Refactor MX4 Kernel to operate on flat tensors #2836
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This pull request was exported from Phabricator. Differential Revision: D59653809 |
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Reviewed By: sryap Differential Revision: D59653809
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Reviewed By: sryap Differential Revision: D59653809
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) Summary: X-link: facebookresearch/FBGEMM#20 Pull Request resolved: pytorch#2816 As noted in [this doc](https://docs.google.com/document/d/156Du0hBRH6umG_i-OrYC574XhpQMUU5SJYG0RTS2tTg/edit#heading=h.akfcp7xpg8cr), using a ceiling round for scale calculation does a better job of not truncating some mantissa bits. This diff switches triton's floor rounding to ceil rounding. Note that currently mx4_test doesnt pass as the cuda kernel now has different behavior than triton. Once we rebase this diff onto a similar change to the cuda kernel, we should see exact matching outputs again. Differential Revision: D59527463 Reviewed By: jianyuh
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
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This pull request was exported from Phabricator. Differential Revision: D59653809 |
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Reviewed By: sryap Differential Revision: D59653809
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Reviewed By: sryap Differential Revision: D59653809
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jwfromm
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
jwfromm
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
jwfromm
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Jul 12, 2024
Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
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Summary: Pull Request resolved: pytorch#2836 Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array. The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes. Differential Revision: D59653809 Reviewed By: sryap
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This pull request has been merged in f5906e0. |
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Summary:
Rather than try to reshape inputs to 2D matrices with each thread operating on one row, this refactor uses 1D inputs and has each thread operate on an offset of the array.
The main benefit of this is that it avoid ragged tensors where we cant divide an input into even sized rows. This should enable us to be compatible with more shapes.
Reviewed By: sryap
Differential Revision: D59653809