Add support for compressed-tensors w8a8 int checkpoints #2745
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This change adds a loader for w8a8 int checkpoints. One large benefit of int8 support is that the corresponding cutlass matmul kernels also work on compute capability 7.5. Evaluation on neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8: | Tasks |Version| Filter |n-shot| Metric | |Value | |Stderr| |---------------|------:|----------------|-----:|-----------------------|---|-----:|---|------| |gsm8k_cot_llama| 3|flexible-extract| 8|exact_match |↑ |0.8431|± |0.0100| | | |strict-match | 8|exact_match |↑ |0.8393|± |0.0101| |ifeval | 4|none | 0|inst_level_loose_acc |↑ |0.8597|± | N/A| | | |none | 0|inst_level_strict_acc |↑ |0.8201|± | N/A| | | |none | 0|prompt_level_loose_acc |↑ |0.7967|± |0.0173| | | |none | 0|prompt_level_strict_acc|↑ |0.7468|± |0.0187| Which is the same ballpark as vLLM. As usual, lots of thanks to Neural Magic/vLLM for the kernels.
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What does this PR do?
This change adds a loader for w8a8 int checkpoints. One large benefit of int8 support is that the corresponding cutlass matmul kernels also work on compute capability 7.5.
Evaluation on neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8:
Which is the same ballpark as vLLM.
Note: output of static input quantization with zero points (asymmetric) seems really bad. But it's hard to tell if it's a bug in this PR or just static input quantization just being bad in general (for the expected reasons). vLLM also doesn't give good output in this case, though it seems slightly better? (E.g. on
nm-testing/asym-w8w8-int8-static-per-tensor-tiny-llama.)As usual, lots of thanks to Neural Magic/vLLM for the kernels.
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