Refactor core architecture and optimize CUDA features (#2)

* exp(#58)

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* refactor(ci): optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* Added MIT LICENSE to this project Quadtrix.cpp

* Refactor Dockerfile to use ARG for CUDA version

* Refactor Dockerfile for backend dependencies

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* Delete .devops/Dockerfile.frontend

* Delete .devops/Dockerfile.dev.frontend

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactor(ui): rewrite ThinkingIndicator to use inline styles and CSS keyframes

* refactor : message bubble layout to use inline styles

* refactor(ui): complete inline-style migration and update auto-scroll implementation

* refactor(ui): complete inline-style migration for MessageAvatar component

* refactor(ui): rewrite EmptyState component using pure inline styles

* refactored(tensor): vectorize element-wise addition and scalar scaling using AVX/SSE

- Added SIMD vectorization support (`__AVX__` and `__SSE__`) for element-wise `add`, `add_inplace`, and `scale` operations.
- Maintained scalar fallback paths for non-vectorized bounds and platforms lacking hardware extensions.
- Explicitly defined rule-of-five constructors (`default` and `noexcept` moves) within the `Tensor` struct layout.
- Optimized vector initialization across the core construct layer via `std::move` and `std::vector::reserve`.

* refactor(main): redesign training loop to log per-step and sample during evaluation

- Replaced the periodic block evaluation layout with standard, per-step logging metrics (`loss`, `ms`, and `tok/s`).
- Shifted initial validation loss calculation out of the iteration cycle to establish a zero-state baseline.
- Restructured token streaming so that generations are triggered conditionally inside the training loop post-evaluation windows.
- Streamlined architecture parameter reporting and consolidated command-line configuration visual prints.

* feat: implement GPT training loop with multi-GPU and memory optimizations

- Add advanced memory footprint optimization using forward-activation recomputation for LayerNorm and GeLU.
- Optimize layer-wise activation buffer layout using a centralized `TensorSpec` registry to support large batch scaling.
- Integrate cuBLASLt matmul fusions, optional cuDNN attention layers, and stochastic rounding options.
- Fall back gracefully to `cudaMallocManaged` under heavy loads to prevent Outlier/OOM crashes.

* Update README.md with new banner for qudtrix.cpp

---------

Co-authored-by: Max <eamon5174@gmail.com>

* chore: clang-format configuration file based on LLVM (#63)

Co-authored-by: Eamon <eamon112009@gmail.com>

* ci: add manual PR checks workflow with slash command support

* ci: add manual PR checks workflow with slash command support

* ci: add manual PR checks workflow with slash command support

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

* feat(cuda): add LayerNorm forward and backward kernel declarations

* refactor(ci): organize workflow into push-triggered QA and manual docker builds

Updated CI workflow to restrict branches for push events and improved input descriptions for image selection and push options.

* Fix formatting and update CI workflow steps

* Enhance CI with macOS binary build and release

Added macOS binary build and release steps to CI workflow.

* feat(docker): add Dockerfile for frontend application

* feat(docker): add Dockerfile for frontend application

* refactor(ci): remove release job from GitHub actions

* ci: add unified release and docker build workflow

* ci: add unified release and docker build workflow

* Refactor macOS build workflow for arm64 architecture

* Update release workflow to remove macOS x64 build

Removed dependency on build-macos-x64 for the release job.

* perf: update execution time benchmarks in csv

Co-Authored-By: codeenthusiasm23 <273188204+codeenthusiasm23@users.noreply.github.com>
Co-Authored-By: Eamon Sippy <eamon112009@gmail.com>

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* Remove frontend job from Docker Images workflow

* Update release workflow to remove s390x and add notes

Removed s390x build configurations and added a step to write detailed release notes.

* feat: add local orchestration script for frontend and backend servers

Introduces a central Python execution script to concurrently manage and
orchestrate the development environment for both the frontend and backend.
- Detects system OS to invoke correct `npm` and `python` (virtualenv) binary variants.
- Verifies existence of the local PyTorch `.pt` model checkpoint before starting.
- Configures environment variables dynamically for Uvicorn (FastAPI) and Vite.
- Handles cross-origin setups (CORS) linking ports interactively.
- Gracefully handles process termination (`Ctrl+C`) by forwarding termination signals.
- Automatically launches the frontend application in the system web browser.

* chore(deps): bump actions/github-script from 7 to 9 (#71)

Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 9.
- [Release notes](https://github.com/actions/github-script/releases)
- [Commits](https://github.com/actions/github-script/compare/v7...v9)

---
updated-dependencies:
- dependency-name: actions/github-script
  dependency-version: '9'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* feat(cuda): introduce log_message utility and LogLevel enum

* feat(cuda): add cuBLAS handle wrapper and matmul operations

* feat(cuda): implement core Tensor, TensorShape, and TensorView abstractions

* refactor: untie embedding and lm_head weights and to quadtrix

* feat(cuda): add NCCL communicator wrapper and all-reduce primitives

* Update README.md with workflow badges

Added badges for release, package, and CI workflows.

* kernels: add AdamW optimization kernel with stochastic rounding  Introduces the AdamW fused CUDA kernel including linear interpolation  optimizations (`lerp`), multi-slice batching support via 2D grids, and  `init_from_master` utility functions for low-precision parameter handling.

* cudnn: implement cached SDPA forward graph using cuDNN frontend

* feat(cuda): implement Packed128 memory vectorization utilities

* feat: add distributed sharded DataLoader for binary token files

* feat(multi-gpu): add foundational utilities for ZeRO sharding

* feat(utils): add  I/O and memory error-checking wrappers

* feat : add PyTorch-compatible Mersenne Twister random utilities

* README : Enhance README with header and workflow badges

Updated README to include a header and badges for release, package, and CI workflows.

* utils:`fopenCheck`, `freadCheck`, `fwriteCheck`, `fcloseCheck`, and `fseekCheck` with explicit crash details and project-specific troubleshooting hints. - Add cross-platform socket closure wrappers (`scloseCheck` and `closesocketCheck`) for Linux and Windows.

* mfu: add GPU specifications database and utilities for MFU estimation

* Modify project title in README.md

Changed the project title to include 'llm.cpp' for clarity.

* Update README to remove image and clean up content

Removed image from README and adjusted formatting.

* Refactor core architecture and optimize CUDA features (#75)

* exp(#58)

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* refactor(ci): optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* Added MIT LICENSE to this project Quadtrix.cpp

* Refactor Dockerfile to use ARG for CUDA version

* Refactor Dockerfile for backend dependencies

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* Delete .devops/Dockerfile.frontend

* Delete .devops/Dockerfile.dev.frontend

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactor(ui): rewrite ThinkingIndicator to use inline styles and CSS keyframes

* refactor : message bubble layout to use inline styles

* refactor(ui): complete inline-style migration and update auto-scroll implementation

* refactor(ui): complete inline-style migration for MessageAvatar component

* refactor(ui): rewrite EmptyState component using pure inline styles

* refactored(tensor): vectorize element-wise addition and scalar scaling using AVX/SSE

- Added SIMD vectorization support (`__AVX__` and `__SSE__`) for element-wise `add`, `add_inplace`, and `scale` operations.
- Maintained scalar fallback paths for non-vectorized bounds and platforms lacking hardware extensions.
- Explicitly defined rule-of-five constructors (`default` and `noexcept` moves) within the `Tensor` struct layout.
- Optimized vector initialization across the core construct layer via `std::move` and `std::vector::reserve`.

* refactor(main): redesign training loop to log per-step and sample during evaluation

- Replaced the periodic block evaluation layout with standard, per-step logging metrics (`loss`, `ms`, and `tok/s`).
- Shifted initial validation loss calculation out of the iteration cycle to establish a zero-state baseline.
- Restructured token streaming so that generations are triggered conditionally inside the training loop post-evaluation windows.
- Streamlined architecture parameter reporting and consolidated command-line configuration visual prints.

* feat: implement GPT training loop with multi-GPU and memory optimizations

- Add advanced memory footprint optimization using forward-activation recomputation for LayerNorm and GeLU.
- Optimize layer-wise activation buffer layout using a centralized `TensorSpec` registry to support large batch scaling.
- Integrate cuBLASLt matmul fusions, optional cuDNN attention layers, and stochastic rounding options.
- Fall back gracefully to `cudaMallocManaged` under heavy loads to prevent Outlier/OOM crashes.

* Update README.md with new banner for qudtrix.cpp

---------



* ci: add manual PR checks workflow with slash command support

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------



* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

* feat(cuda): add LayerNorm forward and backward kernel declarations

* refactor(ci): organize workflow into push-triggered QA and manual docker builds

Updated CI workflow to restrict branches for push events and improved input descriptions for image selection and push options.

* Fix formatting and update CI workflow steps

* Enhance CI with macOS binary build and release

Added macOS binary build and release steps to CI workflow.

* feat(docker): add Dockerfile for frontend application

* feat(docker): add Dockerfile for frontend application

* refactor(ci): remove release job from GitHub actions

* ci: add unified release and docker build workflow

* ci: add unified release and docker build workflow

* Refactor macOS build workflow for arm64 architecture

* Update release workflow to remove macOS x64 build

Removed dependency on build-macos-x64 for the release job.

* perf: update execution time benchmarks in csv




* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* Remove frontend job from Docker Images workflow

* Update release workflow to remove s390x and add notes

Removed s390x build configurations and added a step to write detailed release notes.

* feat: add local orchestration script for frontend and backend servers

Introduces a central Python execution script to concurrently manage and
orchestrate the development environment for both the frontend and backend.
- Detects system OS to invoke correct `npm` and `python` (virtualenv) binary variants.
- Verifies existence of the local PyTorch `.pt` model checkpoint before starting.
- Configures environment variables dynamically for Uvicorn (FastAPI) and Vite.
- Handles cross-origin setups (CORS) linking ports interactively.
- Gracefully handles process termination (`Ctrl+C`) by forwarding termination signals.
- Automatically launches the frontend application in the system web browser.

* chore(deps): bump actions/github-script from 7 to 9 (#71)

Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 9.
- [Release notes](https://github.com/actions/github-script/releases)
- [Commits](https://github.com/actions/github-script/compare/v7...v9)

---
updated-dependencies:
- dependency-name: actions/github-script
  dependency-version: '9'
  dependency-type: direct:production
  update-type: version-update:semver-major
...




* feat(cuda): introduce log_message utility and LogLevel enum

* feat(cuda): add cuBLAS handle wrapper and matmul operations

* feat(cuda): implement core Tensor, TensorShape, and TensorView abstractions

* refactor: untie embedding and lm_head weights and to quadtrix

* feat(cuda): add NCCL communicator wrapper and all-reduce primitives

* Update README.md with workflow badges

Added badges for release, package, and CI workflows.

* kernels: add AdamW optimization kernel with stochastic rounding  Introduces the AdamW fused CUDA kernel including linear interpolation  optimizations (`lerp`), multi-slice batching support via 2D grids, and  `init_from_master` utility functions for low-precision parameter handling.

* cudnn: implement cached SDPA forward graph using cuDNN frontend

* feat(cuda): implement Packed128 memory vectorization utilities

* feat: add distributed sharded DataLoader for binary token files

* feat(multi-gpu): add foundational utilities for ZeRO sharding

* feat(utils): add  I/O and memory error-checking wrappers

* feat : add PyTorch-compatible Mersenne Twister random utilities

* README : Enhance README with header and workflow badges

Updated README to include a header and badges for release, package, and CI workflows.

* utils:`fopenCheck`, `freadCheck`, `fwriteCheck`, `fcloseCheck`, and `fseekCheck` with explicit crash details and project-specific troubleshooting hints. - Add cross-platform socket closure wrappers (`scloseCheck` and `closesocketCheck`) for Linux and Windows.

* mfu: add GPU specifications database and utilities for MFU estimation

* Modify project title in README.md

Changed the project title to include 'llm.cpp' for clarity.

* Update README to remove image and clean up content

Removed image from README and adjusted formatting.

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Eamon Sippy <eamon112009@gmail.com>
Co-authored-by: codeenthusiasm23 <273188204+codeenthusiasm23@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Add CUDA kernels, optimize CI, and update documentation (#74)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

* CUDA header declarations for (LayerNorm) forward and backward  (#66)

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

---------

Co-authored-by: Max <eamon5174@gmail.com>

* Add CUDA attention kernels, gradient norms, and CI improvements (#69)

* exp(#58)

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* refactor(ci): optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* Added MIT LICENSE to this project Quadtrix.cpp

* Refactor Dockerfile to use ARG for CUDA version

* Refactor Dockerfile for backend dependencies

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* Delete .devops/Dockerfile.frontend

* Delete .devops/Dockerfile.dev.frontend

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactor(ui): rewrite ThinkingIndicator to use inline styles and CSS keyframes

* refactor : message bubble layout to use inline styles

* refactor(ui): complete inline-style migration and update auto-scroll implementation

* refactor(ui): complete inline-style migration for MessageAvatar component

* refactor(ui): rewrite EmptyState component using pure inline styles

* refactored(tensor): vectorize element-wise addition and scalar scaling using AVX/SSE

- Added SIMD vectorization support (`__AVX__` and `__SSE__`) for element-wise `add`, `add_inplace`, and `scale` operations.
- Maintained scalar fallback paths for non-vectorized bounds and platforms lacking hardware extensions.
- Explicitly defined rule-of-five constructors (`default` and `noexcept` moves) within the `Tensor` struct layout.
- Optimized vector initialization across the core construct layer via `std::move` and `std::vector::reserve`.

* refactor(main): redesign training loop to log per-step and sample during evaluation

- Replaced the periodic block evaluation layout with standard, per-step logging metrics (`loss`, `ms`, and `tok/s`).
- Shifted initial validation loss calculation out of the iteration cycle to establish a zero-state baseline.
- Restructured token streaming so that generations are triggered conditionally inside the training loop post-evaluation windows.
- Streamlined architecture parameter reporting and consolidated command-line configuration visual prints.

* feat: implement GPT training loop with multi-GPU and memory optimizations

- Add advanced memory footprint optimization using forward-activation recomputation for LayerNorm and GeLU.
- Optimize layer-wise activation buffer layout using a centralized `TensorSpec` registry to support large batch scaling.
- Integrate cuBLASLt matmul fusions, optional cuDNN attention layers, and stochastic rounding options.
- Fall back gracefully to `cudaMallocManaged` under heavy loads to prevent Outlier/OOM crashes.

* Update README.md with new banner for qudtrix.cpp

---------

Co-authored-by: Max <eamon5174@gmail.com>

* ci: add manual PR checks workflow with slash command support

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------

Co-authored-by: Max <eamon5174@gmail.com>

* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

* feat(cuda): add LayerNorm forward and backward kernel declarations

* refactor(ci): organize workflow into push-triggered QA and manual docker builds

Updated CI workflow to restrict branches for push events and improved input descriptions for image selection and push options.

* Fix formatting and update CI workflow steps

* Enhance CI with macOS binary build and release

Added macOS binary build and release steps to CI workflow.

* feat(docker): add Dockerfile for frontend application

* feat(docker): add Dockerfile for frontend application

* refactor(ci): remove release job from GitHub actions

* ci: add unified release and docker build workflow

* ci: add unified release and docker build workflow

* Refactor macOS build workflow for arm64 architecture

* Update release workflow to remove macOS x64 build

Removed dependency on build-macos-x64 for the release job.

* perf: update execution time benchmarks in csv

Co-Authored-By: codeenthusiasm23 <273188204+codeenthusiasm23@users.noreply.github.com>
Co-Authored-By: Eamon Sippy <eamon112009@gmail.com>

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* Remove frontend job from Docker Images workflow

* Update release workflow to remove s390x and add notes

Removed s390x build configurations and added a step to write detailed release notes.

* feat: add local orchestration script for frontend and backend servers

Introduces a central Python execution script to concurrently manage and
orchestrate the development environment for both the frontend and backend.
- Detects system OS to invoke correct `npm` and `python` (virtualenv) binary variants.
- Verifies existence of the local PyTorch `.pt` model checkpoint before starting.
- Configures environment variables dynamically for Uvicorn (FastAPI) and Vite.
- Handles cross-origin setups (CORS) linking ports interactively.
- Gracefully handles process termination (`Ctrl+C`) by forwarding termination signals.
- Automatically launches the frontend application in the system web browser.

* chore(deps): bump actions/github-script from 7 to 9 (#71)

Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 9.
- [Release notes](https://github.com/actions/github-script/releases)
- [Commits](https://github.com/actions/github-script/compare/v7...v9)

---
updated-dependencies:
- dependency-name: actions/github-script
  dependency-version: '9'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* feat(cuda): introduce log_message utility and LogLevel enum

* feat(cuda): add cuBLAS handle wrapper and matmul operations

* feat(cuda): implement core Tensor, TensorShape, and TensorView abstractions

* refactor: untie embedding and lm_head weights and to quadtrix

* feat(cuda): add NCCL communicator wrapper and all-reduce primitives

* Update README.md with workflow badges

Added badges for release, package, and CI workflows.

* kernels: add AdamW optimization kernel with stochastic rounding  Introduces the AdamW fused CUDA kernel including linear interpolation  optimizations (`lerp`), multi-slice batching support via 2D grids, and  `init_from_master` utility functions for low-precision parameter handling.

* cudnn: implement cached SDPA forward graph using cuDNN frontend

* feat(cuda): implement Packed128 memory vectorization utilities

* feat: add distributed sharded DataLoader for binary token files

* feat(multi-gpu): add foundational utilities for ZeRO sharding

* feat(utils): add  I/O and memory error-checking wrappers

* feat : add PyTorch-compatible Mersenne Twister random utilities

* README : Enhance README with header and workflow badges

Updated README to include a header and badges for release, package, and CI workflows.

* utils:`fopenCheck`, `freadCheck`, `fwriteCheck`, `fcloseCheck`, and `fseekCheck` with explicit crash details and project-specific troubleshooting hints. - Add cross-platform socket closure wrappers (`scloseCheck` and `closesocketCheck`) for Linux and Windows.

* mfu: add GPU specifications database and utilities for MFU estimation

* Modify project title in README.md

Changed the project title to include 'llm.cpp' for clarity.

* Update README to remove image and clean up content

Removed image from README and adjusted formatting.

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Max <eamon5174@gmail.com>
Co-authored-by: codeenthusiasm23 <273188204+codeenthusiasm23@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Eamon <eamon112009@gmail.com>
Co-authored-by: codeenthusiasm23 <273188204+codeenthusiasm23@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

* Optimize CI workflow and Docker configurations with refactoring (#72) (#76)

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* refactor(ci): optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* Added MIT LICENSE to this project Quadtrix.cpp

* Refactor Dockerfile to use ARG for CUDA version

* Refactor Dockerfile for backend dependencies

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* Delete .devops/Dockerfile.frontend

* Delete .devops/Dockerfile.dev.frontend

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactor(ui): rewrite ThinkingIndicator to use inline styles and CSS keyframes

* refactor : message bubble layout to use inline styles

* refactor(ui): complete inline-style migration and update auto-scroll implementation

* refactor(ui): complete inline-style migration for MessageAvatar component

* refactor(ui): rewrite EmptyState component using pure inline styles

* refactored(tensor): vectorize element-wise addition and scalar scaling using AVX/SSE

- Added SIMD vectorization support (`__AVX__` and `__SSE__`) for element-wise `add`, `add_inplace`, and `scale` operations.
- Maintained scalar fallback paths for non-vectorized bounds and platforms lacking hardware extensions.
- Explicitly defined rule-of-five constructors (`default` and `noexcept` moves) within the `Tensor` struct layout.
- Optimized vector initialization across the core construct layer via `std::move` and `std::vector::reserve`.

* refactor(main): redesign training loop to log per-step and sample during evaluation

- Replaced the periodic block evaluation layout with standard, per-step logging metrics (`loss`, `ms`, and `tok/s`).
- Shifted initial validation loss calculation out of the iteration cycle to establish a zero-state baseline.
- Restructured token streaming so that generations are triggered conditionally inside the training loop post-evaluation windows.
- Streamlined architecture parameter reporting and consolidated command-line configuration visual prints.

* feat: implement GPT training loop with multi-GPU and memory optimizations

- Add advanced memory footprint optimization using forward-activation recomputation for LayerNorm and GeLU.
- Optimize layer-wise activation buffer layout using a centralized `TensorSpec` registry to support large batch scaling.
- Integrate cuBLASLt matmul fusions, optional cuDNN attention layers, and stochastic rounding options.
- Fall back gracefully to `cudaMallocManaged` under heavy loads to prevent Outlier/OOM crashes.

* Update README.md with new banner for qudtrix.cpp

* docs:report [run_20260530_165216](~791 tok/s) (#60)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs: report [run_20260530_165216] (~791 tok/s) (#62)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



---------



* chore: clang-format configuration file based on LLVM (#63)



* ci: add manual PR checks workflow with slash command support

* ci: add manual PR checks workflow with slash command support

* ci: add manual PR checks workflow with slash command support

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------



* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

* feat(cuda): add LayerNorm forward and backward kernel declarations

* refactor(ci): organize workflow into push-triggered QA and manual docker builds

Updated CI workflow to restrict branches for push events and improved input descriptions for image selection and push options.

* Fix formatting and update CI workflow steps

* Enhance CI with macOS binary build and release

Added macOS binary build and release steps to CI workflow.

* feat(docker): add Dockerfile for frontend application

* feat(docker): add Dockerfile for frontend application

* refactor(ci): remove release job from GitHub actions

* ci: add unified release and docker build workflow

* ci: add unified release and docker build workflow

* Refactor macOS build workflow for arm64 architecture

* Update release workflow to remove macOS x64 build

Removed dependency on build-macos-x64 for the release job.

* perf: update execution time benchmarks in csv




* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* Remove frontend job from Docker Images workflow

* Update release workflow to remove s390x and add notes

Removed s390x build configurations and added a step to write detailed release notes.

* feat: add local orchestration script for frontend and backend servers

Introduces a central Python execution script to concurrently manage and
orchestrate the development environment for both the frontend and backend.
- Detects system OS to invoke correct `npm` and `python` (virtualenv) binary variants.
- Verifies existence of the local PyTorch `.pt` model checkpoint before starting.
- Configures environment variables dynamically for Uvicorn (FastAPI) and Vite.
- Handles cross-origin setups (CORS) linking ports interactively.
- Gracefully handles process termination (`Ctrl+C`) by forwarding termination signals.
- Automatically launches the frontend application in the system web browser.

* chore(deps): bump actions/github-script from 7 to 9 (#71)

Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 9.
- [Release notes](https://github.com/actions/github-script/releases)
- [Commits](https://github.com/actions/github-script/compare/v7...v9)

---
updated-dependencies:
- dependency-name: actions/github-script
  dependency-version: '9'
  dependency-type: direct:production
  update-type: version-update:semver-major
...




* feat(cuda): introduce log_message utility and LogLevel enum

* feat(cuda): add cuBLAS handle wrapper and matmul operations

* feat(cuda): implement core Tensor, TensorShape, and TensorView abstractions

* refactor: untie embedding and lm_head weights and to quadtrix

* feat(cuda): add NCCL communicator wrapper and all-reduce primitives

* Update README.md with workflow badges

Added badges for release, package, and CI workflows.

* kernels: add AdamW optimization kernel with stochastic rounding  Introduces the AdamW fused CUDA kernel including linear interpolation  optimizations (`lerp`), multi-slice batching support via 2D grids, and  `init_from_master` utility functions for low-precision parameter handling.

* cudnn: implement cached SDPA forward graph using cuDNN frontend

* feat(cuda): implement Packed128 memory vectorization utilities

* feat: add distributed sharded DataLoader for binary token files

* feat(multi-gpu): add foundational utilities for ZeRO sharding

* feat(utils): add  I/O and memory error-checking wrappers

* feat : add PyTorch-compatible Mersenne Twister random utilities

* README : Enhance README with header and workflow badges

Updated README to include a header and badges for release, package, and CI workflows.

* utils:`fopenCheck`, `freadCheck`, `fwriteCheck`, `fcloseCheck`, and `fseekCheck` with explicit crash details and project-specific troubleshooting hints. - Add cross-platform socket closure wrappers (`scloseCheck` and `closesocketCheck`) for Linux and Windows.

* mfu: add GPU specifications database and utilities for MFU estimation

* Modify project title in README.md

Changed the project title to include 'llm.cpp' for clarity.

* Update README to remove image and clean up content

Removed image from README and adjusted formatting.

* Refactor core architecture and optimize CUDA features (#75)

* exp(#58)

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* refactor(ci): optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* Added MIT LICENSE to this project Quadtrix.cpp

* Refactor Dockerfile to use ARG for CUDA version

* Refactor Dockerfile for backend dependencies

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* Delete .devops/Dockerfile.frontend

* Delete .devops/Dockerfile.dev.frontend

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactor(ui): rewrite ThinkingIndicator to use inline styles and CSS keyframes

* refactor : message bubble layout to use inline styles

* refactor(ui): complete inline-style migration and update auto-scroll implementation

* refactor(ui): complete inline-style migration for MessageAvatar component

* refactor(ui): rewrite EmptyState component using pure inline styles

* refactored(tensor): vectorize element-wise addition and scalar scaling using AVX/SSE

- Added SIMD vectorization support (`__AVX__` and `__SSE__`) for element-wise `add`, `add_inplace`, and `scale` operations.
- Maintained scalar fallback paths for non-vectorized bounds and platforms lacking hardware extensions.
- Explicitly defined rule-of-five constructors (`default` and `noexcept` moves) within the `Tensor` struct layout.
- Optimized vector initialization across the core construct layer via `std::move` and `std::vector::reserve`.

* refactor(main): redesign training loop to log per-step and sample during evaluation

- Replaced the periodic block evaluation layout with standard, per-step logging metrics (`loss`, `ms`, and `tok/s`).
- Shifted initial validation loss calculation out of the iteration cycle to establish a zero-state baseline.
- Restructured token streaming so that generations are triggered conditionally inside the training loop post-evaluation windows.
- Streamlined architecture parameter reporting and consolidated command-line configuration visual prints.

* feat: implement GPT training loop with multi-GPU and memory optimizations

- Add advanced memory footprint optimization using forward-activation recomputation for LayerNorm and GeLU.
- Optimize layer-wise activation buffer layout using a centralized `TensorSpec` registry to support large batch scaling.
- Integrate cuBLASLt matmul fusions, optional cuDNN attention layers, and stochastic rounding options.
- Fall back gracefully to `cudaMallocManaged` under heavy loads to prevent Outlier/OOM crashes.

* Update README.md with new banner for qudtrix.cpp

---------



* ci: add manual PR checks workflow with slash command support

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------



* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

* feat(cuda): add LayerNorm forward and backward kernel declarations

* refactor(ci): organize workflow into push-triggered QA and manual docker builds

Updated CI workflow to restrict branches for push events and improved input descriptions for image selection and push options.

* Fix formatting and update CI workflow steps

* Enhance CI with macOS binary build and release

Added macOS binary build and release steps to CI workflow.

* feat(docker): add Dockerfile for frontend application

* feat(docker): add Dockerfile for frontend application

* refactor(ci): remove release job from GitHub actions

* ci: add unified release and docker build workflow

* ci: add unified release and docker build workflow

* Refactor macOS build workflow for arm64 architecture

* Update release workflow to remove macOS x64 build

Removed dependency on build-macos-x64 for the release job.

* perf: update execution time benchmarks in csv




* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* ci(docker): refactor image build workflow and add frontend job

* Remove frontend job from Docker Images workflow

* Update release workflow to remove s390x and add notes

Removed s390x build configurations and added a step to write detailed release notes.

* feat: add local orchestration script for frontend and backend servers

Introduces a central Python execution script to concurrently manage and
orchestrate the development environment for both the frontend and backend.
- Detects system OS to invoke correct `npm` and `python` (virtualenv) binary variants.
- Verifies existence of the local PyTorch `.pt` model checkpoint before starting.
- Configures environment variables dynamically for Uvicorn (FastAPI) and Vite.
- Handles cross-origin setups (CORS) linking ports interactively.
- Gracefully handles process termination (`Ctrl+C`) by forwarding termination signals.
- Automatically launches the frontend application in the system web browser.

* chore(deps): bump actions/github-script from 7 to 9 (#71)

Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 9.
- [Release notes](https://github.com/actions/github-script/releases)
- [Commits](https://github.com/actions/github-script/compare/v7...v9)

---
updated-dependencies:
- dependency-name: actions/github-script
  dependency-version: '9'
  dependency-type: direct:production
  update-type: version-update:semver-major
...




* feat(cuda): introduce log_message utility and LogLevel enum

* feat(cuda): add cuBLAS handle wrapper and matmul operations

* feat(cuda): implement core Tensor, TensorShape, and TensorView abstractions

* refactor: untie embedding and lm_head weights and to quadtrix

* feat(cuda): add NCCL communicator wrapper and all-reduce primitives

* Update README.md with workflow badges

Added badges for release, package, and CI workflows.

* kernels: add AdamW optimization kernel with stochastic rounding  Introduces the AdamW fused CUDA kernel including linear interpolation  optimizations (`lerp`), multi-slice batching support via 2D grids, and  `init_from_master` utility functions for low-precision parameter handling.

* cudnn: implement cached SDPA forward graph using cuDNN frontend

* feat(cuda): implement Packed128 memory vectorization utilities

* feat: add distributed sharded DataLoader for binary token files

* feat(multi-gpu): add foundational utilities for ZeRO sharding

* feat(utils): add  I/O and memory error-checking wrappers

* feat : add PyTorch-compatible Mersenne Twister random utilities

* README : Enhance README with header and workflow badges

Updated README to include a header and badges for release, package, and CI workflows.

* utils:`fopenCheck`, `freadCheck`, `fwriteCheck`, `fcloseCheck`, and `fseekCheck` with explicit crash details and project-specific troubleshooting hints. - Add cross-platform socket closure wrappers (`scloseCheck` and `closesocketCheck`) for Linux and Windows.

* mfu: add GPU specifications database and utilities for MFU estimation

* Modify project title in README.md

Changed the project title to include 'llm.cpp' for clarity.

* Update README to remove image and clean up content

Removed image from README and adjusted formatting.

---------






* Add CUDA kernels, optimize CI, and update documentation (#74)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

* CUDA header declarations for (LayerNorm) forward and backward  (#66)

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

---------



* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

---------



* Add CUDA attention kernels, gradient norms, and CI improvements (#69)

* exp(#58)

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* feat(ci): optimize workflow pipeline and update docker configurations

* refactor(ci): optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* refactor : optimize workflow pipeline and update docker configurations

* Added MIT LICENSE to this project Quadtrix.cpp

* Refactor Dockerfile to use ARG for CUDA version

* Refactor Dockerfile for backend dependencies

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* Delete .devops/Dockerfile.frontend

* Delete .devops/Dockerfile.dev.frontend

* refactor : Dockerfile.backend optimize workflow pipeline

* refactor : Dockerfile.backend optimize workflow pipeline

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactored (CI): consolidated manual Docker build jobs into a matrix strategy to reduce duplication

* refactor(ui): rewrite ThinkingIndicator to use inline styles and CSS keyframes

* refactor : message bubble layout to use inline styles

* refactor(ui): complete inline-style migration and update auto-scroll implementation

* refactor(ui): complete inline-style migration for MessageAvatar component

* refactor(ui): rewrite EmptyState component using pure inline styles

* refactored(tensor): vectorize element-wise addition and scalar scaling using AVX/SSE

- Added SIMD vectorization support (`__AVX__` and `__SSE__`) for element-wise `add`, `add_inplace`, and `scale` operations.
- Maintained scalar fallback paths for non-vectorized bounds and platforms lacking hardware extensions.
- Explicitly defined rule-of-five constructors (`default` and `noexcept` moves) within the `Tensor` struct layout.
- Optimized vector initialization across the core construct layer via `std::move` and `std::vector::reserve`.

* refactor(main): redesign training loop to log per-step and sample during evaluation

- Replaced the periodic block evaluation layout with standard, per-step logging metrics (`loss`, `ms`, and `tok/s`).
- Shifted initial validation loss calculation out of the iteration cycle to establish a zero-state baseline.
- Restructured token streaming so that generations are triggered conditionally inside the training loop post-evaluation windows.
- Streamlined architecture parameter reporting and consolidated command-line configuration visual prints.

* feat: implement GPT training loop with multi-GPU and memory optimizations

- Add advanced memory footprint optimization using forward-activation recomputation for LayerNorm and GeLU.
- Optimize layer-wise activation buffer layout using a centralized `TensorSpec` registry to support large batch scaling.
- Integrate cuBLASLt matmul fusions, optional cuDNN attention layers, and stochastic rounding options.
- Fall back gracefully to `cudaMallocManaged` under heavy loads to prevent Outlier/OOM crashes.

* Update README.md with new banner for qudtrix.cpp

---------



* ci: add manual PR checks workflow with slash command support

* feat(cuda): add attention forward backward kernel declarations (#64)

* docs: report [run_20260530_165216] (~791 tok/s)

 Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900

* docs:report [run_20260530_165216](~791 tok/s)  (#61)

Includes metrics for generalization gap, throughput (~791 tok/s), and gradient norms.
Parameters: 6.68M | lr: 1e-3 | batch: 16 | steps: 6000 - Achieved best validation loss of 4.1319 at step 3900



* feat(cuda): add attention forward and backward kernel declarations

Introduces the header declarations for `attention_forward` and
`attention_backward` operations inside the `quadtrix::cuda` namespace.
Configured with support for custom CUDA streams and head partitioning.

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* feat(cuda): add checkpoint metadata struct and stub functions

* feat(cuda): introduce core type definitions and error handling utilities

- Defines `DType` and `DeviceKind` enums supporting standard types (F32, F16, BF16, I32, U8).
- Implements `dtype_name` and `dtype_size` metadata helper functions.
- Adds an explicit `Status` struct for non-throwing error propagation alongside `checked_mul` for safe allocation size computation.
- Introduces `check_cuda` and `abort_on_cuda` error macros and handling mechanisms, exposed via the `QUADTRIX_CUDA_CHECK` macro.

* feat(cuda): add TokenBatchView struct and DataLoader stub class

* feat(cuda): add GeLU activation forward and backward declarations

- Introduces the `GeluMode` enum to toggle between `Exact` and `Approximate` mathematical variants.
- Declares the `gelu_forward` and `gelu_backward` kernel entrypoints.
- Configures both signatures with optional stream execution and a default mode of `GeluMode::Approximate`.

* feat(cuda): add gradient norm calculation and clipping interfaces

* feat(cuda): add LayerNorm forward and backward kernel declarations

* refactor(ci): organize workflow into push-triggered QA and manual docker builds

Updated CI workflow to restrict branches for push events and improved input descriptions for image selection and push options.

* Fix formatting and update CI workflow steps

* Enhance CI with macOS binary build and release

Added macOS binary build and release steps to CI workflow.

* feat(docker): add Dockerfile for frontend application

* feat(docker): add Dockerfile for frontend application

* refactor(ci): remove release job from GitHub actions

* ci: add unified release and docker build workflow

* ci: add un…

Author: 4 people