Make op_glu_test input asymmetric #11294
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11294
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit cf337e7 with merge base 5ef38d3 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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…nal views to avoid copying) These were reverted due to internal test failures. Sending this as an exported internal diff so that we can make sure we get internal signal. Original summary for #11294 (to make the GLU test input asymmetric): This way it will produce different results along each tested dim. Original summaryfor #11295: GLU requires slicing the input Tensor into two halves. Currently, we accomplish this by copying; ExecuTorch does not support views in general because it requires Tensors to be contiguous. However, nothing stops us from implementing [the ATen that uses views](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/GatedLinearUnit.cpp#L35) entirely internally to the op. To support this, I added `support_noncontiguous_tensors` as an optional template argument to BroadcastIndexesRange and plumbed it through to the elementwise_util functions as an optional SupportNonContiguousTensors parameter. Differential Revision: [D76311585](https://our.internmc.facebook.com/intern/diff/D76311585/) [ghstack-poisoned]
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…nal views to avoid copying) These were reverted due to internal test failures. Sending this as an exported internal diff so that we can make sure we get internal signal. Original summary for #11294 (to make the GLU test input asymmetric): This way it will produce different results along each tested dim. Original summaryfor #11295: GLU requires slicing the input Tensor into two halves. Currently, we accomplish this by copying; ExecuTorch does not support views in general because it requires Tensors to be contiguous. However, nothing stops us from implementing [the ATen that uses views](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/GatedLinearUnit.cpp#L35) entirely internally to the op. To support this, I added `support_noncontiguous_tensors` as an optional template argument to BroadcastIndexesRange and plumbed it through to the elementwise_util functions as an optional SupportNonContiguousTensors parameter. Differential Revision: [D76311585](https://our.internmc.facebook.com/intern/diff/D76311585/) ghstack-source-id: 289287609 Pull Request resolved: #11509
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…d implement using internal views to avoid copying)" These were reverted due to internal test failures. Sending this as an exported internal diff so that we can make sure we get internal signal. Original summary for #11294 (to make the GLU test input asymmetric): This way it will produce different results along each tested dim. Original summaryfor #11295: GLU requires slicing the input Tensor into two halves. Currently, we accomplish this by copying; ExecuTorch does not support views in general because it requires Tensors to be contiguous. However, nothing stops us from implementing [the ATen that uses views](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/GatedLinearUnit.cpp#L35) entirely internally to the op. To support this, I added `support_noncontiguous_tensors` as an optional template argument to BroadcastIndexesRange and plumbed it through to the elementwise_util functions as an optional SupportNonContiguousTensors parameter. Differential Revision: [D76311585](https://our.internmc.facebook.com/intern/diff/D76311585/) [ghstack-poisoned]
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…nal views to avoid copying) Pull Request resolved: #11509 These were reverted due to internal test failures. Sending this as an exported internal diff so that we can make sure we get internal signal. Original summary for #11294 (to make the GLU test input asymmetric): This way it will produce different results along each tested dim. Original summaryfor #11295: GLU requires slicing the input Tensor into two halves. Currently, we accomplish this by copying; ExecuTorch does not support views in general because it requires Tensors to be contiguous. However, nothing stops us from implementing [the ATen that uses views](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/GatedLinearUnit.cpp#L35) entirely internally to the op. To support this, I added `support_noncontiguous_tensors` as an optional template argument to BroadcastIndexesRange and plumbed it through to the elementwise_util functions as an optional SupportNonContiguousTensors parameter. Differential Revision: [D76311585](https://our.internmc.facebook.com/intern/diff/D76311585/) ghstack-source-id: 289429482
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…using internal views to avoid copying)" These were reverted due to internal test failures. Sending this as an exported internal diff so that we can make sure we get internal signal. Original summary for #11294 (to make the GLU test input asymmetric): This way it will produce different results along each tested dim. Original summaryfor #11295: GLU requires slicing the input Tensor into two halves. Currently, we accomplish this by copying; ExecuTorch does not support views in general because it requires Tensors to be contiguous. However, nothing stops us from implementing [the ATen that uses views](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/GatedLinearUnit.cpp#L35) entirely internally to the op. To support this, I added `support_noncontiguous_tensors` as an optional template argument to BroadcastIndexesRange and plumbed it through to the elementwise_util functions as an optional SupportNonContiguousTensors parameter. Differential Revision: [D76311585](https://our.internmc.facebook.com/intern/diff/D76311585/) [ghstack-poisoned]
swolchok
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…nal views to avoid copying) Pull Request resolved: #11509 These were reverted due to internal test failures. Sending this as an exported internal diff so that we can make sure we get internal signal. Original summary for #11294 (to make the GLU test input asymmetric): This way it will produce different results along each tested dim. Original summaryfor #11295: GLU requires slicing the input Tensor into two halves. Currently, we accomplish this by copying; ExecuTorch does not support views in general because it requires Tensors to be contiguous. However, nothing stops us from implementing [the ATen that uses views](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/GatedLinearUnit.cpp#L35) entirely internally to the op. To support this, I added `support_noncontiguous_tensors` as an optional template argument to BroadcastIndexesRange and plumbed it through to the elementwise_util functions as an optional SupportNonContiguousTensors parameter. ghstack-source-id: 289432903 Differential Revision: [D76311585](https://our.internmc.facebook.com/intern/diff/D76311585/)
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…nal views to avoid copying) (#11539) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: #11509 by @swolchok ^ Please use this as the source of truth for the PR details, comments, and reviews ghstack PR base: https://github.com/pytorch/executorch/tree/gh/swolchok/451/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/swolchok/451/head Merge bot PR base: https://github.com/pytorch/executorch/tree/main Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/swolchok/451/orig @diff-train-skip-merge Co-authored-by: Scott Wolchok <[email protected]>
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This way it will produce different results along each tested dim.