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This commit introduces a series of enhancements to the quantization library and its benchmarking capabilities.

Key improvements include:

  1. Core Refactoring:

    • Standardized BaseQuantizer and DeviceManager usage across AWQ, GPTQ, and GGUF quantizers for improved consistency and reduced code duplication.
  2. Quantizer Enhancements & Fixes:

    • AWQ: Fixed bugs in activation statistics collection, removed redundant code, and ensured robust device handling.
    • GPTQ: Added extensive logging to clarify use_triton status, Hessian matrix size, and the current utilization of the Hessian in the quantization algorithm. Ensured device consistency.
    • GGUF: Fully integrated a cpu_offload parameter to allow for CPU offloading during quantization and GGUF file conversion, significantly aiding in low-GPU memory scenarios. Ensured robust device handling.
  3. Benchmarking Utility:

    • QuantizationBenchmark now provides more granular performance metrics, including detailed timings for various steps (model copy, quantizer init, quantization, inference) and peak memory usage (GB) at various stages.
  4. Unit Tests:

    • Added a comprehensive suite of unit tests for AWQ, GPTQ, and GGUF quantizers. Tests cover various parameters (bits, group_size, method-specifics), CPU/GPU execution, output consistency, and features like GGUF conversion and cpu_offload.
  5. Documentation:

    • Updated API reference (quantization.rst) and code docstrings to reflect all changes, new features, and clarifications (e.g., GGUF's cpu_offload, GPTQ's Triton/Hessian usage, new benchmark metrics).
    • Added missing __init__ docstrings to all quantizer classes.
    • Resolved a dangling reference to an example file in the documentation.

These changes aim to make the quantization library more robust, understandable, memory-efficient (especially GGUF), and maintainable, while providing better tools for performance analysis.

This commit introduces a series of enhancements to the quantization library and its benchmarking capabilities. Key improvements include: 1. **Core Refactoring:** * Standardized `BaseQuantizer` and `DeviceManager` usage across AWQ, GPTQ, and GGUF quantizers for improved consistency and reduced code duplication. 2. **Quantizer Enhancements & Fixes:** * **AWQ:** Fixed bugs in activation statistics collection, removed redundant code, and ensured robust device handling. * **GPTQ:** Added extensive logging to clarify `use_triton` status, Hessian matrix size, and the current utilization of the Hessian in the quantization algorithm. Ensured device consistency. * **GGUF:** Fully integrated a `cpu_offload` parameter to allow for CPU offloading during quantization and GGUF file conversion, significantly aiding in low-GPU memory scenarios. Ensured robust device handling. 3. **Benchmarking Utility:** * `QuantizationBenchmark` now provides more granular performance metrics, including detailed timings for various steps (model copy, quantizer init, quantization, inference) and peak memory usage (GB) at various stages. 4. **Unit Tests:** * Added a comprehensive suite of unit tests for AWQ, GPTQ, and GGUF quantizers. Tests cover various parameters (bits, group_size, method-specifics), CPU/GPU execution, output consistency, and features like GGUF conversion and `cpu_offload`. 5. **Documentation:** * Updated API reference (`quantization.rst`) and code docstrings to reflect all changes, new features, and clarifications (e.g., GGUF's `cpu_offload`, GPTQ's Triton/Hessian usage, new benchmark metrics). * Added missing `__init__` docstrings to all quantizer classes. * Resolved a dangling reference to an example file in the documentation. These changes aim to make the quantization library more robust, understandable, memory-efficient (especially GGUF), and maintainable, while providing better tools for performance analysis.
@codewithdark-git codewithdark-git merged commit 31938b5 into main May 21, 2025
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