Refactor core architecture and optimize CUDA features (#73)

[codeaddict-119]

• exp(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 (feat(cuda): add attention forward and 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) (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 (chore(deps): bump actions/github-script from 7 to 9 #71)

Bumps actions/github-script from 7 to 9.

• Release notes
• Commits

---

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.

---

[codeaddict-119]

/run-checks

[github-actions]

✅ All checks passed!