Merge branch 'ggml-org:master' into carbon-fns-sampler

Author: kashif

.devops/s390x.Dockerfile

@@ -4,20 +4,6 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
-ARG NODE_VERSION=24
-
-FROM docker.io/node:$NODE_VERSION AS web
-
-ARG APP_VERSION
-
-WORKDIR /app/tools/ui
-
-COPY tools/ui/package.json tools/ui/package-lock.json ./
-RUN npm ci
-
-COPY tools/ui/ ./
-RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
-
 ### Build Llama.cpp stage
 FROM docker.io/gcc:${GCC_VERSION} AS build
 
@@ -34,8 +20,6 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
 WORKDIR /app
 COPY . .
 
-COPY --from=web /app/tools/ui/dist tools/ui/dist
-
 RUN --mount=type=cache,target=/root/.ccache \
     --mount=type=cache,target=/app/build \
     cmake -S . -B build -G Ninja \

.dockerignore

@@ -11,7 +11,6 @@
 build*/
 
 tools/ui/node_modules/
-tools/ui/dist/
 
 models/*
 

.github/workflows/docker.yml

@@ -58,6 +58,13 @@ jobs:
           git tag ${{ steps.srctag.outputs.name }} || exit 0
           git push origin ${{ steps.srctag.outputs.name }} || exit 0
 
+  build_ui:
+    name: Build UI
+    needs: create_tag
+    uses: ./.github/workflows/ui-build.yml
+    with:
+      hf_ui_version: ${{ needs.create_tag.outputs.source_tag }}
+
   prepare_matrices:
     name: Prepare Docker matrices
     runs-on: ubuntu-24.04
@@ -79,7 +86,7 @@ jobs:
           [
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
-            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x" },
+            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x", "prebuilt_ui": true },
             { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
             { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
             { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.3.0", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
@@ -135,7 +142,7 @@ jobs:
 
   push_to_registry:
     name: Push Docker image to Docker Registry
-    needs: [prepare_matrices, create_tag]
+    needs: [prepare_matrices, create_tag, build_ui]
 
     runs-on: ${{ matrix.config.runs_on }}
     strategy:
@@ -150,6 +157,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ needs.create_tag.outputs.source_tag }}
 
+      - name: Download prebuilt UI
+        if: ${{ matrix.config.prebuilt_ui == true }}
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
+        with:
+          name: ui-build
+          path: tools/ui/dist
+
       - name: Set up QEMU
         if: ${{ contains(matrix.config.platforms, 'linux/amd64') }}
         uses: docker/setup-qemu-action@ce360397dd3f832beb865e1373c09c0e9f86d70a # v4

common/arg.cpp

@@ -17,6 +17,7 @@
 #   define NOMINMAX
 #endif
 #include <windows.h>
+#include <shellapi.h>
 #endif
 
 #define JSON_ASSERT GGML_ASSERT
@@ -302,7 +303,6 @@ static handle_model_result common_params_handle_model(struct common_params_model
 
     if (!model.docker_repo.empty()) {
         model.path = common_docker_resolve_model(model.docker_repo);
-        model.name = model.docker_repo;
     } else if (!model.hf_repo.empty()) {
         // If -m was used with -hf, treat the model "path" as the hf_file to download
         if (model.hf_file.empty() && !model.path.empty()) {
@@ -322,7 +322,6 @@ static handle_model_result common_params_handle_model(struct common_params_model
             throw std::runtime_error("failed to download model from Hugging Face");
         }
 
-        model.name = model.hf_repo;
         model.path = download_result.model_path;
 
         if (!download_result.mmproj_path.empty()) {
@@ -893,7 +892,44 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
     return true;
 }
 
+#ifdef _WIN32
+struct utf8_argv {
+    std::vector<std::string> buf;
+    std::vector<char*> ptrs;
+};
+
+static utf8_argv make_utf8_argv() {
+    utf8_argv out;
+    int wargc = 0;
+    LPWSTR* wargv = CommandLineToArgvW(GetCommandLineW(), &wargc);
+    if (!wargv) return out;
+
+    out.buf.reserve(wargc);
+    for (int i = 0; i < wargc; ++i) {
+        int n = WideCharToMultiByte(CP_UTF8, WC_ERR_INVALID_CHARS, wargv[i], -1, nullptr, 0, nullptr, nullptr);
+        if (n <= 0) { out.buf.emplace_back(); continue; }
+        auto& s = out.buf.emplace_back();
+        s.resize(static_cast<size_t>(n - 1));
+        (void)WideCharToMultiByte(CP_UTF8, 0, wargv[i], -1, s.data(), n, nullptr, nullptr);
+    }
+    LocalFree(wargv);
+
+    out.ptrs.reserve(out.buf.size() + 1);
+    for (auto& s : out.buf) out.ptrs.push_back(s.data());
+    out.ptrs.push_back(nullptr);
+    return out;
+}
+#endif
+
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+#ifdef _WIN32
+    auto utf8 = make_utf8_argv();
+    if (!utf8.ptrs.empty()) {
+        argc = static_cast<int>(utf8.buf.size());
+        argv = utf8.ptrs.data();
+    }
+#endif
+
     auto ctx_arg = common_params_parser_init(params, ex, print_usage);
     const common_params params_org = ctx_arg.params; // the example can modify the default params
 
@@ -2911,15 +2947,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
     add_opt(common_arg(
         {"--api-key-file"}, "FNAME",
-        "path to file containing API keys (default: none)",
+        "path to file containing API keys, one per line; lines starting with a hash are treated as comments (default: none)",
         [](common_params & params, const std::string & value) {
             std::ifstream key_file(value);
             if (!key_file) {
                 throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             std::string key;
             while (std::getline(key_file, key)) {
-                if (!key.empty()) {
+                if (!key.empty() && key[0] != '#') {
                     params.api_keys.push_back(key);
                 }
             }

common/common.cpp

@@ -1074,6 +1074,18 @@ std::vector<common_file_info> fs_list(const std::string & path, bool include_dir
     return files;
 }
 
+std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode) {
+#ifdef _WIN32
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, NULL, 0);
+    if (!wlen) { return std::ifstream(); }
+    std::vector<wchar_t> wfname(wlen);
+    (void)MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, wfname.data(), wlen);
+    return std::ifstream(wfname.data(), mode);
+#else
+    return std::ifstream(fname, mode);
+#endif
+}
+
 //
 // TTY utils
 //

common/common.h

@@ -296,7 +296,16 @@ struct common_params_model {
     std::string hf_repo     = ""; // HF repo                                                // NOLINT
     std::string hf_file     = ""; // HF file                                                // NOLINT
     std::string docker_repo = ""; // Docker repo                                            // NOLINT
-    std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
+
+    std::string get_name() {
+        if (!hf_repo.empty()) {
+            return hf_repo;
+        }
+        if (!docker_repo.empty()) {
+            return docker_repo;
+        }
+        return path;
+    }
 };
 
 // draft-model-based speculative decoding parameters
@@ -843,6 +852,9 @@ struct common_file_info {
 };
 std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
 
+// fs open, also handle UTF8 on Windows
+std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode);
+
 //
 // TTY utils
 //

conversion/bailingmoe.py

@@ -126,7 +126,7 @@ def set_gguf_parameters(self):
         if (rope_dim := hparams.get("head_dim")) is None:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])

conversion/base.py

@@ -1119,15 +1119,21 @@ def __init__(self, *args, **kwargs):
 
         rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
         local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)
+        partial_rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"], optional=True)
+        original_max_position_embeddings = self.find_hparam(["original_max_position_embeddings"], optional=True)
 
-        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
+        # Ensure global params are mirrored in rope_parameters
         if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
             if local_rope_theta is not None:
                 self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
             if "rope_theta" not in self.rope_parameters and rope_theta is not None:
                 self.rope_parameters["rope_theta"] = rope_theta
             if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
                 self.rope_parameters["rope_type"] = rope_type
+            if "partial_rotary_factor" not in self.rope_parameters and partial_rotary_factor is not None:
+                self.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+            if "original_max_position_embeddings" not in self.rope_parameters and original_max_position_embeddings is not None:
+                self.rope_parameters["original_max_position_embeddings"] = original_max_position_embeddings
 
     @classmethod
     def __init_subclass__(cls):

conversion/chatglm.py

@@ -148,7 +148,7 @@ def set_gguf_parameters(self):
             rope_dim = self.hparams["attention_dim"]
         else:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_add_bos_token(False)
         rope_freq = 10000
         if "rope_ratio" in self.hparams:

conversion/deci.py

@@ -161,7 +161,7 @@ def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor