA has an output gate: attn = attn * sigmoid(g_proj(x)) before o_proj +// - MoE experts run in a latent space: down -> experts -> weighted sum -> RMSNorm -> up +// with the router operating on the full hidden state +// - AttnRes: the residual stream restarts every attn_res_block_size layers; snapshots are +// banked and re-mixed via a learned softmax mixture before attention, before the FFN +// and at the model output + +void llama_model_kimi_k3::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl); + ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); + ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); + ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda); + ml.get_key(LLM_KV_KDA_GATE_LOWER_BOUND, hparams.kda_gate_lower_bound); + + ml.get_key(LLM_KV_SITU_BETA, hparams.situ_beta); + ml.get_key(LLM_KV_SITU_LINEAR_BETA, hparams.situ_linear_beta); + ml.get_key(LLM_KV_ATTN_RES_BLOCK_SIZE, hparams.attn_res_block_size); + + // Mark KDA layers as recurrent using the n_head_kv pattern (like Kimi Linear) + for (uint32_t i = 0; i < hparams.n_layer(); ++i) { + hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0; + } + + // Stable LatentMoE + ml.get_key(LLM_KV_MOE_LATENT_SIZE, hparams.moe_latent_size); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); + + // K3 always renormalizes the top-k sigmoid weights (moe_renormalize = true) + hparams.expert_weights_norm = true; + + GGML_ASSERT(hparams.attn_res_block_size > 0 && "Kimi-K3 requires attn_res_block_size"); + GGML_ASSERT(hparams.moe_latent_size > 0 && "Kimi-K3 requires moe_latent_size"); + + switch (hparams.n_layer()) { + case 93: type = LLM_TYPE_2_8T_A104B; break; // Kimi-K3 + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_kimi_k3::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + const int64_t moe_latent = hparams.moe_latent_size; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + // AttnRes output mixture + output_res_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_RES_NORM, "weight"), {n_embd}, 0); + output_res_proj = create_tensor(tn(LLM_TENSOR_OUTPUT_RES_PROJ, "weight"), {n_embd}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + // AttnRes per-layer mixtures + layer.attn_res_norm = create_tensor(tn(LLM_TENSOR_ATTN_RES_NORM, "weight", i), {n_embd}, 0); + layer.attn_res_proj = create_tensor(tn(LLM_TENSOR_ATTN_RES_PROJ, "weight", i), {n_embd}, 0); + layer.ffn_res_norm = create_tensor(tn(LLM_TENSOR_FFN_RES_NORM, "weight", i), {n_embd}, 0); + layer.ffn_res_proj = create_tensor(tn(LLM_TENSOR_FFN_RES_PROJ, "weight", i), {n_embd}, 0); + + const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda; + const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda; + const int64_t ssm_d_conv = hparams.ssm_d_conv; + const int64_t d_inner = n_embd_head_k_kda * n_head; + + if (hparams.is_recr(i)) { + // === KDA layer === + // Conv1d weights: 4D [d_conv, 1, d_inner, 1] with 3D fallback + layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_q_conv) { + layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, d_inner}, 0); + } + layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_k_conv) { + layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, d_inner}, 0); + } + layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_v_conv) { + layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, d_inner}, 0); + } + + // q, k, v projections + create_tensor_qkv(layer, i, n_embd, d_inner, d_inner, d_inner, 0); + + // forget gate projections (low-rank) + layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); + layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, d_inner}, 0); + + // b_proj (beta mixing coefficient) + layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0); + + // exp(A_log), per head (sliced from [head_dim] to [n_head] at conversion) + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {n_head}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_a) { + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0); + } + + // dt_bias + layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0); + + // full-rank output gate (use_full_rank_gate = true) + layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, d_inner}, 0); + + // o_norm + layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); + + // o_proj + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0); + } else { + // === Gated MLA layer (NoPE) === + const int64_t q_lora_rank = hparams.n_lora_q; + const int64_t kv_lora_rank = hparams.n_lora_kv; + const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); + const int64_t qk_rope_head_dim = hparams.n_rot(); + + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); + + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0); + + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0); + // Support legacy GGUFs that don't split wkv_b (MLA KV cache disabled) + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), + {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); + if (!layer.wkv_b) { // MLA KV cache enabled + layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0); + layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0); + } + + // output gate: attn = attn * sigmoid(g_proj(x)) before o_proj + layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head * n_embd_head_v_mla}, 0); + + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0); + } + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + const int64_t n_ff_exp = hparams.n_ff_exp; + + if (i < (int) hparams.n_layer_dense_lead) { + // Dense FFN layer (SiTU-GLU) + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } else { + // Stable LatentMoE: router on full hidden, experts in the latent space + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + + layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_latent}, 0); + layer.ffn_latent_norm = create_tensor(tn(LLM_TENSOR_FFN_LATENT_NORM, "weight", i), {moe_latent}, 0); + layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_latent, n_embd}, 0); + + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {moe_latent, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, moe_latent, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_latent, n_ff_exp, n_expert}, 0); + + // Shared experts operate on the full hidden state + const int64_t n_ff_shexp = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + } + } +} + +std::unique_ptr llama_model_kimi_k3::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +// Causal Conv1d for Q/K/V (identical to Kimi Linear) +// qkv: 0 = Q, 1 = K, 2 = V +static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all, ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w, int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) { + const int64_t d_inner = head_dim * n_head; + const int64_t conv_state_size = (d_conv - 1) * d_inner; + const int64_t n_embd_r_total = 3 * conv_state_size; // Q + K + V + + ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs, + (d_conv - 1) * ggml_element_size(conv_state_all), + n_embd_r_total * ggml_element_size(conv_state_all), + qkv * conv_state_size * ggml_element_size(conv_state_all)); + + ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x); + + ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs); + + ggml_tensor * conv_x = ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0); + + // Save last (d_conv-1) columns back to the conv state + ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, + conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]); + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, last_conv_x, + ggml_view_3d(ctx0, conv_states_all, + d_conv - 1, d_inner, n_seqs, + (d_conv - 1) * ggml_element_size(conv_states_all), + n_embd_r_total * ggml_element_size(conv_states_all), + (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all)))); + + ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner); + + ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight); + Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens); + Xcur = ggml_silu(ctx0, Xcur); + + return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs); +} + +// AttnRes mixture: softmax over [bank rows..., prefix] scored by a scalar projection +// of the RMS-normed rows; the mixture combines the raw (unnormalized) rows. +// k = rms_norm(v) +// scores = sum(k * (norm_w * proj_w), dim=embd) +// probs = softmax(scores over rows) +// out = sum_r probs_r * v_r +ggml_tensor * llama_model_kimi_k3::graph::build_attn_res_mix( + ggml_tensor * prefix, + const std::vector & bank, + ggml_tensor * norm_w, + ggml_tensor * proj_w, + int il) { + const int64_t n_embd = prefix->ne[0]; + const int64_t n_toks = prefix->ne[1]; + const int64_t n_rows = (int64_t) bank.size() + 1; + + // v: [n_embd, n_rows, n_toks] + ggml_tensor * v = nullptr; + for (ggml_tensor * b : bank) { + ggml_tensor * r = ggml_reshape_3d(ctx0, b, n_embd, 1, n_toks); + v = v ? ggml_concat(ctx0, v, r, 1) : r; + } + { + ggml_tensor * r = ggml_reshape_3d(ctx0, prefix, n_embd, 1, n_toks); + v = v ? ggml_concat(ctx0, v, r, 1) : r; + } + + ggml_tensor * k = ggml_rms_norm(ctx0, v, hparams.f_norm_rms_eps); + cb(k, "attn_res_k", il); + + // score weight: norm.weight * proj.weight, [n_embd] + ggml_tensor * sw = ggml_mul(ctx0, norm_w, proj_w); + + // scores: [1, n_rows, n_toks] + ggml_tensor * scores = ggml_mul_mat(ctx0, ggml_reshape_2d(ctx0, sw, n_embd, 1), k); + scores = ggml_reshape_2d(ctx0, scores, n_rows, n_toks); + cb(scores, "attn_res_scores", il); + + ggml_tensor * probs = ggml_soft_max(ctx0, scores); + cb(probs, "attn_res_probs", il); + + // weighted sum of raw rows + ggml_tensor * w = ggml_mul(ctx0, v, ggml_reshape_3d(ctx0, probs, 1, n_rows, n_toks)); + w = ggml_cont(ctx0, ggml_permute(ctx0, w, 1, 0, 2, 3)); // [n_rows, n_embd, n_toks] + ggml_tensor * out = ggml_sum_rows(ctx0, w); // [1, n_embd, n_toks] + out = ggml_reshape_2d(ctx0, out, n_embd, n_toks); + cb(out, "attn_res_mix", il); + + return out; +} + +llama_model_kimi_k3::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_build_delta_net_base(params), model(model) { + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + cb(inpL, "model.embed_tokens", -1); + + // K3 uses no positional embeddings anywhere (KDA recurrence + NoPE MLA) + + auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr; + auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr; + auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr(); + auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr; + auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + const int64_t n_head = hparams.n_head(); + const int64_t head_dim = hparams.n_embd_head_kda; + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = n_head * head_dim; + const int64_t n_seqs = ubatch.n_seqs; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + + GGML_ASSERT(n_seqs != 0); + GGML_ASSERT(ubatch.equal_seqs()); + GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); + + // MLA params + const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); + const int64_t kv_lora_rank = hparams.n_lora_kv; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; + const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla); + + const uint32_t res_block = hparams.attn_res_block_size; + GGML_ASSERT(res_block > 0); + + // AttnRes state: snapshot bank + current prefix sum of the residual stream + std::vector res_bank; + ggml_tensor * prefix = inpL; + + for (int il = 0; il < n_layer; ++il) { + const auto & layer = model.layers[il]; + + // pre-attention mixture (bank is empty only before the first snapshot) + ggml_tensor * h = res_bank.empty() + ? prefix + : build_attn_res_mix(prefix, res_bank, layer.attn_res_norm, layer.attn_res_proj, il); + + // snapshot + restart of the residual stream + const bool snapshot = (il % (int) res_block) == 0; + if (snapshot) { + res_bank.push_back(prefix); + } + + cur = build_norm(h, layer.attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + ggml_build_forward_expand(gf, cur); + + if (hparams.is_recr(il)) { + // === KDA layer (Kimi Delta Attention) === + const auto * mctx_cur = inp_rs->mctx; + const auto kv_head = mctx_cur->get_head(); + + ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); + cb(conv_states_all, "conv_states_all", il); + ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs); + ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + + // K3 safe gate: g1 = lower_bound * sigmoid(exp(A_log) * (f_b(f_a(x)) + dt_bias)) + // ssm_a stores exp(A_log) (positive), per head + ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur); + ggml_tensor * g1 = ggml_mul_mat(ctx0, layer.ssm_f_b, f_a); + cb(g1, "kda_g1_raw", il); + g1 = ggml_add(ctx0, g1, layer.ssm_dt_b); + g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens); + + ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head, 1); + g1 = ggml_mul(ctx0, g1, A); + g1 = ggml_sigmoid(ctx0, g1); + g1 = ggml_scale(ctx0, g1, hparams.kda_gate_lower_bound); + cb(g1, "kda_g1", il); + + g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs); + + // beta (mixing coefficient) + ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur); + beta = ggml_reshape_4d(ctx0, beta, 1, n_head, n_seq_tokens, n_seqs); + beta = ggml_sigmoid(ctx0, beta); + cb(beta, "kda_beta", il); + + // KDA recurrence + ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); + ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs); + state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs); + + const float eps_norm = hparams.f_norm_rms_eps; + + Qcur = ggml_l2_norm(ctx0, Qcur, eps_norm); + Kcur = ggml_l2_norm(ctx0, Kcur, eps_norm); + + auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il); + + ggml_tensor * output = ggml_cont(ctx0, attn_out.first); + ggml_tensor * new_state = attn_out.second; + cb(output, "attn_output", il); + + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, new_state, + ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, + kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all)))); + + // full-rank output gate g2 = g_proj(x) + ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.wqkv_gate, cur); + cb(g2, "kda_g2", il); + g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_tokens); + + // o_norm with sigmoid gating: out = RMSNorm(o) * sigmoid(g2) + ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, output, head_dim, n_head, n_tokens); + ggml_tensor * normed = build_norm(attn_out_final, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il); + cb(normed, "kda_normed", il); + ggml_tensor * gated = ggml_mul(ctx0, normed, ggml_sigmoid(ctx0, g2)); + + gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens); + cur = ggml_mul_mat(ctx0, layer.wo, gated); + cb(cur, "kda_out", il); + } else { + // === Gated MLA layer (NoPE) === + // Q: q_b(q_a_norm(q_a(x))) + ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.wq_a, cur); + Qcur = build_norm(Qcur, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + Qcur = ggml_mul_mat(ctx0, layer.wq_b, Qcur); + cb(Qcur, "mla_q", il); + + // KV compression + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur); + + ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + // NoPE: k_pe is a positional-encoding-free shared key dimension, no RoPE applied + kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + + ggml_tensor * attn_pregate = nullptr; + + if (layer.wk_b && layer.wv_b) { // MLA KV cache enabled (absorption) + ggml_tensor * q_nope = + ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla), + ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0); + ggml_tensor * q_pe = ggml_view_3d( + ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla), + ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, ggml_row_size(Qcur->type, n_embd_head_qk_nope)); + + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope); + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + + ggml_tensor * Qmla = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qmla, "mla_q_absorbed", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + ggml_tensor * Vcur = kv_cmpr; + + attn_pregate = build_attn(inp_attn_k, nullptr, nullptr, nullptr, Qmla, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il); + } else { // MLA KV cache disabled: fall back to MHA + ggml_tensor * Qmla = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens); + ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr); + const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla; + + ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(kv->type, kv_per_head), + ggml_row_size(kv->type, kv_per_head * n_head), 0); + ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens, + ggml_row_size(kv->type, kv_per_head), + ggml_row_size(kv->type, kv_per_head * n_head), + ggml_row_size(kv->type, n_embd_head_qk_nope)); + Vcur = ggml_cont(ctx0, Vcur); + + ggml_tensor * k_pe_target = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens); + ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target); + ggml_tensor * Kcur = ggml_concat(ctx0, k_pe_repeated, k_nope, 0); + + attn_pregate = build_attn(inp_attn_kv, nullptr, nullptr, nullptr, Qmla, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il); + } + cb(attn_pregate, "mla_pregate", il); + + // output gate: attn = attn * sigmoid(g_proj(x)), then o_proj + ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur); + gate = ggml_sigmoid(ctx0, gate); + cb(gate, "mla_gate", il); + + cur = ggml_mul(ctx0, attn_pregate, gate); + cur = build_lora_mm(layer.wo, cur, layer.wo_s); + cb(cur, "mla_out", il); + } + + // residual stream update (restarts at snapshot layers) + prefix = snapshot ? cur : ggml_add(ctx0, prefix, cur); + cb(prefix, "attn_prefix", il); + + // pre-FFN mixture (unconditional: bank is never empty here) + ggml_tensor * h2 = build_attn_res_mix(prefix, res_bank, layer.ffn_res_norm, layer.ffn_res_proj, il); + + cur = build_norm(h2, layer.ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + // Dense FFN (SiTU-GLU) + cur = build_ffn(cur, + layer.ffn_up, NULL, NULL, + layer.ffn_gate, NULL, NULL, + layer.ffn_down, NULL, NULL, + NULL, LLM_FFN_SITU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // Stable LatentMoE + // router operates on the full hidden state + ggml_tensor * router_logits = build_lora_mm(layer.ffn_gate_inp, cur); + cb(router_logits, "ffn_moe_logits", il); + + // experts run in the latent space + ggml_tensor * latent = ggml_mul_mat(ctx0, layer.ffn_latent_down, cur); + cb(latent, "ffn_moe_latent", il); + + ggml_tensor * moe_out = build_moe_ffn(latent, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + hparams.n_expert, + hparams.n_expert_used, + LLM_FFN_SITU, + hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + router_logits); + cb(moe_out, "ffn_moe_out", il); + + // latent norm applies AFTER the weighted expert sum, then project back up + moe_out = build_norm(moe_out, layer.ffn_latent_norm, NULL, LLM_NORM_RMS, il); + moe_out = ggml_mul_mat(ctx0, layer.ffn_latent_up, moe_out); + cb(moe_out, "ffn_moe_out_up", il); + + // shared experts on the full hidden state (SiTU-GLU) + ggml_tensor * ffn_shexp = build_ffn(cur, + layer.ffn_up_shexp, NULL, NULL, + layer.ffn_gate_shexp, NULL, NULL, + layer.ffn_down_shexp, NULL, NULL, + NULL, LLM_FFN_SITU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + + // residual stream update + prefix = ggml_add(ctx0, prefix, cur); + prefix = build_cvec(prefix, il); + cb(prefix, "l_out", il); + } + + // output mixture over the final prefix sum and the snapshot bank + cur = build_attn_res_mix(prefix, res_bank, model.output_res_norm, model.output_res_proj, -1); + + // select only the output tokens (AttnRes needs the full token set until here) + if (inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/laguna.cpp b/src/models/laguna.cpp new file mode 100644 index 000000000000..fb55ec12f934 --- /dev/null +++ b/src/models/laguna.cpp @@ -0,0 +1,332 @@ +// Laguna (poolside): sigmoid-routed MoE with a score-correction bias, one shared +// expert, a softplus attention output gate, QK-norm, and per-layer-type RoPE +// (YaRN on full-attention layers, plain RoPE on sliding-window layers). XS.2 is +// hybrid full/SWA with a per-head gate; M.1 is full-attention with a per-element +// gate. Shares the MoE/gate structure with afmoe. + +#include "models.h" + +void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + + // Laguna ships one shared expert and stores its size directly (routed and + // shared experts may differ), so read the size from expert_shared_feed_forward_length. + // The count is not in the config; default to 1 but read the key if present. + hparams.n_expert_shared = 1; + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false); + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); + if (hparams.n_ff_shexp == 0) { + // Weightless fixtures (test-llama-archs) omit this key; derive a nonzero + // size so the shared expert is still built. Real GGUFs always carry the + // exact value (routed and shared FF lengths may differ). + hparams.n_ff_shexp = hparams.n_ff_exp * hparams.n_expert_shared; + } + + // Sliding-window attention is OPTIONAL. XS.2 is hybrid (full / SWA / SWA / + // SWA repeating, period 4 starting with full); M.1 has no sliding window + // (all layers full attention). When sliding_window is absent or zero we + // leave swa_type = NONE and skip the SWA-specific per-layer-type RoPE. + hparams.n_swa = 0; + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); + if (hparams.n_swa > 0) { + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + + uint32_t swa_period = 4; + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period, /*dense_first=*/true); // XS.2: FULL at il%4==0 + + // Per-layer-type RoPE: full layers use YaRN θ=500000 over 64 dims; + // SWA layers use default RoPE θ=10000 over 128 dims. Base load_hparams + // already reads ROPE_FREQ_BASE and ROPE_DIMENSION_COUNT into the + // non-SWA fields; we explicitly pull the SWA mirrors here. + hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; + hparams.rope_freq_scale_train_swa = 1.0f; // SWA uses plain RoPE (no YaRN scaling); do NOT inherit full layers 1/factor + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); + ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false); + } + + // Default the expert gating function to SIGMOID when the key is absent + // (matches the HF reference). + if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { + hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID; + } + + switch (hparams.n_layer()) { + case 40: type = LLM_TYPE_30B_A3B; break; // Laguna-XS.2 + case 70: type = LLM_TYPE_230B_A10B; break; // Laguna-M.1 + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_laguna::load_arch_tensors(llama_model_loader & ml) { + LLAMA_LOAD_LOCALS; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + if (output == NULL) { + // tied embeddings fallback + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + const int64_t n_ff_exp = hparams.n_ff_exp; + const int64_t n_ff_shexp = hparams.n_ff_shexp; + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + // Per-layer head count — Laguna varies n_head between full and SWA + // layers (48 vs 64 in XS.2). KV head count is uniform. + const int64_t n_head_il = hparams.n_head(i); + const int64_t n_head_kv_il = hparams.n_head_kv(i); + const int64_t n_embd_q_il = n_embd_head_k * n_head_il; + const int64_t n_embd_k_il = n_embd_head_k * n_head_kv_il; + const int64_t n_embd_v_il = n_embd_head_v * n_head_kv_il; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + create_tensor_qkv(layer, i, n_embd, n_embd_q_il, n_embd_k_il, n_embd_v_il, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q_il, n_embd}, 0); + + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + + // Attention output gate. XS.2 is per-head (g_proj -> n_head, one scalar + // per head broadcast over head_dim at multiply time); M.1 is per-element + // (g_proj -> n_head*head_dim, like afmoe). Detect from the stored tensor + // shape so a single arch handles both; the graph mirrors this check. + // Gate width selects per-head vs per-element. Real GGUFs always carry the + // gate tensor, so read the width from it and require EXACTLY one of the two + // valid widths -- never guess between them. Weightless fixtures + // (test-llama-archs) have no gate tensor; fall back to the per-head layout so + // the per-head reshape path is still exercised. + const int64_t n_gate_per_head = n_head_il; + const int64_t n_gate_per_elem = n_embd_head_k * n_head_il; + const ggml_tensor * gate_meta = ml.get_tensor_meta(tn(LLM_TENSOR_ATTN_GATE, "weight", i).str().c_str()); + int64_t n_gate_out; + if (gate_meta != nullptr) { + n_gate_out = gate_meta->ne[1]; + if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) { + GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d " + "(expected %lld per-head or %lld per-element)", + (long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem); + } + } else { + n_gate_out = n_gate_per_head; + } + layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_gate_out}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + if ((uint32_t)i >= hparams.n_layer_dense_lead) { + // MoE layer + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + + // Always-on shared expert. + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); + } else { + // Dense layer (the leading n_layer_dense_lead layers) + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + } + } +} + +std::unique_ptr llama_model_laguna::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + // No MuP embedding scale (laguna omits this; afmoe scales by sqrt(hidden)). + + ggml_tensor * inp_pos = build_inp_pos(); + // XS.2 is hybrid SWA -> interleaved-SWA KV input; M.1 is all-full -> plain + // KV input. Pick the matching input (and build_attn overload) per swa_type. + const bool has_swa = hparams.swa_type != LLAMA_SWA_TYPE_NONE; + llm_graph_input_attn_kv * inp_attn_kv = has_swa ? nullptr : build_attn_inp_kv(); + llm_graph_input_attn_kv_iswa * inp_attn_iswa = has_swa ? build_attn_inp_kv_iswa() : nullptr; + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + const float kq_scale = 1.0f / sqrtf(float(n_embd_head)); + + for (int il = 0; il < n_layer; ++il) { + const bool is_swa_il = hparams.is_swa(il); + const int64_t n_head_il = hparams.n_head(il); + const int64_t n_head_kv_il = hparams.n_head_kv(il); + + // Per-layer-type RoPE config. SWA layers run plain rope (no YaRN), + // achieved by zeroing the YaRN ext/beta params for those layers. + const int n_rot_l = is_swa_il ? hparams.n_rot_swa : n_rot; + const float freq_base_l = is_swa_il ? hparams.rope_freq_base_train_swa : freq_base; + const float freq_scale_l = is_swa_il ? hparams.rope_freq_scale_train_swa : freq_scale; + const float ext_factor_l = is_swa_il ? 0.0f : ext_factor; + // YaRN magnitude scaling (mscale) is already handled by the framework: + // llama_context pre-divides cparams.yarn_attn_factor by (1 + 0.1*ln(factor)) + // to cancel ggml rope_yarn's internal mscale *= 1 + 0.1*ln(1/freq_scale). + // Pass attn_factor straight through (like every other arch); SWA layers run + // plain RoPE (ext_factor 0, no mscale) so force 1.0 there. + const float attn_factor_l = is_swa_il ? 1.0f : attn_factor; + const float beta_fast_l = is_swa_il ? 0.0f : beta_fast; + const float beta_slow_l = is_swa_il ? 0.0f : beta_slow; + const int n_ctx_orig_l = is_swa_il ? hparams.n_ctx_train : n_ctx_orig; + + ggml_tensor * inpSA = inpL; + + // Pre-norm + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // Self-attention + { + ggml_tensor * attn_inp = cur; // saved for the gate projection + + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head_il, n_head_kv_il, il); + + // g_proj on the *pre-attention* hidden state (matches HF + // reference: gate is computed from the same `hidden_states` + // input as q/k/v, not from the attn output). + ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp); + cb(gate, "attn_gate_proj", il); + + // QK RMSNorm at head_dim level (Qwen3 style) + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + cb(Kcur, "Kcur_normed", il); + + Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, + n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l, + ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l); + Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, + n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l, + ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l); + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); + + cur = has_swa + ? build_attn(inp_attn_iswa, + NULL, NULL, NULL, // o_proj deferred until after gating + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il) + : build_attn(inp_attn_kv, + NULL, NULL, NULL, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + + // Softplus output gate (the unary kernel computes softplus in fp32 + // and casts back). Two shapes, distinguished by the g_proj output + // dim (matching the load-time detection): + // XS.2 per-head : gate [n_head_il, n_tokens] -> reshape to + // [1, n_head_il, n_tokens] and broadcast over + // head_dim against cur [head_dim, n_head, T]. + // M.1 per-element : gate [n_head_il*head_dim, n_tokens] spans the + // full attention output -> direct ggml_mul. + gate = ggml_softplus(ctx0, gate); + cb(gate, "attn_gate_softplus", il); + + const int64_t n_tokens = cur->ne[1]; + if (model.layers[il].wqkv_gate->ne[1] == n_head_il) { + cur = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_il, n_tokens); + gate = ggml_reshape_3d(ctx0, gate, 1, n_head_il, n_tokens); + cur = ggml_mul(ctx0, cur, gate); + cur = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens); + } else { + cur = ggml_mul(ctx0, cur, gate); + } + cb(cur, "attn_gated", il); + + cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); + cb(cur, "attn_o_proj", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // Pre-norm only (no post-attn norm) + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t)il >= hparams.n_layer_dense_lead) { + // MoE: sigmoid routing + score-correction bias + sum-norm + + // routed_scaling_factor (all handled by build_moe_ffn). + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, + hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "ffn_moe_out", il); + + // Always-on shared expert, summed in parallel. + ggml_tensor * ffn_shexp = build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } else { + // Dense FFN for the leading n_layer_dense_lead layers (XS.2: 1, M.1: 3) + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } + + // No post-ffn norm + cur = ggml_add(ctx0, cur, ffn_inp); + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = inpL; + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = build_lora_mm(model.output, cur); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/minimax-m3.cpp b/src/models/minimax-m3.cpp new file mode 100644 index 000000000000..6068fc6b87a2 --- /dev/null +++ b/src/models/minimax-m3.cpp @@ -0,0 +1,562 @@ +#include "models.h" +#include "llama-kv-cache.h" +#include +#include +#include +#include + +// MiniMax-M3: MiniMax-M2 style GQA (per-head QK-norm, partial rotary) with +// DeepSeek-V3 leading-dense + routed/shared experts (sigmoid gating, routed scaling), +// swigluoai activation, and MiniMax Sparse Attention (MSA). MTP is not in released model weights. +// Notes: Blocks are anchored to absolute KV cache slots. + +void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); + ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head); + ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size); + ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k); + ml.get_key(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size); + ml.get_key(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks); + msa_p = { (int) hparams.indexer_block_size, (int) hparams.indexer_top_k, (int) hparams.indexer_local_blocks }; + hparams.indexer_kv = true; + + switch (hparams.n_layer()) { + case 60: type = LLM_TYPE_428B_A23B; break; + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_minimax_m3::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + const int64_t n_expert_shared = hparams.n_expert_shared; + const int64_t n_ff_exp = hparams.n_ff_exp; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0); + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + // per-head QK-norm: a single head_dim vector applied to every head + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + if (i < (int) hparams.n_layer_dense_lead) { + // leading dense layers + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } else { + // routed experts + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + + // shared expert + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + + // indexer + layer.index_q_proj = create_tensor(tn(LLM_TENSOR_INDEXER_Q_PROJ, "weight", i), {n_embd, hparams.indexer_n_head * hparams.indexer_head_size}, 0); + layer.index_k_proj = create_tensor(tn(LLM_TENSOR_INDEXER_K_PROJ, "weight", i), {n_embd, hparams.indexer_head_size}, 0); + layer.index_q_norm = create_tensor(tn(LLM_TENSOR_INDEXER_Q_NORM, "weight", i), {hparams.indexer_head_size}, 0); + layer.index_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, 0); + } + } +} + +std::unique_ptr llama_model_minimax_m3::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +// per-query local-force bias for MSA selection +// local window always wins a slot +class llm_graph_input_msa_local : public llm_graph_input_i { +public: + llm_graph_input_msa_local(int blk, int local, int64_t nblk) : blk(blk), local(local), nblk(nblk) {} + + void set_input(const llama_ubatch * ubatch) override { + if (!bias || !ubatch->pos) { + return; + } + const int64_t n_tokens = ubatch->n_tokens; + std::vector data((size_t) nblk * n_tokens, 0.0f); + for (int64_t i = 0; i < n_tokens; ++i) { + const int64_t L = ubatch->pos[i] / blk; + for (int l = 0; l < local && L - l >= 0; ++l) { + if (L - l < nblk) { + data[(size_t) i * nblk + (L - l)] = 1e30f; + } + } + } + ggml_backend_tensor_set(bias, data.data(), 0, data.size() * sizeof(float)); + } + + // valid as long as the bias tensor dims still match the new ubatch/cache window + bool can_reuse(const llm_graph_params & params) override { + const auto * mctx = static_cast(params.mctx); + + bool res = true; + res &= bias->ne[1] == params.ubatch.n_tokens; + res &= bias->ne[0] * blk == (int64_t) mctx->get_n_kv(); + return res; + } + + ggml_tensor * bias = nullptr; + int blk; + int local; + int64_t nblk; +}; + +// pooled score of a block with no visible token: -inf from the mask, or -FLT_MAX from the +// max-pool identity when every element of the block is -inf +static inline bool msa_score_masked(float x) { return x <= -1e30f; } + +// MSA block selection (batch regime) +// CPU custom op, the token-level expansion and the combination with the causal mask happen on the GPU. +static void msa_block_mask_op(struct ggml_tensor * dst, int ith, int nth, void * userdata) { + const struct ggml_tensor * bs = dst->src[0]; + const struct ggml_tensor * bias = dst->src[1]; + const msa_params * p = (const msa_params *) userdata; + + const int nblk = (int) bs->ne[0]; + const int Hd = (int) bs->ne[1]; + const int S = (int) bs->ne[2]; + + GGML_ASSERT(bs->type == GGML_TYPE_F32 && ggml_is_contiguous(bs)); + GGML_ASSERT(bias->type == GGML_TYPE_F32 && ggml_is_contiguous(bias)); + GGML_ASSERT(dst->type == GGML_TYPE_F16 && ggml_is_contiguous(dst)); + GGML_ASSERT(dst->ne[0] == nblk && dst->ne[1] == S && dst->ne[2] == Hd); + GGML_ASSERT(bias->ne[0] == nblk && bias->ne[1] == S); + + const int topk = p->topk_blocks < nblk ? p->topk_blocks : nblk; + + const ggml_fp16_t f16_zero = ggml_fp32_to_fp16(0.0f); + const ggml_fp16_t f16_ninf = ggml_fp32_to_fp16(-INFINITY); + + std::vector rank(nblk); + std::vector valid(nblk); + std::vector ord(nblk); + + ggml_fp16_t * out = (ggml_fp16_t *) dst->data; + + for (int i = ith; i < S; i += nth) { + const float * bias_col = (const float *) bias->data + (size_t) i * nblk; + for (int h = 0; h < Hd; ++h) { + const float * bs_col = (const float *) bs->data + ((size_t) i * Hd + h) * nblk; + + for (int bk = 0; bk < nblk; ++bk) { + // a block is selectable if it has a visible token or is locally forced + valid[bk] = !msa_score_masked(bs_col[bk]) || bias_col[bk] > 0.0f; + rank [bk] = bias_col[bk] > 0.0f ? bias_col[bk] : bs_col[bk]; + ord [bk] = bk; + } + + std::partial_sort(ord.begin(), ord.begin() + topk, ord.end(), + [&](int a, int b) { return rank[a] > rank[b]; }); + + ggml_fp16_t * dst_col = out + ((size_t) h * S + i) * nblk; + for (int bk = 0; bk < nblk; ++bk) { + dst_col[bk] = f16_ninf; + } + for (int t = 0; t < topk; ++t) { + const int bk = ord[t]; + if (!valid[bk]) { + break; // sorted desc: first invalid -> fewer than topk selectable blocks + } + dst_col[bk] = f16_zero; + } + } + } +} + +// One FA call for all GQA groups (and at multi-stream decode, all streams) by mapping them onto the FA sequence dim (ne[3]) +ggml_tensor * llama_model_minimax_m3::graph::build_attn_msa_fa( + ggml_tensor * q_cur, // [D, HQ, T] + ggml_tensor * k, // [D, n_keys, 1, C] + ggml_tensor * v, // [D, n_keys, 1, C] + ggml_tensor * mask, // [n_keys, R, 1, C] f16, contiguous + int64_t Gp, float kq_scale, int il) const { + + const int64_t D = q_cur->ne[0]; + const int64_t HQ = q_cur->ne[1]; + const int64_t T = q_cur->ne[2]; + const int64_t C = k->ne[3]; + const int64_t R = HQ*T/(Gp*C); + GGML_ASSERT(Gp*C*R == HQ*T); + GGML_ASSERT(mask->type == GGML_TYPE_F16); + + // [D, HQ, T] -> [D, Gp, C, R] -> [D, R, Gp, C] + // batch (C=HKV, R=T): channel = group + // decode (C=HKV*ns, R=1): channel = (group, stream), group innermost + ggml_tensor * q = ggml_reshape_4d(ctx0, q_cur, D, Gp, C, R); + q = ggml_permute(ctx0, q, 0, 2, 3, 1); + + ggml_tensor * o = ggml_flash_attn_ext(ctx0, q, k, v, mask, kq_scale, + hparams.f_max_alibi_bias, 0.0f); + ggml_flash_attn_ext_set_prec(o, GGML_PREC_F32); + cb(o, "msa_fattn", il); + + // [D, Gp, R, C] -> [D, Gp, C, R] -> [n_embd, T] + o = ggml_permute(ctx0, o, 0, 1, 3, 2); + if (!ggml_is_contiguous(o)) { + o = ggml_cont(ctx0, o); // no-op layout at decode (R == 1), copy at batch + } + return ggml_reshape_2d(ctx0, o, D*HQ, T); +} + +llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v(); + const auto & mm = static_cast(model); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + // partial rotary: head_dim != n_rot, so don't assert n_embd_head == n_rot + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + ggml_tensor * inp_pos = build_inp_pos(); + auto inp_attn = build_attn_inp_kv(); + + // MSA calls ggml_flash_attn_ext directly and assumes the non-transposed V layout that + // llama.cpp only provides when flash attention is enabled. Block selection is anchored + // to absolute KV cache slots, which equal positions only for append-only per-stream + // caches either a single sequence, or multiple sequences with kv_unified == false (each + // stream then has its own slot space). A unified cache with multiple sequences + // interleaves slots and would silently break block anchoring so it falls back to dense. + const bool fa_on = cparams.flash_attn; + const bool streams_ok = cparams.n_seq_max == 1 || !cparams.kv_unified; + const bool msa_enabled = fa_on && streams_ok; + + static bool warned_no_fa = false; + if (!fa_on && !warned_no_fa) { + LLAMA_LOG_WARN("%s: flash attention disabled; MSA requires it -> running DENSE attention " + "(output may be degraded). Enable flash attention for MSA.\n", __func__); + warned_no_fa = true; + } + static bool warned_unified = false; + if (fa_on && !streams_ok && !warned_unified) { + LLAMA_LOG_WARN("%s: unified KV cache with n_seq_max > 1; MSA needs per-sequence streams " + "-> running DENSE attention. Output may be degraded. Drop --kv-unified to enable MSA.\n", __func__); + warned_unified = true; + } + + // hoisted per-graph MSA state (shared by every sparse layer) + llm_graph_input_msa_local * msa_loc = nullptr; + ggml_tensor * msa_kqm = nullptr; + ggml_tensor * msa_mf = nullptr; + int64_t n_kv = 0, nblk = 0, ns = 1, n_tps = 0; + bool msa_decode = false; // gather (1 token per stream) vs mask + const int blk = mm.msa_p.blk; + const int64_t Hd = hparams.indexer_n_head; // one indexer head per GQA group + + if (msa_enabled) { + msa_kqm = inp_attn->get_kq_mask(); + n_kv = msa_kqm->ne[0]; + n_tps = msa_kqm->ne[1]; // tokens per stream + ns = msa_kqm->ne[3]; // streams in this ubatch + GGML_ASSERT(msa_kqm->type == GGML_TYPE_F16 && "MSA requires the FA (f16) mask"); + GGML_ASSERT(n_tps*ns == n_tokens); + GGML_ASSERT(n_kv % blk == 0 && + "MSA: KV/mask n_kv must be a multiple of indexer.block_size (128); " + "the flash-attention KV padding must be a multiple of the block size. " + "A non-multiple would silently drop the partial tail block."); + nblk = n_kv / blk; + msa_decode = n_tps == 1; + + msa_mf = ggml_cast(ctx0, msa_kqm, GGML_TYPE_F32); + + auto loc = std::make_unique(blk, mm.msa_p.local, nblk); + loc->bias = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, nblk, n_tokens); // stream-grouped tokens + ggml_set_input(loc->bias); + msa_loc = (llm_graph_input_msa_local *) res->add_input(std::move(loc)); + } + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // self-attention + { + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + // per-head QK RMSNorm (weights already include Gemma's +1) + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Kcur, "Kcur_normed", il); + + // partial rotary: only the first n_rot dims are rotated + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + const bool is_sparse = msa_enabled && il >= (int) hparams.n_layer_dense_lead; + + if (!is_sparse) { + cur = build_attn(inp_attn, model.layers[il].wo, NULL, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, + 1.0f/sqrtf(float(n_embd_head)), il); + } else { + const int64_t n_idx_dim = hparams.indexer_head_size; // 128 + + GGML_ASSERT(!inp_attn->self_k_rot && !inp_attn->self_v_rot && "MSA: attn-rot not supported"); + + // Index Branch, project, norm, partial RoPE, cache + ggml_tensor * iq = build_lora_mm(model.layers[il].index_q_proj, cur); + ggml_tensor * ik = build_lora_mm(model.layers[il].index_k_proj, cur); + iq = ggml_reshape_3d(ctx0, iq, n_idx_dim, Hd, n_tokens); + ik = ggml_reshape_3d(ctx0, ik, n_idx_dim, 1, n_tokens); + iq = build_norm(iq, model.layers[il].index_q_norm, NULL, LLM_NORM_RMS, il); // +1 baked + ik = build_norm(ik, model.layers[il].index_k_norm, NULL, LLM_NORM_RMS, il); + iq = ggml_rope_ext(ctx0, iq, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + ik = ggml_rope_ext(ctx0, ik, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + + const auto * mctx_cur = inp_attn->mctx; + ggml_build_forward_expand(gf, mctx_cur->cpy_k_idx(ctx0, ik, inp_attn->get_k_idxs(), il)); + ggml_tensor * ik_kv = mctx_cur->get_k_idx(ctx0, il); + + // Main branch: store K/V, take cache views + ggml_build_forward_expand(gf, Qcur); + ggml_build_forward_expand(gf, Kcur); + ggml_build_forward_expand(gf, Vcur); + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il)); + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = mctx_cur->get_v(ctx0, il); + GGML_ASSERT(!(v->nb[1] > v->nb[2]) && "MSA assumes v_trans=false (FA on)"); + + const int64_t D = k->ne[0]; + const int64_t HKV = k->ne[1]; + const int64_t Gp = n_head/HKV; + GGML_ASSERT(HKV == Hd && "MSA: one indexer head per GQA group"); + GGML_ASSERT(k->ne[3] == ns); + const int K = mm.msa_p.topk_blocks < (int) nblk ? mm.msa_p.topk_blocks : (int) nblk; + + const float kq_scale = 1.0f/sqrtf(float(n_embd_head)); + + if (msa_decode) { + // decode: batched over streams top-k + gather, one grouped FA + // scores: per-stream batched matmul over the stream dim (ne[3]). + // the cache views are not contiguous across streams (stride = kv_size, not n_kv) + ggml_tensor * ikv4 = ggml_view_4d(ctx0, ik_kv, n_idx_dim, n_kv, 1, ns, + ik_kv->nb[2], ik_kv->nb[3], ik_kv->nb[3], 0); + ggml_tensor * iq4 = ggml_reshape_4d(ctx0, iq, n_idx_dim, Hd, 1, ns); + ggml_tensor * sc = ggml_mul_mat(ctx0, ikv4, iq4); + ggml_mul_mat_set_prec(sc, GGML_PREC_F32); + sc = ggml_add_inplace(ctx0, sc, msa_mf); + ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0); + cb(bs, "msa_bs", il); + + ggml_tensor * bsf = ggml_add(ctx0, bs, + ggml_reshape_4d(ctx0, msa_loc->bias, nblk, 1, 1, ns)); + ggml_tensor * idx = ggml_top_k(ctx0, bsf, K); + + // token idx: tj[t,k,h,s] = blk*idx[k,h,s] + t (for the mask gather) + // row idx: tr[t,k,h,s] = tj*HKV + h (for the per-stream K/V gather) + ggml_tensor * a = ggml_scale(ctx0, ggml_cast(ctx0, idx, GGML_TYPE_F32), (float) blk); + a = ggml_reshape_4d(ctx0, a, 1, K, Hd, ns); + ggml_tensor * tj = ggml_add(ctx0, + ggml_repeat_4d(ctx0, a, blk, K, Hd, ns), + ggml_reshape_3d(ctx0, ggml_arange(ctx0, 0.0f, (float) blk, 1.0f), blk, 1, 1)); + ggml_tensor * tr = ggml_add(ctx0, + ggml_scale(ctx0, tj, (float) HKV), + ggml_reshape_3d(ctx0, ggml_arange(ctx0, 0.0f, (float) HKV, 1.0f), 1, 1, Hd)); + + ggml_tensor * tokj = ggml_cast(ctx0, ggml_reshape_2d(ctx0, tj, (int64_t) blk*K*Hd, ns), GGML_TYPE_I32); + ggml_tensor * tokr = ggml_cast(ctx0, ggml_reshape_2d(ctx0, tr, (int64_t) blk*K*Hd, ns), GGML_TYPE_I32); + + ggml_tensor * k3 = ggml_view_3d(ctx0, k, D, HKV*n_kv, ns, k->nb[1], k->nb[3], 0); + ggml_tensor * v3 = ggml_view_3d(ctx0, v, D, HKV*n_kv, ns, v->nb[1], v->nb[3], 0); + ggml_tensor * m3 = ggml_reshape_3d(ctx0, msa_kqm, 1, n_kv, ns); + + ggml_tensor * kg = ggml_get_rows(ctx0, k3, tokr); + ggml_tensor * vg = ggml_get_rows(ctx0, v3, tokr); + ggml_tensor * mg = ggml_get_rows(ctx0, m3, tokj); + + // fold (group, stream) onto the FA channel dim + const ggml_type kt = ggml_is_quantized(k->type) ? GGML_TYPE_F16 : k->type; + const ggml_type vt = ggml_is_quantized(v->type) ? GGML_TYPE_F16 : v->type; + ggml_tensor * kfa = ggml_reshape_4d(ctx0, kg, D, (int64_t) blk*K, 1, Hd*ns); + ggml_tensor * vfa = ggml_reshape_4d(ctx0, vg, D, (int64_t) blk*K, 1, Hd*ns); + if (kfa->type != kt) { kfa = ggml_cast(ctx0, kfa, kt); } + if (vfa->type != vt) { vfa = ggml_cast(ctx0, vfa, vt); } + // the FA mask must be F16 + ggml_tensor * mfa = ggml_cast(ctx0, ggml_reshape_4d(ctx0, mg, (int64_t) blk*K, 1, 1, Hd*ns), GGML_TYPE_F16); + + cur = build_attn_msa_fa(Qcur, kfa, vfa, mfa, Gp, kq_scale, il); + } else { + // batch: per-stream loop + std::vector outs(ns); + for (int64_t st = 0; st < ns; ++st) { + ggml_tensor * iq_s = ggml_view_3d(ctx0, iq, n_idx_dim, Hd, n_tps, + iq->nb[1], iq->nb[2], st*n_tps*iq->nb[2]); + ggml_tensor * ik_s = ggml_view_2d(ctx0, ik_kv, n_idx_dim, n_kv, + ik_kv->nb[2], st*ik_kv->nb[3]); + ggml_tensor * mf_s = ggml_view_3d(ctx0, msa_mf, n_kv, 1, n_tps, + msa_mf->nb[1], msa_mf->nb[1], st*msa_mf->nb[3]); + ggml_tensor * km_s = ggml_view_3d(ctx0, msa_kqm, n_kv, n_tps, 1, + msa_kqm->nb[1], msa_kqm->nb[3], st*msa_kqm->nb[3]); + ggml_tensor * bias_s = ggml_view_2d(ctx0, msa_loc->bias, nblk, n_tps, + msa_loc->bias->nb[1], st*n_tps*msa_loc->bias->nb[1]); + ggml_tensor * q_s = ggml_view_3d(ctx0, Qcur, D, n_head, n_tps, + Qcur->nb[1], Qcur->nb[2], st*n_tps*Qcur->nb[2]); + ggml_tensor * k_s = ggml_view_4d(ctx0, k, D, HKV, n_kv, 1, + k->nb[1], k->nb[2], k->nb[3], st*k->nb[3]); + ggml_tensor * v_s = ggml_view_4d(ctx0, v, D, HKV, n_kv, 1, + v->nb[1], v->nb[2], v->nb[3], st*v->nb[3]); + + // block scores: bs = maxpool_blk(idx_q * idx_k^T + causal mask) + // scores are unscaled, only the top-k ordering matters + ggml_tensor * sc = ggml_mul_mat(ctx0, ik_s, + ggml_reshape_2d(ctx0, iq_s, n_idx_dim, Hd*n_tps)); + // indexer scores run in F32 + ggml_mul_mat_set_prec(sc, GGML_PREC_F32); + sc = ggml_reshape_3d(ctx0, sc, n_kv, Hd, n_tps); + sc = ggml_add_inplace(ctx0, sc, mf_s); + ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0); + cb(bs, "msa_bs", il); + + // block-level 0/-inf keep mask on the CPU, tiny transfer + ggml_tensor * srcs[2] = { bs, bias_s }; + ggml_tensor * bm = ggml_custom_4d(ctx0, GGML_TYPE_F16, + nblk, n_tps, Hd, 1, + srcs, 2, msa_block_mask_op, GGML_N_TASKS_MAX, + const_cast(&mm.msa_p)); + cb(bm, "msa_block_mask", il); + + // expand block -> token granularity on the GPU (j = bk*blk + t), + // then combine with the causal mask in place + ggml_tensor * bmx = ggml_repeat_4d(ctx0, + ggml_reshape_3d(ctx0, bm, 1, nblk, n_tps*Hd), + blk, nblk, n_tps*Hd, 1); + bmx = ggml_reshape_3d(ctx0, bmx, n_kv, n_tps, Hd); + ggml_tensor * mask4 = ggml_add_inplace(ctx0, bmx, km_s); + mask4 = ggml_reshape_4d(ctx0, mask4, n_kv, n_tps, 1, Hd); + cb(mask4, "msa_mask4", il); + + // cache views with groups on ne[3]; + ggml_tensor * kfa = ggml_permute(ctx0, k_s, 0, 3, 1, 2); + ggml_tensor * vfa = ggml_permute(ctx0, v_s, 0, 3, 1, 2); + + outs[st] = build_attn_msa_fa(q_s, kfa, vfa, mask4, Gp, kq_scale, il); + } + cur = outs[0]; + for (int64_t st = 1; st < ns; ++st) { + cur = ggml_concat(ctx0, cur, outs[st], 1); + } + } + + cb(cur, "kqv_out", il); + if (model.layers[il].wo) { + cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); + } + } + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + // leading dense FFN (swigluoai) + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SWIGLU_OAI_MOE, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // routed experts (swigluoai MoE) + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SWIGLU_OAI_MOE, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "ffn_moe_out", il); + + // shared expert (swigluoai) + ggml_tensor * ffn_shexp = build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SWIGLU_OAI_MOE, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur, model.output_s); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/models.h b/src/models/models.h index beab9f6bc7df..b72ee82efc0c 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -424,6 +424,22 @@ struct llama_model_mellum : public llama_model_base { std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; +struct llama_model_nanbeige : public llama_model_base { + llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + int n_loops = 1; + int n_layer_phys = 0; + bool skip_loop_final_norm = false; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + struct llama_model_qwen : public llama_model_base { llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; @@ -1187,9 +1203,10 @@ struct llama_model_deepseek4 : public llama_model_base { float kq_scale, int il) const; - ggml_tensor * build_hc_weighted_sum( + ggml_tensor * build_hc_pre( ggml_tensor * x, - ggml_tensor * weights) const; + ggml_tensor * weights, + int il) const; ggml_tensor * build_hc_sinkhorn( ggml_tensor * comb, @@ -1216,7 +1233,9 @@ struct llama_model_glm_dsa : public llama_model_base { void load_arch_hparams(llama_model_loader & ml) override; void load_arch_tensors(llama_model_loader & ml) override; - using graph = llama_model_deepseek2::graph; + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -1680,6 +1699,19 @@ struct llama_model_afmoe : public llama_model_base { }; +struct llama_model_laguna : public llama_model_base { + llama_model_laguna(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_ernie4_5 : public llama_model_base { llama_model_ernie4_5(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; @@ -1886,6 +1918,29 @@ struct llama_model_minimax_m2 : public llama_model_base { std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; +struct msa_params { + int blk; + int topk_blocks; + int local; +}; + +struct llama_model_minimax_m3 : public llama_model_base { + llama_model_minimax_m3(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + msa_params msa_p; + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + + ggml_tensor * build_attn_msa_fa( + ggml_tensor * q_cur, // [D, HQ, S] f32 + ggml_tensor * k, // [D, n_keys, 1, C] C = HKV or HKV*n_stream + ggml_tensor * v, // [D, n_keys, 1, C] + ggml_tensor * mask, // [n_keys, R, 1, C] f16, R = HQ*T/(Gp*C) + int64_t Gp, float kq_scale, int il) const; + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; struct llama_model_cogvlm : public llama_model_base { llama_model_cogvlm(const struct llama_model_params & params) : llama_model_base(params) {} @@ -2108,6 +2163,29 @@ struct llama_model_kimi_linear : public llama_model_base { }; +struct llama_model_kimi_k3 : public llama_model_base { + llama_model_kimi_k3(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + struct graph : public llm_build_delta_net_base { + graph(const llama_model & model, const llm_graph_params & params); + + // AttnRes: softmax mixture over residual-stream snapshots + current prefix sum + ggml_tensor * build_attn_res_mix( + ggml_tensor * prefix, + const std::vector & bank, + ggml_tensor * norm_w, + ggml_tensor * proj_w, + int il); + + const llama_model & model; + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_step35 : public llama_model_base { llama_model_step35(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/src/models/nanbeige.cpp b/src/models/nanbeige.cpp new file mode 100644 index 000000000000..3a546600fa27 --- /dev/null +++ b/src/models/nanbeige.cpp @@ -0,0 +1,184 @@ +#include "models.h" + +void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + uint32_t n_loops_u = 1; + ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false); + GGML_ASSERT(n_loops_u >= 1); + + skip_loop_final_norm = false; + ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false); + + n_layer_phys = (int) hparams.n_layer(); + + // Bound-check before casting: signed int mul can overflow and bypass the guard. + GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS); + n_loops = (int) n_loops_u; + + // Expand logical layer count before load_tensors() allocates layers / KV. + if (n_loops > 1) { + for (int j = 1; j < n_loops; ++j) { + for (int i = 0; i < n_layer_phys; ++i) { + const int dst = i + j * n_layer_phys; + hparams.n_head_arr[dst] = hparams.n_head_arr[i]; + hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i]; + hparams.n_ff_arr[dst] = hparams.n_ff_arr[i]; + hparams.is_swa_impl[dst] = hparams.is_swa_impl[i]; + hparams.is_recr_impl[dst] = hparams.is_recr_impl[i]; + } + } + hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops); + } + + type = LLM_TYPE_UNKNOWN; +} + +void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer; + for (int i = 0; i < n_phys; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, + TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } + + // Share physical weights across loops; each slot still has its own KV index. + if (n_loops > 1) { + for (int j = 1; j < n_loops; ++j) { + for (int i = 0; i < n_phys; ++i) { + layers[i + j * n_phys] = layers[i]; + } + } + } +} + +std::unique_ptr llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const auto & nb = static_cast(model); + + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + const int n_phys = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer; + const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1; + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv(); + + const float kq_scale = hparams.f_attention_scale == 0.0f + ? 1.0f / sqrtf(float(n_embd_head)) + : hparams.f_attention_scale; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + { + ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); + + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + + if (n_loops > 1 && + ((il + 1) % n_phys) == 0 && + (il + 1) < n_layer && + !nb.skip_loop_final_norm) { + cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "loop_norm", il); + inpL = cur; + } + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = build_lora_mm(model.output, cur, model.output_s); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 855295c152fa..7a93b19a0765 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -148,6 +148,8 @@ if (LLAMA_LLGUIDANCE) llama_build_and_test(test-grammar-llguidance.cpp ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf) endif () +llama_build(test-recurrent-state-rollback.cpp get-model.cpp) + if (NOT WIN32 OR NOT BUILD_SHARED_LIBS) # these tests are disabled on Windows because they use internal functions not exported with LLAMA_API (when building with shared libraries) llama_build_and_test(test-sampling.cpp) @@ -193,6 +195,28 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS) # llama_build_and_test(test-double-float.cpp) # SLOW llama_build_and_test(test-llama-archs.cpp) + + set(MODEL_DIR "${CMAKE_CURRENT_BINARY_DIR}/test-models/") + file(MAKE_DIRECTORY "${MODEL_DIR}") + + llama_test( + test-llama-archs + NAME test-generate-models + LABEL main + ARGS -o "${MODEL_DIR}" + ) + set_tests_properties(test-generate-models PROPERTIES + FIXTURES_SETUP generate-models + ) + + llama_test( + test-recurrent-state-rollback + LABEL main + ARGS -m "${MODEL_DIR}/qwen35-dense.gguf" + ) + set_tests_properties(test-recurrent-state-rollback PROPERTIES + FIXTURES_REQUIRED generate-models + ) endif() llama_build_and_test(test-chat-peg-parser.cpp peg-parser/simple-tokenize.cpp) @@ -250,9 +274,6 @@ llama_build_and_test(test-backend-sampler.cpp LABEL "model") llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -m "${MODEL_DEST}") set_tests_properties(test-state-restore-fragmented PROPERTIES FIXTURES_REQUIRED test-download-model) -llama_build_and_test(test-recurrent-state-rollback.cpp LABEL "model" ARGS -m "${MODEL_DEST}") -set_tests_properties(test-recurrent-state-rollback PROPERTIES FIXTURES_REQUIRED test-download-model) - # Test state save/load functionality llama_build_and_test(test-save-load-state.cpp LABEL "model" ARGS -m "${MODEL_DEST}") set_tests_properties(test-save-load-state PROPERTIES FIXTURES_REQUIRED test-download-model) diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index e83ee85dd4ba..1d3584f903c4 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -1,6 +1,7 @@ #include "arg.h" #include "common.h" #include "download.h" +#include "llama.h" #include #include @@ -102,11 +103,9 @@ static void test(void) { argv = {"binary_name", "--draft", "123"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING)); - // negated arg - argv = {"binary_name", "--no-mmap"}; + argv = {"binary_name", "-lm", "hello"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); - printf("test-arg-parser: test valid usage\n\n"); argv = {"binary_name", "-m", "model_file.gguf"}; @@ -132,6 +131,26 @@ static void test(void) { assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE)); assert(params.speculative.draft.n_max == 123); + argv = {"binary_name", "-lm", "none"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_NONE); + + argv = {"binary_name", "-lm", "mmap"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP); + + argv = {"binary_name", "-lm", "mlock"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MLOCK); + + argv = {"binary_name", "-lm", "mmap+mlock"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK); + + argv = {"binary_name", "-lm", "dio"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_DIRECT_IO); + // multi-value args (CSV) argv = {"binary_name", "--lora", "file1.gguf,\"file2,2.gguf\",\"file3\"\"3\"\".gguf\",file4\".gguf"}; assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); @@ -158,13 +177,37 @@ static void test(void) { assert(params.model.path == "blah.gguf"); assert(params.cpuparams.n_threads == 1010); + setenv("LLAMA_ARG_LOAD_MODE", "blah", true); + argv = {"binary_name"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + + setenv("LLAMA_ARG_LOAD_MODE", "mmap", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP); + + setenv("LLAMA_ARG_LOAD_MODE", "mlock", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MLOCK); + + setenv("LLAMA_ARG_LOAD_MODE", "mmap+mlock", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK); + + setenv("LLAMA_ARG_LOAD_MODE", "dio", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_DIRECT_IO); + printf("test-arg-parser: test negated environment variables\n\n"); - setenv("LLAMA_ARG_MMAP", "0", true); + setenv("LLAMA_ARG_LOAD_MODE", "none", true); setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format argv = {"binary_name"}; assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); - assert(params.use_mmap == false); + assert(params.load_mode == LLAMA_LOAD_MODE_NONE); assert(params.no_perf == true); printf("test-arg-parser: test environment variables being overwritten\n\n"); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ae49d2d0d181..e7cd6d0cb668 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2435,13 +2435,17 @@ struct test_set_rows : public test_case { } double max_nmse_err() override { - if (type_dst == GGML_TYPE_Q4_0 || type_dst == GGML_TYPE_Q4_1 || type_dst == GGML_TYPE_IQ4_NL || + if (type_dst == GGML_TYPE_Q2_0 || type_dst == GGML_TYPE_Q4_0 || type_dst == GGML_TYPE_Q4_1 || + type_dst == GGML_TYPE_IQ4_NL || type_dst == GGML_TYPE_Q5_0 || type_dst == GGML_TYPE_Q5_1 || type_dst == GGML_TYPE_Q8_0) { // estimate what the max nmse error would be if one quantized value is // off by one. The test values are distributed in [-1,1], so it'll be // roughly (2.0 / 2^bits)^2, divided by the mean square value of the reference, // which is roughly 0.25 times the number of elements. double err_estimate = 1.0f/8.0f; + if (type_src == GGML_TYPE_F16 && type_dst == GGML_TYPE_Q2_0) { + err_estimate *= 4.0f; + } if (type_dst == GGML_TYPE_Q5_0 || type_dst == GGML_TYPE_Q5_1) { err_estimate /= 2.0f; } @@ -3751,6 +3755,167 @@ struct test_snake_fuse : public test_case { } }; + +struct test_dsv4_hc : public test_case { + static constexpr int64_t hc = 4; + + ggml_tensor * out = nullptr; + + static uint32_t tensor_seed(const ggml_tensor * t) { + uint32_t seed = 2166136261u; + for (const char * p = ggml_get_name(t); *p; ++p) { + seed ^= (uint8_t) *p; + seed *= 16777619u; + } + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + seed ^= (uint32_t) t->ne[i]; + seed *= 16777619u; + } + return seed; + } + + static bool tensor_range(const std::string & name, float & lo, float & hi) { + if (name == "mixes") { + lo = -2.0f; hi = 2.0f; return true; + } + if (name == "scale") { + lo = -0.5f; hi = 0.5f; return true; + } + if (name == "base") { + lo = -0.25f; hi = 0.25f; return true; + } + if (name == "weights" || name == "comb") { + lo = 0.0f; hi = 1.0f; return true; + } + if (name == "post") { + lo = 0.0f; hi = 2.0f; return true; + } + if (name == "x" || name == "residual") { + lo = -1.0f; hi = 1.0f; return true; + } + return false; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + const std::string name = ggml_get_name(t); + float lo; + float hi; + if (!tensor_range(name, lo, hi)) { + init_tensor_uniform(t); + continue; + } + + GGML_ASSERT(t->type == GGML_TYPE_F32); + std::mt19937 rng(tensor_seed(t)); + std::uniform_real_distribution dist(lo, hi); + std::vector data(ggml_nelements(t)); + for (float & v : data) { + v = dist(rng); + } + ggml_backend_tensor_set(t, data.data(), 0, data.size()*sizeof(float)); + } + } +}; + +struct test_dsv4_hc_comb : public test_dsv4_hc { + const int64_t n_tokens; + const int32_t n_iter; + const float eps; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "DSV4_HC_COMB"; + } + + std::string vars() override { + return VARS_TO_STR3(n_tokens, n_iter, eps); + } + + test_dsv4_hc_comb(int64_t n_tokens = 17, int32_t n_iter = 4, float eps = 1e-6f) + : n_tokens(n_tokens), n_iter(n_iter), eps(eps) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * mixes = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, (2 + hc)*hc, n_tokens); + ggml_set_name(mixes, "mixes"); + + ggml_tensor * scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3); + ggml_set_name(scale, "scale"); + + ggml_tensor * base = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (2 + hc)*hc); + ggml_set_name(base, "base"); + + out = ggml_dsv4_hc_comb(ctx, mixes, scale, base, eps, n_iter); + ggml_set_name(out, "out"); + return out; + } +}; + +struct test_dsv4_hc_pre : public test_dsv4_hc { + const int64_t n_embd; + const int64_t n_tokens; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "DSV4_HC_PRE"; + } + + std::string vars() override { + return VARS_TO_STR2(n_embd, n_tokens); + } + + test_dsv4_hc_pre(int64_t n_embd = 31, int64_t n_tokens = 17) + : n_embd(n_embd), n_tokens(n_tokens) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, hc, n_tokens); + ggml_set_name(x, "x"); + + ggml_tensor * weights = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hc, n_tokens); + ggml_set_name(weights, "weights"); + + out = ggml_dsv4_hc_pre(ctx, x, weights); + ggml_set_name(out, "out"); + return out; + } +}; + +struct test_dsv4_hc_post : public test_dsv4_hc { + const int64_t n_embd; + const int64_t n_tokens; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "DSV4_HC_POST"; + } + + std::string vars() override { + return VARS_TO_STR2(n_embd, n_tokens); + } + + test_dsv4_hc_post(int64_t n_embd = 31, int64_t n_tokens = 17) + : n_embd(n_embd), n_tokens(n_tokens) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * x = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); + ggml_set_name(x, "x"); + + ggml_tensor * residual = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, hc, n_tokens); + ggml_set_name(residual, "residual"); + + ggml_tensor * post = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hc, n_tokens); + ggml_set_name(post, "post"); + + ggml_tensor * comb = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hc, hc, n_tokens); + ggml_set_name(comb, "comb"); + + out = ggml_dsv4_hc_post(ctx, x, residual, post, comb); + ggml_set_name(out, "out"); + return out; + } +}; + + // GGML_OP_SSM_CONV struct test_ssm_conv : public test_case { const ggml_type type; @@ -5555,7 +5720,7 @@ struct test_concat : public test_case { const std::array ne_a; const int64_t ne_b_d; const int dim; - const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b) + const int v; // view (1 << 0: non-cont a (first 3 dim), 1 << 1: non-cont b (first 3 dim), 1 << 2: non-cont a (last 2 dim), 1 << 3: non-cont b (last 2 dim)) std::string vars() override { return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v); @@ -5576,6 +5741,13 @@ struct test_concat : public test_case { a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_name(a, "a"); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); + ggml_set_name(a, "view_of_a"); + } else if (v & 4) { + auto ne = ne_a; ne[2] *= 2; ne[3] *= 4; + a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_name(a, "a"); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); ggml_set_name(a, "view_of_a"); } else { @@ -5588,6 +5760,13 @@ struct test_concat : public test_case { b = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_name(b, "b"); + b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0); + ggml_set_name(b, "view_of_b"); + } else if (v & 8) { + auto ne = ne_b; ne[2] *= 3; ne[3] *= 2; + b = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_name(b, "b"); + b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0); ggml_set_name(b, "view_of_b"); } else { @@ -5781,6 +5960,7 @@ enum MoeGatingFunc { GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT, + GATING_FUNC_SQRT_SOFTPLUS, }; struct test_topk_moe : public test_case { @@ -5824,7 +6004,8 @@ struct test_topk_moe : public test_case { ggml_tensor * logits = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data()); ggml_tensor * probs = (gating_func == GATING_FUNC_SOFTMAX) ? ggml_soft_max(ctx, logits) : - (gating_func == GATING_FUNC_SIGMOID) ? ggml_sigmoid(ctx, logits) : logits; + (gating_func == GATING_FUNC_SIGMOID) ? ggml_sigmoid(ctx, logits) : + (gating_func == GATING_FUNC_SQRT_SOFTPLUS) ? ggml_sqrt(ctx, ggml_softplus(ctx, logits)) : logits; ggml_set_name(probs, "probs"); ggml_tensor * selection_probs = probs; @@ -7790,6 +7971,7 @@ static const ggml_type all_types[] = { GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0, GGML_TYPE_Q1_0, + GGML_TYPE_Q2_0, GGML_TYPE_MXFP4, GGML_TYPE_NVFP4, GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, @@ -7804,6 +7986,7 @@ static const ggml_type base_types[] = { GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q8_0, // for I8MM tests GGML_TYPE_Q1_0, + GGML_TYPE_Q2_0, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, // for I8MM tests GGML_TYPE_Q4_K, @@ -7868,6 +8051,19 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_snake_fuse(type, { 64, 32, 2, 3})); // ne[2] > 1 and ne[3] > 1 } + test_cases.emplace_back(new test_dsv4_hc_comb(1, 1)); + test_cases.emplace_back(new test_dsv4_hc_comb(17, 4)); + test_cases.emplace_back(new test_dsv4_hc_comb(257, 8)); + + test_cases.emplace_back(new test_dsv4_hc_pre(1, 1)); + test_cases.emplace_back(new test_dsv4_hc_pre(31, 17)); + test_cases.emplace_back(new test_dsv4_hc_pre(128, 257)); + test_cases.emplace_back(new test_dsv4_hc_pre(4096, 21)); + + test_cases.emplace_back(new test_dsv4_hc_post(1, 1)); + test_cases.emplace_back(new test_dsv4_hc_post(31, 17)); + test_cases.emplace_back(new test_dsv4_hc_post(128, 257)); + // glu ops for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) { for (int v : {0, 1}) { @@ -9089,8 +9285,10 @@ static std::vector> make_test_cases_eval() { } for (ggml_type type_a : { GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0 }) { - for (int dim : { 0, 1, 2, 3, }) { - test_cases.emplace_back(new test_concat(type_a, {128, 12, 13, 14}, dim == 0 ? 256 : 7, dim, 0)); + for (int v : { 0, 4, 8, 12 }) { + for (int dim : { 0, 1, 2, 3, }) { + test_cases.emplace_back(new test_concat(type_a, {128, 12, 13, 14}, dim == 0 ? 256 : 7, dim, v)); + } } } @@ -9388,7 +9586,7 @@ static std::vector> make_test_cases_eval() { } } - for (auto gate : {GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT}) { + for (auto gate : {GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT, GATING_FUNC_SQRT_SOFTPLUS}) { for (bool with_norm : {false, true}) { for (bool bias_probs : {false, true}) { for (float scale_w : {0.0f, 2.0f}) { @@ -9400,6 +9598,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w)); test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w)); test_cases.emplace_back(new test_topk_moe({160, 4, 1, 1}, 160, with_norm, bias_probs, gate, scale_w)); + test_cases.emplace_back(new test_topk_moe({256, 22, 1, 1}, 6, with_norm, bias_probs, gate, scale_w)); // Used by DeepSeek-V4 test_cases.emplace_back(new test_topk_moe({288, 22, 1, 1}, 8, with_norm, bias_probs, gate, scale_w)); // Used by StepFun 3.7 } } diff --git a/tests/test-chat-auto-parser.cpp b/tests/test-chat-auto-parser.cpp index 78e42c65a50c..4218f8d5747d 100644 --- a/tests/test-chat-auto-parser.cpp +++ b/tests/test-chat-auto-parser.cpp @@ -57,6 +57,15 @@ static void test_seed_oss_tool_with_reasoning(testing & t); static void test_nemotron_analysis(testing & t); static void test_nemotron_reasoning_detection(testing & t); static void test_nemotron_tool_format(testing & t); +static void test_laguna_analysis(testing & t); +static void test_laguna_reasoning_detection(testing & t); +static void test_laguna_tool_format(testing & t); +static void test_laguna_s_analysis(testing & t); +static void test_laguna_s_reasoning_detection(testing & t); +static void test_laguna_s_tool_format(testing & t); +static void test_laguna_xs2_analysis(testing & t); +static void test_laguna_xs2_reasoning_detection(testing & t); +static void test_laguna_xs2_tool_format(testing & t); // CohereForAI template analysis tests static void test_cohere_reasoning_detection(testing & t); @@ -101,6 +110,9 @@ int main(int argc, char * argv[]) { t.test("seed_oss_diffs", test_seed_oss_tool_analysis); t.test("cohere", test_cohere_analysis); t.test("nemotron", test_nemotron_analysis); + t.test("laguna", test_laguna_analysis); + t.test("laguna-s", test_laguna_s_analysis); + t.test("laguna-xs2", test_laguna_xs2_analysis); t.test("smollm3", test_smollm3_analysis); t.test("standard_json_tools", test_standard_json_tools_formats); t.test("normalize_quotes_to_json", test_normalize_quotes_to_json); @@ -1378,6 +1390,94 @@ static void test_nemotron_tool_format(testing & t) { t.assert_true("should support tools", analysis.jinja_caps.supports_tools); } +// ============================================================================ +// Laguna Template Analysis Tests +// ============================================================================ +static common_chat_template load_laguna_template(testing & t) { + return load_template(t, "models/templates/poolside-Laguna-XS-2.1.jinja"); +} + +static void test_laguna_reasoning_detection(testing & t) { + common_chat_template tmpl = load_laguna_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + // Laguna's template renders reasoning delimiters with formatting whitespace + // ("\n") that the model does not emit; the Laguna patch trims them. + t.assert_equal("reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("reasoning_end should be ''", "", analysis.reasoning.end); + t.assert_equal("reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode); +} + +static void test_laguna_tool_format(testing & t) { + common_chat_template tmpl = load_laguna_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("arg_value_suffix should be ''", "", analysis.tools.arguments.value_suffix); +} + +static void test_laguna_stop_string(testing & t) { + // The turn terminator can be emitted as ordinary text tokens + // (not the single eot token), so it must also be a literal stop string. + common_chat_template tmpl = load_laguna_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + bool has_stop = false; + for (const auto & stop : analysis.additional_stops) { + if (stop == "") { has_stop = true; break; } + } + t.assert_true("Laguna additional_stops contains ", has_stop); +} + +static void test_laguna_analysis(testing & t) { + t.test("Laguna reasoning detection", test_laguna_reasoning_detection); + t.test("Laguna tool format", test_laguna_tool_format); + t.test("Laguna stop string", test_laguna_stop_string); +} + +static common_chat_template load_laguna_s_template(testing & t) { + return load_template(t, "models/templates/poolside-Laguna-S-2.1.jinja"); +} +static void test_laguna_s_reasoning_detection(testing & t) { + common_chat_template tmpl = load_laguna_s_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-S(v8) reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("Laguna-S(v8) reasoning_end should be ''", "", analysis.reasoning.end); + t.assert_equal("Laguna-S(v8) reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode); +} +static void test_laguna_s_tool_format(testing & t) { + common_chat_template tmpl = load_laguna_s_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-S(v8) arg_value_suffix should be ''", "", analysis.tools.arguments.value_suffix); +} +static void test_laguna_s_analysis(testing & t) { + t.test("Laguna-S(v8) reasoning detection", test_laguna_s_reasoning_detection); + t.test("Laguna-S(v8) tool format", test_laguna_s_tool_format); +} + +static common_chat_template load_laguna_xs2_template(testing & t) { + return load_template(t, "models/templates/poolside-Laguna-XS.2.jinja"); +} +static void test_laguna_xs2_reasoning_detection(testing & t) { + common_chat_template tmpl = load_laguna_xs2_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-XS.2(v5) reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("Laguna-XS.2(v5) reasoning_end should be ''", "", analysis.reasoning.end); + t.assert_equal("Laguna-XS.2(v5) reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode); +} +static void test_laguna_xs2_tool_format(testing & t) { + common_chat_template tmpl = load_laguna_xs2_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-XS.2(v5) arg_value_suffix should be ''", "", analysis.tools.arguments.value_suffix); +} +static void test_laguna_xs2_analysis(testing & t) { + t.test("Laguna-XS.2(v5) reasoning detection", test_laguna_xs2_reasoning_detection); + t.test("Laguna-XS.2(v5) tool format", test_laguna_xs2_tool_format); +} + static common_chat_template load_cohere_template(testing & t) { return load_template(t, "models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja"); } diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index 93685ec8ff42..71155c24121d 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -109,6 +109,15 @@ static void assert_contains(const std::string & haystack, const std::string & ne } } +static void assert_not_contains(const std::string & haystack, const std::string & needle) { + if (haystack.find(needle) != std::string::npos) { + LOG_ERR("Expected NOT to contain: %s\n", needle.c_str()); + LOG_ERR("Actual: %s\n", haystack.c_str()); + common_log_flush(common_log_main()); + throw std::runtime_error("Test failed"); + } +} + static void assert_ends_with(const std::string & str, const std::string & suffix) { if (str.size() < suffix.size() || str.compare(str.size() - suffix.size(), suffix.size(), suffix) != 0) { @@ -1135,7 +1144,7 @@ static void test_peg_parser(common_chat_templates * tmpls, // budget sampler inhibits grammar application while inside thinking blocks — // triggers inside ... are suppressed. bool use_reasoning_budget_path = false; - if (parser.params_.grammar_lazy && !parser.params_.thinking_end_tag.empty()) { + if (parser.params_.grammar_lazy && !parser.params_.thinking_end_tags.empty()) { use_reasoning_budget_path = true; for (const auto & trigger : parser.params_.grammar_triggers) { if (trigger.type != COMMON_GRAMMAR_TRIGGER_TYPE_WORD) { @@ -1153,7 +1162,7 @@ static void test_peg_parser(common_chat_templates * tmpls, // Walk through full_input tracking thinking state; only match triggers // when outside thinking blocks. const auto & think_start = parser.params_.thinking_start_tag; - const auto & think_end = parser.params_.thinking_end_tag; + const auto & think_ends = parser.params_.thinking_end_tags; bool in_thinking = false; for (size_t i = 0; i < full_input.size(); ++i) { @@ -1163,12 +1172,14 @@ static void test_peg_parser(common_chat_templates * tmpls, i += think_start.size() - 1; continue; } - if (in_thinking && full_input.compare(i, think_end.size(), think_end) == 0) { - in_thinking = false; - i += think_end.size() - 1; - continue; - } if (in_thinking) { + for (const auto & think_end : think_ends) { + if (full_input.compare(i, think_end.size(), think_end) == 0) { + in_thinking = false; + i += think_end.size() - 1; + break; + } + } continue; } // Outside thinking — check if any trigger word starts here @@ -2840,6 +2851,17 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect(message_assist) .run(); + // JSON output schema + tst.test( + "I need to output the invoice details in JSON<|END_THINKING|>" + "<|START_TEXT|>{\"amount\": 123.45, \"date\": \"2025-12-03\"}<|END_TEXT|>") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .json_schema(invoice_schema) + .tools({ special_function_tool }) + .expect_reasoning("I need to output the invoice details in JSON") + .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})") + .run(); + // Single tool call with reasoning. tst.test( "I'm\nthinking<|END_THINKING|>" @@ -4016,6 +4038,132 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .run(); } + // DeepSeek V4 tests - same DSML markup as V3.2, but the tool call block is named + // "tool_calls" and the non-thinking generation prompt ends in a bare + // instead of an empty pair. + { + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V4.jinja", detailed_debug); + + // Pure content (non-thinking mode; generation prompt ends with ) + tst.test("Hello, world!\nWhat's up?") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist) + .run(); + + // Thinking + content + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .run(); + + // Thinking + tool call (single, string param) + tst.test( + "Let me check the time\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"get_time\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Tokyo\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ get_time_tool }) + .expect(message_with_tool_calls_and_reasoning("get_time", R"({"city": "Tokyo"})", "Let me check the time")) + .run(); + + // Tool call without reasoning (non-thinking mode), integer param (string="false") + tst.test( + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"special_function\">\n" + "<｜DSML｜parameter name=\"arg1\" string=\"false\">1\n" + "\n" + "") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + // Multiple parallel tool calls with reasoning + tst.test( + "Calling both\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"get_time\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Paris\n" + "\n" + "<｜DSML｜invoke name=\"get_weather\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Paris\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .parallel_tool_calls(true) + .tools({ get_time_tool, get_weather_tool }) + .expect(message_with_reasoning_content_and_multiple_tool_calls( + "Calling both", "", + { { "get_time", R"({"city": "Paris"})" }, { "get_weather", R"({"city": "Paris"})" } })) + .run(); + + // Tool call with content before tool calls + tst.test( + "Thinking about it" + "Let me call the function.\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"special_function\">\n" + "<｜DSML｜parameter name=\"arg1\" string=\"false\">1\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect_reasoning("Thinking about it") + .expect_content("Let me call the function.") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + }) + .run(); + + // Tool call with multiple params (mixed types) + tst.test( + "Multi-arg call\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"magic_int\">\n" + "<｜DSML｜parameter name=\"ref\" string=\"false\">42\n" + "<｜DSML｜parameter name=\"name\" string=\"true\">foo bar\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ magic_int_tool }) + .expect_reasoning("Multi-arg call") + .expect_tool_calls({ + { "magic_int", R"({"ref": 42, "name": "foo bar"})", {} }, + }) + .run(); + + // Continuation tests + tst.test("world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .enable_thinking(true) + .messages({ message_user, message_assist_prefill_content }) + .add_generation_prompt(false) + .continue_final_message(COMMON_CHAT_CONTINUATION_CONTENT) + .expect_reasoning("I'm thinking") + .expect_content("Hello, world!\nWhat's up?") + .run(); + + tst.test(" thinkingHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .enable_thinking(true) + .messages({ message_user, message_assist_prefill_reasoning }) + .add_generation_prompt(false) + .continue_final_message(COMMON_CHAT_CONTINUATION_REASONING) + .expect_reasoning("I'm thinking") + .expect_content("Hello, world!\nWhat's up?") + .run(); + } + // GLM-4.6 tests - format: function_name\n...\n...\n { auto tst = peg_tester("models/templates/GLM-4.6.jinja", detailed_debug); @@ -4190,6 +4338,111 @@ static void test_template_output_peg_parsers(bool detailed_debug) { } } + // Kimi-K3 tests - custom parser + // Unique feature: XTML-ish tags built from <|open|>/<|close|>/<|sep|>, and a + // generation prompt that leaves the think section already open. + { + auto tst = peg_tester("models/templates/Kimi-K3.jinja", detailed_debug); + + // Content only. The response section is explicit even with no reasoning. + tst.test("<|open|>response<|sep|>Hello, world!\nWhat's up?<|close|>response<|sep|>" + "<|close|>message<|sep|>") + .expect(message_assist) + .run(); + + // Reasoning with NO opening tag - the generation prompt already opened + // it. This is the case that silently loses reasoning if unhandled. + tst.test("I'm thinking about this<|close|>think<|sep|>" + "<|open|>response<|sep|>Hello, world!\nWhat's up?<|close|>response<|sep|>" + "<|close|>message<|sep|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(simple_assist_msg("Hello, world!\nWhat's up?", "I'm thinking about this")) + .run(); + + // Prose that mentions the tag names must survive intact. + tst.test("<|open|>response<|sep|>Use the response tag, then message the handler." + "<|close|>response<|sep|><|close|>message<|sep|>") + .expect(simple_assist_msg("Use the response tag, then message the handler.")) + .run(); + + // Truncated mid-reasoning (hit the token budget): keep the reasoning. + tst.test("I was still thinking when the budget ran out") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect_reasoning("I was still thinking when the budget ran out") + .run(); + + // Single tool call, one argument. + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .tools({ special_function_tool }) + .expect_tool_calls({ + { "special_function", R"({"arg1":1})", "" }, + }) + .run(); + + // Tool call preceded by reasoning (no opening think tag) and content. + tst.test("I should call it<|close|>think<|sep|>" + "<|open|>response<|sep|>On it.<|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(simple_assist_msg("On it.", "I should call it", "special_function", + R"({"arg1":1})", "")) + .run(); + + // Multiple typed arguments: the type lives in an attribute, and the + // value must come back as a JSON number, not the string "2". + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function_with_opt\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|open|>argument key=\"arg2\" type=\"number\"<|sep|>2<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .tools({ special_function_tool_with_optional_param }) + .expect_tool_calls({ + { "special_function_with_opt", R"({"arg1":1,"arg2":2})", "" }, + }) + .run(); + + // Parallel tool calls in one <|open|>tools<|sep|> section. + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|close|>call<|sep|>" + "<|open|>call tool=\"special_function_with_opt\" index=\"2\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|open|>argument key=\"arg2\" type=\"number\"<|sep|>2<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .parallel_tool_calls(true) + .tools({ special_function_tool, special_function_tool_with_optional_param }) + .expect_tool_calls({ + { "special_function", R"({"arg1":1})", "" }, + { "special_function_with_opt", R"({"arg1":1,"arg2":2})", "" }, + }) + .run(); + + // String-typed argument keeps its literal text (no JSON coercion). + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"python\" index=\"1\"<|sep|>" + "<|open|>argument key=\"code\" type=\"string\"<|sep|>print('hey')" + "<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .tools({ python_tool }) + .expect_tool_calls({ + // custom delimiter: the payload itself contains )" + { "python", R"JSON({"code":"print('hey')"})JSON", "" }, + }) + .run(); + } + // Kimi-K2-Thinking tests - custom parser // Unique feature: tool call ID embeds function name as functions.: { @@ -5918,6 +6171,144 @@ static void test_developer_role_to_system_workaround() { } } +// Verify reasoning-trace retention rules in the DeepSeek-V4 template: +// all traces are retained unless drop_thinking is true AND the conversation +// has no tool calls, in which case only the last (after-final-user) trace is +// kept and earlier ones are dropped. +static void test_deepseek_v4_thinking_retention() { + LOG_DBG("%s\n", __func__); + + auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja"); + + common_chat_msg user_q1; user_q1.role = "user"; user_q1.content = "Question 1"; + common_chat_msg user_q2; user_q2.role = "user"; user_q2.content = "Question 2"; + common_chat_msg asst_a1 = simple_assist_msg("Answer 1", "thinking A1"); + common_chat_msg asst_a2 = simple_assist_msg("Answer 2", "thinking A2"); + + common_chat_msg tool_assist = message_with_tool_calls("special_function", "{\"arg1\": 1}"); + common_chat_msg tool_result; tool_result.role = "tool"; + tool_result.tool_name = "special_function"; tool_result.tool_call_id = "0"; tool_result.content = "result"; + + // The template uses U+FF5C as the role separator and literal think tags + // for the reasoning block. + const std::string asst_marker = "<\xef\xbd\x9c" "Assistant" "\xef\xbd\x9c>"; + // Built via concatenation so the thinking tokens are not interpreted by + // tooling processing this source file. + const std::string think_start = "<" "think" ">"; + const std::string think_end = ""; + + const std::string think_a1 = asst_marker + think_start + "thinking A1" + think_end; + const std::string think_a2 = asst_marker + think_start + "thinking A2" + think_end; + const std::string asst_no_think = asst_marker + think_end; + + auto render = [&](const std::vector & messages, bool drop_thinking) { + common_chat_templates_inputs inputs; + inputs.messages = messages; + inputs.add_generation_prompt = false; + inputs.chat_template_kwargs["thinking"] = "true"; + inputs.chat_template_kwargs["drop_thinking"] = drop_thinking ? "true" : "false"; + return common_chat_templates_apply(tmpls.get(), inputs).prompt; + }; + + // No tools, drop_thinking=false: all reasoning is retained. + { + auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ false); + assert_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + } + + // No tools, drop_thinking=true: only the last reasoning trace is kept, + // earlier ones are dropped (the assistant block emits just the end token). + { + auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ true); + assert_not_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + // The dropped assistant turn still opens with the marker + bare end token. + assert_contains(prompt, asst_no_think + "Answer 1"); + } + + // Single assistant turn, drop_thinking=true: the only trace is the last + // one, so it must be retained even with drop_thinking set. + { + auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ true); + assert_contains(prompt, think_a1); + } + + // Single assistant turn, drop_thinking=false: reasoning is retained. + { + auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ false); + assert_contains(prompt, think_a1); + } + + // With tool calls, drop_thinking=true: tool presence forces all reasoning + // to be retained, including the pre-tool-call trace. + { + auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 }, + /* drop_thinking = */ true); + assert_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + } + + // With tool calls, drop_thinking=false: all reasoning retained. + { + auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 }, + /* drop_thinking = */ false); + assert_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + } +} + +// Verify that consecutive tool results are rendered in the tool call order of the +// preceding assistant message (matched by tool call id), as required by the reference +// DeepSeek-V4 implementation. +static void test_deepseek_v4_tool_result_ordering() { + LOG_DBG("%s\n", __func__); + + auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja"); + + common_chat_msg user_q; user_q.role = "user"; user_q.content = "Question"; + + common_chat_msg assist_calls; + assist_calls.role = "assistant"; + assist_calls.tool_calls.push_back({ "get_time", "{\"city\": \"Paris\"}", "call_1" }); + assist_calls.tool_calls.push_back({ "get_weather", "{\"city\": \"Paris\"}", "call_2" }); + + common_chat_msg time_result; time_result.role = "tool"; + time_result.tool_name = "get_time"; time_result.tool_call_id = "call_1"; time_result.content = "12:00"; + common_chat_msg weather_result; weather_result.role = "tool"; + weather_result.tool_name = "get_weather"; weather_result.tool_call_id = "call_2"; weather_result.content = "sunny"; + + auto render = [&](const std::vector & messages) { + common_chat_templates_inputs inputs; + inputs.messages = messages; + inputs.add_generation_prompt = false; + return common_chat_templates_apply(tmpls.get(), inputs).prompt; + }; + + // Results sent out of order are reordered to match the tool call order. + { + auto prompt = render({ user_q, assist_calls, weather_result, time_result }); + assert_contains(prompt, "12:00\n\nsunny"); + } + + // Results already in call order stay put. + { + auto prompt = render({ user_q, assist_calls, time_result, weather_result }); + assert_contains(prompt, "12:00\n\nsunny"); + } + + // Without tool call ids there is nothing to match against; order is preserved. + { + auto no_id_calls = assist_calls; + no_id_calls.tool_calls[0].id = ""; + no_id_calls.tool_calls[1].id = ""; + auto no_id_weather = weather_result; no_id_weather.tool_call_id = ""; + auto no_id_time = time_result; no_id_time.tool_call_id = ""; + auto prompt = render({ user_q, no_id_calls, no_id_weather, no_id_time }); + assert_contains(prompt, "sunny\n\n12:00"); + } +} + static void test_reasoning_budget_tokens_per_request() { LOG_DBG("%s\n", __func__); // Use Qwen3 template which has ... reasoning markers. @@ -6139,6 +6530,8 @@ int main(int argc, char ** argv) { test_tools_oaicompat_json_conversion(); test_convert_responses_to_chatcmpl(); test_developer_role_to_system_workaround(); + test_deepseek_v4_thinking_retention(); + test_deepseek_v4_tool_result_ordering(); test_template_generation_prompt(); test_reasoning_budget_tokens_per_request(); test_reasoning_budget_message_per_request(); diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index 90bdbc445d52..1ac5b57decca 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -4,7 +4,7 @@ #include #include -#include +#include "subproc.h" #include "jinja/runtime.h" #include "jinja/parser.h" @@ -2135,21 +2135,20 @@ static void test_template_py(testing & t, const std::string & name, const std::s const char * python_executable = "python3"; #endif - const char * command_line[] = {python_executable, "-c", py_script.c_str(), NULL}; + std::vector args = {python_executable, "-c", py_script, }; - struct subprocess_s subprocess; + common_subproc subprocess; int options = subprocess_option_combined_stdout_stderr | subprocess_option_no_window | subprocess_option_inherit_environment | subprocess_option_search_user_path; - int result = subprocess_create(command_line, options, &subprocess); - if (result != 0) { - t.log("Failed to create subprocess, error code: " + std::to_string(result)); + if (!subprocess.create(args, options)) { + t.log("Failed to create subprocess"); t.assert_true("subprocess creation", false); return; } - FILE * p_stdin = subprocess_stdin(&subprocess); + FILE * p_stdin = subprocess.stdin_file(); // Write input std::string input = merged.dump(); @@ -2157,24 +2156,22 @@ static void test_template_py(testing & t, const std::string & name, const std::s if (written != input.size()) { t.log("Failed to write complete input to subprocess stdin"); t.assert_true("subprocess stdin write", false); - subprocess_destroy(&subprocess); + subprocess.close_stdin(); + subprocess.join(); return; } fflush(p_stdin); - fclose(p_stdin); // Close stdin to signal EOF to the Python process - subprocess.stdin_file = nullptr; + subprocess.close_stdin(); // Close stdin to signal EOF to the Python process // Read output std::string output; char buffer[1024]; - FILE * p_stdout = subprocess_stdout(&subprocess); + FILE * p_stdout = subprocess.stdout_file(); while (fgets(buffer, sizeof(buffer), p_stdout)) { output += buffer; } - int process_return; - subprocess_join(&subprocess, &process_return); - subprocess_destroy(&subprocess); + int process_return = subprocess.join(); if (process_return != 0) { t.log("Python script failed with exit code: " + std::to_string(process_return)); diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index 2cdf35739820..d02e65c9ead0 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -40,8 +40,10 @@ static double nmse(const std::vector & a, const std::vector & b) { } static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) { + size_t seed = *(const size_t *) userdata; std::hash hasher; - std::mt19937 gen(hasher(tensor->name) + *(const size_t *) userdata); + seed ^= hasher(tensor->name); + std::mt19937 gen(seed); std::normal_distribution dis(0.0f, 1.0e-2f); const int64_t ne = ggml_nelements(tensor); @@ -166,6 +168,9 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, uint32_t(64)); ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA, uint32_t(192)); ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, uint32_t(128)); + } else if (arch == LLM_ARCH_MINIMAX_M3) { + // partial rotary: n_rot must not exceed the indexer key length (64) + ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, uint32_t(64)); } ms.add_kv(LLM_KV_ATTENTION_CLAMP_KQV, 1.0f); ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, 1e-5f); @@ -196,9 +201,13 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(2)); } - ms.add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, uint32_t(1)); - ms.add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, uint32_t(64)); - ms.add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, uint32_t(8)); + // MSA requires one indexer head per GQA (KV) head, unlike the DSA archs where the + // indexer head count is independent of the main attention head count. + ms.add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, arch == LLM_ARCH_MINIMAX_M3 ? n_head : uint32_t(1)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, uint32_t(64)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, uint32_t(8)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, uint32_t(4)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, uint32_t(1)); ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector({n_embd_head/4, n_embd_head/4, n_embd_head/4, n_embd_head/4})); ms.add_kv(LLM_KV_TOKENIZER_MODEL, "no_vocab"); // ms.add_kv(LLM_KV_DENSE_2_FEAT_OUT, n_embd); @@ -353,6 +362,7 @@ static bool moe_mandatory(const llm_arch arch) { case LLM_ARCH_LLADA_MOE: case LLM_ARCH_GROVEMOE: case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_MINIMAX_M3: case LLM_ARCH_RND1: case LLM_ARCH_PADDLEOCR: case LLM_ARCH_MIMO2: @@ -360,6 +370,7 @@ static bool moe_mandatory(const llm_arch arch) { case LLM_ARCH_STEP35: case LLM_ARCH_MISTRAL4: case LLM_ARCH_MELLUM: + case LLM_ARCH_LAGUNA: return true; default: return false; @@ -465,7 +476,7 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml if (!moe && moe_mandatory(arch)) { continue; } - if (!llama_model_saver_supports_arch(arch)) { + if (!llama_model_saver_supports_arch(arch) || !arch_supported(arch)) { LOG_INF("%s: %s model (%s) is unsupported, skipping\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense"); continue; } diff --git a/tests/test-model-load-cancel.cpp b/tests/test-model-load-cancel.cpp index 9095826fa988..ecc302271167 100644 --- a/tests/test-model-load-cancel.cpp +++ b/tests/test-model-load-cancel.cpp @@ -16,7 +16,7 @@ int main(int argc, char *argv[] ) { llama_backend_init(); auto params = llama_model_params{}; - params.use_mmap = false; + params.load_mode = LLAMA_LOAD_MODE_NONE; params.progress_callback = [](float progress, void * ctx){ (void) ctx; return progress > 0.50; diff --git a/tests/test-quantize-stats.cpp b/tests/test-quantize-stats.cpp index e53a7b355318..c65557534025 100644 --- a/tests/test-quantize-stats.cpp +++ b/tests/test-quantize-stats.cpp @@ -312,7 +312,7 @@ int main(int argc, char ** argv) { { auto mparams = llama_model_default_params(); - mparams.use_mlock = false; + mparams.load_mode = LLAMA_LOAD_MODE_NONE; model = llama_model_load_from_file(params.model.c_str(), mparams); diff --git a/tests/test-reasoning-budget.cpp b/tests/test-reasoning-budget.cpp index f54cff4f8a27..3bcc77e1733c 100644 --- a/tests/test-reasoning-budget.cpp +++ b/tests/test-reasoning-budget.cpp @@ -20,8 +20,8 @@ static void test_reasoning_budget( const char * test_name, const std::vector & sequence, - const std::vector & start_tokens, - const std::vector & end_tokens, + const std::vector & start_seqs, + const std::vector & end_seqs, const std::vector & forced_tokens, int32_t budget, common_reasoning_budget_state initial_state, @@ -31,8 +31,12 @@ static void test_reasoning_budget( // Find the maximum token ID to ensure our vocab covers all tokens llama_token max_token = 0; for (auto t : sequence) max_token = std::max(max_token, t); - for (auto t : start_tokens) max_token = std::max(max_token, t); - for (auto t : end_tokens) max_token = std::max(max_token, t); + for (const auto & seq : start_seqs) { + for (auto t : seq) max_token = std::max(max_token, t); + } + for (const auto & seq : end_seqs) { + for (auto t : seq) max_token = std::max(max_token, t); + } for (auto t : forced_tokens) max_token = std::max(max_token, t); // Create a minimal sampler with mock vocabulary @@ -40,8 +44,8 @@ static void test_reasoning_budget( // The UTF-8 boundary check will treat all tokens as complete (safe fallback) auto * sampler = common_reasoning_budget_init( nullptr, // vocab - not used for basic state machine tests - start_tokens, - end_tokens, + start_seqs, + end_seqs, forced_tokens, budget, initial_state @@ -152,7 +156,7 @@ static void test_reasoning_budget_clone_mid_counting() { const std::vector end = {101}; const std::vector forced = {102, 101}; - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 2, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 2, REASONING_BUDGET_IDLE); llama_sampler_accept(sampler, 100); // COUNTING, remaining=2 llama_sampler_accept(sampler, 50); // COUNTING, remaining=1 @@ -171,7 +175,7 @@ static void test_reasoning_budget_clone_mid_forcing() { const std::vector end = {101}; const std::vector forced = {102, 101}; - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 0, REASONING_BUDGET_FORCING); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 0, REASONING_BUDGET_FORCING); GGML_ASSERT(get_forced_token(sampler, 102) == 102); llama_sampler_accept(sampler, 102); // advance to the second forced token @@ -191,7 +195,7 @@ static void test_reasoning_budget_force_manual() { // if COUNTING, force() succeeds and begins forcing the end sequence from the start { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 5, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 5, REASONING_BUDGET_IDLE); llama_sampler_accept(sampler, 100); // COUNTING, remaining=5 llama_sampler_accept(sampler, 50); // COUNTING, remaining=4 @@ -212,7 +216,7 @@ static void test_reasoning_budget_force_manual() { // if IDLE, force() is a no-op { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 5, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 5, REASONING_BUDGET_IDLE); GGML_ASSERT(!common_reasoning_budget_force(sampler) && "force() must not transition from IDLE"); GGML_ASSERT(common_reasoning_budget_get_state(sampler) == REASONING_BUDGET_IDLE); @@ -222,7 +226,7 @@ static void test_reasoning_budget_force_manual() { // if DONE, force() is a no-op { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 5, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 5, REASONING_BUDGET_IDLE); llama_sampler_accept(sampler, 100); // COUNTING llama_sampler_accept(sampler, 101); // natural end -> DONE @@ -236,7 +240,7 @@ static void test_reasoning_budget_force_manual() { // if FORCING, force() is a no-op and must not rewind the force position { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 0, REASONING_BUDGET_FORCING); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 0, REASONING_BUDGET_FORCING); GGML_ASSERT(get_forced_token(sampler, 102) == 102); llama_sampler_accept(sampler, 102); // advance to the second forced token (force_pos=1) @@ -254,6 +258,81 @@ static void test_reasoning_budget_force_manual() { fprintf(stderr, " Test 'manual force transition' passed\n"); } +static void test_reasoning_budget_end_match() { + const std::vector start = {{100}}; + const std::vector end = {{101}, {103, 104}}; + + // natural end records the sequence that matched; re-arming clears it + { + auto * sampler = common_reasoning_budget_init(nullptr, start, end, {102, 101}, 5, REASONING_BUDGET_IDLE); + + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + + llama_sampler_accept(sampler, 100); // COUNTING + llama_sampler_accept(sampler, 50); + llama_sampler_accept(sampler, 103); + llama_sampler_accept(sampler, 104); // end matched via {103, 104}, DONE + + const llama_tokens * matched = common_reasoning_budget_get_end_match(sampler); + GGML_ASSERT(matched != nullptr); + GGML_ASSERT(*matched == llama_tokens({103, 104})); + + llama_sampler_accept(sampler, 100); // re-arm, COUNTING + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + + llama_sampler_free(sampler); + } + + // overlapping end sequences: the longest one ending at the position wins + { + const std::vector end_overlap = {{104}, {103, 104}}; + + auto * sampler = common_reasoning_budget_init(nullptr, start, end_overlap, {102, 104}, 5, REASONING_BUDGET_IDLE); + + llama_sampler_accept(sampler, 100); // COUNTING + llama_sampler_accept(sampler, 103); + llama_sampler_accept(sampler, 104); // both {104} and {103, 104} end here + + const llama_tokens * matched = common_reasoning_budget_get_end_match(sampler); + GGML_ASSERT(matched != nullptr); + GGML_ASSERT(*matched == llama_tokens({103, 104})); + + llama_sampler_free(sampler); + } + + // forcing records the end sequence terminating forced_tokens + { + auto * sampler = common_reasoning_budget_init(nullptr, start, end, {102, 103, 104}, 0, REASONING_BUDGET_FORCING); + + llama_sampler_accept(sampler, 102); + llama_sampler_accept(sampler, 103); + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + llama_sampler_accept(sampler, 104); // forced sequence complete, DONE + + const llama_tokens * matched = common_reasoning_budget_get_end_match(sampler); + GGML_ASSERT(matched != nullptr); + GGML_ASSERT(*matched == llama_tokens({103, 104})); + + llama_sampler_free(sampler); + } + + // forced_tokens not ending with a known end sequence records nothing + { + auto * sampler = common_reasoning_budget_init(nullptr, start, end, {102}, 0, REASONING_BUDGET_FORCING); + + llama_sampler_accept(sampler, 102); // forced sequence complete, DONE + GGML_ASSERT(common_reasoning_budget_get_state(sampler) == REASONING_BUDGET_DONE); + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + + llama_sampler_free(sampler); + } + + // a null sampler is safely ignored + GGML_ASSERT(common_reasoning_budget_get_end_match(nullptr) == nullptr); + + fprintf(stderr, " Test 'matched end sequence' passed\n"); +} + // UTF-8 boundary detection unit test // Tests common_utf8_is_complete() from reasoning-budget.h static void test_utf8_boundary_detection() { @@ -290,7 +369,7 @@ int main(void) { const std::vector forced = {102}; // forced token (not used in this test) const std::vector sequence = {100, 50, 51, 101, 52}; // start, two tokens, end, one more - test_reasoning_budget("natural end before budget exhausted", sequence, start, end, forced, + test_reasoning_budget("natural end before budget exhausted", sequence, {start}, {end}, forced, 5, // budget of 5 tokens REASONING_BUDGET_IDLE, SIZE_MAX, SIZE_MAX); // no forcing expected (natural end) @@ -306,7 +385,7 @@ int main(void) { const std::vector forced = {102, 101}; // forced message + end const std::vector sequence = {100, 50, 51, 52, 53}; // start + 4 tokens (budget=2) - test_reasoning_budget("budget exhausted forcing", sequence, start, end, forced, + test_reasoning_budget("budget exhausted forcing", sequence, {start}, {end}, forced, 2, // budget of 2 tokens REASONING_BUDGET_IDLE, 3, // forcing starts at i=3 (accept at i=2 depletes budget, apply at i=3 forces) @@ -321,7 +400,7 @@ int main(void) { const std::vector forced = {102, 101}; const std::vector sequence = {100, 50, 51, 52}; // start token first, then 3 tokens - test_reasoning_budget("activate immediately budget=0", sequence, start, end, forced, + test_reasoning_budget("activate immediately budget=0", sequence, {start}, {end}, forced, 0, // budget of 0 tokens REASONING_BUDGET_COUNTING, // starts counting, promoted to FORCING since budget=0 0, // forcing starts at i=0 (initialized in FORCING, apply forces immediately) @@ -335,7 +414,7 @@ int main(void) { const std::vector forced = {102}; const std::vector sequence = {50, 51, 52, 53}; - test_reasoning_budget("no start/end configured", sequence, start, end, forced, + test_reasoning_budget("no start/end configured", sequence, {start}, {end}, forced, 2, // budget REASONING_BUDGET_IDLE, SIZE_MAX, SIZE_MAX); // no forcing (no start/end configured) @@ -350,7 +429,7 @@ int main(void) { const std::vector forced = {102, 101}; const std::vector sequence = {50, 51, 52, 53}; - test_reasoning_budget("activate immediately with budget", sequence, start, end, forced, + test_reasoning_budget("activate immediately with budget", sequence, {start}, {end}, forced, 2, // budget of 2 tokens REASONING_BUDGET_COUNTING, 2, // forcing starts at i=2 (after 2 accepts deplete budget, apply at i=2 forces) @@ -373,18 +452,50 @@ int main(void) { const std::vector forced = {102, 101}; const std::vector sequence = {100, 50, 101, 100, 60, 61, 62, 63}; - test_reasoning_budget("multi-block re-arms budget after DONE", sequence, start, end, forced, + test_reasoning_budget("multi-block re-arms budget after DONE", sequence, {start}, {end}, forced, 2, // budget of 2 tokens (per block) REASONING_BUDGET_IDLE, 6, // forcing starts at i=6 (after second block exhausts at i=5) 7); // forcing continues through i=7 } + // Test 7: Multiple start sequences - the second sequence activates counting + // Flow: i=0 accept(110), i=1 accept(111)->COUNTING rem=2; i=2 accept(50)->rem=1; + // i=3 accept(51)->rem=0->FORCING; i=4..5 apply() forces the end sequence + { + const std::vector start = {{100}, {110, 111}}; + const std::vector end = {{101}}; + const std::vector forced = {102, 101}; + const std::vector sequence = {110, 111, 50, 51, 52, 53}; + + test_reasoning_budget("multiple start sequences", sequence, start, end, forced, + 2, // budget of 2 tokens + REASONING_BUDGET_IDLE, + 4, // forcing starts at i=4 (accept at i=3 depletes budget) + 5); // forcing continues through i=5 + } + + // Test 8: Multiple end sequences - natural end via the second sequence + // Flow: i=0 accept(100)->COUNTING rem=5; i=1 accept(50)->rem=4; + // i=2 accept(103)->partial end, rem=3; i=3 accept(104)->end matched, DONE + { + const std::vector start = {{100}}; + const std::vector end = {{101}, {103, 104}}; + const std::vector forced = {102, 101}; + const std::vector sequence = {100, 50, 103, 104, 52}; + + test_reasoning_budget("multiple end sequences", sequence, start, end, forced, + 5, // budget of 5 tokens + REASONING_BUDGET_IDLE, + SIZE_MAX, SIZE_MAX); // no forcing expected (natural end) + } + test_reasoning_budget_clone_mid_counting(); test_reasoning_budget_clone_mid_forcing(); test_reasoning_budget_force_manual(); + test_reasoning_budget_end_match(); - printf("OK (9 tests passed)\n"); + printf("OK (12 tests passed)\n"); printf("Testing UTF-8 boundary detection... "); test_utf8_boundary_detection(); diff --git a/tests/test-recurrent-state-rollback.cpp b/tests/test-recurrent-state-rollback.cpp index be19316db8a7..8e2eace6a195 100644 --- a/tests/test-recurrent-state-rollback.cpp +++ b/tests/test-recurrent-state-rollback.cpp @@ -20,7 +20,7 @@ static llama_context * make_ctx(const common_params & params, llama_model * mode static bool decode_tokens(llama_context * ctx, const std::vector & tokens, uint32_t count) { llama_batch batch = llama_batch_init(count, 0, 1); for (uint32_t pos = 0; pos < count; ++pos) { - common_batch_add(batch, tokens[pos], pos, { 0 }, false); + common_batch_add(batch, tokens[pos], pos, { 0 }, pos + 1 == count); } const bool ok = llama_decode(ctx, batch) == 0; llama_batch_free(batch); @@ -79,7 +79,12 @@ int main(int argc, char ** argv) { return 0; } - std::vector tokens = common_tokenize(ctx_src, "The quick brown fox jumps", true); + std::vector tokens; + if (llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_NONE) { + tokens = { 1, 2, 3, 4, 5, 6, 7, 8, 9 }; + } else { + tokens = common_tokenize(ctx_src, "The quick brown fox jumps", true); + } const uint32_t n_rs_seq = llama_n_rs_seq(ctx_src); if (tokens.size() > n_rs_seq + 1) { tokens.resize(n_rs_seq + 1); diff --git a/tools/cli/README.md b/tools/cli/README.md index f93ae914ce27..972ea04dc7d7 100644 --- a/tools/cli/README.md +++ b/tools/cli/README.md @@ -54,9 +54,11 @@ | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | +| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC) | +| `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available
(env: LLAMA_ARG_DIO) | +| `-lm, --load-mode MODE` | model loading mode (default: mmap)
- none: no special loading mode
- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
- mlock: force system to keep model in RAM rather than swapping or compressing
- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
- dio: use DirectIO if available

(env: LLAMA_ARG_LOAD_MODE) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -142,6 +144,7 @@ | Argument | Explanation | | -------- | ----------- | +| `--server-base URL` | connect to this server instead of starting a new one, example: 'http://localhost:8080' (default: none) | | `--verbose-prompt` | print a verbose prompt before generation (default: false) | | `--display-prompt, --no-display-prompt` | whether to print prompt at generation (default: true) | | `-co, --color [on\|off\|auto]` | Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal | @@ -164,17 +167,19 @@ | `--image, --audio, --video FILE` | path to an image, audio, or video file. use with multimodal models, use comma-separated values for multiple files | | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MIN_TOKENS) | | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MAX_TOKENS) | +| `-o, --output, --output-file FNAME` | output file (default: '') | | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'
(env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) | | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(env: LLAMA_ARG_JINJA) | | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK) | | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))
(env: LLAMA_ARG_REASONING) | | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) | +| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)
compatible with certain templates having 'supports_preserve_reasoning' capability
example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking
(env: LLAMA_ARG_REASONING_PRESERVE) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING) | | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles | -| `--log-prompts-dir PATH` | Log prompts to directory (only used for debugging, default: disabled) | +| `--log-prompts-dir PATH` | Log prompts to directory (auto-created if not present; only used for debugging, default: disabled) | | `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft /[:quant]` | Same as --hf-repo, but for the draft model (default: unused)
(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) | | `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) | | `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) | @@ -198,7 +203,7 @@ | `--spec-draft-device, -devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | | `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_SPEC_DRAFT_MODEL) | -| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | +| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,draft-dflash,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | | `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) | | `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) | | `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) | diff --git a/tools/completion/README.md b/tools/completion/README.md index d90f81748662..bce71d68d949 100644 --- a/tools/completion/README.md +++ b/tools/completion/README.md @@ -137,9 +137,11 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | +| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC) | +| `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available
(env: LLAMA_ARG_DIO) | +| `-lm, --load-mode MODE` | model loading mode (default: mmap)
- none: no special loading mode
- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
- mlock: force system to keep model in RAM rather than swapping or compressing
- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
- dio: use DirectIO if available

(env: LLAMA_ARG_LOAD_MODE) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -253,6 +255,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))
(env: LLAMA_ARG_REASONING) | | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) | +| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)
compatible with certain templates having 'supports_preserve_reasoning' capability
example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking
(env: LLAMA_ARG_REASONING_PRESERVE) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING) | diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index 0756893881ff..c17a27b54019 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -26,6 +26,7 @@ #include "fit.h" #include "ggml.h" #include "llama.h" +#include "log.h" #ifdef _WIN32 # define WIN32_LEAN_AND_MEAN @@ -339,14 +340,13 @@ struct cmd_params { std::vector n_gpu_layers; std::vector n_cpu_moe; std::vector split_mode; + std::vector load_mode; std::vector main_gpu; std::vector no_kv_offload; std::vector flash_attn; std::vector> devices; std::vector> tensor_split; std::vector> tensor_buft_overrides; - std::vector use_mmap; - std::vector use_direct_io; std::vector embeddings; std::vector no_op_offload; std::vector no_host; @@ -384,14 +384,13 @@ static const cmd_params cmd_params_defaults = { /* n_gpu_layers */ { -1 }, /* n_cpu_moe */ { 0 }, /* split_mode */ { LLAMA_SPLIT_MODE_LAYER }, + /* load_mode */ { LLAMA_LOAD_MODE_MMAP }, /* main_gpu */ { 0 }, /* no_kv_offload */ { false }, /* flash_attn */ { LLAMA_FLASH_ATTN_TYPE_AUTO }, /* devices */ { {} }, /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, /* tensor_buft_overrides*/ { std::vector{ { nullptr, nullptr } } }, - /* use_mmap */ { true }, - /* use_direct_io */ { false }, /* embeddings */ { false }, /* no_op_offload */ { false }, /* no_host */ { false }, @@ -430,44 +429,45 @@ static void print_usage(int /* argc */, char ** argv) { } printf("\n"); printf("test parameters:\n"); - printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -hf, -hfr, --hf-repo /[:quant] Hugging Face model repository; quant is optional, case-insensitive\n"); - printf(" default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"); - printf(" example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M\n"); - printf(" (default: unused)\n"); - printf(" -hff, --hf-file Hugging Face model file. If specified, it will override the quant in --hf-repo\n"); - printf(" (default: unused)\n"); - printf(" -hft, --hf-token Hugging Face access token\n"); - printf(" (default: value from HF_TOKEN environment variable)\n"); - printf(" --offline Offline mode: forces use of cache, prevents network access\n"); - printf(" (default: disabled)\n"); - printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); - printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); - printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); - printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - printf(" -ncmoe, --n-cpu-moe (default: %s)\n", join(cmd_params_defaults.n_cpu_moe, ",").c_str()); - printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -fa, --flash-attn (default: %s)\n", join(transform_to_str(cmd_params_defaults.flash_attn, llama_flash_attn_type_name), ",").c_str()); - printf(" -dev, --device (default: auto)\n"); - printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -dio, --direct-io <0|1> (default: %s)\n", join(cmd_params_defaults.use_direct_io, ",").c_str()); - printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); - printf(" -ts, --tensor-split (default: 0)\n"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -hf, -hfr, --hf-repo /[:quant] Hugging Face model repository; quant is optional, case-insensitive\n"); + printf(" default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"); + printf(" example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M\n"); + printf(" (default: unused)\n"); + printf(" -hff, --hf-file Hugging Face model file. If specified, it will override the quant in --hf-repo\n"); + printf(" (default: unused)\n"); + printf(" -hft, --hf-token Hugging Face access token\n"); + printf(" (default: value from HF_TOKEN environment variable)\n"); + printf(" --offline Offline mode: forces use of cache, prevents network access\n"); + printf(" (default: disabled)\n"); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); + printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); + printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -ncmoe, --n-cpu-moe (default: %s)\n", join(cmd_params_defaults.n_cpu_moe, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn (default: %s)\n", join(transform_to_str(cmd_params_defaults.flash_attn, llama_flash_attn_type_name), ",").c_str()); + printf(" -dev, --device (default: auto)\n"); + printf(" -lm, --load-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.load_mode, llama_load_mode_name), ",").c_str()); + printf(" -mmp, --mmap <0|1> (DEPRECATED IN FAVOUR OF --load-mode)\n"); + printf(" -dio, --direct-io <0|1> (DEPRECATED IN FAVOUR OF --load-mode)\n"); + printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); + printf(" -ts, --tensor-split (default: 0)\n"); printf(" -ot --override-tensor =;...\n"); - printf(" (default: disabled)\n"); - printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); - printf(" --no-host <0|1> (default: %s)\n", join(cmd_params_defaults.no_host, ",").c_str()); + printf(" (default: disabled)\n"); + printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); + printf(" --no-host <0|1> (default: %s)\n", join(cmd_params_defaults.no_host, ",").c_str()); printf("\n"); printf( "Multiple values can be given for each parameter by separating them with ','\n" @@ -670,22 +670,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } } else if (arg == "--list-devices") { - std::vector devices; - for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { - auto * dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { - devices.push_back(dev); - } - } - printf("Available devices:\n"); - if (devices.empty()) { - printf(" (none)\n"); - } - for (auto * dev : devices) { - size_t free, total; - ggml_backend_dev_memory(dev, &free, &total); - printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); - } + common_print_available_devices(); exit(0); } else if (arg == "-t" || arg == "--threads") { if (++i >= argc) { @@ -769,6 +754,36 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); + } else if (arg == "-lm" || arg == "--load-mode") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + + std::vector modes; + for (const auto & m : p) { + llama_load_mode mode; + if (m == "none") { + mode = LLAMA_LOAD_MODE_NONE; + } else if (m == "mmap") { + mode = LLAMA_LOAD_MODE_MMAP; + } else if (m == "mlock") { + mode = LLAMA_LOAD_MODE_MLOCK; + } else if (m == "mmap+mlock") { + mode = LLAMA_LOAD_MODE_MMAP_MLOCK; + } else if (m == "dio") { + mode = LLAMA_LOAD_MODE_DIRECT_IO; + } else { + invalid_param = true; + break; + } + modes.push_back(mode); + } + if (invalid_param) { + break; + } + params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end()); } else if (arg == "-mg" || arg == "--main-gpu") { if (++i >= argc) { invalid_param = true; @@ -829,15 +844,39 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } + LOG_WRN("DEPRECATED: -mmp and --mmap are deprecated in favour of --load-mode. Please use --load-mode mmap instead."); auto p = string_split(argv[i], split_delim); - params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); + + std::vector modes; + for (const auto & m : p) { + llama_load_mode mode; + if (m) { + mode = LLAMA_LOAD_MODE_MMAP; + } else { + mode = LLAMA_LOAD_MODE_NONE; + } + modes.push_back(mode); + } + params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end()); } else if (arg == "-dio" || arg == "--direct-io") { if (++i >= argc) { invalid_param = true; break; } + LOG_WRN("DEPRECATED: -dio and --direct-io are deprecated in favour of --load-mode. Please use --load-mode dio instead."); auto p = string_split(argv[i], split_delim); - params.use_direct_io.insert(params.use_direct_io.end(), p.begin(), p.end()); + + std::vector modes; + for (const auto & m : p) { + llama_load_mode mode; + if (m) { + mode = LLAMA_LOAD_MODE_DIRECT_IO; + } else { + mode = LLAMA_LOAD_MODE_NONE; + } + modes.push_back(mode); + } + params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end()); } else if (arg == "-embd" || arg == "--embeddings") { if (++i >= argc) { invalid_param = true; @@ -1093,6 +1132,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } + if (params.load_mode.empty()) { + params.load_mode = cmd_params_defaults.load_mode; + } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } @@ -1111,12 +1153,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.tensor_buft_overrides.empty()) { params.tensor_buft_overrides = cmd_params_defaults.tensor_buft_overrides; } - if (params.use_mmap.empty()) { - params.use_mmap = cmd_params_defaults.use_mmap; - } - if (params.use_direct_io.empty()) { - params.use_direct_io = cmd_params_defaults.use_direct_io; - } if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } @@ -1164,14 +1200,13 @@ struct cmd_params_instance { int n_gpu_layers; int n_cpu_moe; llama_split_mode split_mode; + llama_load_mode load_mode; int main_gpu; bool no_kv_offload; llama_flash_attn_type flash_attn; std::vector devices; std::vector tensor_split; std::vector tensor_buft_overrides; - bool use_mmap; - bool use_direct_io; bool embeddings; bool no_op_offload; bool no_host; @@ -1186,10 +1221,9 @@ struct cmd_params_instance { mparams.devices = const_cast(devices.data()); } mparams.split_mode = split_mode; + mparams.load_mode = load_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); - mparams.use_mmap = use_mmap; - mparams.use_direct_io = use_direct_io; mparams.no_host = no_host; if (n_cpu_moe <= 0) { @@ -1235,9 +1269,7 @@ struct cmd_params_instance { return model == other.model && n_gpu_layers == other.n_gpu_layers && n_cpu_moe == other.n_cpu_moe && split_mode == other.split_mode && main_gpu == other.main_gpu && tensor_split == other.tensor_split && - use_mmap == other.use_mmap && use_direct_io == other.use_direct_io && - devices == other.devices && - no_host == other.no_host && + load_mode == other.load_mode && devices == other.devices && no_host == other.no_host && vec_tensor_buft_override_equal(tensor_buft_overrides, other.tensor_buft_overrides); } @@ -1270,12 +1302,11 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nl : params.n_gpu_layers) for (const auto & ncmoe : params.n_cpu_moe) for (const auto & sm : params.split_mode) + for (const auto & lm : params.load_mode) for (const auto & mg : params.main_gpu) for (const auto & devs : params.devices) for (const auto & ts : params.tensor_split) for (const auto & ot : params.tensor_buft_overrides) - for (const auto & mmp : params.use_mmap) - for (const auto & dio : params.use_direct_io) for (const auto & noh : params.no_host) for (const auto & embd : params.embeddings) for (const auto & nopo : params.no_op_offload) @@ -1295,34 +1326,33 @@ static std::vector get_cmd_params_instances(const cmd_param continue; } cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ n_prompt, - /* .n_gen = */ 0, - /* .n_depth = */ nd, - /* .n_batch = */ nb, - /* .n_ubatch = */ nub, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .cpu_mask = */ cm, - /* .cpu_strict = */ cs, - /* .poll = */ pl, - /* .n_gpu_layers = */ nl, - /* .n_cpu_moe = */ ncmoe, - /* .split_mode = */ sm, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .flash_attn = */ fa, - /* .devices = */ devs, - /* .tensor_split = */ ts, + /* .model = */ m, + /* .n_prompt = */ n_prompt, + /* .n_gen = */ 0, + /* .n_depth = */ nd, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .n_cpu_moe = */ ncmoe, + /* .split_mode = */ sm, + /* .load_mode = */ lm, + /* .main_gpu = */ mg, + /* .no_kv_offload = */ nkvo, + /* .flash_attn = */ fa, + /* .devices = */ devs, + /* .tensor_split = */ ts, /* .tensor_buft_overrides = */ ot, - /* .use_mmap = */ mmp, - /* .use_direct_io= */ dio, - /* .embeddings = */ embd, - /* .no_op_offload= */ nopo, - /* .no_host = */ noh, - /* .fit_target = */ fpt, - /* .fit_min_ctx = */ fpc, + /* .embeddings = */ embd, + /* .no_op_offload = */ nopo, + /* .no_host = */ noh, + /* .fit_target = */ fpt, + /* .fit_min_ctx = */ fpc, }; instances.push_back(instance); } @@ -1332,34 +1362,33 @@ static std::vector get_cmd_params_instances(const cmd_param continue; } cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ 0, - /* .n_gen = */ n_gen, - /* .n_depth = */ nd, - /* .n_batch = */ nb, - /* .n_ubatch = */ nub, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .cpu_mask = */ cm, - /* .cpu_strict = */ cs, - /* .poll = */ pl, - /* .n_gpu_layers = */ nl, - /* .n_cpu_moe = */ ncmoe, - /* .split_mode = */ sm, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .flash_attn = */ fa, - /* .devices = */ devs, - /* .tensor_split = */ ts, + /* .model = */ m, + /* .n_prompt = */ 0, + /* .n_gen = */ n_gen, + /* .n_depth = */ nd, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .n_cpu_moe = */ ncmoe, + /* .split_mode = */ sm, + /* .load_mode = */ lm, + /* .main_gpu = */ mg, + /* .no_kv_offload = */ nkvo, + /* .flash_attn = */ fa, + /* .devices = */ devs, + /* .tensor_split = */ ts, /* .tensor_buft_overrides = */ ot, - /* .use_mmap = */ mmp, - /* .use_direct_io= */ dio, - /* .embeddings = */ embd, - /* .no_op_offload= */ nopo, - /* .no_host = */ noh, - /* .fit_target = */ fpt, - /* .fit_min_ctx = */ fpc, + /* .embeddings = */ embd, + /* .no_op_offload = */ nopo, + /* .no_host = */ noh, + /* .fit_target = */ fpt, + /* .fit_min_ctx = */ fpc, }; instances.push_back(instance); } @@ -1369,34 +1398,33 @@ static std::vector get_cmd_params_instances(const cmd_param continue; } cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ n_pg.first, - /* .n_gen = */ n_pg.second, - /* .n_depth = */ nd, - /* .n_batch = */ nb, - /* .n_ubatch = */ nub, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .cpu_mask = */ cm, - /* .cpu_strict = */ cs, - /* .poll = */ pl, - /* .n_gpu_layers = */ nl, - /* .n_cpu_moe = */ ncmoe, - /* .split_mode = */ sm, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .flash_attn = */ fa, - /* .devices = */ devs, - /* .tensor_split = */ ts, + /* .model = */ m, + /* .n_prompt = */ n_pg.first, + /* .n_gen = */ n_pg.second, + /* .n_depth = */ nd, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .n_cpu_moe = */ ncmoe, + /* .split_mode = */ sm, + /* .load_mode = */ lm, + /* .main_gpu = */ mg, + /* .no_kv_offload = */ nkvo, + /* .flash_attn = */ fa, + /* .devices = */ devs, + /* .tensor_split = */ ts, /* .tensor_buft_overrides = */ ot, - /* .use_mmap = */ mmp, - /* .use_direct_io= */ dio, - /* .embeddings = */ embd, - /* .no_op_offload= */ nopo, - /* .no_host = */ noh, - /* .fit_target = */ fpt, - /* .fit_min_ctx = */ fpc, + /* .embeddings = */ embd, + /* .no_op_offload = */ nopo, + /* .no_host = */ noh, + /* .fit_target = */ fpt, + /* .fit_min_ctx = */ fpc, }; instances.push_back(instance); } @@ -1426,14 +1454,13 @@ struct test { int n_gpu_layers; int n_cpu_moe; llama_split_mode split_mode; + llama_load_mode load_mode; int main_gpu; bool no_kv_offload; llama_flash_attn_type flash_attn; std::vector devices; std::vector tensor_split; std::vector tensor_buft_overrides; - bool use_mmap; - bool use_direct_io; bool embeddings; bool no_op_offload; bool no_host; @@ -1466,14 +1493,13 @@ struct test { n_gpu_layers = inst.n_gpu_layers; n_cpu_moe = inst.n_cpu_moe; split_mode = inst.split_mode; + load_mode = inst.load_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; flash_attn = inst.flash_attn; devices = inst.devices; tensor_split = inst.tensor_split; tensor_buft_overrides = inst.tensor_buft_overrides; - use_mmap = inst.use_mmap; - use_direct_io = inst.use_direct_io; embeddings = inst.embeddings; no_op_offload = inst.no_op_offload; no_host = inst.no_host; @@ -1535,8 +1561,8 @@ struct test { "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", "n_cpu_moe", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "devices", "tensor_split", - "tensor_buft_overrides", "use_mmap", "use_direct_io", "embeddings", - "no_op_offload", "no_host", "fit_target", "fit_min_ctx", + "tensor_buft_overrides", "load_mode", "embeddings", + "no_op_offload", "no_host", "fit_target", "fit_min_ctx", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" }; @@ -1554,12 +1580,15 @@ struct test { return INT; } if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || - field == "use_mmap" || field == "use_direct_io" || field == "embeddings" || field == "no_host") { + field == "embeddings" || field == "no_host") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { return FLOAT; } + if (field == "load_mode") { + return STRING; + } return STRING; } @@ -1626,8 +1655,7 @@ struct test { devices_to_string(devices), tensor_split_str, tensor_buft_overrides_str, - std::to_string(use_mmap), - std::to_string(use_direct_io), + llama_load_mode_name(load_mode), std::to_string(embeddings), std::to_string(no_op_offload), std::to_string(no_host), @@ -1806,18 +1834,15 @@ struct markdown_printer : public printer { if (field == "split_mode") { return 6; } + if (field == "load_mode") { + return 10; + } if (field == "flash_attn") { return 3; } if (field == "devices") { return -12; } - if (field == "use_mmap") { - return 4; - } - if (field == "use_direct_io") { - return 3; - } if (field == "test") { return 15; } @@ -1852,11 +1877,8 @@ struct markdown_printer : public printer { if (field == "flash_attn") { return "fa"; } - if (field == "use_mmap") { - return "mmap"; - } - if (field == "use_direct_io") { - return "dio"; + if (field == "load_mode") { + return "lm"; } if (field == "embeddings") { return "embd"; @@ -1945,11 +1967,8 @@ struct markdown_printer : public printer { if (params.tensor_buft_overrides.size() > 1 || !vec_vec_tensor_buft_override_equal(params.tensor_buft_overrides, cmd_params_defaults.tensor_buft_overrides)) { fields.emplace_back("tensor_buft_overrides"); } - if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { - fields.emplace_back("use_mmap"); - } - if (params.use_direct_io.size() > 1 || params.use_direct_io != cmd_params_defaults.use_direct_io) { - fields.emplace_back("use_direct_io"); + if (params.load_mode.size() > 1 || params.load_mode != cmd_params_defaults.load_mode) { + fields.emplace_back("load_mode"); } if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) { fields.emplace_back("embeddings"); diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index ea684d9f156d..e4752cb51cb6 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -1,8 +1,15 @@ # mtmd -set(MTMD_VIDEO ON CACHE BOOL "enable video support in mtmd (requires ffmpeg binary in PATH)") +set(MTMD_VIDEO_HELP "enable video support in mtmd (requires ffmpeg binary in PATH)") + +set(MTMD_VIDEO ON CACHE BOOL "${MTMD_VIDEO_HELP}") # TODO: add MTMD_VIDEO_METHOD in the future to select between ffmpeg and other backends +if (MTMD_VIDEO AND NOT LLAMA_SUBPROCESS) + message(STATUS "Disabling MTMD_VIDEO because LLAMA_SUBPROCESS is OFF") + set(MTMD_VIDEO OFF CACHE BOOL "${MTMD_VIDEO_HELP}" FORCE) +endif() + find_package(Threads REQUIRED) add_library(mtmd @@ -31,6 +38,7 @@ add_library(mtmd models/granite4-vision.cpp models/hunyuanvl.cpp models/internvl.cpp + models/kimik3.cpp models/kimivl.cpp models/kimik25.cpp models/nemotron-v2-vl.cpp @@ -40,6 +48,7 @@ add_library(mtmd models/paddleocr.cpp models/pixtral.cpp models/qwen2vl.cpp + models/minimax-m3.cpp models/qwen3vl.cpp models/mimovl.cpp models/qwen3a.cpp diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 5b413681f040..1021a9b50efd 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -41,6 +41,7 @@ #define KEY_PROJ_DIM "clip.%s.projection_dim" #define KEY_N_HEAD "clip.%s.attention.head_count" #define KEY_N_HEAD_KV "clip.%s.attention.head_count_kv" +#define KEY_N_EMBD_HEAD "clip.%s.attention.head_dim" #define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" #define KEY_FEATURE_LAYERS "clip.%s.feature_layer" @@ -131,6 +132,8 @@ #define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3 #define TN_MM_PROJECTOR "mm.model.fc.%s" // idefics3, deepseekocr #define TN_MM_PATCH_MERGER "mm.patch_merger.%s" // mistral small 3.1, glm4v +#define TN_MM_MERGER_FC1 "mm.merger.fc1.%s" // minimax-m3 patch-merge MLP +#define TN_MM_MERGER_FC2 "mm.merger.fc2.%s" #define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral #define TN_TOK_GLM_BOI "adapter.boi" // glm-edge (these embeddings are not in text model) #define TN_TOK_GLM_EOI "adapter.eoi" // glm-edge (these embeddings are not in text model) @@ -364,12 +367,14 @@ enum projector_type { PROJECTOR_TYPE_YOUTUVL, PROJECTOR_TYPE_YASA2, PROJECTOR_TYPE_KIMIK25, + PROJECTOR_TYPE_KIMIK3, PROJECTOR_TYPE_NEMOTRON_V2_VL, PROJECTOR_TYPE_HUNYUANVL, PROJECTOR_TYPE_EXAONE4_5, PROJECTOR_TYPE_MINICPMV4_6, PROJECTOR_TYPE_GRANITE_SPEECH, PROJECTOR_TYPE_MIMOVL, + PROJECTOR_TYPE_MINIMAX_M3, PROJECTOR_TYPE_GRANITE4_VISION, PROJECTOR_TYPE_UNKNOWN, }; @@ -418,12 +423,14 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_YOUTUVL, "youtuvl"}, { PROJECTOR_TYPE_YASA2, "yasa2"}, { PROJECTOR_TYPE_KIMIK25, "kimik25"}, + { PROJECTOR_TYPE_KIMIK3, "kimik3"}, { PROJECTOR_TYPE_NEMOTRON_V2_VL, "nemotron_v2_vl"}, { PROJECTOR_TYPE_EXAONE4_5, "exaone4_5"}, { PROJECTOR_TYPE_HUNYUANVL, "hunyuanvl"}, { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"}, { PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"}, { PROJECTOR_TYPE_MIMOVL, "mimovl"}, + { PROJECTOR_TYPE_MINIMAX_M3, "minimax_m3"}, { PROJECTOR_TYPE_GRANITE4_VISION, "granite4_vision"}, }; diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index 6d4336c4010b..e8726ce2dd51 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -54,6 +54,8 @@ struct clip_hparams { int32_t projection_dim = 0; int32_t n_head = 0; int32_t n_head_kv = 0; + // 0 = derive from n_embd; set when qkv width != n_embd + int32_t n_embd_head = 0; int32_t n_layer = 0; int32_t n_merge = 1; // number of patch merges **per-side** @@ -397,6 +399,10 @@ struct clip_model { ggml_tensor * mm_0_b = nullptr; ggml_tensor * mm_2_w = nullptr; ggml_tensor * mm_2_b = nullptr; + ggml_tensor * mm_merger_fc1_w = nullptr; // minimax-m3 + ggml_tensor * mm_merger_fc1_b = nullptr; + ggml_tensor * mm_merger_fc2_w = nullptr; + ggml_tensor * mm_merger_fc2_b = nullptr; ggml_tensor * image_newline = nullptr; ggml_tensor * view_seperator = nullptr; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index b8866506493e..93e853d6fa9c 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -253,7 +253,7 @@ clip_graph::clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : n_embd(hparams.n_embd), n_head(hparams.n_head), n_head_kv(hparams.n_head_kv), - d_head(n_head > 0 ? n_embd / n_head : 0), + d_head(hparams.n_embd_head > 0 ? hparams.n_embd_head : (n_head > 0 ? n_embd / n_head : 0)), n_layer(hparams.n_layer), n_mmproj_embd(clip_n_mmproj_embd(ctx)), eps(hparams.eps), @@ -367,13 +367,13 @@ ggml_tensor * clip_graph::build_vit( /* nb1 */ ggml_row_size(cur->type, d_head), /* nb2 */ cur->nb[1], /* nb3 */ cur->nb[1] * n_pos, - /* offset */ ggml_row_size(cur->type, n_embd)); + /* offset */ ggml_row_size(cur->type, n_head * d_head)); Vcur = ggml_view_4d(ctx0, cur, d_head, n_head, n_pos, B, /* nb1 */ ggml_row_size(cur->type, d_head), /* nb2 */ cur->nb[1], /* nb3 */ cur->nb[1] * n_pos, - /* offset */ ggml_row_size(cur->type, 2 * n_embd)); + /* offset */ ggml_row_size(cur->type, 2 * n_head * d_head)); if (layer.q_norm) { GGML_ASSERT(layer.q_norm->ne[0] == Qcur->ne[0]); @@ -915,6 +915,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_STEP3VL: { builder = std::make_unique(ctx, img); @@ -960,6 +964,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_KIMIK3: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_COGVLM: { builder = std::make_unique(ctx, img); @@ -1169,6 +1177,7 @@ struct clip_model_loader { const char * prefix = is_vision ? "vision" : "audio"; get_u32(string_format(KEY_N_EMBD, prefix), hparams.n_embd); get_u32(string_format(KEY_N_HEAD, prefix), hparams.n_head); + get_u32(string_format(KEY_N_EMBD_HEAD, prefix), hparams.n_embd_head, false); get_u32(string_format(KEY_N_FF, prefix), hparams.n_ff); get_u32(string_format(KEY_N_BLOCK, prefix), hparams.n_layer); get_u32(string_format(KEY_PROJ_DIM, prefix), hparams.projection_dim); @@ -1406,6 +1415,23 @@ struct clip_model_loader { hparams.rope_theta = 10000.0f; get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + int min_pixels = 0, max_pixels = 0; + get_u32(KEY_IMAGE_MIN_PIXELS, min_pixels, false); + get_u32(KEY_IMAGE_MAX_PIXELS, max_pixels, false); + if (min_pixels > 0 && max_pixels > 0) { + hparams.image_min_pixels = min_pixels; + hparams.image_max_pixels = max_pixels; + hparams.warmup_image_size = static_cast(std::sqrt(max_pixels)); + } else { + hparams.set_limit_image_tokens(2, 4096); + } + } break; + case PROJECTOR_TYPE_KIMIK3: + { + hparams.image_resize_algo = RESIZE_ALGO_BILINEAR; + hparams.rope_theta = 10000.0f; + get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + int min_pixels = 0, max_pixels = 0; get_u32(KEY_IMAGE_MIN_PIXELS, min_pixels, false); get_u32(KEY_IMAGE_MAX_PIXELS, max_pixels, false); @@ -1469,6 +1495,17 @@ struct clip_model_loader { LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__); } } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + hparams.n_merge = 2; // spatial_merge_size + hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW; + hparams.image_resize_pad = PAD_NONE; + get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false); + hparams.rope_theta = 10000.0f; // vision_config.rope_theta + // MiniMax-M3: max_pixels 451584 (=672^2) -> 576 merged tokens (image_seq_length) + hparams.set_limit_image_tokens(8, 576); + hparams.set_warmup_n_tokens(16*16); + } break; case PROJECTOR_TYPE_MIMOVL: { hparams.n_merge = 2; // spatial_merge_size @@ -2089,6 +2126,19 @@ struct clip_model_loader { model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + // per-patch MLP: mm.1 -> gelu -> mm.2 + model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight")); + model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias")); + model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); + // 2x2 merge MLP: mm.merge.fc1 -> gelu -> mm.merge.fc2 + model.mm_merger_fc1_w = get_tensor(string_format(TN_MM_MERGER_FC1, "weight")); + model.mm_merger_fc1_b = get_tensor(string_format(TN_MM_MERGER_FC1, "bias")); + model.mm_merger_fc2_w = get_tensor(string_format(TN_MM_MERGER_FC2, "weight")); + model.mm_merger_fc2_b = get_tensor(string_format(TN_MM_MERGER_FC2, "bias")); + } break; case PROJECTOR_TYPE_STEP3VL: { model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); @@ -2315,6 +2365,13 @@ struct clip_model_loader { model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); } break; + case PROJECTOR_TYPE_KIMIK3: + { + // patchmergerv2, bias-free, norm after the projection + model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight")); + model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + model.mm_post_norm_w = get_tensor(string_format(TN_MM_POST_NORM, "weight")); + } break; case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_KIMIK25: @@ -3360,6 +3417,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { case PROJECTOR_TYPE_QWEN3VL: case PROJECTOR_TYPE_EXAONE4_5: case PROJECTOR_TYPE_MIMOVL: + case PROJECTOR_TYPE_MINIMAX_M3: case PROJECTOR_TYPE_GLM4V: case PROJECTOR_TYPE_YOUTUVL: { @@ -3395,6 +3453,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: { // dynamic size int out_patch_size = params.patch_size * ctx->model.hparams.n_merge; @@ -3866,6 +3925,24 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 set_input_i32("positions", positions); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + const int n_merge = hparams.n_merge; + const int gh = image_size_height / patch_size; + const int gw = image_size_width / patch_size; + std::vector pos_h, pos_w; + pos_h.reserve(gh * gw); + pos_w.reserve(gh * gw); + for (int bh = 0; bh < gh / n_merge; bh++) + for (int bw = 0; bw < gw / n_merge; bw++) + for (int mh = 0; mh < n_merge; mh++) + for (int mw = 0; mw < n_merge; mw++) { + pos_h.push_back(bh * n_merge + mh); + pos_w.push_back(bw * n_merge + mw); + } + set_input_i32("minimax_pos_h", pos_h); + set_input_i32("minimax_pos_w", pos_w); + } break; case PROJECTOR_TYPE_DOTS_OCR: { const int pw = image_size_width / patch_size; @@ -4061,6 +4138,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: case PROJECTOR_TYPE_LIGHTONOCR: { // set the 2D positions @@ -4569,6 +4647,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->model.mm_ffn_down_w->ne[1]; case PROJECTOR_TYPE_GLM_EDGE: return ctx->model.mm_model_mlp_3_w->ne[1]; + case PROJECTOR_TYPE_MINIMAX_M3: + return ctx->model.mm_merger_fc2_b->ne[0]; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_EXAONE4_5: @@ -4612,6 +4692,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: case PROJECTOR_TYPE_YASA2: return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_HUNYUANVL: diff --git a/tools/mtmd/models/kimik3.cpp b/tools/mtmd/models/kimik3.cpp new file mode 100644 index 000000000000..fe06c7fdbb38 --- /dev/null +++ b/tools/mtmd/models/kimik3.cpp @@ -0,0 +1,80 @@ +#include "models.h" + +#include +#include + +// Kimi-K3 MoonViT-3d, image path. +// Follows clip_graph_kimik25, but with RMSNorm, no biases, qkv width != n_embd, and a post-norm patchmergerv2 projector. +// Images only: at t == 1 the temporal pool and the temporal position term vanish. + +ggml_tensor * clip_graph_kimik3::resize_position_embeddings_3d(uint32_t interpolation_mode) { + ggml_tensor * pos_embd = model.position_embeddings; + const int height = img.ny() / patch_size; + const int width = img.nx() / patch_size; + + GGML_ASSERT(pos_embd); + + const int64_t stored_c = pos_embd->ne[0]; + const int64_t orig_w = pos_embd->ne[1]; + const int64_t orig_h = pos_embd->ne[2]; + + GGML_ASSERT(stored_c == n_embd); + + if (height == (int) orig_h && width == (int) orig_w) { + return ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); + } + + pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3); + pos_embd = ggml_interpolate(ctx0, pos_embd, height, width, n_embd, 1, interpolation_mode); + pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3); + pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); + return pos_embd; +} + +ggml_cgraph * clip_graph_kimik3::build() { + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * learned_pos_embd = resize_position_embeddings_3d(GGML_SCALE_MODE_BILINEAR); + + // Q/K are de-interleaved during conversion. + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + }; + + ggml_tensor * inp = build_inp(); + inp = ggml_add(ctx0, inp, learned_pos_embd); + + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_RMS, + hparams.ffn_op, + nullptr, + add_pos); + cb(cur, "vit_out", -1); + + { + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + cur = build_ffn(cur, + model.mm_1_w, nullptr, + nullptr, nullptr, + model.mm_2_w, nullptr, + FFN_GELU, + -1); + cb(cur, "proj_mlp_out", -1); + + cur = build_norm(cur, model.mm_post_norm_w, nullptr, NORM_TYPE_RMS, hparams.eps, -1); + cb(cur, "proj_out", -1); + } + + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/minimax-m3.cpp b/tools/mtmd/models/minimax-m3.cpp new file mode 100644 index 000000000000..447621754e69 --- /dev/null +++ b/tools/mtmd/models/minimax-m3.cpp @@ -0,0 +1,84 @@ +#include "models.h" + +ggml_tensor * clip_graph_minimax_m3::apply_rope( + ggml_tensor * x, ggml_tensor * pos_h, ggml_tensor * pos_w) { + const int64_t Hn = x->ne[1]; + const int64_t P = x->ne[2]; + const size_t es = ggml_element_size(x); + const int dh = (int) x->ne[0]; + const int axd = 2 * ((2 * (dh / 2) / 3) / 2); + + GGML_ASSERT(x->nb[0] == es); + GGML_ASSERT(3 * axd <= dh); + + const float th = hparams.rope_theta; + + // layout of x is [t, h, w, pad] + // t is unrotated, h and w are rotated, pad is unrotated + // note: everything from n_dims onward untouched, so w and pad are rotated in one call. + auto sl = [&](int off, int n) { + return ggml_cont(ctx0, ggml_view_3d(ctx0, x, n, Hn, P, x->nb[1], x->nb[2], (size_t) off * es)); + }; + ggml_tensor * t = sl(0, axd); + ggml_tensor * h = sl(axd, axd); + ggml_tensor * w = sl(2 * axd, dh - 2 * axd); // w + pad + + h = ggml_rope_ext(ctx0, h, pos_h, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + w = ggml_rope_ext(ctx0, w, pos_w, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + return ggml_concat(ctx0, ggml_concat(ctx0, t, h, 0), w, 0); +} + +ggml_cgraph * clip_graph_minimax_m3::build() { + GGML_ASSERT(model.patch_bias == nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + GGML_ASSERT(model.patch_embeddings_0 && model.patch_embeddings_1); + GGML_ASSERT(model.mm_1_w && model.mm_2_w); + GGML_ASSERT(model.mm_merger_fc1_w && model.mm_merger_fc2_w); + + const int batch_size = 1; + const int n_pos = n_patches; + const int merge = hparams.n_merge; + + // patch embedding + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_add(ctx0, + ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1), + ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1)); + + // spatial merge + { + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); + inp = ggml_cont_4d(ctx0, inp, n_embd * merge, n_patches_x / merge, n_patches_y, batch_size); + inp = ggml_reshape_4d(ctx0, inp, n_embd * merge, n_patches_x / merge, merge, batch_size * (n_patches_y / merge)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d(ctx0, inp, n_embd, n_patches_x * n_patches_y, batch_size); + } + + // t (time axis) is always 0 for now, so we leave it unrotated + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_h, "minimax_pos_h"); ggml_set_input(pos_h); + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_w, "minimax_pos_w"); ggml_set_input(pos_w); + + ggml_tensor * inpL = build_vit( + inp, n_pos, NORM_TYPE_NORMAL, FFN_GELU_ERF, nullptr, + [&](ggml_tensor * c, const clip_layer &) { + return apply_rope(c, pos_h, pos_w); + }); + + // projector + ggml_tensor * emb = inpL; + emb = build_ffn(emb, model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, FFN_GELU_ERF, -1); + + const int64_t proj = emb->ne[0]; + emb = ggml_reshape_2d(ctx0, emb, proj * merge * merge, n_pos / (merge * merge)); + + emb = build_ffn(emb, model.mm_merger_fc1_w, model.mm_merger_fc1_b, + nullptr, nullptr, + model.mm_merger_fc2_w, model.mm_merger_fc2_b, FFN_GELU_ERF, -1); + + ggml_build_forward_expand(gf, emb); + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h index 5f1493fa603e..02d8ba0cc1b6 100644 --- a/tools/mtmd/models/models.h +++ b/tools/mtmd/models/models.h @@ -40,6 +40,12 @@ struct clip_graph_qwen3vl : clip_graph_qwen2vl { ggml_cgraph * build() override; }; +struct clip_graph_minimax_m3 : clip_graph { + clip_graph_minimax_m3(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; + ggml_tensor * apply_rope(ggml_tensor * x, ggml_tensor * pos_h, ggml_tensor * pos_w); +}; + struct clip_graph_mimovl : clip_graph { clip_graph_mimovl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; @@ -211,6 +217,13 @@ struct clip_graph_kimik25 : clip_graph { ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode); }; +struct clip_graph_kimik3 : clip_graph { + clip_graph_kimik3(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; + + ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode); +}; + struct clip_graph_exaone4_5 : clip_graph { clip_graph_exaone4_5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; diff --git a/tools/mtmd/models/qwen3vl.cpp b/tools/mtmd/models/qwen3vl.cpp index 261e77a198af..48626b221fbb 100644 --- a/tools/mtmd/models/qwen3vl.cpp +++ b/tools/mtmd/models/qwen3vl.cpp @@ -37,7 +37,7 @@ ggml_cgraph * clip_graph_qwen3vl::build() { } // calculate absolute position embedding and apply - ggml_tensor * learned_pos_embd = resize_position_embeddings(); + ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS); learned_pos_embd = ggml_cont_4d( ctx0, learned_pos_embd, n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); diff --git a/tools/mtmd/mtmd-helper.cpp b/tools/mtmd/mtmd-helper.cpp index 3c73db4431e7..90451d02ebd8 100644 --- a/tools/mtmd/mtmd-helper.cpp +++ b/tools/mtmd/mtmd-helper.cpp @@ -238,6 +238,29 @@ struct decode_embd_batch { } }; +// Helper class to set non-causal attention via RAII +class scope_non_causal { +public: + scope_non_causal(llama_context * context, bool enabled) : context_(context), enabled_(enabled) { + if (enabled_) { + // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image + llama_set_causal_attn(context_, false); + } + } + ~scope_non_causal() { + if (enabled_) { + llama_set_causal_attn(context_, true); + } + } + + scope_non_causal(const scope_non_causal &) = delete; + scope_non_causal & operator=(const scope_non_causal &) = delete; + +private: + llama_context * context_; + bool enabled_; +}; + // Helper function for decoding an image whose embeddings have already been calculated int32_t mtmd_helper_decode_image_chunk( mtmd_context * ctx, @@ -288,10 +311,7 @@ int32_t mtmd_helper_decode_image_chunk( } const bool use_non_causal = mtmd_decode_use_non_causal(ctx, chunk); - if (use_non_causal) { - llama_set_causal_attn(lctx, false); - // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image - } + const scope_non_causal non_causal(lctx, use_non_causal); while (i_batch < n_img_batches) { // split into batches int pos_offset = i_batch*n_batch; @@ -304,9 +324,6 @@ int32_t mtmd_helper_decode_image_chunk( int32_t ret = llama_decode(lctx, batch_embd_view); if (ret != 0) { LOG_ERR("failed to decode %s\n", name); - if (use_non_causal) { - llama_set_causal_attn(lctx, true); - } return ret; } @@ -314,9 +331,6 @@ int32_t mtmd_helper_decode_image_chunk( ret = callback(batch_embd_view, user_data); if (ret != 0) { LOG_ERR("post-decode callback failed\n"); - if (use_non_causal) { - llama_set_causal_attn(lctx, true); - } return ret; } } @@ -329,9 +343,6 @@ int32_t mtmd_helper_decode_image_chunk( n_past += mtmd_input_chunk_get_n_pos(chunk); *new_n_past = n_past; - if (use_non_causal) { - llama_set_causal_attn(lctx, true); - } return 0; } @@ -629,6 +640,7 @@ bool mtmd_helper_support_video(mtmd_context * ctx) { #ifdef MTMD_VIDEO return mtmd_support_vision(ctx); #else + GGML_UNUSED(ctx); return false; #endif } @@ -996,6 +1008,9 @@ mtmd_helper_video * mtmd_helper_video_init( return ctx; #else + GGML_UNUSED(mctx); + GGML_UNUSED(path); + GGML_UNUSED(params); LOG_ERR("%s: video is not supported in this build (MTMD_VIDEO is set to OFF)\n", __func__); return nullptr; #endif @@ -1028,6 +1043,10 @@ mtmd_helper_video * mtmd_helper_video_init_from_buf( return ctx; #else + GGML_UNUSED(mctx); + GGML_UNUSED(buf); + GGML_UNUSED(len); + GGML_UNUSED(params); LOG_ERR("%s: video is not supported in this build (MTMD_VIDEO is set to OFF)\n", __func__); return nullptr; #endif @@ -1039,6 +1058,7 @@ void mtmd_helper_video_free(mtmd_helper_video * ctx) { ctx->stop_ffmpeg(); delete ctx; #else + GGML_UNUSED(ctx); LOG_ERR("%s: video is not supported in this build (MTMD_VIDEO is set to OFF)\n", __func__); #endif } @@ -1047,6 +1067,7 @@ mtmd_helper_video_info mtmd_helper_video_get_info(const mtmd_helper_video * ctx) #ifdef MTMD_VIDEO return ctx->info; #else + GGML_UNUSED(ctx); GGML_ASSERT(false && "video is not supported in this build (MTMD_VIDEO is set to OFF)"); #endif } @@ -1057,6 +1078,9 @@ int32_t mtmd_helper_video_read_next(mtmd_helper_video * ctx, if (!ctx) return -2; return ctx->read_next(out_bitmap, out_text); #else + GGML_UNUSED(ctx); + GGML_UNUSED(out_bitmap); + GGML_UNUSED(out_text); GGML_ASSERT(false && "video is not supported in this build (MTMD_VIDEO is set to OFF)"); #endif } diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index e10ccf186b14..7828dd8821d3 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -463,6 +463,13 @@ struct mtmd_context { img_end = "<|vision_end|>"; image_preproc = std::make_unique(ctx_v); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + // ]<]start of image[>[ ... (image embeddings) ... ]<]end of image[>[ + img_beg = "]<]start of image[>["; + img_end = "]<]end of image[>["; + image_preproc = std::make_unique(ctx_v); + } break; case PROJECTOR_TYPE_YOUTUVL: { // <|vision_start|> ... (image embeddings) ... <|vision_end|> @@ -554,10 +561,19 @@ struct mtmd_context { image_preproc = std::make_unique(ctx_v); } break; case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: { - // <|media_begin|> ... (image embeddings) ... <|media_end|> - img_beg = "<|media_begin|>"; - img_end = "<|media_end|>"; + // GLM-5.2-V reuses the Kimi-K2.5 vision encoder and projector, but marks + // images with its own tokens, so decide based on the text model vocab + if (lookup_token("<|begin_of_image|>") != LLAMA_TOKEN_NULL) { + // <|begin_of_image|> ... (image embeddings) ... <|end_of_image|> + img_beg = "<|begin_of_image|>"; + img_end = "<|end_of_image|>"; + } else { + // <|media_begin|> ... (image embeddings) ... <|media_end|> + img_beg = "<|media_begin|>"; + img_end = "<|media_end|>"; + } image_preproc = std::make_unique(ctx_v); } break; case PROJECTOR_TYPE_LIGHTONOCR: diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt index b5c40884fd6e..280bd9e19dca 100644 --- a/tools/server/CMakeLists.txt +++ b/tools/server/CMakeLists.txt @@ -19,6 +19,8 @@ add_library(${TARGET} STATIC server-stream.h server-tools.cpp server-tools.h + server-mcp.cpp + server-mcp.h server-schema.cpp server-schema.h ) diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md index e81336e5e26b..b41d70c63ac8 100644 --- a/tools/server/README-dev.md +++ b/tools/server/README-dev.md @@ -136,13 +136,13 @@ Producer side: `server_res_generator` extends `server_res_spipe`, which keeps al Lifetime safety: the session holds no back reference to the response, so `spipe` is a plain `unique_ptr` touched only by the http worker. `cancel` raises an atomic the producer polls; the producer finalizes the session from its destructor, which also runs `~server_response_reader::stop()` to cancel the generation at the queue level. A `DELETE` stops work by raising the flag and letting the worker unwind. -Consumer side: `GET /v1/stream/?from=N` opens a `text/event-stream` that replays buffered bytes from offset `N` and blocks for live bytes, so the browser reattaches like a fresh EventSource. An offset below the dropped prefix returns 400. +Consumer side: `GET /v1/stream?conv_id=&from=N` opens a `text/event-stream` that replays buffered bytes from offset `N` and blocks for live bytes, so the browser reattaches like a fresh EventSource. An offset below the dropped prefix returns 400. Routes: -- `GET /v1/stream/:conv_id?from=N`: replay or live reattach. +- `GET /v1/stream?conv_id=&from=N`: replay or live reattach. The id travels in the query string because it can embed a model name containing slashes. - `POST /v1/streams/lookup` with `{"conversation_ids": [...]}`: returns session status only for ids the caller already owns. There is no listing route, so live sessions cannot be enumerated (an earlier `GET /v1/streams` was removed for exactly this reason). -- `DELETE /v1/stream/:conv_id`: explicit Stop, idempotent (`evict_and_cancel`). +- `DELETE /v1/stream?conv_id=`: explicit Stop, idempotent (`evict_and_cancel`). Router mode binds the same paths to proxy handlers. A `conv_id -> child` map (`conv_models`), populated when a POST is routed, resolves the owning child in one lookup with no polling. The lookup groups ids per child; GET and DELETE proxy straight to the owner. This loopback REST hop is expected to move to a websocket IPC later, swapping only the transport. @@ -166,8 +166,8 @@ graph TD GC[GC thread] -- drop after TTL --> Sess end Sess -- read_from offset --> Cons[stream_pipe_consumer] - Cons -- "GET /v1/stream/:id?from=N" --> Client - DEL[DELETE /v1/stream/:id] -- evict_and_cancel --> Sess + Cons -- "GET /v1/stream?conv_id=id&from=N" --> Client + DEL[DELETE /v1/stream?conv_id=id] -- evict_and_cancel --> Sess ``` The diagram shows the buffer touch points. The live wire (chunks streamed to the original client during a normal generation) is the producer's default output, described under "Producer side" above. @@ -189,7 +189,7 @@ This endpoint is intended to be used internally by the Web UI and subject to cha Get a list of tools, each tool has these fields: - `tool` (string): the ID name of the tool, to be used in POST call. Example: `read_file` - `display_name` (string): the name to be displayed on UI. Example: `Read file` -- `type` (string): always be `"builtin"` for now +- `type` (string): `"builtin"` for a built-in tool, or `"mcp"` for a tool exposed by an MCP server - `permissions` (object): a mapping string --> boolean that indicates the permission required by this tool. This is useful for the UI to ask the user before calling the tool. For now, the only permission supported is `"write"` - `definition` (object): the OAI-compat definition of this tool @@ -199,7 +199,7 @@ Invoke a tool call, request body is a JSON object with: - `tool` (string): the name of the tool - `params` (object): a mapping from argument name (string) to argument value -Returns JSON object. There are two response formats: +Returns JSON object. There are two response formats (MCP tools use the same two formats: their result content is concatenated into `plain_text_response`, and RPC or tool errors are surfaced as the `error` string): Format 1: Plain text. The text will be placed into a field called `plain_text_response`, example: diff --git a/tools/server/README.md b/tools/server/README.md index 365a944659a9..25aacf9f516f 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -71,9 +71,11 @@ For the full list of features, please refer to [server's changelog](https://gith | `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | +| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC) | +| `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available
(env: LLAMA_ARG_DIO) | +| `-lm, --load-mode MODE` | model loading mode (default: mmap)
- none: no special loading mode
- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
- mlock: force system to keep model in RAM rather than swapping or compressing
- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
- dio: use DirectIO if available

(env: LLAMA_ARG_LOAD_MODE) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -162,7 +164,7 @@ For the full list of features, please refer to [server's changelog](https://gith | `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) | | `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) | | `-ctxcp, --ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 32)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS) | -| `-cms, --checkpoint-min-step N` | minimum spacing between context checkpoints in tokens (default: 256, 0 = no minimum)
(env: LLAMA_ARG_CHECKPOINT_MIN_SPACING_NT) | +| `-cms, --checkpoint-min-step N` | minimum spacing between context checkpoints in tokens (default: 8192, 0 = no minimum)
(env: LLAMA_ARG_CHECKPOINT_MIN_SPACING_NT) | | `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM) | | `-kvu, --kv-unified, -no-kvu, --no-kv-unified` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)
(env: LLAMA_ARG_KV_UNIFIED) | | `--cache-idle-slots, --no-cache-idle-slots` | save idle slots to the prompt cache on new task, and clear them when using unified KV (default: enabled, requires cache-ram)
(env: LLAMA_ARG_CACHE_IDLE_SLOTS) | @@ -188,12 +190,16 @@ For the full list of features, please refer to [server's changelog](https://gith | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | | `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)
(env: LLAMA_ARG_REUSE_PORT) | | `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | +| `--cors-origins ORIGINS` | comma-separated list of allowed origins for CORS (default: *)
if set to special value 'localhost', reflect the Origin header only if it is localhost
(env: LLAMA_ARG_CORS_ORIGINS) | +| `--cors-methods METHODS` | comma-separated list of allowed methods for CORS (default: GET, POST, DELETE, OPTIONS)
(env: LLAMA_ARG_CORS_METHODS) | +| `--cors-headers HEADERS` | comma-separated list of allowed headers for CORS (default: *)
(env: LLAMA_ARG_CORS_HEADERS) | +| `--cors-credentials, --no-cors-credentials` | whether to allow credentials for CORS (default: enabled)
note: if this is enabled and --cors-origins is set to * (default), the Origin header will be echoed back, and credentials will always be allowed
(env: LLAMA_ARG_CORS_CREDENTIALS) | | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )
(env: LLAMA_ARG_API_PREFIX) | | `--ui-config, --webui-config JSON` | JSON that provides default UI settings (overrides UI defaults)
(env: LLAMA_ARG_UI_CONFIG) | | `--ui-config-file, --webui-config-file PATH` | JSON file that provides default UI settings (overrides UI defaults)
(env: LLAMA_ARG_UI_CONFIG_FILE) | | `--ui-mcp-proxy, --webui-mcp-proxy, --no-ui-mcp-proxy, --no-webui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)
(env: LLAMA_ARG_UI_MCP_PROXY) | -| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)
specify "all" to enable all tools
available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff, get_datetime
(env: LLAMA_ARG_TOOLS) | -| `-ag, --agent, -no-ag, --no-agent` | whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)
(env: LLAMA_ARG_AGENT) | +| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)
specify "all" to enable all tools
available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime
note: for security reasons, this will limit --cors-origins to localhost by default
(env: LLAMA_ARG_TOOLS) | +| `-ag, --agent, -no-ag, --no-agent` | whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)
note: for security reasons, this will limit --cors-origins to localhost by default
(env: LLAMA_ARG_AGENT) | | `--ui, --webui, --no-ui, --no-webui` | whether to enable the Web UI (default: enabled)
(env: LLAMA_ARG_UI) | | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | | `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING) | @@ -221,6 +227,7 @@ For the full list of features, please refer to [server's changelog](https://gith | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))
(env: LLAMA_ARG_REASONING) | | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) | +| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)
compatible with certain templates having 'supports_preserve_reasoning' capability
example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking
(env: LLAMA_ARG_REASONING_PRESERVE) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING) | @@ -252,7 +259,7 @@ For the full list of features, please refer to [server's changelog](https://gith | `--spec-draft-device, -devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | | `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_SPEC_DRAFT_MODEL) | -| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | +| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,draft-dflash,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | | `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) | | `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) | | `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) | @@ -1240,6 +1247,8 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type": `chat_template_kwargs`: Allows sending additional parameters to the json templating system. For example: `{"enable_thinking": false}` +`reasoning_effort`: If set to `none`, reasoning will be disabled for this request. Other values (e.g., `low`, `max`) have no effect on reasoning. + `reasoning_format`: The reasoning format to be parsed. If set to `none`, it will output the raw generated text. `reasoning_control`: Arms realtime reasoning control for this completion so it can be ended early via `/v1/chat/completions/control`. Defaults to `false`. diff --git a/tools/server/server-chat.cpp b/tools/server/server-chat.cpp index 31f94e023307..0322e54ccea8 100644 --- a/tools/server/server-chat.cpp +++ b/tools/server/server-chat.cpp @@ -283,6 +283,15 @@ json server_chat_convert_responses_to_chatcmpl(const json & response_body) { chatcmpl_body["max_tokens"] = response_body["max_output_tokens"]; } + if (response_body.contains("reasoning")) { + // Only "effort" is handled so far + const json & reasoning = response_body.at("reasoning"); + if (reasoning.contains("effort")) { + chatcmpl_body["reasoning_effort"] = reasoning.at("effort"); + } + chatcmpl_body.erase("reasoning"); + } + return chatcmpl_body; } diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index 78b5c6819b03..c9109fc9626e 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -1086,6 +1086,14 @@ json oaicompat_chat_params_parse( throw std::invalid_argument("invalid type for \"enable_thinking\" (expected boolean, got string)"); } + // Parse also the OAI "reasoning_effort": "none" specific value + if (body.contains("reasoning_effort")) { + auto reasoning_effort = json_value(body, "reasoning_effort", std::string("")); + if (reasoning_effort == "none") { + inputs.enable_thinking = false; + } // other reasoning_effort values are model-specific and not yet handled + } + inputs.force_pure_content = opt.force_pure_content; // Apply chat template to the list of messages @@ -1123,10 +1131,10 @@ json oaicompat_chat_params_parse( reasoning_budget = opt.reasoning_budget; } - if (!chat_params.thinking_end_tag.empty()) { + if (!chat_params.thinking_end_tags.empty()) { llama_params["reasoning_budget_tokens"] = reasoning_budget; llama_params["reasoning_budget_start_tag"] = chat_params.thinking_start_tag; - llama_params["reasoning_budget_end_tag"] = chat_params.thinking_end_tag; + llama_params["reasoning_budget_end_tags"] = chat_params.thinking_end_tags; llama_params["reasoning_budget_message"] = json_value(body, "reasoning_budget_message", opt.reasoning_budget_message); llama_params["reasoning_control"] = json_value(body, "reasoning_control", false); } diff --git a/tools/server/server-common.h b/tools/server/server-common.h index c0eaec6b0267..6ef797ebb473 100644 --- a/tools/server/server-common.h +++ b/tools/server/server-common.h @@ -9,9 +9,15 @@ #define JSON_ASSERT GGML_ASSERT #include +#include +#include +#include +#include +#include +#include +#include #include #include -#include using json = nlohmann::ordered_json; @@ -207,6 +213,9 @@ struct server_tokens { bool empty() const { return tokens.empty(); } + // true if the sequence actually contains image/audio chunks. + bool has_media() const { return !map_idx_to_media.empty(); } + void clear() { map_idx_to_media.clear(); tokens.clear(); @@ -373,3 +382,67 @@ server_tokens format_prompt_rerank( mtmd_context * mctx, const std::string & query, const std::string & doc); + +// simple implementation of a pipe +// used for streaming data between threads +template +struct server_pipe { + std::mutex mutex; + std::condition_variable cv; + std::queue queue; + std::atomic writer_closed{false}; + std::atomic reader_closed{false}; + + // 0 = unbounded (default) + // > 0, write() drops the oldest item once the queue is full + size_t max_size = 0; + + void close_write() { + writer_closed.store(true, std::memory_order_relaxed); + cv.notify_all(); + } + + void close_read() { + reader_closed.store(true, std::memory_order_relaxed); + cv.notify_all(); + } + + // close_on_stop = true: should_stop means the reader is gone for good, so the writer is told the pipe is broken. + // close_on_stop = false: should_stop is a per-read deadline and further reads still come, so the pipe stays usable. + bool read(T & output, const std::function & should_stop, bool close_on_stop = true) { + std::unique_lock lk(mutex); + constexpr auto poll_interval = std::chrono::milliseconds(500); + while (true) { + if (!queue.empty()) { + output = std::move(queue.front()); + queue.pop(); + return true; + } + if (writer_closed.load()) { + return false; // clean EOF + } + if (should_stop && should_stop()) { // a null should_stop means "never stop" + if (close_on_stop) { + close_read(); // signal broken pipe to writer + } + return false; // cancelled / deadline reached + } + cv.wait_for(lk, poll_interval); + } + } + + bool write(T && data) { + std::lock_guard lk(mutex); + if (reader_closed.load()) { + return false; // broken pipe + } + if (max_size > 0) { + while (queue.size() >= max_size) { + queue.pop(); // drop oldest to stay bounded + } + } + queue.push(std::move(data)); + cv.notify_one(); + return true; + } +}; diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 7564ad4e9cfb..744593c760a3 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -1152,6 +1152,11 @@ struct server_context_impl { return false; } + if (ctx_tgt == nullptr) { + SRV_ERR("failed to create_context with model '%s'\n", params_base.model.path.c_str()); + return false; + } + vocab = llama_model_get_vocab(model_tgt); n_ctx = llama_n_ctx(ctx_tgt); @@ -2011,10 +2016,13 @@ struct server_context_impl { queue_results.send(std::move(res)); } - // if multimodal is enabled, send an error and return false - bool check_no_mtmd(const int id_task) { - if (mctx) { - send_error(id_task, "This feature is not supported by multimodal", ERROR_TYPE_NOT_SUPPORTED); + // Gate slot save/restore/erase on slot content (does it hold media), + // not model capability: a multimodal model may hold a pure-text slot. + bool check_slot_no_media(const server_slot & slot, const int id_task) { + if (slot.prompt.tokens.has_media()) { + send_error(id_task, + "This operation is not supported while the slot holds image/audio tokens (a pure-text prefix is supported)", + ERROR_TYPE_NOT_SUPPORTED); return false; } return true; @@ -2502,16 +2510,15 @@ struct server_context_impl { } break; case SERVER_TASK_TYPE_SLOT_SAVE: { - if (!check_no_mtmd(task.id)) { - break; - } - const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + if (!check_slot_no_media(*slot, task.id)) { + break; + } if (slot->is_processing()) { // if requested slot is unavailable, we defer this task for processing later SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); @@ -2519,13 +2526,13 @@ struct server_context_impl { break; } - const size_t token_count = slot->prompt.tokens.size(); const int64_t t_start = ggml_time_us(); std::string filename = task.slot_action.filename; std::string filepath = task.slot_action.filepath; - const llama_tokens & tokens = slot->prompt.tokens.get_tokens(); + const llama_tokens tokens = slot->prompt.tokens.get_text_tokens(); + const size_t token_count = tokens.size(); const size_t nwrite = llama_state_seq_save_file(ctx_tgt, filepath.c_str(), slot->id, tokens.data(), token_count); const int64_t t_end = ggml_time_us(); @@ -2543,7 +2550,6 @@ struct server_context_impl { } break; case SERVER_TASK_TYPE_SLOT_RESTORE: { - if (!check_no_mtmd(task.id)) break; const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { @@ -2590,15 +2596,16 @@ struct server_context_impl { } break; case SERVER_TASK_TYPE_SLOT_ERASE: { - if (!check_no_mtmd(task.id)) { - break; - } const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + // Gate on slot content, consistent with save/restore. + if (!check_slot_no_media(*slot, task.id)) { + break; + } if (slot->is_processing()) { // if requested slot is unavailable, we defer this task for processing later SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp index 24a38452aab7..783b01b82d11 100644 --- a/tools/server/server-http.cpp +++ b/tools/server/server-http.cpp @@ -283,9 +283,9 @@ bool server_http_context::init(const common_params & params) { } else if (params.cors_origins == "localhost") { // special case: only reflect the Origin header if it is a localhost origin std::string origin = req.get_header_value("Origin"); - if (origin_is_localhost(origin)) { + if (!origin.empty() && origin_is_localhost(origin)) { res.set_header("Access-Control-Allow-Origin", origin); - } else { + } else if (!origin.empty()) { SRV_WRN("(CORS) skip non-localhost origin: %s\n", origin.c_str()); } } else { diff --git a/tools/server/server-mcp.cpp b/tools/server/server-mcp.cpp new file mode 100644 index 000000000000..93db6164d34e --- /dev/null +++ b/tools/server/server-mcp.cpp @@ -0,0 +1,820 @@ +#include "server-mcp.h" + +#include "subproc.h" + +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) +# include +# include +#else +# include +# include +# include +# include +extern char ** environ; +#endif + +// read NDJSON lines from a child pipe, calling on_line per line until `running` clears, EOF/error, or on_line returns false. +// polled, not blocking: a grandchild can inherit the pipe's write end and hold it open (terminate() kills only the direct child), so a blocking read would hang teardown on an EOF that never comes. +static void mcp_pump_ndjson(FILE * f, std::atomic & running, + const std::function & on_line) { + if (!f) { + return; + } + const int poll_ms = 50; + const size_t max_line = 8 * 1024 * 1024; // drop any single NDJSON line larger than this, so a child that never emits '\n' can't grow buf without bound +#if defined(_WIN32) + HANDLE h = (HANDLE) _get_osfhandle(_fileno(f)); +#else + int fd = fileno(f); + int fl = fcntl(fd, F_GETFL, 0); + if (fl >= 0) { + fcntl(fd, F_SETFL, fl | O_NONBLOCK); + } +#endif + std::string buf; + bool skipping = false; // discarding an over-long line until its terminating newline + char chunk[4096]; + while (running.load()) { + size_t n = 0; +#if defined(_WIN32) + DWORD avail = 0; + if (!PeekNamedPipe(h, NULL, 0, NULL, &avail, NULL)) { + break; // pipe broken / child gone + } + if (avail == 0) { + std::this_thread::sleep_for(std::chrono::milliseconds(poll_ms)); + continue; + } + DWORD to_read = avail < (DWORD) sizeof(chunk) ? avail : (DWORD) sizeof(chunk); + DWORD got = 0; + if (!ReadFile(h, chunk, to_read, &got, NULL) || got == 0) { + break; + } + n = (size_t) got; +#else + struct pollfd pfd; + pfd.fd = fd; + pfd.events = POLLIN; + pfd.revents = 0; + int pr = poll(&pfd, 1, poll_ms); + if (pr < 0) { + if (errno == EINTR) { + continue; + } + break; + } + if (pr == 0) { + continue; // timeout -> re-check running + } + if (pfd.revents & (POLLERR | POLLNVAL)) { + break; + } + ssize_t r = read(fd, chunk, sizeof(chunk)); + if (r < 0) { + if (errno == EINTR || errno == EAGAIN || errno == EWOULDBLOCK) { + continue; + } + break; + } + if (r == 0) { + break; // EOF: child (and any pipe writers) closed the stream + } + n = (size_t) r; +#endif + buf.append(chunk, n); + + // resync after an over-long, unterminated line: discard bytes until the next newline + if (skipping) { + size_t nl = buf.find('\n'); + if (nl == std::string::npos) { + if (buf.size() > max_line) { + buf.clear(); // stay bounded while waiting for a terminator + } + continue; + } + buf.erase(0, nl + 1); + skipping = false; + } + + size_t pos; + while ((pos = buf.find('\n')) != std::string::npos) { + std::string line = buf.substr(0, pos); + buf.erase(0, pos + 1); + if (!line.empty() && line.back() == '\r') { + line.pop_back(); + } + if (line.empty()) { + continue; + } + if (!on_line(std::move(line))) { + return; + } + } + + // a partial line already larger than the cap and still no newline: drop it to avoid unbounded growth + if (buf.size() > max_line) { + SRV_WRN("MCP: dropping oversized line (> %zu bytes) from child pipe\n", max_line); + buf.clear(); + skipping = true; + } + } +} + +// +// server_mcp_server_config +// + +std::vector server_mcp_server_config::parse_from_json(const std::string & json_str) { + return parse_cursor_format(json::parse(json_str)); +} + +std::vector server_mcp_server_config::parse_cursor_format(const json & j) { + std::vector result; + + if (!j.contains("mcpServers") || !j.at("mcpServers").is_object()) { + return result; + } + + for (const auto & [name, cfg] : j.at("mcpServers").items()) { + server_mcp_server_config sc; + sc.name = name; + sc.command = cfg.value("command", std::string()); + sc.cwd = cfg.value("cwd", std::string()); + sc.timeout_ms = cfg.value("timeout_ms", sc.timeout_ms); + + if (cfg.contains("args") && cfg.at("args").is_array()) { + for (const auto & a : cfg.at("args")) { + sc.args.push_back(a.get()); + } + } + if (cfg.contains("env") && cfg.at("env").is_object()) { + for (const auto & [k, v] : cfg.at("env").items()) { + sc.env[k] = v.get(); + } + } + + if (sc.command.empty()) { + SRV_WRN("MCP server '%s' has no command, skipping\n", name.c_str()); + continue; + } + result.push_back(std::move(sc)); + } + + return result; +} + + +// +// server_mcp_transport +// + +static constexpr const char * MCP_PROTOCOL_VERSION = "2024-11-05"; + +static std::string rpc_error_message(const json & resp) { + if (resp.contains("error")) { + const json & e = resp.at("error"); + if (e.is_object()) { + return e.value("message", "unknown error"); + } + if (e.is_string()) { + return e.get(); + } + } + return "unknown error"; +} + +// normalize an MCP tools/call result to the /tools contract (see README-dev.md): +// concat text parts of result.content[], and surface an isError result +static json mcp_result_to_response(const json & result) { + std::string text; + if (result.contains("content") && result.at("content").is_array()) { + for (const auto & part : result.at("content")) { + if (part.is_object() && part.value("type", "") == "text") { + if (!text.empty()) { + text += "\n"; + } + text += part.value("text", ""); + } + } + } + if (result.is_object() && result.value("isError", false)) { + return {{"error", text.empty() ? "MCP tool returned an error" : text}}; + } + return {{"plain_text_response", text}}; +} + +json server_mcp_transport::send_rpc(const json & request, const std::function & should_stop) { + if (!to_server.write(request.dump())) { + return {{"error", {{"code", -32603}, {"message", "transport closed"}}}}; + } + + const bool has_id = request.contains("id"); + const auto deadline = std::chrono::steady_clock::now() + std::chrono::milliseconds(timeout_ms); + auto stop = [&]() { + return (should_stop && should_stop()) || std::chrono::steady_clock::now() >= deadline; + }; + + std::string frame; + while (from_server.read(frame, stop, false)) { + json reply; + try { + reply = json::parse(frame); + } catch (...) { + if (std::chrono::steady_clock::now() >= deadline) { + break; + } + continue; // skip malformed frame + } + // no id: a notification. mismatched id: a stale reply from a timed-out request (ids are monotonic, never a future one) + if (!has_id || (reply.contains("id") && reply.at("id") == request.at("id"))) { + return reply; + } + if (std::chrono::steady_clock::now() >= deadline) { + break; // a flood of notifications must not outrun the deadline + } + } + + if (should_stop && should_stop()) { + return {{"error", {{"code", -32603}, {"message", "cancelled"}}}}; + } + if (std::chrono::steady_clock::now() >= deadline) { + return {{"error", {{"code", -32603}, {"message", "request timed out"}}}}; + } + return {{"error", {{"code", -32603}, {"message", "transport closed"}}}}; +} + +bool server_mcp_transport::ensure_init(const std::function & should_stop) { + if (initialized) { + return true; + } + + json init_req = { + {"jsonrpc", "2.0"}, + {"id", next_id++}, + {"method", "initialize"}, + {"params", { + {"protocolVersion", MCP_PROTOCOL_VERSION}, + {"capabilities", json::object()}, + {"clientInfo", {{"name", "llama.cpp"}, {"version", "1.0"}}}, + }}, + }; + json resp = send_rpc(init_req, should_stop); + if (!resp.contains("result")) { + last_error = "initialize failed: " + rpc_error_message(resp); + return false; + } + + // notifications/initialized: no id, no reply expected + json notif = {{"jsonrpc", "2.0"}, {"method", "notifications/initialized"}}; + to_server.write(notif.dump()); + + initialized = true; + return true; +} + +std::vector server_mcp_transport::list_tools(const std::function & should_stop) { + std::lock_guard lock(rpc_mutex); + if (!ensure_init(should_stop)) { + return {}; + } + if (!tools.empty()) { + return tools; + } + + json req = {{"jsonrpc", "2.0"}, {"id", next_id++}, {"method", "tools/list"}}; + json resp = send_rpc(req, should_stop); + if (!resp.contains("result")) { + last_error = "tools/list failed: " + rpc_error_message(resp); + return {}; + } + + const json & result = resp.at("result"); + if (result.contains("tools") && result.at("tools").is_array()) { + for (const auto & t : result.at("tools")) { + server_mcp_tool_def def; + def.server_name = name; + def.name = t.value("name", ""); + def.description = t.value("description", ""); + if (t.contains("inputSchema")) { + def.input_schema = t.at("inputSchema"); + } + tools.push_back(std::move(def)); + } + } + return tools; +} + +json server_mcp_transport::call_tool(const std::string & tool_name, + const json & arguments, + const std::function & should_stop) { + std::lock_guard lock(rpc_mutex); + if (!ensure_init(should_stop)) { + return {{"error", last_error}}; + } + + json req = { + {"jsonrpc", "2.0"}, + {"id", next_id++}, + {"method", "tools/call"}, + {"params", {{"name", tool_name}, {"arguments", arguments}}}, + }; + json resp = send_rpc(req, should_stop); + if (resp.contains("error")) { + return {{"error", rpc_error_message(resp)}}; + } + if (resp.contains("result")) { + return mcp_result_to_response(resp.at("result")); + } + return {{"error", "invalid response from MCP server"}}; +} + +// +// server_mcp_stdio +// + +struct server_mcp_stdio::process_handle { + common_subproc sp; + FILE * in = nullptr; // child stdin + FILE * out = nullptr; // child stdout + FILE * err = nullptr; // child stderr +}; + +#if defined(_WIN32) +// config strings are UTF-8 (from JSON) and subprocess.h converts them with CP_UTF8, so inputs must be UTF-8, not the active code page +static std::wstring windows_utf8_to_wide(const std::string & s) { + if (s.empty()) { + return std::wstring(); + } + int n = MultiByteToWideChar(CP_UTF8, 0, s.data(), (int) s.size(), NULL, 0); + if (n <= 0) { + return std::wstring(); + } + std::wstring w((size_t) n, L'\0'); + MultiByteToWideChar(CP_UTF8, 0, s.data(), (int) s.size(), &w[0], n); + return w; +} + +static std::string windows_wide_to_utf8(const wchar_t * s, int len /* -1 for NUL-terminated */) { + int n = WideCharToMultiByte(CP_UTF8, 0, s, len, NULL, 0, NULL, NULL); + if (n <= 0) { + return std::string(); + } + std::string out((size_t) n, '\0'); + WideCharToMultiByte(CP_UTF8, 0, s, len, &out[0], n, NULL, NULL); + if (len == -1 && !out.empty() && out.back() == '\0') { + out.pop_back(); // drop the terminator WideCharToMultiByte counts for -1 + } + return out; +} +#endif + +static std::string mcp_resolve_command(const std::string & command) { +#if defined(_WIN32) + // For Windows: make sure we handle ".exe" correctly, as well as UTF-8 + std::wstring wcmd = windows_utf8_to_wide(command); + wchar_t buf[MAX_PATH * 4]; + const DWORD cap = (DWORD) (sizeof(buf) / sizeof(buf[0])); + + auto search = [&](const wchar_t * ext) -> std::string { + DWORD n = SearchPathW(NULL, wcmd.c_str(), ext, cap, buf, NULL); + return (n > 0 && n < cap) ? windows_wide_to_utf8(buf, (int) n) : std::string(); + }; + + std::string found = search(NULL); // exact path / already-extensioned / .exe on PATH + if (!found.empty()) { + return found; + } + + std::wstring pathext; + DWORD need = GetEnvironmentVariableW(L"PATHEXT", NULL, 0); + if (need > 0) { + pathext.resize(need); + DWORD got = GetEnvironmentVariableW(L"PATHEXT", &pathext[0], need); + pathext.resize(got); + } + if (pathext.empty()) { + pathext = L".COM;.EXE;.BAT;.CMD"; + } + for (size_t start = 0; start <= pathext.size();) { + size_t sep = pathext.find(L';', start); + std::wstring ext = pathext.substr(start, sep == std::wstring::npos ? std::wstring::npos : sep - start); + if (!ext.empty()) { + found = search(ext.c_str()); + if (!found.empty()) { + return found; + } + } + if (sep == std::wstring::npos) { + break; + } + start = sep + 1; + } + return command; // give up and let subprocess.h report the spawn error +#else + return command; +#endif // _WIN32 +} + +static std::vector mcp_parent_env() { + std::vector env; +#if defined(_WIN32) + LPWCH block = GetEnvironmentStringsW(); + if (block) { + for (LPWCH e = block; *e; e += wcslen(e) + 1) { + env.emplace_back(windows_wide_to_utf8(e, -1)); + } + FreeEnvironmentStringsW(block); + } +#else + if (environ) { + for (char ** e = environ; *e; ++e) { + env.emplace_back(*e); + } + } +#endif + return env; +} + +// parent env with the config overrides applied, in "KEY=VALUE" form +static std::vector mcp_build_env(const std::map & overrides) { + std::vector env; + for (auto & e : mcp_parent_env()) { + size_t eq = e.find('='); + std::string key = eq == std::string::npos ? e : e.substr(0, eq); + if (overrides.find(key) == overrides.end()) { + env.push_back(e); + } + } + for (auto & [k, v] : overrides) { + env.push_back(k + "=" + v); + } + return env; +} + +server_mcp_stdio::server_mcp_stdio(const server_mcp_server_config & config) : config(config) { + name = config.name; + timeout_ms = config.timeout_ms; + // bound the reply queue: send_rpc only drains during a call, so unsolicited notifications would otherwise grow it without limit + from_server.max_size = 65536; +} + +server_mcp_stdio::~server_mcp_stdio() { + join_pumps(); +} + +bool server_mcp_stdio::start() { + std::vector argv_s; + argv_s.push_back(mcp_resolve_command(config.command)); + argv_s.insert(argv_s.end(), config.args.begin(), config.args.end()); + + int options = subprocess_option_no_window | subprocess_option_search_user_path; + std::vector envp_s; + if (config.env.empty()) { + options |= subprocess_option_inherit_environment; + } else { + envp_s = mcp_build_env(config.env); + } + + auto handle = std::make_unique(); + bool ok = handle->sp.create(argv_s, options, envp_s, config.cwd.empty() ? nullptr : config.cwd.c_str()); + if (!ok) { + SRV_WRN("MCP '%s': failed to spawn '%s'\n", config.name.c_str(), config.command.c_str()); + return false; + } + handle->in = handle->sp.stdin_file(); + handle->out = handle->sp.stdout_file(); + handle->err = handle->sp.stderr_file(); + + proc = std::move(handle); + running.store(true); + reader = std::thread([this] { reader_loop(); }); + writer = std::thread([this] { writer_loop(); }); + errlog = std::thread([this] { errlog_loop(); }); + return true; +} + +void server_mcp_stdio::close() { + join_pumps(); +} + +bool server_mcp_stdio::is_alive() const { + return running.load(); +} + +std::string server_mcp_stdio::diagnostics() { + std::string out; + { + std::lock_guard lock(rpc_mutex); // last_error is written by send_rpc's callers + out = last_error; + } + std::lock_guard lk(err_mu); + if (!err_tail.empty()) { + if (!out.empty()) { + out += "; "; + } + out += "last stderr: " + err_tail; + } + return out; +} + +void server_mcp_stdio::reader_loop() { + mcp_pump_ndjson(proc->out, running, [this](std::string && line) { + return from_server.write(std::move(line)); // false => consumer gone, stop + }); + running.store(false); + to_server.close_write(); // stop the writer + from_server.close_write(); // EOF to any waiting caller +} + +// write all of `data` to child stdin, non-blocking and polled so teardown never hangs (a grandchild can hold the read end of a full pipe open). returns false on error/close/shutdown. +static bool mcp_write_all(FILE * f, const std::string & data, std::atomic & running) { + if (!f) { + return false; + } + size_t total = 0; +#if defined(_WIN32) + HANDLE h = (HANDLE) _get_osfhandle(_fileno(f)); + DWORD nowait = PIPE_NOWAIT; + SetNamedPipeHandleState(h, &nowait, NULL, NULL); + while (total < data.size() && running.load()) { + DWORD written = 0; + BOOL ok = WriteFile(h, data.data() + total, (DWORD) (data.size() - total), &written, NULL); + if (ok && written > 0) { + total += written; + continue; + } + if (!ok) { + DWORD err = GetLastError(); + if (err != ERROR_NO_DATA && err != ERROR_PIPE_BUSY) { + return false; + } + } + // backpressure (pipe full) is rare for small JSON-RPC frames; sleep rather than spin. + // no writable-wait exists for a PIPE_NOWAIT anonymous pipe, so this polls like the POSIX poll() path. + std::this_thread::sleep_for(std::chrono::milliseconds(10)); + } +#else + int fd = fileno(f); + int fl = fcntl(fd, F_GETFL, 0); + if (fl >= 0) { + fcntl(fd, F_SETFL, fl | O_NONBLOCK); + } + while (total < data.size() && running.load()) { + ssize_t n = write(fd, data.data() + total, data.size() - total); + if (n > 0) { + total += (size_t) n; + continue; + } + if (n == 0) { + return false; + } + if (errno == EINTR) { + continue; + } + if (errno != EAGAIN && errno != EWOULDBLOCK) { + return false; + } + struct pollfd pfd; + pfd.fd = fd; + pfd.events = POLLOUT; + pfd.revents = 0; + int pr = poll(&pfd, 1, 50); + if (pr < 0) { + if (errno == EINTR) { + continue; + } + return false; + } + if (pfd.revents & (POLLERR | POLLNVAL | POLLHUP)) { + return false; + } + } +#endif + return total == data.size(); +} + +void server_mcp_stdio::writer_loop() { + auto should_stop = [this] { return !running.load(); }; + std::string msg; + while (to_server.read(msg, should_stop)) { + msg.push_back('\n'); + if (!mcp_write_all(proc->in, msg, running)) { + break; // child gone or shutting down + } + } + running.store(false); + to_server.close_read(); // fail fast on any further send_rpc write + from_server.close_write(); // wake any caller waiting for a reply +} + +void server_mcp_stdio::errlog_loop() { + static constexpr size_t ERR_TAIL_MAX = 4096; + // drain stderr (an undrained pipe blocks the child): + // log it, and keep a bounded tail for reporting when the server dies + mcp_pump_ndjson(proc->err, running, [this](std::string && line) { + SRV_DBG("MCP '%s' stderr: %s\n", name.c_str(), line.c_str()); + std::lock_guard lk(err_mu); + err_tail += line; + err_tail += '\n'; + if (err_tail.size() > ERR_TAIL_MAX) { + err_tail.erase(0, err_tail.size() - ERR_TAIL_MAX); + } + return true; + }); +} + +void server_mcp_stdio::join_pumps() { + if (!proc) { + return; + } + running.store(false); + to_server.close_write(); // wake the writer if it waits for a message + from_server.close_write(); // wake any caller waiting for a reply + + proc->sp.terminate(); // child death unblocks the blocked fread/fwrite + + if (writer.joinable()) writer.join(); + if (reader.joinable()) reader.join(); + if (errlog.joinable()) errlog.join(); + + proc->sp.join(); // reap the child: never waiting would leave the pid a zombie for the process lifetime + proc.reset(); +} + + +// +// server_mcp +// + +static constexpr int MCP_COOLDOWN_SECONDS = 5; +static constexpr int MCP_WARMUP_TIMEOUT_SECONDS = 10; // cap per-server tool discovery at startup + +server_mcp::~server_mcp() { + shutdown(); + + std::vector> to_close; + { + std::lock_guard lock(mutex); + for (auto & [name, t] : transports) { + to_close.push_back(std::move(t)); + } + transports.clear(); + } + for (auto & t : to_close) { + t->close(); + } +} + +std::shared_ptr server_mcp::create_transport(const server_mcp_server_config & cfg) { + return std::make_shared(cfg); +} + +void server_mcp::shutdown() { + stopping.store(true); +} + +const server_mcp_server_config * server_mcp::find_config(const std::string & name) const { + for (const auto & c : configs) { + if (c.name == name) { + return &c; + } + } + return nullptr; +} + +void server_mcp::start(const common_params & params) { + auto append = [this](const std::string & json_str) { + try { + auto parsed = server_mcp_server_config::parse_from_json(json_str); + if (parsed.empty()) { + SRV_WRN("%s", "MCP config: no servers found in JSON\n"); + } + for (auto & p : parsed) { + // names must be unique across both config sources: get_or_create / find_config key on the name + if (find_config(p.name)) { + SRV_WRN("MCP config: duplicate server name '%s', skipping\n", p.name.c_str()); + continue; + } + configs.push_back(std::move(p)); + } + } catch (const std::exception & e) { + throw std::runtime_error(std::string("failed to parse MCP config JSON: ") + e.what()); + } + }; + if (!params.mcp_servers_config.empty()) { + std::ifstream f = fs_open_ifstream(params.mcp_servers_config, std::ios::in); + if (!f) { + throw std::runtime_error("failed to open MCP config file: " + params.mcp_servers_config); + } + std::stringstream ss; + ss << f.rdbuf(); + append(ss.str()); + } + if (!params.mcp_servers_json.empty()) { + append(params.mcp_servers_json); + } + + if (configs.empty()) { + return; + } + + std::vector discovered; + for (const auto & cfg : configs) { + auto t = create_transport(cfg); + if (!t->start()) { + SRV_WRN("MCP warmup: failed to spawn '%s': %s\n", cfg.name.c_str(), t->diagnostics().c_str()); + continue; + } + // bound warmup per server so an unresponsive one can't stall startup for the full per-call timeout + const auto deadline = std::chrono::steady_clock::now() + std::chrono::seconds(MCP_WARMUP_TIMEOUT_SECONDS); + auto should_stop = [this, deadline]() { + return stopping.load() || std::chrono::steady_clock::now() >= deadline; + }; + auto tools = t->list_tools(should_stop); + SRV_INF("MCP warmup: '%s' discovered %zu tools\n", cfg.name.c_str(), tools.size()); + discovered.insert(discovered.end(), tools.begin(), tools.end()); + t->close(); + } + + std::lock_guard lock(mutex); + registry.swap(discovered); +} + +std::vector server_mcp::list_tools() const { + std::lock_guard lock(mutex); + return registry; +} + +json server_mcp::call_tool(const std::string & server_name, + const std::string & tool_name, + const json & arguments, + const std::function & should_stop) { + auto transport = get_or_create(server_name); + if (!transport) { + return {{"error", "MCP server unavailable: " + server_name}}; + } + + auto stop = [this, &should_stop]() { + return stopping.load() || (should_stop && should_stop()); + }; + return transport->call_tool(tool_name, arguments, stop); +} + +std::shared_ptr server_mcp::get_or_create(const std::string & name) { + std::vector> to_close; // closed after unlock + std::shared_ptr result; + + { + std::lock_guard lock(mutex); + if (stopping.load()) { + return nullptr; + } + + auto now = std::chrono::steady_clock::now(); + auto dead_it = dead_servers.find(name); + if (dead_it != dead_servers.end()) { + if (now < dead_it->second) { + return nullptr; + } + dead_servers.erase(dead_it); + } + + auto it = transports.find(name); + if (it != transports.end()) { + if (it->second->is_alive()) { + return it->second; + } + SRV_WRN("MCP '%s' is no longer alive: %s\n", name.c_str(), it->second->diagnostics().c_str()); + to_close.push_back(std::move(it->second)); + transports.erase(it); + } + + const server_mcp_server_config * cfg = find_config(name); + if (cfg) { + auto fresh = create_transport(*cfg); + if (fresh->start() && fresh->is_alive()) { + transports[name] = fresh; + result = fresh; + } else { + SRV_WRN("MCP '%s': failed to start: %s\n", name.c_str(), fresh->diagnostics().c_str()); + to_close.push_back(std::move(fresh)); + dead_servers[name] = now + std::chrono::seconds(MCP_COOLDOWN_SECONDS); + } + } + } + + for (auto & t : to_close) { + t->close(); // blocking call, no leaks + } + + return result; +} + diff --git a/tools/server/server-mcp.h b/tools/server/server-mcp.h new file mode 100644 index 000000000000..c7f33a3f797d --- /dev/null +++ b/tools/server/server-mcp.h @@ -0,0 +1,176 @@ +#pragma once + +#include "server-common.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// +// Configuration (Cursor-compatible "mcpServers" JSON) +// + +struct server_mcp_server_config { + std::string name; // config key, e.g. "filesystem" + std::string command; + std::vector args; + std::map env; // merged over the parent env + std::string cwd; + int timeout_ms = 30000; // per-tool-call timeout + + // throw on parse errors; missing "mcpServers" yields an empty list; entries without a "command" are skipped + static std::vector parse_from_json(const std::string & json_str); + static std::vector parse_cursor_format(const json & j); +}; + +// a tool advertised by an MCP server +struct server_mcp_tool_def { + std::string server_name; + std::string name; // bare tool name, no "_" prefix + std::string description; + json input_schema; // JSON Schema for the arguments, or null +}; + +// +// server_mcp_transport: one MCP server session. +// +// caller --send_rpc--> to_server --[writer]--> framing --> server +// caller <--send_rpc-- from_server <--[reader]-- framing <-- server +// +// each queue item is one complete serialized JSON message. +// subclass owns byte I/O and framing; base owns JSON and the JSON-RPC session (handshake, id correlation). +// + +struct server_mcp_transport { + std::string name; + int timeout_ms = 30000; + + server_pipe to_server; // serialized messages we send to the server + server_pipe from_server; // serialized messages read from the server + + virtual ~server_mcp_transport() = default; + + virtual bool start() = 0; + virtual void close() = 0; // blocking and idempotent + virtual bool is_alive() const = 0; // never blocks behind an in-flight send_rpc() + + // human-readable diagnostics for logging when the transport fails/dies + // (example: last RPC error, plus any transport-specific detail) + // may run on a different thread than send_rpc(), so last_error is read under rpc_mutex + virtual std::string diagnostics() { + std::lock_guard lock(rpc_mutex); + return last_error; + } + + std::vector list_tools(const std::function & should_stop); + + json call_tool(const std::string & tool_name, + const json & arguments, + const std::function & should_stop); + +protected: + // per-transport: send_rpc() holds it across the reply wait, so sharing it would stall every server behind one slow call. guards all members below. + std::mutex rpc_mutex; + uint64_t next_id = 1; // reset to 1 per (re)spawn + bool initialized = false; + std::string last_error; + std::vector tools; + + // both assume rpc_mutex is already held by the public caller + bool ensure_init(const std::function & should_stop); // initialize handshake, once + json send_rpc(const json & request, const std::function & should_stop); // returns the reply or an {"error": ...} +}; + +// +// server_mcp_stdio: child process, NDJSON JSON-RPC over stdio (stderr drained to the debug log) +// + +struct server_mcp_stdio : server_mcp_transport { + explicit server_mcp_stdio(const server_mcp_server_config & config); + ~server_mcp_stdio() override; + + bool start() override; + void close() override; + bool is_alive() const override; + std::string diagnostics() override; + +private: + server_mcp_server_config config; + + // defined in the .cpp so stays out of this header + struct process_handle; + std::unique_ptr proc; + + std::thread reader; // child stdout -> NDJSON de-framing -> from_server + std::thread writer; // to_server -> NDJSON framing -> child stdin + std::thread errlog; // child stderr -> debug log (must be drained or the child blocks) + + // cleared by close() or by the reader on stdout EOF; read without rpc_mutex + std::atomic running{false}; + + // bounded tail of the child's stderr, for diagnostics when it dies + std::mutex err_mu; + std::string err_tail; + + void reader_loop(); + void writer_loop(); + void errlog_loop(); + void join_pumps(); +}; + +// +// server_mcp +// declare before the HTTP context so it outlives every /tools handler. +// + +class server_mcp { +public: + server_mcp() = default; + ~server_mcp(); + + // parse the MCP config from params (file and/or inline JSON), + // then spawn each server once, list its tools, and shut it down + // throws on config parse errors; spawn failures are logged. + void start(const common_params & params); + + // true until start() has parsed at least one server from the config + bool empty() const { return configs.empty(); } + + std::vector list_tools() const; + + // lazily (re)spawns the transport. returns the MCP result or an {"error": ...}. should_stop is OR-ed with the manager's cancel flag. + json call_tool(const std::string & server_name, + const std::string & tool_name, + const json & arguments, + const std::function & should_stop = nullptr); + + // flip the cancel flag so in-flight calls return; blocking teardown is in the destructor. call before the HTTP server drains. + // note: multiple calls are idempotent + void shutdown(); + +private: + std::vector configs; + + mutable std::mutex mutex; // guards transports, dead_servers, registry + + // shared_ptr: call_tool() hands a transport to the caller and drops the lock for the blocking RPC, so a concurrent evict/respawn must not destroy it mid-call + std::map> transports; + std::map dead_servers; // spawn-failure cooldown + std::vector registry; + + std::atomic stopping{false}; + + const server_mcp_server_config * find_config(const std::string & name) const; + + // the only place that names a concrete transport + std::shared_ptr create_transport(const server_mcp_server_config & cfg); + + // nullptr during cooldown or shutdown + std::shared_ptr get_or_create(const std::string & name); +}; diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 7c6bfda0ff67..188a72a3741c 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -8,10 +8,10 @@ #include "preset.h" #include "download.h" #include "http.h" +#include "subproc.h" #include // TODO: remove this once we use HTTP client from download.h #include -#include #include #include @@ -49,43 +49,24 @@ extern char **environ; #define CHILD_ADDR "127.0.0.1" struct server_subproc { - std::optional sproc; // empty while in DOWNLOADING state + common_subproc sproc; // not yet spawned while in DOWNLOADING state std::atomic stopped{false}; // set to cancel a download or signal child process exit - subprocess_s & get() { - GGML_ASSERT(sproc.has_value() && "subprocess not initialized"); - return sproc.value(); - } - bool is_alive() { - return sproc.has_value() && subprocess_alive(&sproc.value()); + return sproc.alive(); } void request_exit() { - if (sproc.has_value()) { - FILE * stdin_file = subprocess_stdin(&sproc.value()); - if (stdin_file) { - fprintf(stdin_file, "%s\n", CMD_ROUTER_TO_CHILD_EXIT); - fflush(stdin_file); - } + FILE * stdin_file = sproc.stdin_file(); + if (stdin_file) { + fprintf(stdin_file, "%s\n", CMD_ROUTER_TO_CHILD_EXIT); + fflush(stdin_file); } stopped.store(true, std::memory_order_relaxed); } void terminate() { - if (!sproc.has_value()) { - return; - } -#if defined(_WIN32) - if (sproc->hProcess == NULL) { - return; - } -#else - if (sproc->child <= 0) { - return; - } -#endif - subprocess_terminate(&sproc.value()); + sproc.terminate(); } }; @@ -711,18 +692,6 @@ std::optional server_models::get_meta(const std::string & nam return std::nullopt; } -// helper to convert vector to char ** -// pointers are only valid as long as the original vector is valid -static std::vector to_char_ptr_array(const std::vector & vec) { - std::vector result; - result.reserve(vec.size() + 1); - for (const auto & s : vec) { - result.push_back(const_cast(s.c_str())); - } - result.push_back(nullptr); - return result; -} - std::vector server_models::get_all_meta() { std::unique_lock lk(mutex); if (need_reload) { @@ -845,15 +814,10 @@ void server_models::load(const std::string & name, const load_options & opts) { } inst.meta.args = child_args; // save for debugging - std::vector argv = to_char_ptr_array(child_args); - std::vector envp = to_char_ptr_array(child_env); - // TODO @ngxson : maybe separate stdout and stderr in the future // so that we can use stdout for commands and stderr for logging int options = subprocess_option_no_window | subprocess_option_combined_stdout_stderr; - inst.subproc->sproc.emplace(); - int result = subprocess_create_ex(argv.data(), options, envp.data(), &inst.subproc->get()); - if (result != 0) { + if (!inst.subproc->sproc.create(child_args, options, child_env)) { throw std::runtime_error("failed to spawn server instance"); } } @@ -867,8 +831,8 @@ void server_models::load(const std::string & name, const load_options & opts) { stop_timeout = inst.meta.stop_timeout, child_mode = opts.mode ]() { - FILE * stdin_file = subprocess_stdin(&child_proc->get()); - FILE * stdout_file = subprocess_stdout(&child_proc->get()); // combined stdout/stderr + FILE * stdin_file = child_proc->sproc.stdin_file(); + FILE * stdout_file = child_proc->sproc.stdout_file(); // combined stdout/stderr std::thread log_thread([&]() { // read stdout/stderr and forward to main server log @@ -942,9 +906,7 @@ void server_models::load(const std::string & name, const load_options & opts) { } // get the exit code - int exit_code = 0; - subprocess_join(&child_proc->get(), &exit_code); - subprocess_destroy(&child_proc->get()); + int exit_code = child_proc->sproc.join(); // update status and exit code if (child_mode == SERVER_CHILD_MODE_DOWNLOAD) { @@ -1210,7 +1172,7 @@ bool server_models::ensure_model_ready(const std::string & name) { return true; } -server_http_res_ptr server_models::proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used) { +server_http_res_ptr server_models::proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used, bool detached) { auto meta = get_meta(name); if (!meta.has_value()) { throw std::runtime_error("model name=" + name + " is not found"); @@ -1236,7 +1198,10 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co req.headers, req.body, req.files, - req.should_stop, + // a detached request belongs to a replay session that outlives the client socket: + // it reaches the child even when the downstream died during the load wait, the + // session buffer is the recipient and DELETE remains the stop + detached ? std::function([]() { return false; }) : req.should_stop, base_params.timeout_read, base_params.timeout_write ); @@ -1507,13 +1472,9 @@ static bool router_validate_model(std::string & name, server_models & models, bo } // resolve alias to canonical model name name = meta->name; - if (models_autoload) { - models.ensure_model_ready(name); - } else { - if (!meta->is_running()) { - res_err(res, format_error_response("model is not loaded", ERROR_TYPE_INVALID_REQUEST)); - return false; - } + if (!models_autoload && !meta->is_running()) { + res_err(res, format_error_response("model is not loaded", ERROR_TYPE_INVALID_REQUEST)); + return false; } return true; } @@ -1567,6 +1528,10 @@ static std::optional resolve_child_for_conv( } void server_models_routes::init_routes() { + if (!common_subproc::is_supported()) { + throw std::runtime_error("subprocess is not enabled on this build"); + } + this->get_router_props = [this](const server_http_req & req) { std::string name = req.get_param("model"); if (name.empty()) { @@ -1602,6 +1567,9 @@ void server_models_routes::init_routes() { if (!router_validate_model(name, models, autoload, error_res)) { return error_res; } + if (autoload) { + models.ensure_model_ready(name); + } return models.proxy_request(req, method, name, false); }; @@ -1615,12 +1583,23 @@ void server_models_routes::init_routes() { return error_res; } // remember which child serves this conversation so the stream routes can route straight - // to it without polling, keyed on the exact conv id from the header + // to it without polling, keyed on the exact conv id from the header. registered before + // the load wait so a stop issued while the model loads can erase the entry and cancel + // this request instead of leaving an orphan generation std::string conv_id = server_stream_conv_id_from_headers(req.headers); - if (!conv_id.empty()) { - models.conv_models.remember(conv_id, name); + uint64_t ticket = models.conv_models.remember(conv_id, name); + bool waited = autoload && models.ensure_model_ready(name); + if (ticket != 0 && !models.conv_models.alive(conv_id, ticket)) { + SRV_INF("request for conv_id=%s cancelled while model name=%s was loading\n", + conv_id.c_str(), name.c_str()); + res_err(error_res, format_error_response( + "request cancelled by a stop while the model was loading", ERROR_TYPE_INVALID_REQUEST)); + return error_res; } - return models.proxy_request(req, method, name, true); // update last usage for POST request only + // a session request that waited for a load detaches from the client socket: the + // client may have dropped during the wait (page reload) and the session buffer must + // still receive the generation for a later resume + return models.proxy_request(req, method, name, true, waited && ticket != 0); // update last usage for POST request only }; this->post_router_models_load = [this](const server_http_req & req) { @@ -1813,7 +1792,7 @@ void server_models_routes::init_routes() { }; this->router_stream_get = [this](const server_http_req & req) { - // GET /v1/stream/?from=N. resolve the owning child from the conv_id -> model + // GET /v1/stream?conv_id=&from=N. resolve the owning child from the conv_id -> model // map, 404 when nothing maps auto res = std::make_unique(); std::string conv_id = req.get_param("conv_id"); @@ -1823,13 +1802,24 @@ void server_models_routes::init_routes() { } std::optional owner = resolve_child_for_conv(models, conv_id); if (!owner.has_value()) { - res_err(res, format_error_response("Stream not found or expired", ERROR_TYPE_NOT_FOUND)); + // a registered conv whose model is still loading earns a retry: the session appears + // once the load ends and the pending request reaches the child + auto tracked = models.conv_models.lookup(conv_id); + auto meta = tracked.has_value() ? models.get_meta(*tracked) : std::nullopt; + bool transient = meta.has_value() && (meta->status == SERVER_MODEL_STATUS_LOADING || + meta->status == SERVER_MODEL_STATUS_DOWNLOADING || + meta->status == SERVER_MODEL_STATUS_DOWNLOADED); + if (transient) { + res_err(res, format_error_response("Stream owner model is loading, retry later", ERROR_TYPE_UNAVAILABLE)); + } else { + res_err(res, format_error_response("Stream not found or expired", ERROR_TYPE_NOT_FOUND)); + } return res; } std::string from = req.get_param("from"); - std::string child_path = "/v1/stream/" + encode_qs(conv_id); + std::string child_path = "/v1/stream?conv_id=" + encode_qs(conv_id); if (!from.empty()) { - child_path += "?from=" + from; + child_path += "&from=" + from; } SRV_TRC("proxying stream resume to model %s on port %d, path=%s\n", owner->name.c_str(), owner->port, child_path.c_str()); @@ -1909,7 +1899,7 @@ void server_models_routes::init_routes() { }; this->router_stream_delete = [this](const server_http_req & req) { - // DELETE /v1/stream/. resolve the owning child via the map and forward only to + // DELETE /v1/stream?conv_id=. resolve the owning child via the map and forward only to // it, evict_and_cancel is idempotent on the child auto res = std::make_unique(); std::string conv_id = req.get_param("conv_id"); @@ -1917,7 +1907,7 @@ void server_models_routes::init_routes() { res_err(res, format_error_response("Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST)); return res; } - std::string child_path = "/v1/stream/" + encode_qs(conv_id); + std::string child_path = "/v1/stream?conv_id=" + encode_qs(conv_id); auto owner = resolve_child_for_conv(models, conv_id); if (owner.has_value()) { httplib::Client cli(CHILD_ADDR, owner->port); @@ -1926,6 +1916,11 @@ void server_models_routes::init_routes() { cli.set_write_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000); auto resp = cli.Delete(child_path.c_str()); (void) resp; // the child logs its own miss when the session is unknown there + } else if (auto tracked = models.conv_models.lookup(conv_id); tracked.has_value()) { + // the entry exists but its model is still loading: the forget below erases it, + // which cancels the request parked in proxy_post before the generation starts + SRV_INF("router stop for conv_id=%s while model name=%s is loading, cancelling the pending request\n", + conv_id.c_str(), tracked->c_str()); } else { SRV_WRN("router stop for unknown conv_id=%s, no owning child in the conv map\n", conv_id.c_str()); @@ -1944,53 +1939,6 @@ void server_models_routes::init_routes() { // server_http_proxy // -// simple implementation of a pipe -// used for streaming data between threads -template -struct pipe_t { - std::mutex mutex; - std::condition_variable cv; - std::queue queue; - std::atomic writer_closed{false}; - std::atomic reader_closed{false}; - void close_write() { - writer_closed.store(true, std::memory_order_relaxed); - cv.notify_all(); - } - void close_read() { - reader_closed.store(true, std::memory_order_relaxed); - cv.notify_all(); - } - bool read(T & output, const std::function & should_stop) { - std::unique_lock lk(mutex); - constexpr auto poll_interval = std::chrono::milliseconds(500); - while (true) { - if (!queue.empty()) { - output = std::move(queue.front()); - queue.pop(); - return true; - } - if (writer_closed.load()) { - return false; // clean EOF - } - if (should_stop()) { - close_read(); // signal broken pipe to writer - return false; // cancelled / reader no longer alive - } - cv.wait_for(lk, poll_interval); - } - } - bool write(T && data) { - std::lock_guard lk(mutex); - if (reader_closed.load()) { - return false; // broken pipe - } - queue.push(std::move(data)); - cv.notify_one(); - return true; - } -}; - static std::string to_lower_copy(const std::string & value) { std::string lowered(value.size(), '\0'); std::transform(value.begin(), value.end(), lowered.begin(), [](unsigned char c) { return std::tolower(c); }); @@ -2100,7 +2048,7 @@ server_http_proxy::server_http_proxy( ) { // shared between reader and writer threads auto cli = std::make_shared(host, port); - auto pipe = std::make_shared>(); + auto pipe = std::make_shared>(); if (scheme == "https") { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT diff --git a/tools/server/server-models.h b/tools/server/server-models.h index 62bed8725b5b..614798186cfc 100644 --- a/tools/server/server-models.h +++ b/tools/server/server-models.h @@ -134,12 +134,24 @@ struct server_models { // proxy_request forwards a POST carrying an X-Conversation-Id. best effort: a stale entry just // makes the child answer not found and the client recovers. owns its lock, one mutex per struct struct conv_model_tracker { - void remember(const std::string & conv_id, const std::string & model) { + // returns the ticket of this registration, 0 when nothing was registered. erasing or + // replacing the entry invalidates the ticket, which is how a stop cancels a request + // parked in the model load wait + uint64_t remember(const std::string & conv_id, const std::string & model) { if (conv_id.empty() || model.empty()) { - return; + return 0; } std::lock_guard lock(mu); - map[conv_id] = model; + uint64_t ticket = next_ticket++; + map[conv_id] = { model, ticket }; + return ticket; + } + + // false means a stop erased the entry or a newer request replaced it + bool alive(const std::string & conv_id, uint64_t ticket) { + std::lock_guard lock(mu); + auto it = map.find(conv_id); + return it != map.end() && it->second.ticket == ticket; } std::optional lookup(const std::string & conv_id) { @@ -151,7 +163,7 @@ struct server_models { if (it == map.end()) { return std::nullopt; } - return it->second; + return it->second.model; } void forget(const std::string & conv_id) { @@ -163,8 +175,13 @@ struct server_models { } private: - std::mutex mu; - std::unordered_map map; + struct entry_t { + std::string model; + uint64_t ticket; + }; + std::mutex mu; + uint64_t next_ticket = 1; + std::unordered_map map; }; common_preset_context ctx_preset; @@ -249,7 +266,7 @@ struct server_models { bool ensure_model_ready(const std::string & name); // proxy an HTTP request to the model instance - server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used); + server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used, bool detached = false); // handle message sent from server_child::notify_to_router() // raw input must starts with CMD_CHILD_TO_ROUTER_STATE, followed by a JSON string diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp index 89026eb4e3f0..e880f4ca728d 100644 --- a/tools/server/server-schema.cpp +++ b/tools/server/server-schema.cpp @@ -390,21 +390,40 @@ std::vector> make_llama_cmpl_schema(const common_params & ctx.params.sampling.reasoning_budget_start = common_tokenize(ctx.vocab, data.at("reasoning_budget_start_tag").get(), false, true); })); - add((new field_str("reasoning_budget_end_tag")) - ->set_desc("Token string marking the end of the reasoning budget section") + add((new field_json("reasoning_budget_end_tags")) + ->add_alias("reasoning_budget_end_tag") + ->set_desc("Token strings marking the end of the reasoning budget section; the first is forced when the budget expires") ->set_handler([&](field_eval_context & ctx, const json & data) { GGML_ASSERT(ctx.vocab != nullptr); - std::string end_tag = data.at("reasoning_budget_end_tag").get(); - ctx.params.sampling.reasoning_budget_end = common_tokenize(ctx.vocab, end_tag, false, true); + ctx.params.sampling.reasoning_budget_end.clear(); + if (data.contains("reasoning_budget_end_tags")) { + for (const auto & t : data.at("reasoning_budget_end_tags")) { + std::string tag = t.get(); + if (!tag.empty()) { + ctx.params.sampling.reasoning_budget_end.push_back(common_tokenize(ctx.vocab, tag, false, true)); + } + } + } else if (data.contains("reasoning_budget_end_tag")) { + std::string tag = data.at("reasoning_budget_end_tag").get(); + if (!tag.empty()) { + ctx.params.sampling.reasoning_budget_end.push_back(common_tokenize(ctx.vocab, tag, false, true)); + } + } })); add((new field_str("reasoning_budget_message")) ->set_desc("Message to prepend to the reasoning budget end tag when forcing it") ->set_handler([&](field_eval_context & ctx, const json & data) { GGML_ASSERT(ctx.vocab != nullptr); - std::string end_tag = json_value(data, "reasoning_budget_end_tag", std::string()); - std::string message = data.at("reasoning_budget_message").get(); - ctx.params.sampling.reasoning_budget_forced = common_tokenize(ctx.vocab, message + end_tag, false, true); + if (!ctx.params.sampling.reasoning_budget_end.empty()) { + llama_tokens end_tag = ctx.params.sampling.reasoning_budget_end.front(); + std::string message = json_value(data, "reasoning_budget_message", std::string()); + if (!message.empty()) { + llama_tokens message_tokens = common_tokenize(ctx.vocab, message, false, true); + end_tag.insert(end_tag.begin(), message_tokens.begin(), message_tokens.end()); + } + ctx.params.sampling.reasoning_budget_forced = std::move(end_tag); + } })); add((new field_json("logit_bias")) @@ -546,7 +565,7 @@ task_params eval_llama_cmpl_schema( // debugging { auto budget = params.sampling.reasoning_budget_tokens; - SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n", + SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu seqs, forced=%zu toks\n", budget, params.sampling.generation_prompt.c_str(), params.sampling.reasoning_budget_start.size(), params.sampling.reasoning_budget_end.size(), diff --git a/tools/server/server-stream.cpp b/tools/server/server-stream.cpp index 19db04988d81..f6b9b8a9f4cc 100644 --- a/tools/server/server-stream.cpp +++ b/tools/server/server-stream.cpp @@ -453,7 +453,7 @@ static server_http_res_ptr make_error_response(int status, const std::string & m server_http_context::handler_t server_stream_make_get_handler() { return [](const server_http_req & req) -> server_http_res_ptr { - // GET /v1/stream/?from=N replays buffered SSE bytes then blocks for live + // GET /v1/stream?conv_id=&from=N replays buffered SSE bytes then blocks for live // bytes until the session finalizes, streamed as text/event-stream for EventSource std::string conv_id = req.get_param("conv_id"); if (conv_id.empty()) { @@ -560,13 +560,13 @@ server_http_context::handler_t server_stream_make_lookup_handler() { server_http_context::handler_t server_stream_make_delete_handler() { return [](const server_http_req & req) -> server_http_res_ptr { - // DELETE /v1/stream/ is the explicit user Stop, cancels the producer and evicts + // DELETE /v1/stream?conv_id= is the explicit user Stop, cancels the producer and evicts // the buffer. idempotent, returns 204 even if the session was already gone std::string conv_id = req.get_param("conv_id"); if (conv_id.empty()) { return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST); } - SRV_TRC("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str()); + SRV_TRC("DELETE /v1/stream conv_id=%s -> evict_and_cancel\n", conv_id.c_str()); g_stream_sessions.evict_and_cancel(conv_id); auto res = std::make_unique(); res->status = 204; @@ -621,7 +621,7 @@ bool server_res_spipe::conn_alive() { bool server_res_spipe::should_stop() { if (spipe) { - // note: if DELETE /v1/stream/ is called, is_cancelled() will be true + // note: if DELETE /v1/stream is called for this conv, is_cancelled() will be true return spipe->is_cancelled(); } else { return !conn_alive(); @@ -632,6 +632,13 @@ void server_res_spipe::on_complete() { if (!spipe || next_finished) { return; } + // an empty next_orig means set_next() never ran: the request failed before streaming + // started, typically a params validation throw. evict the session installed by set_req() + // so the failed request leaves nothing behind for discovery or replay + if (!next_orig) { + g_stream_sessions.evict(server_stream_conv_id_from_headers(req->headers)); + return; + } std::string chunk; while (!spipe->is_cancelled()) { chunk.clear(); diff --git a/tools/server/server-stream.h b/tools/server/server-stream.h index 9753140dd601..1e7461285f43 100644 --- a/tools/server/server-stream.h +++ b/tools/server/server-stream.h @@ -45,7 +45,13 @@ void server_stream_session_manager_start(); void server_stream_session_manager_stop(); // route handler factories wired under /v1/stream/* by server.cpp +// child-side handlers for the resumable stream routes. the conv id travels in the conv_id +// query string because it can embed a model name containing slashes (org/repo), which the +// decoded path would split before the param is captured server_http_context::handler_t server_stream_make_get_handler(); +// POST /v1/streams/lookup with body {"conversation_ids": [...]}: only answers for ids the +// caller already owns (the WebUI passes the convs visible in its sidebar), the server never +// lists ids it has not been asked about, so a random caller cannot enumerate live sessions server_http_context::handler_t server_stream_make_lookup_handler(); server_http_context::handler_t server_stream_make_delete_handler(); diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index f01780100c0d..1fd7cce27bb3 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -63,6 +63,8 @@ json task_params::to_json(bool only_metrics) const { {"mirostat", sampling.mirostat}, {"mirostat_tau", sampling.mirostat_tau}, {"mirostat_eta", sampling.mirostat_eta}, + {"adaptive_target", sampling.adaptive_target}, + {"adaptive_decay", sampling.adaptive_decay}, {"max_tokens", n_predict}, {"n_predict", n_predict}, // TODO: deduplicate? {"n_keep", n_keep}, @@ -114,6 +116,8 @@ json task_params::to_json(bool only_metrics) const { {"mirostat", sampling.mirostat}, {"mirostat_tau", sampling.mirostat_tau}, {"mirostat_eta", sampling.mirostat_eta}, + {"adaptive_target", sampling.adaptive_target}, + {"adaptive_decay", sampling.adaptive_decay}, {"stop", antiprompt}, {"max_tokens", n_predict}, {"n_predict", n_predict}, // TODO: deduplicate? diff --git a/tools/server/server-tools.cpp b/tools/server/server-tools.cpp index a8216d7dbc54..90b7e2a9f0ee 100644 --- a/tools/server/server-tools.cpp +++ b/tools/server/server-tools.cpp @@ -1,18 +1,19 @@ #include "server-tools.h" -#include +#include "subproc.h" #include #include #include #include #include +#include #include #include -#include #include #include #include +#include namespace fs = std::filesystem; @@ -24,7 +25,7 @@ json server_tool::to_json() const { return { {"display_name", display_name}, {"tool", name}, - {"type", "builtin"}, + {"type", type()}, {"permissions", json{ {"write", permission_write} }}, @@ -137,15 +138,14 @@ class tools_io_basic : public tools_io { const std::function & on_chunk = nullptr) const override { exec_result res; - subprocess_s proc; - auto argv = to_cstr_vec(args); + common_subproc proc; int options = subprocess_option_no_window | subprocess_option_combined_stdout_stderr | subprocess_option_inherit_environment | subprocess_option_search_user_path; - if (subprocess_create(argv.data(), options, &proc) != 0) { + if (!proc.create(args, options)) { res.output = "failed to spawn process"; return res; } @@ -158,14 +158,14 @@ class tools_io_basic : public tools_io { while (!done.load()) { if (std::chrono::steady_clock::now() >= deadline) { timed_out.store(true); - subprocess_terminate(&proc); + proc.terminate(); return; } std::this_thread::sleep_for(std::chrono::milliseconds(100)); } }); - FILE * f = subprocess_stdout(&proc); + FILE * f = proc.stdout_file(); std::string output; bool truncated = false; if (f) { @@ -176,7 +176,7 @@ class tools_io_basic : public tools_io { if (output.size() + len <= max_output) { output.append(buf, len); if (on_chunk && !on_chunk(std::string(buf, len))) { - subprocess_terminate(&proc); + proc.terminate(); break; } } else { @@ -194,8 +194,7 @@ class tools_io_basic : public tools_io { timeout_thread.join(); } - subprocess_join(&proc, &res.exit_code); - subprocess_destroy(&proc); + res.exit_code = proc.join(); res.output = output; res.timed_out = timed_out.load(); @@ -206,16 +205,6 @@ class tools_io_basic : public tools_io { } private: - static std::vector to_cstr_vec(const std::vector & v) { - std::vector r; - r.reserve(v.size() + 1); - for (const auto & s : v) { - r.push_back(const_cast(s.c_str())); - } - r.push_back(nullptr); - return r; - } - static const std::unordered_set & junk_dir_names() { static const std::unordered_set names = { ".git", ".svn", ".hg", "node_modules", "__pycache__", @@ -289,7 +278,7 @@ struct server_tool_read_file : server_tool { {"function", { {"name", name}, {"description", "Read the contents of a file. Optionally specify a 1-based line range. " - "If append_loc is true, each line is prefixed with its line number (e.g. \"1\u2192 ...\")."}, + "If append_loc is true, each line is prefixed with its line number (e.g. \"1\u2192...\")."}, {"parameters", { {"type", "object"}, {"properties", { @@ -339,7 +328,7 @@ struct server_tool_read_file : server_tool { std::string out_line; if (append_loc) { - out_line = std::to_string(lineno) + "\u2192 " + line + "\n"; + out_line = std::to_string(lineno) + "\u2192" + line + "\n"; } else { out_line = line + "\n"; } @@ -1048,16 +1037,42 @@ struct server_tool_get_datetime : server_tool { {"type", "function"}, {"function", { {"name", name}, - {"description", "Returns the current date and time"}, + {"description", "Returns the current date and time in UTC"}, + {"parameters", { + {"type", "object"}, + {"properties", { + {"format", { + {"type", "string"}, + {"description", + "strftime()-style format string for the output (default: \"%Y-%m-%dT%H:%M:%SZ\", " + "e.g. ISO 8601). Choose your own format if you need something else, " + "e.g. \"%A, %B %d %Y\" for a human-readable date."}, + }}, + }}, + }}, }}, }; } - json invoke(json, server_tool::stream *) const override { - auto now = std::chrono::system_clock::now(); + json invoke(json params, server_tool::stream *) const override { + std::string format = json_value(params, "format", std::string("%Y-%m-%dT%H:%M:%SZ")); + + auto now = std::chrono::system_clock::now(); auto time = std::chrono::system_clock::to_time_t(now); + std::tm tm_utc; +#ifdef _WIN32 + gmtime_s(&tm_utc, &time); +#else + gmtime_r(&time, &tm_utc); +#endif - return {{"result", std::ctime(&time)}}; + char buf[256]; + size_t len = std::strftime(buf, sizeof(buf), format.c_str(), &tm_utc); + if (len == 0) { + return {{"error", "invalid format string"}}; + } + + return {{"result", std::string(buf, len)}}; } }; @@ -1102,6 +1117,49 @@ struct server_tools_res : server_http_res { } }; +// +// server_mcp_tool: exposes one tool from a running MCP server as a server_tool. +// +struct server_mcp_tool : server_tool { + std::string server_name; + std::string tool_name; + server_mcp_tool_def def; + server_mcp & mcp_mgr; + + server_mcp_tool(server_mcp_tool_def d, server_mcp & mgr) + : server_name(d.server_name) + , tool_name(d.name) + , def(std::move(d)) + , mcp_mgr(mgr) + { + name = server_name + "_" + tool_name; + display_name = name; + permission_write = false; + support_stream = false; + } + + std::string type() const override { return "mcp"; } + + json get_definition() const override { + json schema = def.input_schema; + if (schema.is_null() || !schema.is_object()) { + schema = json::object(); + } + return { + {"type", "function"}, + {"function", { + {"name", name}, + {"description", def.description}, + {"parameters", schema}, + }}, + }; + } + + json invoke(json params, server_tool::stream *) const override { + return mcp_mgr.call_tool(server_name, tool_name, params); + } +}; + static server_tool & find_tool(std::vector> & tools, const std::string & name, bool require_stream) { for (auto & t : tools) { if (t->name == name) { @@ -1130,8 +1188,13 @@ static std::vector> build_tools() { return tools; } -void server_tools::setup(const std::vector & enabled_tools) { +void server_tools::setup(const std::vector & enabled_tools, + server_mcp & mcp_mgr) { if (!enabled_tools.empty()) { + if (!common_subproc::is_supported()) { + throw std::runtime_error("subprocess is not enabled on this build"); + } + std::unordered_set enabled_set(enabled_tools.begin(), enabled_tools.end()); auto all_tools = build_tools(); @@ -1161,6 +1224,29 @@ void server_tools::setup(const std::vector & enabled_tools) { } } + // append MCP tools, skipping any that collide with a built-in or another MCP tool of the same "_" name + if (!mcp_mgr.empty()) { + std::unordered_set seen_names; + for (auto & t : tools) { + seen_names.insert(t->name); + } + size_t n_added = 0; + for (const auto & def : mcp_mgr.list_tools()) { + std::string mcp_name = def.server_name + "_" + def.name; + if (seen_names.count(mcp_name)) { + SRV_WRN("MCP tool \"%s\" from server \"%s\" collides with an existing tool, skipping\n", + mcp_name.c_str(), def.server_name.c_str()); + continue; + } + seen_names.insert(mcp_name); + tools.push_back(std::make_unique(def, mcp_mgr)); + n_added++; + } + if (n_added > 0) { + SRV_INF("Added %zu MCP tools\n", n_added); + } + } + handle_get = [this](const server_http_req &) -> server_http_res_ptr { auto res = std::make_unique(); try { diff --git a/tools/server/server-tools.h b/tools/server/server-tools.h index 6f6528f484f8..601399ee9392 100644 --- a/tools/server/server-tools.h +++ b/tools/server/server-tools.h @@ -3,9 +3,11 @@ #include "server-common.h" #include "server-http.h" #include "server-queue.h" +#include "server-mcp.h" #include #include +#include struct server_tool { std::string name; @@ -15,6 +17,7 @@ struct server_tool { virtual ~server_tool() = default; virtual json get_definition() const = 0; + virtual std::string type() const { return "builtin"; } struct stream { server_response & qr; @@ -34,7 +37,8 @@ struct server_tools { server_response queue_res; std::atomic res_id{0}; - void setup(const std::vector & enabled_tools); + void setup(const std::vector & enabled_tools, + server_mcp & mcp_mgr); server_http_context::handler_t handle_get; server_http_context::handler_t handle_post; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 20effbb14851..a3b2a8b0fe1b 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -88,6 +88,11 @@ static server_http_context::handler_t ex_wrapper(server_http_context::handler_t int llama_server(int argc, char ** argv) { std::setlocale(LC_NUMERIC, "C"); +#ifndef _WIN32 + // Ignore SIGPIPE so the server does not crash if an MCP child exits while we are writing to its stdin + signal(SIGPIPE, SIG_IGN); +#endif + // own arguments required by this example common_params params; @@ -157,6 +162,9 @@ int llama_server(common_params & params, int argc, char ** argv) { params.model_alias.insert(model_name); } + // note: this is guaranteed to out-live ctx_http and tools + server_mcp mcp_mgr; + // struct that contains llama context and inference server_context ctx_server; @@ -264,10 +272,8 @@ int llama_server(common_params & params, int argc, char ** argv) { ctx_http.get ("/slots", ex_wrapper(routes.get_slots)); ctx_http.post("/slots/:id_slot", ex_wrapper(routes.post_slots)); - // resumable streaming, the conversation_id is the session identity end to end. router and - // child wire different handlers under the same paths: a child binds the local session - // factories, the router binds proxies that resolve the owning child through the - // conv_id -> model map + // resumable streaming: a child binds the local session factories, the router binds + // proxies that resolve the owning child, see server-stream.h server_http_context::handler_t stream_get_h; server_http_context::handler_t streams_lookup_h; server_http_context::handler_t stream_delete_h; @@ -280,12 +286,9 @@ int llama_server(common_params & params, int argc, char ** argv) { streams_lookup_h = server_stream_make_lookup_handler(); stream_delete_h = server_stream_make_delete_handler(); } - ctx_http.get ("/v1/stream/:conv_id", ex_wrapper(stream_get_h)); - // POST /v1/streams/lookup with body {"conversation_ids": [...]}. you can only ask for ids - // you already own (the WebUI passes the convs visible in its sidebar). the server never - // lists ids it has not been asked about, so a random caller cannot enumerate live sessions + ctx_http.get ("/v1/stream", ex_wrapper(stream_get_h)); ctx_http.post("/v1/streams/lookup", ex_wrapper(streams_lookup_h)); - ctx_http.del ("/v1/stream/:conv_id", ex_wrapper(stream_delete_h)); + ctx_http.del ("/v1/stream", ex_wrapper(stream_delete_h)); // Google Cloud Platform (Vertex AI) compat ctx_http.register_gcp_compat(); @@ -326,17 +329,28 @@ int llama_server(common_params & params, int argc, char ** argv) { ctx_http.post("/cors-proxy", ex_wrapper(res_403)); } - // EXPERIMENTAL built-in tools - if (!params.server_tools.empty()) { + try { + mcp_mgr.start(params); + } catch (const std::exception & e) { + SRV_ERR("MCP starting failed: %s\n", e.what()); + return 1; + } + + if (!params.server_tools.empty() || !mcp_mgr.empty()) { try { - tools.setup(params.server_tools); + tools.setup(params.server_tools, mcp_mgr); } catch (const std::exception & e) { SRV_ERR("tools setup failed: %s\n", e.what()); return 1; } ctx_http.get ("/tools", ex_wrapper(tools.handle_get)); ctx_http.post("/tools", ex_wrapper(tools.handle_post)); - warn_names.push_back("built-in tools (experimental)"); + if (!params.server_tools.empty()) { + warn_names.push_back("built-in tools (experimental)"); + } + if (!mcp_mgr.empty()) { + warn_names.push_back("MCP servers (experimental)"); + } } else { ctx_http.get ("/tools", ex_wrapper(res_403)); ctx_http.post("/tools", ex_wrapper(res_403)); @@ -378,7 +392,7 @@ int llama_server(common_params & params, int argc, char ** argv) { if (is_router_server) { SRV_INF("%s", "starting server in router mode. models will be automatically loaded on-demand\n"); - clean_up = [&models_routes]() { + clean_up = [&models_routes, &mcp_mgr]() { SRV_INF("%s: cleaning up before exit...\n", __func__); // stop the session GC first, it finalizes live sessions and wakes pending readers server_stream_session_manager_stop(); @@ -386,6 +400,7 @@ int llama_server(common_params & params, int argc, char ** argv) { models_routes->stopping.store(true); // maybe redundant, but just to be safe models_routes->models.unload_all(); } + mcp_mgr.shutdown(); llama_backend_free(); }; @@ -401,17 +416,19 @@ int llama_server(common_params & params, int argc, char ** argv) { // important to disconnect any SSE clients models_routes->stopping.store(true); } + mcp_mgr.shutdown(); ctx_http.stop(); }; } else { // setup clean up function, to be called before exit - clean_up = [&ctx_http, &ctx_server]() { + clean_up = [&ctx_http, &ctx_server, &mcp_mgr]() { SRV_INF("%s: cleaning up before exit...\n", __func__); // stop the session GC first, it finalizes live sessions and wakes pending readers server_stream_session_manager_stop(); ctx_http.stop(); ctx_server.terminate(); + mcp_mgr.shutdown(); llama_backend_free(); }; @@ -444,6 +461,7 @@ int llama_server(common_params & params, int argc, char ** argv) { SRV_INF("%s", "model loaded\n"); shutdown_handler = [&](int) { + mcp_mgr.shutdown(); // this will unblock start_loop() ctx_server.terminate(); }; diff --git a/tools/server/tests/fixtures/mcp_burst_server.py b/tools/server/tests/fixtures/mcp_burst_server.py new file mode 100644 index 000000000000..22892d9a1d72 --- /dev/null +++ b/tools/server/tests/fixtures/mcp_burst_server.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +""" +Minimal MCP server that writes notification + response in a single write() with no flush. +This reproduces the buffering bug where read_message() can strand the response. +""" +import json +import sys +import os + +TOOLS = [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string"} + }, + "required": ["message"] + } + } +] + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "burst-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"tools": TOOLS} + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + notif = { + "jsonrpc": "2.0", + "method": "notifications/progress", + "params": {"progress": 50, "total": 100} + } + response = { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + # Single os.write() call: both lines land in one pipe packet atomically. + # This is the key difference from mcp_malformed_server.py which flushes between writes. + data = (json.dumps(notif) + "\n" + json.dumps(response) + "\n").encode("utf-8") + os.write(sys.stdout.fileno(), data) + return None # already written + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + return response + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + # Use line-buffered text mode for regular responses, but the burst write + # uses os.write() directly to guarantee a single kernel write(). + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + if response is not None: + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_crash_server.py b/tools/server/tests/fixtures/mcp_crash_server.py new file mode 100644 index 000000000000..8dffdc61c0e9 --- /dev/null +++ b/tools/server/tests/fixtures/mcp_crash_server.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +""" +MCP server that crashes after receiving a specific tool call. +""" +import json +import sys +import os + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "crash-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "tools": [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string"} + } + } + }, + { + "name": "crash", + "description": "Crash the server", + "inputSchema": { + "type": "object", + "properties": {} + } + } + ] + } + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + elif tool_name == "crash": + # Send a partial response then exit + sys.stdout.write(json.dumps({"jsonrpc": "2.0", "id": req_id, "result": {"content": [{"type": "text", "text": "crashing..."}]}}) + "\n") + sys.stdout.flush() + os._exit(1) + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_echo_server.py b/tools/server/tests/fixtures/mcp_echo_server.py new file mode 100755 index 000000000000..7acfb358881a --- /dev/null +++ b/tools/server/tests/fixtures/mcp_echo_server.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +""" +Minimal MCP server for testing. +Implements JSON-RPC 2.0 over stdio (line-delimited JSON). +""" +import json +import sys +import os + +# Ensure we use python3 from the current environment +if sys.platform == "win32": + # On Windows, we need to use the same python interpreter + pass + +TOOLS = [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string", "description": "Message to echo"} + }, + "required": ["message"] + } + }, + { + "name": "add", + "description": "Add two numbers", + "inputSchema": { + "type": "object", + "properties": { + "a": {"type": "number"}, + "b": {"type": "number"} + }, + "required": ["a", "b"] + } + }, + { + "name": "fail_once", + "description": "Fails on first call, succeeds on subsequent calls", + "inputSchema": { + "type": "object", + "properties": {} + } + } +] + +_state = {"fail_once_called": False} + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "echo-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"tools": TOOLS} + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + elif tool_name == "add": + a = arguments.get("a", 0) + b = arguments.get("b", 0) + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": str(a + b)}] + } + } + elif tool_name == "fail_once": + if not _state["fail_once_called"]: + _state["fail_once_called"] = True + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32000, "message": "transient error"} + } + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": "ok"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +def handle_ping(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, + "ping": handle_ping, +} + +def main(): + # Use unbuffered output + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_grandchild_server.py b/tools/server/tests/fixtures/mcp_grandchild_server.py new file mode 100644 index 000000000000..2604a77ee80e --- /dev/null +++ b/tools/server/tests/fixtures/mcp_grandchild_server.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +""" +MCP server (NDJSON JSON-RPC over stdio) that spawns a long-lived grandchild which inherits +this process's stdin/stdout/stderr and keeps them open. + +This reproduces the reader-teardown deadlock: killing the direct MCP child (SIGKILL, which is +all subprocess_terminate() does) does NOT close the stdout/stderr pipe write ends, because the +grandchild still holds them. A server that reads those pipes with a blocking read would then +wait forever for an EOF that never arrives, hanging teardown (both warmup shutdown at startup +and process shutdown). The polled, running-aware reader must exit regardless. +""" +import json +import os +import subprocess +import sys + +# Spawn a grandchild that inherits our std handles (fds 0/1/2 = the MCP pipes) and lives well +# past any teardown in the tests. We do NOT redirect its stdio, so it keeps the pipe write ends +# open even after this process is killed. +subprocess.Popen([sys.executable, "-c", "import time; time.sleep(30)"]) + +TOOLS = [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": {"message": {"type": "string", "description": "Message to echo"}}, + "required": ["message"], + }, + } +] + + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "grandchild-test", "version": "1.0"}, + }, + } + + +def handle_tools_list(params, req_id): + return {"jsonrpc": "2.0", "id": req_id, "result": {"tools": TOOLS}} + + +def handle_tools_call(params, req_id): + if params.get("name") == "echo": + message = params.get("arguments", {}).get("message", "") + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"content": [{"type": "text", "text": f"echo: {message}"}]}, + } + return {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32602, "message": "Unknown tool"}} + + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + + +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + if req_id is None: + continue # notification, no response + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": f"Method not found: {method}"}} + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_malformed_server.py b/tools/server/tests/fixtures/mcp_malformed_server.py new file mode 100644 index 000000000000..743333c5fdaa --- /dev/null +++ b/tools/server/tests/fixtures/mcp_malformed_server.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +""" +MCP server that sends malformed responses and notifications during requests. +""" +import json +import sys +import os + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "malformed-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "tools": [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string"} + } + } + } + ] + } + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + # Send a notification first (no id field) + notif = { + "jsonrpc": "2.0", + "method": "notifications/progress", + "params": {"progress": 50, "total": 100} + } + sys.stdout.write(json.dumps(notif) + "\n") + sys.stdout.flush() + # Then send the actual response + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + # Send malformed JSON response + sys.stdout.write("THIS IS NOT JSON\n") + sys.stdout.flush() + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_slow_server.py b/tools/server/tests/fixtures/mcp_slow_server.py new file mode 100644 index 000000000000..7f8e67835acc --- /dev/null +++ b/tools/server/tests/fixtures/mcp_slow_server.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +""" +MCP server that sleeps before responding, for timeout testing. +""" +import json +import sys +import os +import time +import argparse + +TOOLS = [ + { + "name": "sleep", + "description": "Sleep for a given number of seconds", + "inputSchema": { + "type": "object", + "properties": { + "seconds": {"type": "number", "description": "Seconds to sleep"} + }, + "required": ["seconds"] + } + } +] + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "slow-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"tools": TOOLS} + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "sleep": + seconds = arguments.get("seconds", 1) + time.sleep(seconds) + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"slept {seconds}s"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--delay", type=float, default=5.0, help="Delay in seconds for sleep tool") + args = parser.parse_args() + + # Override the sleep duration + global handle_tools_call + def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "sleep": + seconds = arguments.get("seconds", args.delay) + time.sleep(seconds) + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"slept {seconds}s"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/unit/test_completion.py b/tools/server/tests/unit/test_completion.py index 1e0891987a9d..9375e0110e53 100644 --- a/tools/server/tests/unit/test_completion.py +++ b/tools/server/tests/unit/test_completion.py @@ -66,6 +66,8 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp assert server.n_predict is not None assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict) assert data["generation_settings"]["seed"] == server.seed + assert "adaptive_target" in data["generation_settings"] + assert "adaptive_decay" in data["generation_settings"] assert match_regex(re_content, content) else: assert len(data["tokens"]) > 0 diff --git a/tools/server/tests/unit/test_mcp_servers.py b/tools/server/tests/unit/test_mcp_servers.py new file mode 100644 index 000000000000..9ad2241bd029 --- /dev/null +++ b/tools/server/tests/unit/test_mcp_servers.py @@ -0,0 +1,718 @@ +#!/usr/bin/env python3 +""" +Tests for MCP server integration via the /tools endpoint. + +Invariants verified: +1. MCP tools appear in /tools listing when configured +2. MCP tools use _ naming +3. MCP tools can be invoked and return correct results +4. Misconfigured MCP servers do not crash the server +5. Multiple MCP servers can be configured simultaneously +6. Warmup populates the tool list at startup +""" +import json +import os +import sys +import tempfile +import time + +import pytest + +from utils import * + +# Path to the test MCP server fixture +FIXTURES_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "fixtures") +MCP_ECHO_SERVER = os.path.join(FIXTURES_DIR, "mcp_echo_server.py") + +server: ServerProcess + + +def _mcp_config_json(servers: dict) -> str: + """Create a JSON config string for --mcp-servers-json.""" + return json.dumps({"mcpServers": servers}) + + +def _start_server_with_mcp(mcp_json: str, **kwargs) -> ServerProcess: + """Helper to start a router server with MCP config.""" + srv = ServerPreset.router() + srv.server_tools = "all" + srv.no_ui = True + srv.server_port = 8085 # avoid conflict with load_all() which uses 8080 + srv.mcp_servers_json = mcp_json + for k, v in kwargs.items(): + setattr(srv, k, v) + srv.start() + return srv + + +def test_mcp_tools_listed_in_tools_endpoint(): + """MCP tools should appear in GET /tools with server:tool naming.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + assert isinstance(tools, list), f"Expected list, got {type(tools)}" + + # Find MCP tools - name is in "tool" field or definition.function.name + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + mcp_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + assert len(mcp_tools) >= 2, f"Expected at least 2 echo_ tools, got {len(mcp_tools)}: {mcp_tools}" + + tool_names = {get_tool_name(t) for t in mcp_tools} + assert "echo_echo" in tool_names + assert "echo_add" in tool_names + + # Verify tool structure + echo_tool = next(t for t in mcp_tools if get_tool_name(t) == "echo_echo") + assert "description" in echo_tool or "definition" in echo_tool + finally: + server.stop() + + +def test_mcp_tool_invocation(): + """MCP tools should be callable via POST /tools and return correct results.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Call echo_echo + res = server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": "hello world"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + # The result format depends on the tool implementation + # For MCP tools, it should contain the tool result + assert "plain_text_response" in body or "result" in body or "content" in body, body + + # Call echo_add + res = server.make_request("POST", "/tools", data={ + "tool": "echo_add", + "params": {"a": 3, "b": 5} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_bad_command_does_not_crash(): + """A misconfigured MCP server should not crash the llama-server.""" + global server + mcp_json = _mcp_config_json({ + "nonexistent": { + "command": "this_executable_does_not_exist_12345", + "args": [], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Server should still be healthy + res = server.make_request("GET", "/health") + assert res.status_code == 200, res.body + + # Builtin tools should still work + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + # Should have builtin tools but no MCP tools from the bad server + mcp_tools = [t for t in tools if t.get("name", "").startswith("nonexistent_")] + assert len(mcp_tools) == 0, f"Expected no nonexistent_ tools, got {mcp_tools}" + finally: + server.stop() + + +def test_mcp_multiple_servers(): + """Multiple MCP servers can be configured simultaneously.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + }, + "echo2": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + echo_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + echo2_tools = [t for t in tools if get_tool_name(t).startswith("echo2_")] + + assert len(echo_tools) >= 2, f"Expected echo_ tools, got {echo_tools}" + assert len(echo2_tools) >= 2, f"Expected echo2_ tools, got {echo2_tools}" + finally: + server.stop() + + +def test_mcp_tools_not_listed_when_not_configured(): + """Without MCP config, no MCP tools should appear.""" + global server + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.start() + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + # Should only have builtin tools, no server: prefixed tools + mcp_tools = [t for t in tools if ":" in get_tool_name(t)] + assert len(mcp_tools) == 0, f"Expected no MCP tools, got {mcp_tools}" + finally: + server.stop() + + +def test_mcp_fail_once_tool_eventual_success(): + """Test that a tool that fails once eventually succeeds (tests instance respawn).""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # First call should succeed (warmup already spawned and shut down the instance, + # but the first actual tool call will spawn a fresh instance) + res = server.make_request("POST", "/tools", data={ + "tool": "echo_fail_once", + "params": {} + }) + # It might fail on first call if the warmup instance was shut down + # and a new instance is spawned. The fail_once state is per-process, + # so a fresh process will fail once then succeed. + # Actually, warmup spawns, lists, then shuts down. So the first tool call + # spawns a new process which will fail once. + assert res.status_code in (200, 500), res.body + finally: + server.stop() + + +def test_mcp_tools_via_json_config_file(): + """Test that --mcp-servers-config (file) works as well as --mcp-servers-json.""" + global server + config = { + "mcpServers": { + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + } + } + + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json.dump(config, f) + config_path = f.name + + try: + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.mcp_servers_config = config_path + server.start() + + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + mcp_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + assert len(mcp_tools) >= 2, f"Expected echo_ tools, got {mcp_tools}" + finally: + os.unlink(config_path) + server.stop() + + +def test_mcp_tools_slot_independent(): + """MCP tools should work without any slot concept; /tools is slot-independent.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Call /tools without any slot binding - should succeed + res = server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": "hello"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_concurrent_tool_calls(): + """Concurrent POST /tools to same MCP server should all succeed.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + def call_tool(): + return server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": "hi"} + }) + + with ThreadPoolExecutor(max_workers=10) as executor: + futures = [executor.submit(call_tool) for _ in range(10)] + results = [f.result() for f in futures] + + for res in results: + assert res.status_code == 200, res.body + assert "error" not in res.body, res.body + finally: + server.stop() + + +def test_mcp_tool_timeout(): + """Tool call should timeout if MCP server is too slow.""" + global server + MCP_SLOW_SERVER = os.path.join(FIXTURES_DIR, "mcp_slow_server.py") + mcp_json = _mcp_config_json({ + "slow": { + "command": sys.executable, + "args": [MCP_SLOW_SERVER, "--delay", "5"], + "timeout_ms": 500 + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("POST", "/tools", data={ + "tool": "slow_sleep", + "params": {"seconds": 5} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" in body, body + finally: + server.stop() + + +def test_mcp_warmup_partial_failure(): + """Good server's tools should appear even if bad server fails warmup.""" + global server + mcp_json = _mcp_config_json({ + "good": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + }, + "bad": { + "command": "nonexistent", + "args": [] + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + # good server tools should be present + assert any("good_" in get_tool_name(t) for t in tools), f"Expected good: tools in {tools}" + finally: + server.stop() + + +def test_mcp_notification_during_request(): + """Notification during request should not be returned as response.""" + global server + MCP_MALFORMED_SERVER = os.path.join(FIXTURES_DIR, "mcp_malformed_server.py") + mcp_json = _mcp_config_json({ + "notifying": { + "command": sys.executable, + "args": [MCP_MALFORMED_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("POST", "/tools", data={ + "tool": "notifying_echo", + "params": {"message": "hi"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_instance_respawn_after_crash(): + """Tool call after process crash should respawn and succeed.""" + global server + MCP_CRASH_SERVER = os.path.join(FIXTURES_DIR, "mcp_crash_server.py") + mcp_json = _mcp_config_json({ + "crash": { + "command": sys.executable, + "args": [MCP_CRASH_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # First call succeeds + res1 = server.make_request("POST", "/tools", data={ + "tool": "crash_echo", + "params": {"message": "hi"} + }) + assert res1.status_code == 200, res1.body + assert "error" not in res1.body, res1.body + + # Second call should also succeed (respawned instance) + res2 = server.make_request("POST", "/tools", data={ + "tool": "crash_echo", + "params": {"message": "hi2"} + }) + assert res2.status_code == 200, res2.body + assert "error" not in res2.body, res2.body + finally: + server.stop() + + + + +def test_mcp_fail_once_eventual_success_verified(): + """Verify that fail_once tool eventually succeeds after respawn.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # First call may fail (fresh process) + res1 = server.make_request("POST", "/tools", data={ + "tool": "echo_fail_once", + "params": {} + }) + # Second call should succeed + res2 = server.make_request("POST", "/tools", data={ + "tool": "echo_fail_once", + "params": {} + }) + assert res2.status_code == 200, res2.body + assert "error" not in res2.body, res2.body + finally: + server.stop() + + +def test_mcp_config_file_errors(): + """Invalid JSON config and missing file should cause server to fail to start.""" + # Invalid JSON - server should fail to start + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.mcp_servers_json = "not valid json" + try: + server.start() + assert False, "Server should not have started with invalid MCP JSON config" + except RuntimeError: + pass # Expected: server process dies due to bad config + + # Missing file - server should fail to start + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.mcp_servers_config = "/nonexistent/path.json" + try: + server.start() + assert False, "Server should not have started with missing config file" + except RuntimeError: + pass # Expected: server process dies due to missing config + + +def test_mcp_empty_tool_list(): + """MCP server reporting zero tools should result in empty tool list.""" + global server + # Create a minimal server that returns empty tools list + empty_server = os.path.join(FIXTURES_DIR, "_empty_mcp_server.py") + with open(empty_server, "w") as f: + f.write('''#!/usr/bin/env python3 +import json, sys, os +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + for line in sys.stdin: + line = line.strip() + if not line: continue + try: request = json.loads(line) + except: continue + method = request.get("method") + req_id = request.get("id") + if method == "initialize": + resp = {"jsonrpc": "2.0", "id": req_id, "result": {"protocolVersion": "2024-11-05", "capabilities": {"tools": {}}, "serverInfo": {"name": "empty", "version": "1.0"}}} + elif method == "tools/list": + resp = {"jsonrpc": "2.0", "id": req_id, "result": {"tools": []}} + else: + resp = {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": "Method not found"}} + sys.stdout.write(json.dumps(resp) + "\\n") + sys.stdout.flush() +if __name__ == "__main__": + main() +''') + try: + mcp_json = _mcp_config_json({ + "empty": { + "command": sys.executable, + "args": [empty_server], + } + }) + server = _start_server_with_mcp(mcp_json) + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + mcp_tools = [t for t in tools if get_tool_name(t).startswith("empty:")] + assert len(mcp_tools) == 0, f"Expected no empty: tools, got {mcp_tools}" + finally: + os.unlink(empty_server) + server.stop() + + +def test_mcp_rapid_succession_calls(): + """Many rapid calls should increment next_id correctly and correlate responses.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + for i in range(20): + res = server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": f"msg{i}"} + }) + assert res.status_code == 200, res.body + assert "error" not in res.body, res.body + finally: + server.stop() + + +def test_mcp_notification_burst(): + """Notification + response in a single write() with no flush should not strand the response.""" + global server + MCP_BURST_SERVER = os.path.join(FIXTURES_DIR, "mcp_burst_server.py") + mcp_json = _mcp_config_json({ + "burst": { + "command": sys.executable, + "args": [MCP_BURST_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("POST", "/tools", data={ + "tool": "burst_echo", + "params": {"message": "burst test"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_tool_definition_shape_via_chat_completions(): + """MCP tool definitions returned by GET /tools should have the correct shape for chat/completions.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Get MCP tool definitions + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + echo_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + assert len(echo_tools) >= 2, f"Expected echo_ tools, got {echo_tools}" + + echo_tool = next(t for t in echo_tools if get_tool_name(t) == "echo_echo") + definition = echo_tool.get("definition", echo_tool) + + # Verify the definition has the standard function-calling shape + assert definition.get("type") == "function", f"Expected type=function, got {definition.get('type')}" + func = definition.get("function", {}) + assert "name" in func, "Missing function.name" + assert "description" in func, "Missing function.description" + assert "parameters" in func, f"Missing function.parameters, got keys: {list(func.keys())}" + params = func["parameters"] + assert params.get("type") == "object", f"Expected parameters.type=object, got {params.get('type')}" + assert "properties" in params, "Missing parameters.properties" + finally: + server.stop() + + +def test_mcp_slow_tool_call_slot_release(): + """A slow tool call should not stall server shutdown for the full I/O timeout.""" + global server + MCP_SLOW_SERVER = os.path.join(FIXTURES_DIR, "mcp_slow_server.py") + mcp_json = _mcp_config_json({ + "slow": { + "command": sys.executable, + "args": [MCP_SLOW_SERVER, "--delay", "10"], + "timeout_ms": 30000 + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Start a slow tool call in a background thread + def slow_call(): + return server.make_request("POST", "/tools", data={ + "tool": "slow_sleep", + "params": {"seconds": 10} + }) + + with ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(slow_call) + + # Wait a moment for the call to start + time.sleep(2) + + # Stop the server while the tool call is in progress. + # With global MCP instances, close_all() is called explicitly at shutdown + # (not from slot release), so shutdown should complete promptly. + start_time = time.time() + server.stop() + elapsed = time.time() - start_time + + # The server should stop quickly, not wait for the full 30s I/O timeout. + # With the terminating flag, send_rpc() bails out within one select() + # slice (~50ms). This threshold MUST stay below the 5s force-kill + # fallback in ServerProcess.stop(): without the flag, shutdown stalls + # on the instance mutex and only completes when stop() sends SIGKILL + # at ~5s -- which any threshold above 5 would still accept. + assert elapsed < 3, f"Server stop took {elapsed:.1f}s, expected < 3s" + + # Wait for the future to complete (it will get an error response or timeout) + try: + res = future.result(timeout=5) + # If we got a response, it should be an error since the server stopped + if hasattr(res, 'status_code'): + assert res.status_code in (200, 500, 502, 503, 504), f"Unexpected status: {res.status_code}" + except Exception: + # Thread may have raised due to connection error - that's acceptable + pass + finally: + server.stop() + + +def test_mcp_grandchild_holding_pipes_does_not_deadlock(): + """An MCP server that leaves a grandchild inheriting its stdout/stderr must not deadlock + teardown. + + subprocess_terminate() only SIGKILLs the direct MCP child, so the inherited pipe write ends + stay open and a blocking read on them would never see EOF. That hung both warmup shutdown + (the server would never reach "ready") and process shutdown. The polled, running-aware reader + must exit regardless, so the server both starts and stops promptly here. + """ + global server + MCP_GRANDCHILD_SERVER = os.path.join(FIXTURES_DIR, "mcp_grandchild_server.py") + mcp_json = _mcp_config_json({ + "gc": { + "command": sys.executable, + "args": [MCP_GRANDCHILD_SERVER], + } + }) + + # If warmup teardown deadlocked, the server would never become ready and start() would time out. + server = _start_server_with_mcp(mcp_json) + + try: + # invoking the tool spawns a live transport whose reader thread holds the inherited pipe + res = server.make_request("POST", "/tools", data={ + "tool": "gc_echo", + "params": {"message": "hello"} + }) + assert res.status_code == 200, res.body + assert "error" not in res.body, res.body + + # shutdown must be prompt: a deadlocked reader-join would stall until the 5s SIGKILL + # fallback in ServerProcess.stop(), so the threshold has to stay below that + start = time.time() + server.stop() + elapsed = time.time() - start + assert elapsed < 3, f"server shutdown took {elapsed:.1f}s (expected < 3s) — teardown likely deadlocked" + finally: + server.stop() diff --git a/tools/server/tests/unit/test_slot_save.py b/tools/server/tests/unit/test_slot_save.py index 1b428cc2a840..be22d9859efd 100644 --- a/tools/server/tests/unit/test_slot_save.py +++ b/tools/server/tests/unit/test_slot_save.py @@ -1,5 +1,7 @@ import pytest from utils import * +import base64 +import requests server = ServerPreset.tinyllama2() @@ -96,3 +98,127 @@ def test_slot_erase(): assert res.status_code == 200 assert match_regex("(Whiskers|Flana)+", res.body["content"]) assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed + + +# +# Multimodal server (mmproj loaded) slot save/restore. +# +# Regression coverage for issue #21133: slot save/restore/erase must be gated on +# the slot's CONTENT (does it actually hold image/audio tokens) rather than the +# model's CAPABILITY (is an mmproj loaded). A pure-text slot on a multimodal +# server must save/restore/erase normally; a slot that actually holds an image +# must be rejected with ERROR_TYPE_NOT_SUPPORTED (HTTP 501). +# + +IMG_URL_CAT = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/91_cat.png" + + +def _get_img_base64(url: str) -> str: + response = requests.get(url) + response.raise_for_status() # Raise an exception for bad status codes + return base64.b64encode(response.content).decode("utf-8") + + +@pytest.fixture +def mmproj_server(): + # tinygemma3 is a small multimodal model: the mmproj is provided by the HF + # registry API and auto-downloaded on first run. + os.environ['LLAMA_MEDIA_MARKER'] = '<__media__>' + mm_server = ServerPreset.tinygemma3() + mm_server.slot_save_path = "./tmp" + mm_server.temperature = 0.0 + return mm_server + + +def test_slot_save_restore_text_only_on_multimodal(mmproj_server): + server = mmproj_server + server.start() + + # A pure-text prompt processed on slot 1 of a multimodal server. + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + prompt_n = res.body["timings"]["prompt_n"] + assert prompt_n > 0 # all tokens are processed + + # Saving a pure-text slot must succeed even though an mmproj is loaded. + res = server.make_request("POST", "/slots/1?action=save", data={ + "filename": "mm_slot1.bin", + }) + assert res.status_code == 200 + n_saved = res.body["n_saved"] + assert n_saved > 0 # the slot KV (prompt + generated tokens) was written + + # Restore the saved state into slot 0; it must round-trip exactly. + res = server.make_request("POST", "/slots/0?action=restore", data={ + "filename": "mm_slot1.bin", + }) + assert res.status_code == 200 + assert res.body["n_restored"] == n_saved + + # The restored slot is usable for a follow-up completion. We do NOT assert + # prefix reuse here: tinygemma3 is a SWA model, which forces full prompt + # re-processing after a restore (a model property, not the save/restore gate + # under test). + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 0, + "cache_prompt": True, + }) + assert res.status_code == 200 + + +def test_slot_save_rejected_when_slot_holds_image(mmproj_server): + server = mmproj_server + server.start() + + # Process a prompt that actually contains an image on slot 1. + res = server.make_request("POST", "/completions", data={ + "temperature": 0.0, + "top_k": 1, + "id_slot": 1, + "cache_prompt": True, + "prompt": { + "prompt_string": "What is this: <__media__>\n", + "multimodal_data": [ _get_img_base64(IMG_URL_CAT) ], + }, + }) + assert res.status_code == 200 + + # Saving a slot that holds image tokens must be rejected (HTTP 501, + # not_supported_error). + res = server.make_request("POST", "/slots/1?action=save", data={ + "filename": "mm_slot_image.bin", + }) + assert res.status_code != 200 + assert res.body["error"]["type"] == "not_supported_error" + + +def test_slot_erase_text_only_on_multimodal(mmproj_server): + server = mmproj_server + server.start() + + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + prompt_n = res.body["timings"]["prompt_n"] + assert prompt_n > 0 # all tokens are processed + + # Erasing a pure-text slot must succeed even though an mmproj is loaded. + res = server.make_request("POST", "/slots/1?action=erase") + assert res.status_code == 200 + + # Re-running the same prompt should process all tokens again. + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert res.body["timings"]["prompt_n"] == prompt_n # all tokens are processed again diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py index 84cd77e6f2ed..c6568479ca4a 100644 --- a/tools/server/tests/unit/test_speculative.py +++ b/tools/server/tests/unit/test_speculative.py @@ -12,8 +12,9 @@ def create_server(): server = ServerPreset.stories15m_moe() # set default values server.model_draft = download_file(MODEL_DRAFT_FILE_URL) - server.draft_min = 4 - server.draft_max = 8 + server.spec_type = "draft-simple" + server.spec_draft_n_min = 4 + server.spec_draft_n_max = 8 server.fa = "off" @@ -25,6 +26,7 @@ def fixture_create_server(): def test_with_and_without_draft(): global server server.model_draft = None # disable draft model + server.spec_type = None server.start() res = server.make_request("POST", "/completion", data={ "prompt": "I believe the meaning of life is", @@ -46,6 +48,7 @@ def test_with_and_without_draft(): "n_predict": 16, }) assert res.status_code == 200 + assert res.body["timings"]["draft_n"] > 0 content_draft = res.body["content"] assert content_no_draft == content_draft @@ -63,8 +66,8 @@ def test_different_draft_min_draft_max(): last_content = None for draft_min, draft_max in test_values: server.stop() - server.draft_min = draft_min - server.draft_max = draft_max + server.spec_draft_n_min = draft_min + server.spec_draft_n_max = draft_max server.start() res = server.make_request("POST", "/completion", data={ "prompt": "I believe the meaning of life is", diff --git a/tools/server/tests/unit/test_stream.py b/tools/server/tests/unit/test_stream.py new file mode 100644 index 000000000000..a1ef55567bc7 --- /dev/null +++ b/tools/server/tests/unit/test_stream.py @@ -0,0 +1,153 @@ +import json +import socket +import threading +import time +from urllib.parse import quote +import pytest +from utils import * + +server: ServerProcess + +# a model name with slashes exercises the query string routing of the stream routes: the id +# cannot travel as a path param because the decoded slash would split it before capture +MODEL = "ggml-org/tinygemma3-GGUF:Q8_0" +STREAM_ID = f"conv-stream-test::{MODEL}" +QS = "conv_id=" + quote(STREAM_ID, safe="") + + +@pytest.fixture(autouse=True) +def create_server(): + global server + server = ServerPreset.router() + + +def test_stream_resume_and_stop_with_slashed_model_name(): + global server + server.start() + + content = "" + for data in server.make_stream_request("POST", "/chat/completions", data={ + "model": MODEL, + "stream": True, + "max_tokens": 16, + "messages": [{"role": "user", "content": "hello"}], + }, headers={"X-Conversation-Id": STREAM_ID}): + if data["choices"]: + content += data["choices"][0]["delta"].get("content") or "" + assert len(content) > 0 + + # the finished session replays from the beginning through the router + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + assert res.status_code == 200 + assert "data: " in str(res.body) + + # the explicit stop reaches the owning child and evicts the session + res = server.make_request("DELETE", f"/v1/stream?{QS}") + assert res.status_code == 204 + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + assert res.status_code == 404 + + +def test_stream_stop_during_model_load(): + global server + server.start() + + thread_error: list[ServerError] = [] + thread_done = threading.Event() + + def fire_post(): + try: + for _ in server.make_stream_request("POST", "/chat/completions", data={ + "model": MODEL, + "stream": True, + "max_tokens": 512, + "messages": [{"role": "user", "content": "Count from 1 to 1000."}], + }, headers={"X-Conversation-Id": STREAM_ID}): + pass + except ServerError as e: + thread_error.append(e) + finally: + thread_done.set() + + t = threading.Thread(target=fire_post) + t.start() + + # catch the autoload window, tiny models load fast so poll aggressively + saw_loading = False + deadline = time.time() + 5.0 + while time.time() < deadline and not thread_done.is_set(): + res = server.make_request("GET", "/models") + status = next(m["status"]["value"] for m in res.body["data"] if m["id"] == MODEL) + if status == "loading": + saw_loading = True + break + time.sleep(0.002) + if not saw_loading: + t.join() + pytest.skip("load window too short to be observed on this machine") # ty: ignore[too-many-positional-arguments] + + # a stop during the load cancels the parked request instead of leaving an orphan + res = server.make_request("DELETE", f"/v1/stream?{QS}") + assert res.status_code == 204 + assert thread_done.wait(timeout=60) + t.join() + assert len(thread_error) == 1 + assert thread_error[0].code == 400 + assert "cancelled" in json.dumps(thread_error[0].body) + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + assert res.status_code == 404 + + +def test_stream_resumes_after_reload_during_model_load(): + global server + server.start() + + # raw socket client so the connection can be dropped mid load like a page reload + body = json.dumps({ + "model": MODEL, + "stream": True, + "max_tokens": 16, + "messages": [{"role": "user", "content": "hello"}], + }) + request = ( + f"POST /v1/chat/completions HTTP/1.1\r\n" + f"Host: {server.server_host}:{server.server_port}\r\n" + f"Content-Type: application/json\r\n" + f"X-Conversation-Id: {STREAM_ID}\r\n" + f"Content-Length: {len(body)}\r\n" + f"Connection: close\r\n\r\n{body}" + ) + sock = socket.create_connection((server.server_host, server.server_port)) + sock.sendall(request.encode()) + + # drop the client while the model loads, poll aggressively to catch the window + saw_loading = False + saw_503 = False + deadline = time.time() + 5.0 + while time.time() < deadline: + res = server.make_request("GET", "/models") + status = next(m["status"]["value"] for m in res.body["data"] if m["id"] == MODEL) + if status == "loading": + saw_loading = True + break + if status == "loaded": + break + time.sleep(0.002) + sock.close() + if not saw_loading: + pytest.skip("load window too short to be observed on this machine") # ty: ignore[too-many-positional-arguments] + + # while the model loads the resume route answers retry later, then the session appears, + # receives the whole generation despite the dead client, and replays from the beginning + deadline = time.time() + 60.0 + replay = None + while time.time() < deadline: + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + if res.status_code == 503: + saw_503 = True + elif res.status_code == 200 and "data: " in str(res.body): + replay = res + break + time.sleep(0.1) + assert saw_503, "resume during the load did not answer 503" + assert replay is not None, "session never became resumable after the client disconnect" diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py index f4f0e61e6106..ae56bc70a15a 100644 --- a/tools/server/tests/utils.py +++ b/tools/server/tests/utils.py @@ -95,6 +95,7 @@ class ServerProcess: no_models_autoload: bool | None = None lora_files: List[str] | None = None enable_ctx_shift: int | None = False + spec_type: str | None = None spec_draft_n_min: int | None = None spec_draft_n_max: int | None = None no_ui: bool | None = None @@ -114,6 +115,8 @@ class ServerProcess: backend_sampling: bool = False gcp_compat: bool = False server_tools: str | None = None + mcp_servers_config: str | None = None + mcp_servers_json: str | None = None cors_origins: str | None = None # session variables @@ -226,6 +229,8 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None: server_args.extend(["--lora", lora_file]) if self.enable_ctx_shift: server_args.append("--context-shift") + if self.spec_type: + server_args.extend(["--spec-type", self.spec_type]) if self.api_key: server_args.extend(["--api-key", self.api_key]) if self.spec_draft_n_max: @@ -262,6 +267,10 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None: server_args.append("--ui-mcp-proxy") if self.server_tools: server_args.extend(["--tools", self.server_tools]) + if self.mcp_servers_config: + server_args.extend(["--mcp-servers-config", self.mcp_servers_config]) + if self.mcp_servers_json: + server_args.extend(["--mcp-servers-json", self.mcp_servers_json]) if self.backend_sampling: server_args.append("--backend_sampling") if self.gcp_compat: diff --git a/tools/tokenize/tokenize.cpp b/tools/tokenize/tokenize.cpp index 23120ad2e182..77b33c4a4657 100644 --- a/tools/tokenize/tokenize.cpp +++ b/tools/tokenize/tokenize.cpp @@ -103,19 +103,11 @@ int main(int argc, char ** argv) { return 1; } - // which prompt source was requested? - // -p/--prompt and -f/--file both end up in params.prompt (common's -f also - // strips a single trailing newline), but -f additionally records the path - // in params.prompt_file, so we use that to tell them apart. + // -f and -p both land in params.prompt; -f also sets prompt_file. -f and -p + // resolve like the other tools (no mutual exclusion), --stdin takes precedence. const bool use_stdin = params.tokenize_stdin; const bool use_file = !params.prompt_file.empty(); - // sanity check: --stdin is mutually exclusive with -f/--file and -p/--prompt - if (use_stdin && (use_file || !params.prompt.empty())) { - LOG_ERR("error: --stdin is mutually exclusive with --file and --prompt\n"); - return 1; - } - // must have some prompt if (!use_stdin && !use_file && params.prompt.empty()) { LOG_ERR("error: must specify one of: --stdin, --file or --prompt\n"); diff --git a/tools/ui/.gitignore b/tools/ui/.gitignore index 0bb8c9b3c218..7cd35376e199 100644 --- a/tools/ui/.gitignore +++ b/tools/ui/.gitignore @@ -36,3 +36,7 @@ static/favicon* *storybook.log storybook-static *.code-workspace + +# Vitest browser mode failure artifacts +.vitest-attachments/ +tests/**/__screenshots__/ diff --git a/tools/ui/.prettierignore b/tools/ui/.prettierignore index 7bbdcf6a0963..635cf99c7e9c 100644 --- a/tools/ui/.prettierignore +++ b/tools/ui/.prettierignore @@ -16,3 +16,6 @@ build/ /build/ /.svelte-kit/ test-results + +# Vendored third party sources, kept byte identical to upstream +src/lib/vendors/ diff --git a/tools/ui/eslint.config.js b/tools/ui/eslint.config.js index b90376b6c156..fcbf7ee95489 100644 --- a/tools/ui/eslint.config.js +++ b/tools/ui/eslint.config.js @@ -29,6 +29,13 @@ export default ts.config( // This app uses hash-based routing (#/) where resolve() from $app/paths does not apply 'svelte/no-navigation-without-resolve': 'off', + // Snippet bodies often ignore one or more of the parent's params + // (e.g. `{#snippet children(_meta, ctx)}` when only ctx is read). + '@typescript-eslint/no-unused-vars': [ + 'error', + { argsIgnorePattern: '^_', varsIgnorePattern: '^_' } + ], + // Enforce empty line at end of file 'eol-last': 'error' } @@ -52,7 +59,8 @@ export default ts.config( '.svelte-kit/**', 'test-results/**', '.storybook/**/*', - 'src/lib/services/sandbox-worker.js' + 'src/lib/services/sandbox-worker.js', + 'src/lib/vendors/**' ] }, storybook.configs['flat/recommended'] diff --git a/tools/ui/package-lock.json b/tools/ui/package-lock.json index 7216de682340..2fe44f4c34f7 100644 --- a/tools/ui/package-lock.json +++ b/tools/ui/package-lock.json @@ -12,7 +12,7 @@ "@eslint/compat": "1.4.1", "@eslint/js": "9.39.2", "@internationalized/date": "3.12.2", - "@lucide/svelte": "0.515.0", + "@lucide/svelte": "1.25.0", "@modelcontextprotocol/sdk": "1.26.0", "@playwright/test": "1.56.1", "@storybook/addon-a11y": "10.2.4", @@ -3065,9 +3065,9 @@ } }, "node_modules/@lucide/svelte": { - "version": "0.515.0", - "resolved": "https://registry.npmjs.org/@lucide/svelte/-/svelte-0.515.0.tgz", - "integrity": "sha512-CEAyqcZmNBfYzVgaRmK2RFJP5tnbXxekRyDk0XX/eZQRfsJmkDvmQwXNX8C869BgNeryzmrRyjHhUL6g9ZOHNA==", + "version": "1.25.0", + "resolved": "https://registry.npmjs.org/@lucide/svelte/-/svelte-1.25.0.tgz", + "integrity": "sha512-v9m+dD68jxVnqkU3K59mG/RSRFlPGzmKCGSyMfnXcaGv9jODDQMyQkcp1CGvk3Y/cUj9v7f8rw1n//K0B53xGQ==", "dev": true, "license": "ISC", "peerDependencies": { diff --git a/tools/ui/package.json b/tools/ui/package.json index 8b3516a02cf2..4ea2bf703c35 100644 --- a/tools/ui/package.json +++ b/tools/ui/package.json @@ -31,7 +31,7 @@ "@eslint/compat": "1.4.1", "@eslint/js": "9.39.2", "@internationalized/date": "3.12.2", - "@lucide/svelte": "0.515.0", + "@lucide/svelte": "1.25.0", "@modelcontextprotocol/sdk": "1.26.0", "@playwright/test": "1.56.1", "@storybook/addon-a11y": "10.2.4", diff --git a/tools/ui/scripts/vite-plugin-nerdamer.ts b/tools/ui/scripts/vite-plugin-nerdamer.ts new file mode 100644 index 000000000000..218c2fa233d0 --- /dev/null +++ b/tools/ui/scripts/vite-plugin-nerdamer.ts @@ -0,0 +1,49 @@ +import { build } from 'esbuild'; +import { dirname, resolve } from 'path'; +import { fileURLToPath } from 'url'; +import type { Plugin } from 'vite'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); + +const VENDORS_DIR = resolve(__dirname, '../src/lib/vendors'); +const VIRTUAL_ID = 'virtual:nerdamer'; +const RESOLVED_ID = '\0' + VIRTUAL_ID; + +/** + * Bundle the vendored nerdamer-prime source into a minified IIFE string, + * exposed as the `virtual:nerdamer` module. Flags mirror the upstream + * build (esbuild --bundle --minify --format=iife --global-name=nerdamer), + * so only human readable source lives in the repo and minification is a + * build artifact. Vendored under src/lib/vendors/, upstream snapshot: + * https://github.com/together-science/nerdamer-prime/commit/1936145f8af306ec0d883b9bfd7730aedd175c24 + */ +export function nerdamerPlugin(): Plugin { + let bundled: string | null = null; + + return { + name: 'llamacpp:nerdamer', + resolveId(id) { + return id === VIRTUAL_ID ? RESOLVED_ID : undefined; + }, + async load(id) { + if (id !== RESOLVED_ID) return undefined; + if (bundled === null) { + const result = await build({ + entryPoints: [resolve(VENDORS_DIR, 'nerdamer-prime/all.js')], + bundle: true, + minify: true, + format: 'iife', + globalName: 'nerdamer', + alias: { + 'big-integer': resolve(VENDORS_DIR, 'big-integer/BigInteger.js'), + 'decimal.js': resolve(VENDORS_DIR, 'decimal.js/decimal.js') + }, + write: false, + logLevel: 'silent' + }); + bundled = result.outputFiles[0].text; + } + return `export default ${JSON.stringify(bundled)};`; + } + }; +} diff --git a/tools/ui/src/app.css b/tools/ui/src/app.css index 8c4056477dbf..f9b544bebc56 100644 --- a/tools/ui/src/app.css +++ b/tools/ui/src/app.css @@ -193,6 +193,33 @@ -ms-overflow-style: none; scrollbar-width: none; } + + .shimmer-text { + background: linear-gradient( + 90deg, + var(--muted-foreground), + var(--foreground), + var(--muted-foreground) + ); + background-size: 200% 100%; + background-clip: text; + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + font-weight: 500; + animation: shimmer 1s linear infinite; + } + + @keyframes shimmer { + to { + background-position: -200% 0; + } + } + + @media (prefers-reduced-motion: reduce) { + .shimmer-text { + animation: none; + } + } } .mermaidTooltip { diff --git a/tools/ui/src/lib/actions/fade-in-view.svelte.ts b/tools/ui/src/lib/actions/fade-in-view.svelte.ts deleted file mode 100644 index 9a5918131aa9..000000000000 --- a/tools/ui/src/lib/actions/fade-in-view.svelte.ts +++ /dev/null @@ -1,49 +0,0 @@ -import { isElementInViewport } from '$lib/utils/viewport'; - -/** - * Svelte action that fades in an element when it enters the viewport. - * Uses IntersectionObserver for efficient viewport detection. - * - * If skipIfVisible is set and the element is already visible in the viewport - * when the action attaches (e.g. a markdown block promoted from unstable - * during streaming), the fade is skipped entirely to avoid a flash. - */ -export function fadeInView( - node: HTMLElement, - options: { duration?: number; y?: number; delay?: number; skipIfVisible?: boolean } = {} -) { - const { duration = 300, y = 0, delay = 0, skipIfVisible = false } = options; - - if (skipIfVisible && isElementInViewport(node)) { - return; - } - - node.style.opacity = '0'; - node.style.transform = `translateY(${y}px)`; - node.style.transition = `opacity ${duration}ms ease-out, transform ${duration}ms ease-out`; - - $effect(() => { - const observer = new IntersectionObserver( - (entries) => { - for (const entry of entries) { - if (entry.isIntersecting) { - setTimeout(() => { - requestAnimationFrame(() => { - node.style.opacity = '1'; - node.style.transform = 'translateY(0)'; - }); - }, delay); - observer.disconnect(); - } - } - }, - { threshold: 0.05 } - ); - - observer.observe(node); - - return () => { - observer.disconnect(); - }; - }); -} diff --git a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte index 8a86557bb98d..608ff6fab4b2 100644 --- a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte +++ b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte @@ -66,7 +66,14 @@ {#snippet child({ props })} - {@render button(props)} + {#if disabled} + + + {@render button({})} + + {:else} + {@render button(props)} + {/if} {/snippet} diff --git a/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte b/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte index 999f0cba9e78..9b7b370ad085 100644 --- a/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte +++ b/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte @@ -1,4 +1,5 @@ {#if reasoning.modelSupportsThinking} - + {#if reasoning.thinkingEnabled} - + + {:else if reasoning.isOff} + {:else} - + {/if} - {reasoning.thinkingEnabled ? reasoning.currentEffort : 'off'} + {reasoning.currentEffort} @@ -36,19 +37,18 @@ > {#each reasoning.levels as level (level.value)} {@const tokenLabel = reasoning.tokenLabel(level)} - + {/each} diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte index 6ee8eb578f89..1c6bb0c1c6ee 100644 --- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte @@ -1,4 +1,5 @@ - -{#if modelSupportsThinking} - - - {#if thinkingEnabled} - - {:else} - - {/if} - - Thinking - - {#if thinkingEnabled} - {currentEffort} - {:else} - off - {/if} - - - - {#each REASONING_EFFORT_LEVELS as level (level.value)} - - {/each} - - -{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte index 855cf6ce783d..ff6d39fdd48c 100644 --- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte @@ -1,14 +1,16 @@ - - - - - - -
-
- Context - · - - {formatParameters(gauge.contextUsed)} - / {gauge.contextTotal !== null ? formatParameters(gauge.contextTotal) : '-'} - -
- - {#if gauge.activeModelId !== null && !gauge.isActiveModelLoaded} - - {:else if showProgressBar} -
-
-
- -
- - {gauge.contextPercent}% used - - - {formatParameters((gauge.contextTotal ?? 0) - gauge.contextUsed)} remaining - -
- {:else} -
No context info available
- {/if} - - {#if gauge.hasAnyUsage} - - {/if} -
- - +
+ +
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ContextGaugePopup.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ContextGaugePopup.svelte new file mode 100644 index 000000000000..af9ad010e3a3 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ContextGaugePopup.svelte @@ -0,0 +1,113 @@ + + +{#if gaugePopup.open} +
+
+
+ Context + · + + {formatParameters(gauge.contextUsed)} + / {gauge.contextTotal !== null ? formatParameters(gauge.contextTotal) : '-'} + +
+ + {#if gauge.activeModelId !== null && !gauge.isActiveModelLoaded} + + {:else if showProgressBar} +
+
+
+ +
+ + {gauge.contextPercent}% used + + + {formatParameters((gauge.contextTotal ?? 0) - gauge.contextUsed)} remaining + +
+ {:else} +
No context info available
+ {/if} + + {#if gauge.hasAnyUsage} + + {/if} +
+
+{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte index 2b4a3f9d393c..8e8a14ac31bc 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte @@ -7,7 +7,6 @@ import { SYSTEM_MESSAGE_PLACEHOLDER } from '$lib/constants'; import { REASONING_TAGS } from '$lib/constants/agentic'; import { MessageRole, AttachmentType, AgenticSectionType } from '$lib/enums'; - import { fadeInView } from '$lib/actions/fade-in-view.svelte'; import { ChatMessageAssistant, ChatMessageUser, @@ -328,7 +327,7 @@ } -
+
{#if message.role === MessageRole.SYSTEM} {/if}
+ + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte index 6670d9302dc9..199d75fcec95 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte @@ -2,23 +2,20 @@ import { ChatMessageAgenticContent, ChatMessageActionIcons, - ChatMessageEditForm, - ChatMessageStatistics, - ModelBadge, - ModelsSelectorDropdown + ChatMessageAssistantModel, + ChatMessageAssistantProcessingInfo, + ChatMessageAssistantRawOutput, + ChatMessageAssistantStatistics, + ChatMessageEditForm } from '$lib/components/app'; import { getMessageEditContext } from '$lib/contexts'; import { useProcessingState } from '$lib/hooks/use-processing-state.svelte'; - import { isLoading, isChatStreaming } from '$lib/stores/chat.svelte'; - import { copyToClipboard, deriveAgenticSections, modelLoadProgressText } from '$lib/utils'; - import { AgenticSectionType, ChatMessageStatisticsMode } from '$lib/enums'; - import { REASONING_TAGS } from '$lib/constants/agentic'; - import { fade } from 'svelte/transition'; + import { chatStore, isLoading, isChatStreaming } from '$lib/stores/chat.svelte'; + import { modelLoadProgressText } from '$lib/utils'; import { MessageRole } from '$lib/enums'; import { config } from '$lib/stores/settings.svelte'; import { isRouterMode } from '$lib/stores/server.svelte'; import { modelsStore } from '$lib/stores/models.svelte'; - import { ServerModelStatus } from '$lib/enums'; import { hasAgenticContent } from '$lib/utils'; @@ -33,7 +30,6 @@ isLastAssistantMessage?: boolean; message: DatabaseMessage; toolMessages?: DatabaseMessage[]; - messageContent: string | undefined; onCopy: () => void; onConfirmDelete: () => void; onContinue?: () => void; @@ -54,7 +50,6 @@ isLastAssistantMessage = false, message, toolMessages = [], - messageContent, onConfirmDelete, onContinue, onCopy, @@ -77,62 +72,21 @@ let currentConfig = $derived(config()); let isRouter = $derived(isRouterMode()); - let showRawOutput = $state(false); - - let rawOutputContent = $derived.by(() => { - const sections = deriveAgenticSections(message, toolMessages, [], false); - const parts: string[] = []; - - for (const section of sections) { - switch (section.type) { - case AgenticSectionType.REASONING: - case AgenticSectionType.REASONING_PENDING: - parts.push(`${REASONING_TAGS.START}\n${section.content}\n${REASONING_TAGS.END}`); - break; - - case AgenticSectionType.TEXT: - parts.push(section.content); - break; - - case AgenticSectionType.TOOL_CALL: - case AgenticSectionType.TOOL_CALL_PENDING: - case AgenticSectionType.TOOL_CALL_STREAMING: { - const callObj: Record = { name: section.toolName }; - - if (section.toolArgs) { - try { - callObj.arguments = JSON.parse(section.toolArgs); - } catch { - callObj.arguments = section.toolArgs; - } - } - - parts.push(JSON.stringify(callObj, null, 2)); - - if (section.toolResult) { - parts.push(`[Tool Result]\n${section.toolResult}`); - } - - break; - } - } - } - return parts.join('\n\n\n'); - }); + let showRawOutput = $state(false); let displayedModel = $derived(message.model ?? null); - // model being switched to while it loads, so the selector bar tracks it - let pendingModel = $state(null); - let isCurrentlyLoading = $derived(isLoading()); let isStreaming = $derived(isChatStreaming()); let hasNoContent = $derived(!message?.content?.trim()); let isActivelyProcessing = $derived(isCurrentlyLoading || isStreaming); - // during a router auto-load the message has no model yet, so target the selected one - let loadTargetModel = $derived(message.model ?? modelsStore.selectedModelName); + // during a router auto-load the message has no model yet: target the model frozen in the + // persisted stream state (survives a reload), then fall back to the dropdown selection + let loadTargetModel = $derived( + message.model ?? chatStore.getResumeModel(message.convId) ?? modelsStore.selectedModelName + ); let modelLoadProgress = $derived( isRouter && loadTargetModel ? modelsStore.getLoadProgress(loadTargetModel) : null ); @@ -189,10 +143,6 @@ }; }); - function handleCopyModel() { - void copyToClipboard(displayedModel ?? ''); - } - $effect(() => { if (showProcessingInfoTop || showProcessingInfoBottom) { processingState.startMonitoring(); @@ -211,23 +161,14 @@ aria-label="Assistant message with actions" > {#if showProcessingInfoTop} -
-
- - {modelLoadingText ?? - processingState.getPromptProgressText() ?? - processingState.getProcessingMessage() ?? - 'Processing...'} - -
-
+ {/if} {#if editCtx.isEditing} - {:else if message.role === MessageRole.ASSISTANT} + {:else} {#if showRawOutput} - 
{rawOutputContent || ''}
+ {:else} {/if} - {:else} -
- {messageContent} -
{/if} {#if showProcessingInfoBottom} -
-
- - {modelLoadingText ?? - processingState.getPromptProgressText() ?? - processingState.getProcessingMessage() ?? - 'Processing...'} - -
-
+ {/if}
{#if displayedModel}
- {#if isRouter} - { - const status = modelsStore.getModelStatus(modelId); - - if (status !== ServerModelStatus.LOADED) { - pendingModel = modelId; - - try { - await modelsStore.loadModel(modelId); - } finally { - pendingModel = null; - } - } - - onRegenerate(modelName); - return true; - }} - /> - {:else} - - {/if} - - {#if currentConfig.showMessageStats && message.timings && message.timings.predicted_n && message.timings.predicted_ms} - {@const agentic = message.timings.agentic} - - {:else if isLoading() && currentConfig.showMessageStats} - {@const liveStats = processingState.getLiveProcessingStats()} - {@const genStats = processingState.getLiveGenerationStats()} - - {#if genStats} - - {/if} - {/if} + + +
{/if}
@@ -353,47 +244,4 @@ ); } } - - .processing-container { - display: flex; - flex-direction: column; - align-items: flex-start; - gap: 0.5rem; - } - - .processing-text { - background: linear-gradient( - 90deg, - var(--muted-foreground), - var(--foreground), - var(--muted-foreground) - ); - background-size: 200% 100%; - background-clip: text; - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - animation: shine 1s linear infinite; - font-weight: 500; - font-size: 0.875rem; - } - - @keyframes shine { - to { - background-position: -200% 0; - } - } - - .raw-output { - width: 100%; - max-width: 48rem; - margin-top: 1.5rem; - padding: 1rem 1.25rem; - border-radius: 1rem; - background: hsl(var(--muted) / 0.3); - color: var(--foreground); - font-size: 0.875rem; - line-height: 1.6; - white-space: pre-wrap; - word-break: break-word; - } diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte new file mode 100644 index 000000000000..76b45ec94e09 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte @@ -0,0 +1,46 @@ + + +{#if isRouter} + { + const status = modelsStore.getModelStatus(modelId); + + if (status !== ServerModelStatus.LOADED) { + pendingModel = modelId; + + try { + await modelsStore.loadModel(modelId); + } finally { + pendingModel = null; + } + } + + onRegenerate(modelName); + return true; + }} + /> +{:else} + +{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte new file mode 100644 index 000000000000..356512ecb73d --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte @@ -0,0 +1,25 @@ + + +
+
+ + {modelLoadingText ?? + processingState.getPromptProgressText() ?? + processingState.getProcessingMessage() ?? + 'Processing...'} + +
+
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte new file mode 100644 index 000000000000..30ce16be934e --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte @@ -0,0 +1,33 @@ + + +
{rawOutputContent || ''}
+ + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte new file mode 100644 index 000000000000..4cc4080c3b7c --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte @@ -0,0 +1,40 @@ + + +{#if showMessageStats && message.timings && message.timings.predicted_n && message.timings.predicted_ms} + {@const agentic = message.timings.agentic} + +{:else if isLoading && showMessageStats} + {@const liveStats = processingState.getLiveProcessingStats()} + {@const genStats = processingState.getLiveGenerationStats()} + + {#if genStats} + + {/if} +{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte index 9d3d07a2730b..24b3be4c5ff7 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte @@ -7,7 +7,7 @@ import { getMessageEditContext } from '$lib/contexts'; import { KeyboardKey, MessageRole } from '$lib/enums'; import { config } from '$lib/stores/settings.svelte'; - import { isIMEComposing } from '$lib/utils'; + import { autoResizeTextarea, isIMEComposing } from '$lib/utils'; interface Props { class?: string; @@ -91,6 +91,11 @@ resizeObserver.disconnect(); }; }); + $effect(() => { + if (editCtx.isEditing && textareaElement) { + autoResizeTextarea(textareaElement); + } + }); function toggleExpand() { isExpanded = !isExpanded; @@ -105,11 +110,15 @@ {#if editCtx.isEditing}
@@ -157,10 +166,7 @@ > {#if currentConfig.renderUserContentAsMarkdown}
- +
{:else} + import { BuiltInTool } from '$lib/enums'; + import { + extractSearchQuery, + extractSearchResults, + isWebSearchToolName, + type AgenticSection + } from '$lib/utils'; + import type { DatabaseMessageExtra } from '$lib/types'; + import ChatMessageToolCallBlockDefault from './ChatMessageToolCallBlockDefault.svelte'; + import ChatMessageToolCallBlockEditFile from './ChatMessageToolCallBlockEditFile.svelte'; + import ChatMessageToolCallBlockExecShellCommand from './ChatMessageToolCallBlockExecShellCommand.svelte'; + import ChatMessageToolCallBlockFileGlobSearch from './ChatMessageToolCallBlockFileGlobSearch.svelte'; + import ChatMessageToolCallBlockGetDatetime from './ChatMessageToolCallBlockGetDatetime.svelte'; + import ChatMessageToolCallBlockGrepSearch from './ChatMessageToolCallBlockGrepSearch.svelte'; + import ChatMessageToolCallBlockReadFile from './ChatMessageToolCallBlockReadFile.svelte'; + import ChatMessageToolCallBlockRunJavascript from './ChatMessageToolCallBlockRunJavascript.svelte'; + import ChatMessageToolCallBlockSearchResults from './ChatMessageToolCallBlockSearchResults.svelte'; + import ChatMessageToolCallBlockWriteFile from './ChatMessageToolCallBlockWriteFile.svelte'; + + interface Props { + section: AgenticSection; + attachments?: DatabaseMessageExtra[]; + open: boolean; + isStreaming: boolean; + isExecuting?: boolean; + onToggle?: () => void; + } + + let { section, attachments, open, isStreaming, isExecuting, onToggle }: Props = $props(); + + const searchResults = $derived(extractSearchResults(section.toolResult)); + const searchQuery = $derived(extractSearchQuery(section.toolArgs)); + const isSearchCall = $derived( + searchResults.length > 0 || (searchQuery.length > 0 && isWebSearchToolName(section.toolName)) + ); + + +{#if isSearchCall} + +{:else if section.toolName === BuiltInTool.GET_DATETIME} + +{:else if section.toolName === BuiltInTool.READ_FILE} + +{:else if section.toolName === BuiltInTool.EDIT_FILE} + +{:else if section.toolName === BuiltInTool.WRITE_FILE} + +{:else if section.toolName === BuiltInTool.EXEC_SHELL_COMMAND} + +{:else if section.toolName === BuiltInTool.FILE_GLOB_SEARCH} + +{:else if section.toolName === BuiltInTool.GREP_SEARCH} + +{:else if section.toolName === BuiltInTool.RUN_JAVASCRIPT} + +{:else} + +{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockDefault.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockDefault.svelte new file mode 100644 index 000000000000..acf2de12ac64 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockDefault.svelte @@ -0,0 +1,124 @@ + + + + {#snippet children(_meta, ctx)} + {#if ctx.isStreamingCall} +
+ Input + {#if ctx.isStreaming} + + {/if} +
+ {#if section.toolArgs} + + {:else if ctx.isStreaming} +
+ Receiving arguments... +
+ {:else} +
+ Response was truncated +
+ {/if} + {:else} + {@const showInput = Boolean(section.toolArgs)} + {#if showInput} +
+ Input +
+ + {/if} +
+ Output + {#if ctx.isPending} + + {/if} +
+ {#if ctx.isPending} +
+ Waiting for result... +
+ {:else if section.toolResult} + {#if outputKind === ToolResultKind.JSON} + + {:else if outputKind === ToolResultKind.MARKDOWN} + + {:else} +
+ {#each parsedLines as line, i (i)} +
+ {line.text} +
+ {#if line.image} + {line.image.name} + {/if} + {/each} +
+ {/if} + {:else} +
No output
+ {/if} + {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte new file mode 100644 index 000000000000..6f30060f54c4 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte @@ -0,0 +1,166 @@ + + + + {#snippet titleSnippet()} + Edit file + {editFileMeta?.filePath} + {#if editFileMeta?.errorMessage} + (failed) + {/if} + {/snippet} + + {#snippet children(meta, _ctx)} + {#if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta && meta.edits.length > 0} + {#each editDiffs as diffLines, ei (ei)} +
+
+ Edit {ei + 1} of {meta.edits.length} +
+
+
+ {#each diffLines as line, li (li)} +
+ {line.oldLine ?? ''} + {prefixFor(line.kind)} + {line.newLine ?? ''} + {line.text || ' '} +
+ {/each} +
+
+
+ {/each} +
+ {#if meta.resultMessage} + {meta.resultMessage}{meta.editsApplied != null ? RESULT_STAT_SEPARATOR : ''}{/if} + {#if meta.editsApplied != null} + {meta.editsApplied} + {meta.editsApplied === 1 ? 'edit' : 'edits'} applied + {/if} +
+ {:else} +
No edits
+ {/if} + {/snippet} + + + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte new file mode 100644 index 000000000000..5de801d39a48 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte @@ -0,0 +1,293 @@ + + +{#snippet execShellTitle()} + {#if highlightedCommandHtml} + {@html highlightedCommandHtml} + {:else} + {execShellMeta?.command} + {/if} +{/snippet} + + + {#snippet titleSnippet()} + {@render execShellTitle()} + {/snippet} + + {#snippet children(_meta, ctx)} + {#if ctx.isPending} +
+ + Running... +
+ {:else if execShellError} +
+ + {execShellError} +
+ {:else if section.toolResult} +
+ {#each outputLines as line, i (i)} +
{line.text}
+ {#if line.image} + {line.image.name} + {/if} + {/each} + + {#if isExitCodeFinalLine && execShellExitStatus} +
+ {#if execShellExitStatus.timedOut} + + timed out + · + exit {execShellExitStatus.code} + {:else if execShellExitStatus.code === 0} + + exit 0 + {:else} + + exit {execShellExitStatus.code} + {/if} +
+ {/if} +
+ {/if} + {/snippet} + + + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockFileGlobSearch.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockFileGlobSearch.svelte new file mode 100644 index 000000000000..ad082039ff60 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockFileGlobSearch.svelte @@ -0,0 +1,61 @@ + + + + {#snippet titleSnippet()} + {#if fileGlobMeta} + {fileGlobMeta.include === '**' ? 'List files' : 'Search files'}  + {#if fileGlobMeta.include !== '**'} + {fileGlobMeta.include} + {/if} +  in  + {fileGlobMeta.path} + {/if} + {/snippet} + + {#snippet children(meta, ctx)} + {#if ctx.isPending} +
+ Searching... +
+ {:else if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta && meta.matches.length > 0} +
+ {#each meta.matches as match, i (i)} +
{match}
+ {/each} +
+
+ Total matches: {meta.totalMatches ?? meta.matches.length} +
+ {:else} +
No matches
+
+ Total matches: {meta?.totalMatches ?? 0} +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGetDatetime.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGetDatetime.svelte new file mode 100644 index 000000000000..e0c701deaa58 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGetDatetime.svelte @@ -0,0 +1,57 @@ + + +
+ + {#if showSpinner} + Current time + + {:else if dateMeta.errorMessage} + Current time  + - {dateMeta.errorMessage} + {:else if dateMeta.dateString} + Current time is  + {dateMeta.dateString} + {:else} + Current time + {/if} +
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGrepSearch.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGrepSearch.svelte new file mode 100644 index 000000000000..afb06fef7168 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGrepSearch.svelte @@ -0,0 +1,67 @@ + + + + {#snippet titleSnippet()} + {#if grepMeta} + Search for  + {grepMeta.pattern} +  in  + {grepMeta.path} + {/if} + {/snippet} + + {#snippet children(meta, ctx)} + {#if ctx.isPending} +
+ Searching... +
+ {:else if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta && meta.matches.length > 0} +
+ {#each meta.matches as match, mi (mi)} +
+ {match.file} + {#if meta.showLineNumbers && match.line != null} + :{match.line} + {/if} + : + {match.content} +
+ {/each} +
+
+ Total matches: {meta.totalMatches ?? meta.matches.length} + {#if meta.showLineNumbers} +  (with line numbers) + {/if} +
+ {:else} +
No matches
+
+ Total matches: {meta?.totalMatches ?? 0} +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockReadFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockReadFile.svelte new file mode 100644 index 000000000000..a99ff9ceedcd --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockReadFile.svelte @@ -0,0 +1,44 @@ + + + + {#snippet titleSnippet()} + Read file + {readFileMeta?.fileName} + {#if readFileMeta?.lineRange} +  (lines {readFileMeta.lineRange.start}-{readFileMeta.lineRange.end}) + {/if} + {/snippet} + + {#snippet children(_meta, _ctx)} + {#if section.toolResult} + + {:else} +
+ Waiting for file content... +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockRunJavascript.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockRunJavascript.svelte new file mode 100644 index 000000000000..707d83d7377e --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockRunJavascript.svelte @@ -0,0 +1,69 @@ + + + + {#snippet children(meta, ctx)} + {#if ctx.isPending} +
Running...
+ {:else if meta?.errorMessage} +
+ + {meta.errorMessage} +
+
+ +
+ {:else if meta} + +
+ + Console + {#if meta.timeoutMs != null} + · timeout {meta.timeoutMs} ms + {/if} +
+ {#if section.toolResult} +
+ +
+ {:else} +
No output
+ {/if} + {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockSearchResults.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockSearchResults.svelte new file mode 100644 index 000000000000..e4b4adf151d3 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockSearchResults.svelte @@ -0,0 +1,167 @@ + + +{#snippet pill(result: SearchResult)} + {@const faviconUrl = faviconForUrl(result.url)} + {@const safeUrl = sanitizeExternalUrl(result.url)} + {@const showHoverCard = safeUrl !== null && hasDetails(result)} + {#if safeUrl} + + + {#if faviconUrl} + + {:else} + + {/if} + {result.title} + + {#if showHoverCard} + {@const publishDate = formatPublishDate(result.published)} + {@const host = hostFor(safeUrl)} + +
+ {result.title} + {#if publishDate || result.author} +
+ {#if publishDate} + {publishDate} + {/if} + {#if publishDate && result.author} + · + {/if} + {#if result.author} + {result.author} + {/if} +
+ {/if} + {#if result.highlights} +

+ {result.highlights} +

+ {/if} + {#if host} +
{host}
+ {/if} +
+ + {/if} + + {/if} +{/snippet} + + + {#if results.length > 0} +
+ {#each results as result (result.url)} + {@render pill(result)} + {/each} +
+ {:else if showSpinner} +
+ + Searching... +
+ {:else} +
No results
+ {/if} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte new file mode 100644 index 000000000000..eda067662305 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte @@ -0,0 +1,55 @@ + + + + {#snippet titleSnippet()} + Write file + {writeFileMeta?.filePath} + {#if writeFileMeta?.errorMessage} + (failed) + {/if} + {/snippet} + + {#snippet children(meta, ctx)} + {#if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta} + +
+ {#if meta.resultMessage} + {meta.resultMessage}{meta.bytesWritten != null ? RESULT_STAT_SEPARATOR : ''}{/if} + {#if meta.bytesWritten != null} + {meta.bytesWritten} + bytes + {/if} +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ToolCallBlock.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ToolCallBlock.svelte new file mode 100644 index 000000000000..a17a74e16126 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ToolCallBlock.svelte @@ -0,0 +1,129 @@ + + + + {@render children(meta, { + isStreaming, + isPending, + isStreamingCall, + isCodeStreaming + })} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts new file mode 100644 index 000000000000..6114f17b5bde --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts @@ -0,0 +1,49 @@ +// Helpers shared by the per-tool meta parsers under +// `src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/`. +// Each tool needs the same first three steps (tool-name check, +// args-present check, JSON parse) - keeping them here lets each parser +// stay focused on its own format quirks. + +import { BuiltInTool } from '$lib/enums'; +import { parsePartialJsonArgs } from '$lib/utils/parse-partial-json-args'; +import type { AgenticSection } from '$lib/utils/agentic'; + +/** + * Strict (final-state) JSON parser for a tool-args blob. Mirrors the + * behaviour the per-tool components used before extraction: an + * invalid JSON blob, a JSON array, or a JSON primitive all map to + * `null` so callers don't have to guard against surprise shapes. + */ +function parseFinalToolArgs(blob: string): Record | null { + try { + const parsed: unknown = JSON.parse(blob); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + return parsed as Record; + } + return null; + } catch { + return null; + } +} + +/** + * Parse a section's toolArgs against an expected tool name. Returns + * `null` when: + * - the section's toolName doesn't match (component isn't for this + * tool); + * - the section has no args yet (call hasn't started streaming); + * - or the args blob can't be parsed. + * + * Pass `{ partial: true }` for tools that need to render incrementally + * as each token lands (read_file, edit_file, write_file). + */ +export function parseToolArgs( + expected: BuiltInTool, + section: AgenticSection, + options: { partial?: boolean } = {} +): Record | null { + if (section.toolName !== expected || !section.toolArgs) return null; + return options.partial + ? parsePartialJsonArgs(section.toolArgs) + : parseFinalToolArgs(section.toolArgs); +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts new file mode 100644 index 000000000000..4bff25bb54cd --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts @@ -0,0 +1,71 @@ +// Meta parser for `edit_file` tool calls. Reads the file path and the +// array of edits from the streamed args (partial JSON for incremental +// rendering), plus the result blob for `result` / `edits_applied` / +// `error` fields. + +import { BuiltInTool } from '$lib/enums'; +import { FILE_PATH_SEPARATOR_REGEX } from '$lib/constants'; +import { tryParseToolResultObject, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type EditFileEdit = { + oldText: string; + newText: string; +}; + +export type EditFileMeta = { + fileName: string; + filePath: string; + edits: EditFileEdit[]; + resultMessage?: string; + editsApplied?: number; + errorMessage?: string; +}; + +export function parseEditFileMeta(section: AgenticSection): EditFileMeta | null { + const args = parseToolArgs(BuiltInTool.EDIT_FILE, section, { partial: true }); + if (!args) return null; + + const rawPath = args.path ?? args.file_path ?? args.filePath; + if (typeof rawPath !== 'string' || !rawPath) return null; + + const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + + // Filter the streamed edits array strictly: each entry must be an + // object with a non-empty `old_text`. Edits without an old_text + // would diff against empty and render as a full re-write. + const rawEdits = Array.isArray(args.edits) ? args.edits : []; + const edits: EditFileEdit[] = []; + for (const e of rawEdits) { + if (!e || typeof e !== 'object' || Array.isArray(e)) continue; + const obj = e as Record; + const oldText = typeof obj.old_text === 'string' ? obj.old_text : ''; + if (!oldText) continue; + const newText = typeof obj.new_text === 'string' ? obj.new_text : ''; + edits.push({ oldText, newText }); + } + + const resultObj = tryParseToolResultObject(section.toolResult); + let resultMessage: string | undefined; + let editsApplied: number | undefined; + let errorMessage: string | undefined; + if (typeof resultObj?.error === 'string') { + errorMessage = resultObj.error; + } else if (resultObj) { + if (typeof resultObj.result === 'string') { + resultMessage = resultObj.result; + } + if (Number.isFinite(Number(resultObj.edits_applied))) { + editsApplied = Number(resultObj.edits_applied); + } + } + + return { + fileName, + filePath: rawPath, + edits, + resultMessage, + editsApplied, + errorMessage + }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts new file mode 100644 index 000000000000..e8adbd18b06b --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts @@ -0,0 +1,23 @@ +// Meta parser for `exec_shell_command` tool calls. Surfaces the +// command text from args `command` / `cmd` / `shell_command` aliases. +// The exit-status and error parsing live in their own utilities +// (`parse-exec-shell-status.ts` / `parse-exec-shell-error.ts`) - this +// file only deals with what's strictly about *calling* the tool, since +// the error / exit status elide from call-section to result-section. + +import { BuiltInTool } from '$lib/enums'; +import type { AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type ExecShellCommandMeta = { + command: string; +}; + +export function parseExecShellCommandMeta(section: AgenticSection): ExecShellCommandMeta | null { + const args = parseToolArgs(BuiltInTool.EXEC_SHELL_COMMAND, section); + if (!args) return null; + + const commandRaw = args.command ?? args.cmd ?? args.shell_command; + if (typeof commandRaw !== 'string' || !commandRaw) return null; + return { command: commandRaw }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/file-glob-search.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/file-glob-search.ts new file mode 100644 index 000000000000..1ad92b74cf05 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/file-glob-search.ts @@ -0,0 +1,58 @@ +// Meta parser for `file_glob_search` tool calls. Reads the path, +// include pattern, and optional exclude from the args (strict parsing) +// and the matches from the result blob. Like grep_search, the result +// parser keeps the original raw-text fallback for MCP servers that +// emit unparseable output. + +import { BuiltInTool } from '$lib/enums'; +import { splitSearchSummaryList, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type FileGlobSearchMeta = { + path: string; + include: string; + exclude?: string; + matches: string[]; + totalMatches?: number; + errorMessage?: string; +}; + +export function parseFileGlobSearchMeta(section: AgenticSection): FileGlobSearchMeta | null { + const args = parseToolArgs(BuiltInTool.FILE_GLOB_SEARCH, section); + if (!args) return null; + + const path = typeof args.path === 'string' ? args.path : ''; + const include = typeof args.include === 'string' && args.include ? args.include : '**'; + const exclude = typeof args.exclude === 'string' && args.exclude ? args.exclude : undefined; + if (!path) return null; + + let matches: string[] = []; + let totalMatches: number | undefined; + let errorMessage: string | undefined; + + const toolResultString = section.toolResult; + if (toolResultString) { + try { + const parsed: unknown = JSON.parse(toolResultString); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + const obj = parsed as Record; + if (typeof obj.error === 'string') { + errorMessage = obj.error; + } else if (typeof obj.plain_text_response === 'string') { + const split = splitSearchSummaryList(obj.plain_text_response, (total) => { + totalMatches = total; + }); + matches = split.lines; + } + } + } catch { + // See grep-search.ts: same fallback used there. + const split = splitSearchSummaryList(toolResultString, (total) => { + totalMatches = total; + }); + matches = split.lines; + } + } + + return { path, include, exclude, matches, totalMatches, errorMessage }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/grep-search.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/grep-search.ts new file mode 100644 index 000000000000..0e606e193c68 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/grep-search.ts @@ -0,0 +1,108 @@ +// Meta parser for `grep_search` tool calls. Reads the path/pattern +// triplet from args (strict parsing - we wait for the args to +// complete) and the matches from the result blob. The result parser +// keeps the original "scan result as raw text on JSON.parse failure" +// fallback so MCP servers that return unparseable output still get +// surfaced. + +import { BuiltInTool } from '$lib/enums'; +import { splitSearchSummaryList, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type GrepSearchMatch = { + file: string; + line?: number; + content: string; +}; + +export type GrepSearchMeta = { + path: string; + pattern: string; + include: string; + exclude?: string; + showLineNumbers: boolean; + matches: GrepSearchMatch[]; + totalMatches?: number; + errorMessage?: string; +}; + +export function parseGrepSearchMeta(section: AgenticSection): GrepSearchMeta | null { + const args = parseToolArgs(BuiltInTool.GREP_SEARCH, section); + if (!args) return null; + + const path = typeof args.path === 'string' ? args.path : ''; + const pattern = typeof args.pattern === 'string' ? args.pattern : ''; + if (!path || !pattern) return null; + + const include = typeof args.include === 'string' && args.include ? args.include : '**'; + const exclude = typeof args.exclude === 'string' && args.exclude ? args.exclude : undefined; + const showLineNumbers = args.return_line_numbers === true; + + let matches: GrepSearchMatch[] = []; + let totalMatches: number | undefined; + let errorMessage: string | undefined; + + const toolResultString = section.toolResult; + if (toolResultString) { + try { + const parsed: unknown = JSON.parse(toolResultString); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + const obj = parsed as Record; + if (typeof obj.error === 'string') { + errorMessage = obj.error; + } else if (typeof obj.plain_text_response === 'string') { + const split = splitSearchSummaryList(obj.plain_text_response, (total) => { + totalMatches = total; + }); + matches = split.lines.map((line) => parseGrepLine(line, showLineNumbers)); + } + } + } catch { + // Result wasn't JSON: keep behaviour for MCP servers that + // emit raw text and treat each line as a `:` + // (or `::`) match. + const split = splitSearchSummaryList(toolResultString, (total) => { + totalMatches = total; + }); + matches = split.lines.map((line) => parseGrepLine(line, showLineNumbers)); + } + } + + return { + path, + pattern, + include, + exclude, + showLineNumbers, + matches, + totalMatches, + errorMessage + }; +} + +function parseGrepLine(line: string, showLineNumbers: boolean): GrepSearchMatch { + // Server output: + // : when return_line_numbers=false + // :: when return_line_numbers=true + const firstColon = line.indexOf(':'); + if (firstColon === -1) { + return { file: line, content: '' }; + } + const file = line.slice(0, firstColon); + const tail = line.slice(firstColon + 1); + + if (!showLineNumbers) { + return { file, content: tail }; + } + + const secondColon = tail.indexOf(':'); + if (secondColon === -1) { + return { file, content: tail }; + } + const lineNum = parseInt(tail.slice(0, secondColon), 10); + return { + file, + line: Number.isFinite(lineNum) ? lineNum : undefined, + content: tail.slice(secondColon + 1) + }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/read-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/read-file.ts new file mode 100644 index 000000000000..d37dcf5010ea --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/read-file.ts @@ -0,0 +1,52 @@ +// Meta parser for `read_file` tool calls. Reads the file path and an +// optional line range (either `start_line`+`end_line` or +// `start_line`+`line_count`). Args are parsed partially so a header +// can render incrementally as the file path streams in. + +import { BuiltInTool } from '$lib/enums'; +import { + DEFAULT_LANGUAGE, + FILE_PATH_SEPARATOR_REGEX, + TEXT_LANGUAGE_PREFIX_REGEX +} from '$lib/constants'; +import { getFileTypeByExtension, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type ReadFileMeta = { + fileName: string; + lineRange: { start: number; end: number } | null; + language: string; +}; + +export function parseReadFileMeta(section: AgenticSection): ReadFileMeta | null { + const args = parseToolArgs(BuiltInTool.READ_FILE, section, { partial: true }); + if (!args) return null; + + const rawPath = args.path ?? args.file_path ?? args.filePath; + if (typeof rawPath !== 'string' || !rawPath) return null; + + const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + + // Models emit range arguments under several aliases. Accept all to + // stay forgiving across prompt variations. + const startRaw = args.start_line ?? args.line_start ?? args.startLine ?? args.from_line; + const endRaw = args.end_line ?? args.line_end ?? args.endLine ?? args.to_line; + const countRaw = args.line_count ?? args.count ?? args.num_lines; + + let lineRange: { start: number; end: number } | null = null; + const sNum = Number(startRaw); + const eNum = Number(endRaw); + if (startRaw != null && endRaw != null && Number.isFinite(sNum) && Number.isFinite(eNum)) { + lineRange = { start: sNum, end: eNum }; + } else if (startRaw != null && countRaw != null) { + const cNum = Number(countRaw); + if (Number.isFinite(sNum) && Number.isFinite(cNum)) { + lineRange = { start: sNum, end: sNum + cNum - 1 }; + } + } + + const fileType = getFileTypeByExtension(fileName); + const language = fileType ? fileType.replace(TEXT_LANGUAGE_PREFIX_REGEX, '') : DEFAULT_LANGUAGE; + + return { fileName, lineRange, language }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/run-javascript.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/run-javascript.ts new file mode 100644 index 000000000000..9bcba8f03cc4 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/run-javascript.ts @@ -0,0 +1,56 @@ +// Meta parser for `run_javascript` tool calls. Reads the JS code and +// optional timeout from args (strict parsing) and surfaces any error +// from the result blob. SandboxService.formatReply emits a JSON object +// containing an `error` field on failure, but a partial/non-JSON +// failure renders as a flat line beginning with `Error:`. Both shapes +// are handled. + +import { BuiltInTool } from '$lib/enums'; +import type { AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type RunJavascriptMeta = { + code: string; + timeoutMs?: number; + errorMessage?: string; +}; + +export function parseRunJavascriptMeta(section: AgenticSection): RunJavascriptMeta | null { + const args = parseToolArgs(BuiltInTool.RUN_JAVASCRIPT, section); + if (!args) return null; + + const code = typeof args.code === 'string' ? args.code : ''; + if (!code) return null; + + const timeoutRaw = Number(args.timeout_ms); + const timeoutMs = Number.isFinite(timeoutRaw) && timeoutRaw > 0 ? timeoutRaw : undefined; + + let errorMessage: string | undefined; + const toolResultString = section.toolResult; + if (toolResultString) { + // Branches matter here: a JSON object can carry `error`, but a + // JSON array always represents successful output (sandbox returns + // the array of values). Only when the result isn't a JSON object + // do we scan raw lines for the `Error:` prefix. + let parsedObject: Record | null = null; + try { + const parsed: unknown = JSON.parse(toolResultString); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + parsedObject = parsed as Record; + } + } catch { + parsedObject = null; + } + if (typeof parsedObject?.error === 'string') { + errorMessage = parsedObject.error; + } else if (!parsedObject) { + const errorLine = toolResultString + .split('\n') + .map((line) => line.trim()) + .find((line) => line.startsWith('Error:')); + if (errorLine) errorMessage = errorLine.slice('Error:'.length).trim(); + } + } + + return { code, timeoutMs, errorMessage }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts new file mode 100644 index 000000000000..95edc3d95eaf --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts @@ -0,0 +1,54 @@ +// Meta parser for `write_file` tool calls. Reads the path/content from +// the streamed args (partial JSON so we can render before the call +// finishes) and surfaces `bytes`, `result`, and `error` from the +// result blob. + +import { BuiltInTool } from '$lib/enums'; +import { + DEFAULT_LANGUAGE, + FILE_PATH_SEPARATOR_REGEX, + TEXT_LANGUAGE_PREFIX_REGEX +} from '$lib/constants'; +import { getFileTypeByExtension, tryParseToolResultObject, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type WriteFileMeta = { + fileName: string; + filePath: string; + language: string; + content: string; + bytesWritten?: number; + resultMessage?: string; + errorMessage?: string; +}; + +export function parseWriteFileMeta(section: AgenticSection): WriteFileMeta | null { + const args = parseToolArgs(BuiltInTool.WRITE_FILE, section, { partial: true }); + if (!args) return null; + + // Tool contracts drifted over time: some models emit `path`, + // others `file_path` / `filePath`. Accept all three. + const rawPath = args.path ?? args.file_path ?? args.filePath; + if (typeof rawPath !== 'string' || !rawPath) return null; + + const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + const content = typeof args.content === 'string' ? args.content : ''; + const language = + getFileTypeByExtension(rawPath)?.replace(TEXT_LANGUAGE_PREFIX_REGEX, '') ?? DEFAULT_LANGUAGE; + + const resultObj = tryParseToolResultObject(section.toolResult); + const bytesWritten = + resultObj && Number.isFinite(Number(resultObj.bytes)) ? Number(resultObj.bytes) : undefined; + const resultMessage = typeof resultObj?.result === 'string' ? resultObj.result : undefined; + const errorMessage = typeof resultObj?.error === 'string' ? resultObj.error : undefined; + + return { + fileName, + filePath: rawPath, + language, + content, + bytesWritten, + resultMessage, + errorMessage + }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte index 01fc9d365505..04e6715bf058 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte @@ -65,7 +65,7 @@ > {#if renderMarkdown && currentConfig.renderUserContentAsMarkdown}
- +
{:else} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte index 58b3a42e07ac..1cc79fe6bca4 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte @@ -1,6 +1,5 @@
+ import { ICON_CLASS_DEFAULT } from '$lib/constants/css-classes'; import type { Snippet, Component } from 'svelte'; interface Props { @@ -12,7 +13,7 @@
- + {@render message()} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte index f0d03f547862..751d13756271 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte @@ -1,40 +1,29 @@ -{#snippet renderSection(section: (typeof sectionsParsed)[number], index: number)} +{#snippet renderSection(section: AgenticSection, index: number)} {#if section.type === AgenticSectionType.TEXT}
- {:else if section.type === AgenticSectionType.TOOL_CALL_STREAMING} - {@const streamingIcon = isStreaming ? Loader2 : Loader2} - {@const streamingIconClass = isStreaming ? 'h-4 w-4 animate-spin' : 'h-4 w-4'} - - toggleExpanded(index, section)} - > -
-
- Arguments: - - {#if isStreaming} - - {/if} -
- {#if section.toolArgs} - - {:else if isStreaming} -
- Receiving arguments... -
- {:else} -
- Response was truncated -
- {/if} -
- - {:else if section.type === AgenticSectionType.TOOL_CALL || section.type === AgenticSectionType.TOOL_CALL_PENDING} - {@const isPending = section.type === AgenticSectionType.TOOL_CALL_PENDING} - {@const toolIcon = isPending ? Loader2 : Wrench} - {@const toolIconClass = isPending ? 'h-4 w-4 animate-spin' : 'h-4 w-4'} - - toggleExpanded(index, section)} - > - {#if section.toolArgs && section.toolArgs !== '{}'} -
-
Arguments:
- - -
- {/if} - -
-
- Result: - - {#if isPending} - - {/if} -
- {#if isPending} -
- Waiting for result... -
- {:else if section.toolResult} -
- {#each section.parsedLines as line, i (i)} -
- {line.text} -
- {#if line.image} - {line.image.name} - {/if} - {/each} -
- {:else} -
No output
- {/if} -
- - {:else if section.type === AgenticSectionType.REASONING} - {@const reasoningSubtitle = section.wasInterrupted - ? hasReasoningError - ? 'Error' - : 'Cancelled' - : isStreaming - ? '' - : undefined} - - toggleExpanded(index, section)} - > -
- {#if renderThinkingAsMarkdown} - - {:else} -
- {section.content} -
- {/if} -
- - {:else if section.type === AgenticSectionType.REASONING_PENDING} - {@const reasoningTitle = isStreaming ? 'Reasoning...' : 'Reasoning'} - {@const reasoningSubtitle = isStreaming ? '' : hasReasoningError ? 'Error' : 'Cancelled'} - - + {:else if section.type === AgenticSectionType.TOOL_CALL || section.type === AgenticSectionType.TOOL_CALL_PENDING || section.type === AgenticSectionType.TOOL_CALL_STREAMING} + toggleExpanded(index, section)} - > -
- {#if renderThinkingAsMarkdown} - - {:else} -
- {section.content} -
- {/if} -
- + /> {/if} {/snippet} -
+
{#if turnGroups.length > 1} {#each turnGroups as turn, turnIndex (turnIndex)} {@const turnStats = message?.timings?.agentic?.perTurn?.[turnIndex]} -
+
{#each turn.sections as section, sIdx (turn.flatIndices[sIdx])} {@render renderSection(section, turn.flatIndices[sIdx])} {/each} - {#if turnStats && showMessageStats} -
+ {#if turnStats && showAgenticTurnStats} +
{/each} {:else} - {#each sectionsParsed as section, index (index)} + {#each sections as section, index (index)} {@render renderSection(section, index)} {/each} {/if} @@ -404,7 +256,6 @@ flex-direction: column; width: 100%; max-width: 48rem; - gap: 1rem; } .agentic-content > :global(*), diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageReasoningBlock.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageReasoningBlock.svelte new file mode 100644 index 000000000000..833cae5db529 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageReasoningBlock.svelte @@ -0,0 +1,151 @@ + + + +
+ {#if renderThinkingAsMarkdown} + + {:else} +
+ {section.content} +
+ {/if} +
+ + + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte index dce0edd03138..2b5ccb978e16 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte @@ -1,6 +1,4 @@ -
+
{#each displayMessages as { message, toolMessages, isLastAssistantMessage, isLastUserMessage, nextAssistantMessage, siblingInfo } (message.id)} { + if (autoScroll.userScrolledUp) return; + if (activeConversation()?.id !== id) return; + autoScroll.scrollToBottom(); + const height = container.scrollHeight; + stableFrames = height === lastHeight ? stableFrames + 1 : 0; + lastHeight = height; + if (stableFrames >= LANDING_STABLE_FRAMES) return; + if (performance.now() - started > LANDING_SETTLE_MAX_MS) return; + requestAnimationFrame(settle); + }; + requestAnimationFrame(settle); + } + function handleSendLikeScroll() { if (!isMobile.current) { autoScroll.enable(); @@ -246,6 +281,7 @@ {#if !isEmpty} { handleSendLikeScroll(); }} diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte index c470ac729690..dca24afd440f 100644 --- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte @@ -1,4 +1,5 @@ {#if hasError} -
+
{#if isLoadingModel} - + {:else} - + {/if} diff --git a/tools/ui/src/lib/components/app/chat/index.ts b/tools/ui/src/lib/components/app/chat/index.ts index 4f826841e4b7..cd06ec036619 100644 --- a/tools/ui/src/lib/components/app/chat/index.ts +++ b/tools/ui/src/lib/components/app/chat/index.ts @@ -571,6 +571,10 @@ export { default as ChatMessageMcpPromptContent } from './ChatMessages/ChatMessa * Handles streaming state with real-time content updates. */ export { default as ChatMessageAssistant } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte'; +export { default as ChatMessageAssistantModel } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte'; +export { default as ChatMessageAssistantProcessingInfo } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte'; +export { default as ChatMessageAssistantRawOutput } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte'; +export { default as ChatMessageAssistantStatistics } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte'; /** * Inline message editing form. Provides textarea for editing message content with diff --git a/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte b/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte index 3875b449a186..ad703226192a 100644 --- a/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte +++ b/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte @@ -1,12 +1,8 @@ @@ -76,59 +45,59 @@ open = value; onToggle?.(); }} - class="{className} my-0!" + class={cn('group/collapsible', 'my-0!', className)} > - - -
-
- {#if IconComponent} - - {/if} - - {title} - - {#if subtitle} - {subtitle} - {/if} -
- - {#if displayedPreview && !showThoughtInProgress} -
-
- {displayedPreview} -
- {#if displayedOverflow > 0} - {displayedOverflow}+ chars - {/if} -
+ +
+ {#if iconUrl} + + {:else if IconComponent} + + {/if} + + + {#if titleSnippet} + {@render titleSnippet()} + {:else} + {title} {/if} + + + {#if subtitle} + {subtitle} + {/if} +
+ + + + Toggle content + + + + + {#if open} +
+
+ {@render children()} +
- -
- - - Toggle content -
- - - -
- {@render children()} -
- - + {/if} + diff --git a/tools/ui/src/lib/components/app/content/CollapsibleTerminalBlock.svelte b/tools/ui/src/lib/components/app/content/CollapsibleTerminalBlock.svelte new file mode 100644 index 000000000000..5cbe003bd7ad --- /dev/null +++ b/tools/ui/src/lib/components/app/content/CollapsibleTerminalBlock.svelte @@ -0,0 +1,101 @@ + + + { + open = value; + onToggle?.(); + }} + class={cn('group/collapsible', 'overflow-hidden rounded-md', className)} + style="background: var(--code-background); border: 1px solid color-mix(in oklch, var(--border) 30%, transparent);" +> + +
+ {#if iconUrl} + + {:else if IconComponent} + + {/if} + + + {#if titleSnippet} + {@render titleSnippet()} + {:else} + {title} + {/if} + + + {#if subtitle} + {subtitle} + {/if} +
+ + + + Toggle content + + + + + {#if open} +
+ {@render children()} +
+ {/if} + + diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte index 8ac7f94483a2..fc7e314122f7 100644 --- a/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte +++ b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte @@ -78,7 +78,6 @@ import { createAutoScrollController } from '$lib/hooks/use-auto-scroll.svelte'; import type { DatabaseMessageExtra } from '$lib/types/database'; import { config } from '$lib/stores/settings.svelte'; - import { fadeInView } from '$lib/actions/fade-in-view.svelte'; interface Props { attachments?: DatabaseMessageExtra[]; @@ -108,6 +107,15 @@ return null; }); const liveSvgHtml = $derived(streamingSvgCode !== null ? sanitizeSvg(streamingSvgCode) : ''); + + // Derived rather than called inline in the template so it only recomputes when + // the block actually changes. Auto-detection is disabled while streaming: it + // costs ~38ms a call and re-guesses the language on every chunk. + const streamingCodeHtml = $derived( + incompleteCodeBlock + ? highlightCode(incompleteCodeBlock.code, incompleteCodeBlock.language || 'text', false) + : '' + ); let previewDialogOpen = $state(false); let previewCode = $state(''); let previewLanguage = $state('text'); @@ -828,7 +836,7 @@ : ''}" > {#each renderedBlocks as block (block.id)} -
+
{@html block.html}
{/each} @@ -904,10 +912,7 @@ > 
{@html highlightCode(
-								incompleteCodeBlock.code,
-								incompleteCodeBlock.language || 'text'
-							)}{@html streamingCodeHtml}
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css b/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css index b0e04ca6209a..41813f4fda76 100644 --- a/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css +++ b/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css @@ -19,8 +19,16 @@ line-height: 1.75; } +.markdown-content :global(.markdown-block:first-child p:first-child) { + margin-block-start: 0; +} + +.markdown-content :global(.markdown-block:last-child p:last-child) { + margin-block-end: 0; +} + .markdown-content :global(:is(h1, h2, h3, h4, h5, h6):first-child) { - margin-top: 0; + margin-top: 0.5rem; } /* Headers with consistent spacing */ diff --git a/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte b/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte index bb3185f40eda..39540e7a8cd5 100644 --- a/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte +++ b/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte @@ -1,4 +1,5 @@
- + 
{@html highlightedHtml}
diff --git a/tools/ui/src/lib/components/app/content/index.ts b/tools/ui/src/lib/components/app/content/index.ts index 5d2884bb214c..5cfdd1b9c1e0 100644 --- a/tools/ui/src/lib/components/app/content/index.ts +++ b/tools/ui/src/lib/components/app/content/index.ts @@ -68,7 +68,6 @@ export { default as SyntaxHighlightedCode } from './SyntaxHighlightedCode.svelte * ```svelte * @@ -78,6 +77,22 @@ export { default as SyntaxHighlightedCode } from './SyntaxHighlightedCode.svelte */ export { default as CollapsibleContentBlock } from './CollapsibleContentBlock.svelte'; +/** + * **CollapsibleTerminalBlock** - Expandable content card with a terminal-style frame + * + * Same shape as CollapsibleContentBlock, but with a `code-background` + * fill, subtle border, and tightened padding suited for shell command + * output and similar dense / monospace content. + * + * @example + * ```svelte + * + * 
{output}
+ * + * ``` + */ +export { default as CollapsibleTerminalBlock } from './CollapsibleTerminalBlock.svelte'; + /** * **MermaidPreview** - Interactive Mermaid diagram viewer * diff --git a/tools/ui/src/lib/components/app/dialogs/DialogConversationRename.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConversationRename.svelte new file mode 100644 index 000000000000..d85340f3fb6a --- /dev/null +++ b/tools/ui/src/lib/components/app/dialogs/DialogConversationRename.svelte @@ -0,0 +1,85 @@ + + + + + + + + Rename conversation + + + Choose a new title for this conversation. + + +
+ + + +
+ + + Cancel + + + + + diff --git a/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte index 7373250850cc..5f5b2f4ab315 100644 --- a/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte +++ b/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte @@ -37,9 +37,9 @@ - + - + Select Conversations to {mode === 'export' ? 'Export' : 'Import'} @@ -58,6 +58,7 @@ - import * as AlertDialog from '$lib/components/ui/alert-dialog'; - import { Button } from '$lib/components/ui/button'; - - interface Props { - open: boolean; - currentTitle: string; - newTitle: string; - onConfirm: () => void; - onCancel: () => void; - } - - let { open = $bindable(), currentTitle, newTitle, onConfirm, onCancel }: Props = $props(); - - - - - - Update Conversation Title? - - - Do you want to update the conversation title to match the first message content? - - - -
-
-

Current title:

- -

{currentTitle}

-
- -
-

New title would be:

- -

{newTitle}

-
-
- - - - - - - - diff --git a/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte b/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte index c112bde9f682..fe36dce56e52 100644 --- a/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte +++ b/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte @@ -68,6 +68,7 @@ Cancel + + import { ICON_CLASS_DEFAULT } from '$lib/constants/css-classes'; import { FolderOpen, Plus, Loader2, Braces } from '@lucide/svelte'; import { toast } from 'svelte-sonner'; import * as Dialog from '$lib/components/ui/dialog'; @@ -289,7 +290,7 @@ {#if selectedTemplate && !templatePreviewContent}
- + {selectedTemplate.title || selectedTemplate.name} @@ -371,9 +372,9 @@ {#if hasTemplateResult} +
+ +
+ {#each recommendationsToShow as recommendation (recommendation.id)} + handleRecommendationClick(recommendation.id)} + selected={selectedRecommendationId === recommendation.id} + dimmed={hasSelection && selectedRecommendationId !== recommendation.id} + /> + {/each} +
+
+ {/if} +
(newServerUrl = v)} @@ -83,6 +282,8 @@ onUseProxyChange={(v) => (newServerUseProxy = v)} urlError={newServerUrl ? newServerUrlError : null} id="new-server" + bind:wantsAuthorization={newServerWantsAuthorization} + required={authRequired} />
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte b/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte index a6c20291fa0a..89d23cd4b292 100644 --- a/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte +++ b/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte @@ -1,4 +1,5 @@ + + +
+ {#if activeIconUrl} + + {/if} + +

{server.name}

+
+ +

{server.description}

+ diff --git a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte index 8ed4ee8b8023..19778f95b006 100644 --- a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte +++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte @@ -7,13 +7,23 @@ serverId: string; serverUrl: string; serverUseProxy?: boolean; - onSave: (url: string, headers: string, useProxy: boolean) => void; + /** Current automatic label, prefilled so the user can customize it. */ + serverLabel?: string; + onSave: (url: string, headers: string, useProxy: boolean, name?: string) => void; onCancel: () => void; } - let { serverId, serverUrl, serverUseProxy = false, onSave, onCancel }: Props = $props(); + let { + serverId, + serverUrl, + serverUseProxy = false, + serverLabel = '', + onSave, + onCancel + }: Props = $props(); let editUrl = $derived(serverUrl); + let editName = $derived(serverLabel); let editHeaders = $state(''); let editUseProxy = $derived(serverUseProxy); @@ -34,7 +44,12 @@ function handleSave() { if (!canSave) return; - onSave(editUrl.trim(), editHeaders.trim(), editUseProxy); + + // An unchanged prefill keeps following the automatic label; only an + // actual edit becomes a persisted custom display name. + const name = editName.trim() !== serverLabel.trim() ? editName.trim() : undefined; + + onSave(editUrl.trim(), editHeaders.trim(), editUseProxy, name); } function handleSubmit(event: SubmitEvent) { @@ -42,10 +57,11 @@ handleSave(); } - export function setInitialValues(url: string, headers: string, useProxy: boolean) { + export function setInitialValues(url: string, headers: string, useProxy: boolean, name = '') { editUrl = url; editHeaders = headers; editUseProxy = useProxy; + editName = name; } @@ -55,6 +71,8 @@ (editName = v)} headers={editHeaders} useProxy={editUseProxy} onUrlChange={(v) => (editUrl = v)} diff --git a/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte index 7f05d5fef31f..2b8e1226bab8 100644 --- a/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte +++ b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte @@ -5,30 +5,60 @@ import type { KeyValuePair } from '$lib/types'; import { parseHeadersToArray, serializeHeaders } from '$lib/utils'; import { UrlProtocol } from '$lib/enums'; - import { MCP_SERVER_URL_PLACEHOLDER } from '$lib/constants'; + import { + AUTHORIZATION_HEADER, + BEARER_PREFIX, + CLI_FLAGS, + MCP_SERVER_URL_PLACEHOLDER, + REDACTED_HEADERS + } from '$lib/constants'; import { mcpStore } from '$lib/stores/mcp.svelte'; - import { CLI_FLAGS } from '$lib/constants'; interface Props { url: string; headers: string; + name?: string; + onNameChange?: (name: string) => void; + /** Shown in the empty display name field, e.g. the current automatic label. */ + namePlaceholder?: string; useProxy?: boolean; onUrlChange: (url: string) => void; onHeadersChange: (headers: string) => void; onUseProxyChange?: (useProxy: boolean) => void; urlError?: string | null; id?: string; + /** + * "Wants Authorization" is the user's *intent* to add a Bearer token + * (separate from `hasAuthorization` which reflects what's already in + * the headers). Bindable so a parent - e.g. the recommendation cards + * on the "Add New Server" dialog - can flip the switch on when the + * picked server ships a `needsAuthorization: true` flag. + */ + wantsAuthorization?: boolean; + /** + * Marks the "Authorization" field as required. Locks the toggle so the + * user can't dismiss it, and visually marks the field with a red + * asterisk. The parent is expected to gate its submit affordance on + * the bearer token actually being filled. Used by the "Add New Server" + * dialog for recommendations whose `needsAuthorization` flag is true. + */ + required?: boolean; } let { url, headers, + name = '', + onNameChange, + namePlaceholder = 'Name reported by the server', useProxy = false, onUrlChange, onHeadersChange, onUseProxyChange, urlError = null, - id = 'server' + id = 'server', + wantsAuthorization = $bindable(false), + required = false }: Props = $props(); let isWebSocket = $derived( @@ -38,14 +68,11 @@ let headerPairs = $derived(parseHeadersToArray(headers)); - const AUTHORIZATION_HEADER = 'Authorization'; - const BEARER_PREFIX = 'Bearer '; - // Heuristic: this dedicated UI only owns Authorization headers that already // carry a Bearer scheme. Anything else (e.g. Basic, raw tokens) stays in the // KV section so the user can still edit those values verbatim. const matchesAuthorizationKey = (key: string): boolean => - key.trim().toLowerCase() === AUTHORIZATION_HEADER.toLowerCase(); + REDACTED_HEADERS.has(key.trim().toLowerCase()); const isBearerScheme = (value: string): boolean => value.trim().toLowerCase().startsWith(BEARER_PREFIX.toLowerCase()); @@ -55,8 +82,6 @@ let hasAuthorization = $derived(headerPairs.some(ownedByBearerUi)); - let wantsAuthorization = $state(false); - let showAuthorization = $derived(hasAuthorization || wantsAuthorization); let urlInput: HTMLInputElement | null = $state(null); @@ -119,7 +144,7 @@
-
-
- + @@ -139,6 +115,7 @@ + {#if filteredConversations.length === 0} @@ -152,23 +129,28 @@ {:else} {#each filteredConversations as conv (conv.id)} + {@const checked = selectedIds.has(conv.id)} toggleConversation(conv.id, event.shiftKey)} + class="cursor-pointer border-b transition-colors hover:bg-muted/50 {checked + ? 'bg-muted/75' + : ''}" + data-conversation-row={conv.id} + onmousedown={(event) => marquee.rowMouseDown(conv.id, event)} + onclick={(event) => marquee.rowClick(conv.id, event.shiftKey)} > diff --git a/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte b/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte index a04f3956f8a5..d5665901a1a4 100644 --- a/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte +++ b/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte @@ -1,4 +1,5 @@ {#snippet itemIcon(IconComponent: Component)} - + {/snippet} {#if isSearchModeActive} @@ -118,9 +119,7 @@ : onSearchClick} {@const itemTransition = { duration: ICON_STRIP_TRANSITION_DURATION, - delay: !initialized - ? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER - : 0, + delay: !initialized ? i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER : 0, easing: circIn }} @@ -139,10 +138,8 @@ {@render itemIcon(item.icon)} {#if showIcons} - {item.tooltip}{item.tooltip} {/if} @@ -170,9 +167,7 @@ : onSearchClick} {@const itemTransition = { duration: ICON_STRIP_TRANSITION_DURATION, - delay: !initialized - ? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER - : 0, + delay: !initialized ? i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER : 0, easing: circIn }} @@ -183,7 +178,7 @@ tooltip={item.tooltip} tooltipSide={TooltipSide.RIGHT} size="lg" - iconSize="h-4 w-4" + iconSize={ICON_CLASS_DEFAULT} class="h-9 w-9 rounded-full hover:bg-accent! {isActive ? 'bg-accent text-accent-foreground' : ''}" diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte index b1c2b78f65ea..7204d7fec293 100644 --- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte +++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte @@ -1,4 +1,5 @@ -{#if isSearchModeActive} - -{:else} - {#if pinnedConversations.length > 0} -
-
- +
+ {#if isSearchModeActive} + + {:else} + {#if pinnedConversations.length > 0} +
+
+ - Pinned + Pinned +
-
- -
    - {#each pinnedConversations as { conversation, depth } (conversation.id)} -
  • - -
    • - {/each} -
- {/if} - -
- {#if filteredConversations.length > 0} -
- Recent conversations -
- {/if} -
-
    - {#each unpinnedConversations as { conversation, depth } (conversation.id)} +
      + {#each pinnedConversations as { conversation, depth } (conversation.id)}
      • {/each} - - {#if unpinnedConversations.length === 0} -
    • -

      - {recentEmptyMessage} -

      -
      • - {/if}
    + {/if} + +
    + {#if filteredConversations.length > 0} +
    + Recent conversations +
    + {/if} + +
    +
      + {#each unpinnedConversations as { conversation, depth } (conversation.id)} +
    • + +
      • + {/each} + + {#if unpinnedConversations.length === 0} +
    • +

      + {recentEmptyMessage} +

      +
      • + {/if} +
    +
    -
-{/if} + + {#if isSelectionMode} + + {/if} + {/if} +
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte index 92d8fd0bda88..68d6c214366b 100644 --- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte +++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte @@ -7,10 +7,16 @@ searchQuery: string; filteredConversations: DatabaseConversation[]; currentChatId: string | undefined; + isSelectionMode?: boolean; + selectedIds?: Set; onSelect: (id: string) => void; onEdit: (id: string) => void; onDelete: (id: string) => void; onStop: (id: string) => void; + onToggleSelect?: (id: string) => void; + onEnterSelectionMode?: (id: string) => void; + onSelectionClick?: (id: string, options: { shiftKey: boolean }) => void; + onRowMouseDown?: (id: string, event: MouseEvent) => void; } let { @@ -18,10 +24,16 @@ searchQuery, filteredConversations, currentChatId, + isSelectionMode = false, + selectedIds = new Set(), onSelect, onEdit, onDelete, - onStop + onStop, + onToggleSelect, + onEnterSelectionMode, + onSelectionClick, + onRowMouseDown }: Props = $props(); let tree = $derived(buildConversationTree(filteredConversations)); @@ -56,10 +68,16 @@ }} {depth} isActive={currentChatId === conversation.id} + {isSelectionMode} + isSelected={selectedIds.has(conversation.id)} {onSelect} {onEdit} {onDelete} {onStop} + {onToggleSelect} + {onEnterSelectionMode} + {onSelectionClick} + {onRowMouseDown} /> {/each} diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSelectionBar.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSelectionBar.svelte new file mode 100644 index 000000000000..15412e57b62e --- /dev/null +++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSelectionBar.svelte @@ -0,0 +1,163 @@ + + + + + diff --git a/tools/ui/src/lib/components/app/navigation/index.ts b/tools/ui/src/lib/components/app/navigation/index.ts index e07dde6bc901..ea5ad1794012 100644 --- a/tools/ui/src/lib/components/app/navigation/index.ts +++ b/tools/ui/src/lib/components/app/navigation/index.ts @@ -114,6 +114,36 @@ export { default as SidebarNavigation } from './SidebarNavigation/SidebarNavigat */ export { default as SidebarNavigationConversationItem } from './SidebarNavigation/SidebarNavigationConversationItem.svelte'; +/** + * **SidebarNavigationSelectionBar** - Bulk action toolbar for selection mode + * + * Rendered above the conversation list when the sidebar enters selection mode. + * Hosts a master checkbox (with select-all / clear-all semantics over the + * currently-visible items), a selected-count caption, and bulk actions for + * pin/unpin, export, and delete. Delete uses + * {@link DialogConfirmation} before invoking the bulk store method. + * + * Pure-presentational; all operations are delegated via callbacks so the + * sidebar owns selection state and persistence. + * + * @example + * ```svelte + * + * ``` + */ +export { default as SidebarNavigationSelectionBar } from './SidebarNavigation/SidebarNavigationSelectionBar.svelte'; + /** * **SidebarNavigationConversationList** - Grouped conversation list * diff --git a/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte b/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte index 4da0d1ddfa80..d9c4386e4c10 100644 --- a/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte +++ b/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte @@ -1,4 +1,5 @@ {#each fields as field (field.key)} -
- {#if field.type === SettingsFieldType.INPUT} - {@const currentValue = String(localConfig[field.key] ?? '')} - {@const serverDefault = currentModelParams[field.key]} - {@const isCustomRealTime = (() => { - if (serverDefault == null) return false; - if (currentValue === '') return false; + {#if !field.dependsOn || Boolean(localConfig[field.dependsOn])} +
+ {#if field.type === SettingsFieldType.INPUT} + {@const currentValue = String(localConfig[field.key] ?? '')} + {@const serverDefault = currentModelParams[field.key]} + {@const isCustomRealTime = (() => { + if (serverDefault == null) return false; + if (currentValue === '') return false; - const numericInput = parseFloat(currentValue); - const normalizedInput = !isNaN(numericInput) - ? Math.round(numericInput * 1000000) / 1000000 - : currentValue; - const normalizedDefault = - typeof serverDefault === 'number' - ? Math.round(serverDefault * 1000000) / 1000000 - : serverDefault; + const numericInput = parseFloat(currentValue); + const normalizedInput = !isNaN(numericInput) + ? Math.round(numericInput * 1000000) / 1000000 + : currentValue; + const normalizedDefault = + typeof serverDefault === 'number' + ? Math.round(serverDefault * 1000000) / 1000000 + : serverDefault; - return normalizedInput !== normalizedDefault; - })()} + return normalizedInput !== normalizedDefault; + })()} -
- - {#if isCustomRealTime} - - {/if} -
- -
- { - // Update local config immediately for real-time badge feedback - onConfigChange(field.key, e.currentTarget.value); - }} - placeholder={currentModelParams[field.key] != null - ? `Default: ${normalizeFloatingPoint(currentModelParams[field.key])}` - : ''} - class="w-full {isCustomRealTime ? 'pr-8' : ''}" - /> - {#if isCustomRealTime} - - {/if} -
- {#if field.help || SETTING_CONFIG_INFO[field.key]} -

- {@html field.help || SETTING_CONFIG_INFO[field.key]} -

- {/if} - {:else if field.type === SettingsFieldType.TEXTAREA} - {#if field.label} - - {/if} - -
Messages
{ event.preventDefault(); event.stopPropagation(); - toggleConversation(conv.id, event.shiftKey); + marquee.rowClick(conv.id, event.shiftKey); }} /> -
+
{conv.name || 'Untitled conversation'}