Add Preshuffled FP8 x INT4 Grouped Gemm Kernel #3800
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Summary: Working on adding support for stacked mixed dtype grouped gemm with preshuffling. Differential Revision: D70870933
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Summary: Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: Working on adding support for stacked mixed dtype grouped gemm with preshuffling. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Pull Request resolved: pytorch#3800 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Pull Request resolved: pytorch#3800 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: Pull Request resolved: pytorch#3800 Working on adding support for stacked mixed dtype grouped gemm with preshuffling. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Pull Request resolved: pytorch#3800 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#847 Pull Request resolved: pytorch#3766 One of the new interesting changes in the preshuffled F8I4 kernel is that group scales are downcast to FP8. This has the risk of running into dynamic range issues and impacting accuracy. We can mitigate this risk by adding FP32 columnwise scaling to the output. Fortunately, we can do this using EVT so the performance impact is negligible. Differential Revision: D70587477
Summary: X-link: facebookresearch/FBGEMM#855 Pull Request resolved: pytorch#3775 This diff introduces a set of quantization helper functions to fbgemm_gpu/experimental/gen_ai to make it easier to apply the new Int4 packing and preshuffling to weights. Differential Revision: D70643388 Reviewed By: summerdengfb
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933
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Summary: X-link: facebookresearch/FBGEMM#897 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933
Summary: X-link: facebookresearch/FBGEMM#897 Pull Request resolved: pytorch#3800 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933
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Summary: Pull Request resolved: pytorch#3800 Working on adding support for stacked mixed dtype grouped gemm with preshuffling. Differential Revision: D70870933
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This pull request has been merged in a39d2cc. |
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Summary: X-link: https://github.com/facebookresearch/FBGEMM/pull/897 Pull Request resolved: pytorch#3800 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933 fbshipit-source-id: 195fb9feb993ffa7efe27b038173bd70a1db57ed
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hey @jwfromm - thanks for the PR What exactly the intended use of |
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Summary: Pull Request resolved: facebookresearch/FBGEMM#897 X-link: pytorch#3800 Efficient FP8xINT4 grouped gemm with preshuffling and scale packing. This implementation uses the "stacked" API where inputs and outputs are single contiguous tensors and the group boundaries are indicated with an `M_sizes` tensor that contains the number of rows in each group. Reviewed By: jiawenliu64 Differential Revision: D70870933 fbshipit-source-id: 195fb9feb993ffa7efe27b038173bd70a1db57ed
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Summary: Working on adding support for stacked mixed dtype grouped gemm with preshuffling.
Differential Revision: D70870933