Add custom kernel for RMS normalization #16
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Fix ambiguous fma call
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SUMMARY: - Fix bug whereby 2:4 is not being invoked - Eschew SparseTensor based implementation TESTING: - examples/offline_inference_semi_structured_sparse.py --------- Co-authored-by: Lucas Wilkinson <[email protected]>
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SUMMARY: - Fix bug whereby 2:4 is not being invoked - Eschew SparseTensor based implementation TESTING: - examples/offline_inference_semi_structured_sparse.py --------- Co-authored-by: Lucas Wilkinson <[email protected]>
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This PR adds a custom CUDA kernel for RMS normalization, which is used in LLaMA models. The kernel removes the inefficient data movement in the current PyTorch implementation.
Performance (fp16):
Tested models:
Tested GPUs: