Awq re implementation #824
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👋 Hi! Thank you for contributing to llm-compressor. Please add the ready label when the PR is ready for review. |
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Note that we should update the list of supported algorithms in the readme when this lands |
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dsikka
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Apr 21, 2025
SUMMARY: Addition of [`AWQModifier`](https://arxiv.org/pdf/2306.00978), based on [AutoAWQ implementation](https://github.com/casper-hansen/AutoAWQ/blob/main/awq/quantize/quantizer.py#L28). Should be reviewed/merged in conjunction with neuralmagic/compressed-tensors#269 Replaces #181 and #824 TEST PLAN: Some unit tests included, but as this was mostly a port from AutoAWQ, we validated the code by ensuring we could reproduce the evaluation metrics in Table 4 of [the paper](https://arxiv.org/pdf/2306.00978). We achieve the following wikitext PPL scores: Llama-2 7B Group 128: 1. Paper: 5.60 2. AutoAWQ: 5.615 3. This implementation: 5.612 4. we match what the paper reports for just RTN -- 5.73 5. We get reasonable results for channel-wise -- 6.788. AutoAWQ errors out for this (setting "q_group_size": -1 in the quant_config), and results not reported in paper. Llama-2 13B Group 128: 1. We match the results of AutoAWQ and the results shown in the paper: 4.97 2. We match what the paper reports for just RTN -- 4.984 NOTE: We are excluding the clipping logic in this implementation, if we want to add it we should add it as another modifier, they are mutually exclusive and the data model for AWQ doesn't align well with clipping. That might be the reason for the slight deviation of results reported in the paper and in our implementation --------- Signed-off-by: Brian Dellabetta <bdellabe@redhat.com>
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SUMMARY:
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TEST PLAN:
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