Support un-fused batchnorm1d/2d on XNNPACK via decomposition #16533
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/16533
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New FailureAs of commit e81cddd with merge base 4f8dbde ( NEW FAILURE - The following job has failed:
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@GregoryComer has exported this pull request. If you are a Meta employee, you can view the originating Diff in D90422630. |
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…#16533) Summary: Add a new pass - DecomposeBatchNorm - which converts standalone (non-fused) batch norm operators to 1x1 depthwise convolution. This prevents delegation graph breaks when batch norm operators can't be fused. Differential Revision: D90422630
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Updated to add test coverage around dtype, affine=False, memory_format/dim_order, and more comprehensive checks on the conv node created by the pass. Will re-open and request review once CI finshes. |
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…#16533) Summary: Add a new pass - DecomposeBatchNorm - which converts standalone (non-fused) batch norm operators to 1x1 depthwise convolution. This prevents delegation graph breaks when batch norm operators can't be fused. Differential Revision: D90422630
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Test failure is on a core ml test, so it appears to be a flake. |
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Summary: Add a new pass - DecomposeBatchNorm - which converts standalone (non-fused) batch norm operators to 1x1 depthwise convolution. This prevents delegation graph breaks when batch norm operators can't be fused.
Differential Revision: D90422630
cc @digantdesai @cbilgin