autotune target_bits example for llama recipe

[xin3he]

PR Type

Enhancement

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Description

• Add target_bits as float in AutoRoundConfig

• Remove redundant params_list in multiple classes

• Introduce non_tunable_params in TorchBaseConfig

• Add output_dir to AutoRoundConfig

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Diagram Walkthrough

flowchart LR
  A["Add target_bits as float"] -- "in AutoRoundConfig" --> B["Remove redundant params_list"]
  B -- "Introduce non_tunable_params" --> C["Add output_dir"]

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File Walkthrough

	Relevant files
Enhancement	1 files config.py Modify AutoRoundConfig and clean up config classes              +11/-167
Additional files	11 files README.md +217/-0  quantize.py +260/-0  requirements.txt [link]    README.md +0/-125  quantize.py +0/-261  Meta-Llama-3.1-8B-Instruct_7bits.json +0/-2242 Meta-Llama-3.3-70B-Instruct_5bits.json +0/-5602 run_hf_inf.py +0/-29    base_config.py +2/-1      autoround.py +16/-1    auto_accelerator.py +2/-1

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⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Exception Handling The except block in the convert method is too broad. It catches all exceptions without any handling, which can hide errors and make debugging difficult. pass Resource Management The del model statement and self.accelerator.empty_cache() call should be inside a finally block to ensure they are executed even if an exception occurs. del model self.accelerator.empty_cache() logger.info("Quantization is done, reloading model from saved directory...") Garbage Collection Calling gc.collect() in the empty_cache method might not be necessary and could impact performance. Consider removing it unless profiling shows a significant benefit. gc.collect()

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[Copilot]

Pull request overview

This PR enhances AutoRound quantization configuration by improving parameter management and enabling flexible mixed-precision quantization through the target_bits parameter.

Key Changes:

• Modified target_bits from int to float in AutoRoundConfig to support fractional bit-width targets
• Refactored config classes to remove redundant params_list attributes and use dynamic generation instead
• Added non_tunable_params mechanism in TorchBaseConfig to exclude specific parameters from tuning
• Added output_dir parameter to AutoRoundConfig for managing temporary file storage

Reviewed changes

Copilot reviewed 10 out of 12 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
neural_compressor/torch/quantization/config.py	Refactored config classes: removed hardcoded params_list, added non_tunable_params initialization in TorchBaseConfig.__init__, changed target_bits type to float, added output_dir parameter
neural_compressor/common/base_config.py	Updated tuning parameter filtering logic to check against non_tunable_params, added internal parameter filtering in to_dict()
neural_compressor/torch/algorithms/weight_only/autoround.py	Refactored device handling to store accelerator object, added model reloading logic for specific export formats with memory cleanup
neural_compressor/torch/utils/auto_accelerator.py	Enhanced CPU accelerator's empty_cache() to call gc.collect() instead of no-op
examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/quantize.py	New example script demonstrating AutoRound quantization with target_bits parameter
examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/README.md	New comprehensive documentation for Llama3 quantization recipes and inference

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[yiliu30]

Overall, LGTM.
It would be better not to mix example changes and new features in one PR.”

[xin3he]

Overall, LGTM. It would be better not to mix example changes and new features in one PR.”

Right, most issues are found during enabling example, and those are mixed aiming quick development.