Fix batch norm partitioning with Conv3d #13696
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13696
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Summary: Models with a batch norm following a conv3d cause an internal error during lowering. This diff fixes it by updating the partitioning logic to only rely on fusion with 1d and 2d convs. This is because XNNPACK doesn't currently support standalone batch norms and only partitions norms that can be fused. We can't fuse with Conv3d, because XNNPACK doesn't have an implementation. The partitioner constraint was missing logic to exclude Conv3d. Differential Revision: D81069236
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Summary: Models with a batch norm following a conv3d cause an internal error during lowering. This diff fixes it by updating the partitioning logic to only rely on fusion with 1d and 2d convs. This is because XNNPACK doesn't currently support standalone batch norms and only partitions norms that can be fused. We can't fuse with Conv3d, because XNNPACK doesn't have an implementation. The partitioner constraint was missing logic to exclude Conv3d. Differential Revision: D81069236
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This pull request was exported from Phabricator. Differential Revision: D81069236 |
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Summary: Models with a batch norm following a conv3d cause an internal error during lowering. This diff fixes it by updating the partitioning logic to only rely on fusion with 1d and 2d convs. This is because XNNPACK doesn't currently support standalone batch norms and only partitions norms that can be fused. We can't fuse with Conv3d, because XNNPACK doesn't have an implementation. The partitioner constraint was missing logic to exclude Conv3d. Differential Revision: D81069236
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Summary: Models with a batch norm following a conv3d cause an internal error during lowering. This diff fixes it by updating the partitioning logic to only rely on fusion with 1d and 2d convs. This is because XNNPACK doesn't currently support standalone batch norms and only partitions norms that can be fused. We can't fuse with Conv3d, because XNNPACK doesn't have an implementation. The partitioner constraint was missing logic to exclude Conv3d. Differential Revision: D81069236
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Summary: Forward pyre fix https://www.internalfb.com/diff/D81069236 pytorch#13696 bypass-github-export-checks bypass-github-pytorch-ci-checks bypass-github-executorch-ci-checks Reviewed By: digantdesai Differential Revision: D82118651
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Summary: Forward pyre fix https://www.internalfb.com/diff/D81069236 #13696 bypass-github-export-checks bypass-github-pytorch-ci-checks bypass-github-executorch-ci-checks Reviewed By: digantdesai Differential Revision: D82118651
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…ytorch#14170) Summary: Forward pyre fix https://www.internalfb.com/diff/D81069236 pytorch#13696 bypass-github-export-checks bypass-github-pytorch-ci-checks bypass-github-executorch-ci-checks Reviewed By: digantdesai Differential Revision: D82118651
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Summary:
Models with a batch norm following a conv3d cause an internal error during lowering. This diff fixes it by updating the partitioning logic to only rely on fusion with 1d and 2d convs.
This is because XNNPACK doesn't currently support standalone batch norms and only partitions norms that can be fused. We can't fuse with Conv3d, because XNNPACK doesn't have an implementation. The partitioner constraint was missing logic to exclude Conv3d.
Differential Revision: D81069236