Fuse cumsum into stacked FP8 Grouped Gemm #3814
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…torch#3699) Summary: Pull Request resolved: pytorch#3699 X-link: facebookresearch/FBGEMM#780 Currently, the stacked version of FP8 grouped gemm accepts lists of tensor inputs and produces a single tensor output. This reduces quite a bit of overhead when cuda graphs are used, but still requires splitting input tensors in prefill which can be costly. This diff updates the input types of stacked grouped gemm to support single tensors. Notably, since M varies across group and we do no padding, this change requires that we provide a new input tensor called `M_offsets` that indicates the row that each group begins at within in the first input. We create M_offsets by taking the cumulative sum of M for each group, which we may be able to further optimize. This diff also includes a long overdue refactor of grouped gemm setup for nvidia such that we only launch a single kernel rather than one per group. This should reduce overhead by quite a bit in some cases. Differential Revision: D69544396 Reviewed By: jiawenliu64
Summary: Adds the latest stacked variants of grouped gemm to quantize bench. Differential Revision: D70117584
Summary: Rather than require an explicit cumulative sum to be performed on the M sizes of each group to generate the M_offsets argument, we can instead compute the offsets based on the sizes as part of kernel setup. This should be quite a bit faster than doing a separate kernel launch. Differential Revision: D71152287
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This pull request was exported from Phabricator. Differential Revision: D71152287 |
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Summary: Rather than require an explicit cumulative sum to be performed on the M sizes of each group to generate the M_offsets argument, we can instead compute the offsets based on the sizes as part of kernel setup. This should be quite a bit faster than doing a separate kernel launch.
Differential Revision: D71152287