Exclude upsample_bilinear2d.vec from default core ATen decomposition table (#141791) #7126
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… decomposition table (pytorch#7126) Summary: As upsample_bilinear2d.vec and upsample_nearest2d.vec are core ATen ops, they should not be decomposed by default in the export path. Because the operators have CompositeImplicitAutograd dispatch, their decomposition is registered by default. This change adds an override list for CIA decompositions being registered in the default decomp table. In the long-term, we likely will want to exclude decompositions for all core-tagged CIA ops, but this will require all consumers to be ready to handle the remaining two ops, avg_pool1d, and adaptive_avg_pool1d. Until they are ready, I believe an explicit override list is the safest option. Additionally, I've also removed the ExecuTorch XNNPACK delegate ConvertToUpsampleBilinear2d pass, as the pass breaks (and is not needed), given that the op is not decomposed. The purpose of this pass was originally to pattern match the decomposition and un-decomposite it, but this is no longer necessary. X-link: pytorch/pytorch#141791 Reviewed By: StellarrZ Differential Revision: D66575454
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… decomposition table (#141791) Summary: As upsample_bilinear2d.vec and upsample_nearest2d.vec are core ATen ops, they should not be decomposed by default in the export path. Because the operators have CompositeImplicitAutograd dispatch, their decomposition is registered by default. This change adds an override list for CIA decompositions being registered in the default decomp table. In the long-term, we likely will want to exclude decompositions for all core-tagged CIA ops, but this will require all consumers to be ready to handle the remaining two ops, avg_pool1d, and adaptive_avg_pool1d. Until they are ready, I believe an explicit override list is the safest option. Additionally, I've also removed the ExecuTorch XNNPACK delegate ConvertToUpsampleBilinear2d pass, as the pass breaks (and is not needed), given that the op is not decomposed. The purpose of this pass was originally to pattern match the decomposition and un-decomposite it, but this is no longer necessary. X-link: pytorch/executorch#7126 Test Plan: Added a new test (`test_default_decomposition_core_cia_ops`) in test_export.py to verify that upsample_bilinear2d.vec (and in the future, other core-tagged CIA ops) are not decomposed by default. Also, I manually validated end to end with ExecuTorch that the op is not decomposed in to_edge (see N6238522). ``` buck test //caffe2/test:test_export -- test_default_decomposition_core_cia_ops ``` Reviewed By: StellarrZ Differential Revision: D66575454
… decomposition table (pytorch#7126) Summary: As upsample_bilinear2d.vec and upsample_nearest2d.vec are core ATen ops, they should not be decomposed by default in the export path. Because the operators have CompositeImplicitAutograd dispatch, their decomposition is registered by default. This change adds an override list for CIA decompositions being registered in the default decomp table. In the long-term, we likely will want to exclude decompositions for all core-tagged CIA ops, but this will require all consumers to be ready to handle the remaining two ops, avg_pool1d, and adaptive_avg_pool1d. Until they are ready, I believe an explicit override list is the safest option. Additionally, I've also removed the ExecuTorch XNNPACK delegate ConvertToUpsampleBilinear2d pass, as the pass breaks (and is not needed), given that the op is not decomposed. The purpose of this pass was originally to pattern match the decomposition and un-decomposite it, but this is no longer necessary. X-link: pytorch/pytorch#141791 Reviewed By: StellarrZ Differential Revision: D66575454
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… decomposition table (#141791) Summary: As upsample_bilinear2d.vec and upsample_nearest2d.vec are core ATen ops, they should not be decomposed by default in the export path. Because the operators have CompositeImplicitAutograd dispatch, their decomposition is registered by default. This change adds an override list for CIA decompositions being registered in the default decomp table. In the long-term, we likely will want to exclude decompositions for all core-tagged CIA ops, but this will require all consumers to be ready to handle the remaining two ops, avg_pool1d, and adaptive_avg_pool1d. Until they are ready, I believe an explicit override list is the safest option. Additionally, I've also removed the ExecuTorch XNNPACK delegate ConvertToUpsampleBilinear2d pass, as the pass breaks (and is not needed), given that the op is not decomposed. The purpose of this pass was originally to pattern match the decomposition and un-decomposite it, but this is no longer necessary. X-link: pytorch/executorch#7126 Test Plan: Added a new test (`test_default_decomposition_core_cia_ops`) in test_export.py to verify that upsample_bilinear2d.vec (and in the future, other core-tagged CIA ops) are not decomposed by default. Also, I manually validated end to end with ExecuTorch that the op is not decomposed in to_edge (see N6238522). ``` buck test //caffe2/test:test_export -- test_default_decomposition_core_cia_ops ``` Reviewed By: StellarrZ Differential Revision: D66575454
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Summary: Pull Request resolved: pytorch/pytorch#141791
Differential Revision: D66575454