[ET-VK][Ops] aten.index_select#3744
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## The Operator `nn.Module` invocations of [`torch.index_select`](https://pytorch.org/docs/stable/generated/torch.index_select.html) get compiled to `aten.index_select.default` in the Edge Dialect, which carries the following signature. ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` ## Implementation This is a C-packing-only implementation. It is very similar to `aten.slice`: #3171 ``` - func: slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) ``` It features a similar split between a shader for N,H,W and a shader for C, because copying from the C-dimension is more difficult due to C-packing. Both `index_select` and `slice` copy specific indices across 1 dimension. The difference is in the way these indices are specified. - `slice` uses `start=1`/`end=5`/`step=2` as three scalars for indices `1,3`. - `index_select` lists the exact indices inside a tensor e.g. `index=torch.tensor([1,3])`. Hence, `slice` uses a `offset=1` and `step=2` to compute input position. In `index_select`, we read the index tensor to compute input position. Differential Revision: [D57745489](https://our.internmc.facebook.com/intern/diff/D57745489/) [ghstack-poisoned]
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## The Operator `nn.Module` invocations of [`torch.index_select`](https://pytorch.org/docs/stable/generated/torch.index_select.html) get compiled to `aten.index_select.default` in the Edge Dialect, which carries the following signature. ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` ## Implementation This is a C-packing-only implementation. It is very similar to `aten.slice`: #3171 ``` - func: slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) ``` It features a similar split between a shader for N,H,W and a shader for C, because copying from the C-dimension is more difficult due to C-packing. Both `index_select` and `slice` copy specific indices across 1 dimension. The difference is in the way these indices are specified. - `slice` uses `start=1`/`end=5`/`step=2` as three scalars for indices `1,3`. - `index_select` lists the exact indices inside a tensor e.g. `index=torch.tensor([1,3])`. Hence, `slice` uses a `offset=1` and `step=2` to compute input position. In `index_select`, we read the index tensor to compute input position. Differential Revision: [D57745489](https://our.internmc.facebook.com/intern/diff/D57745489/) ghstack-source-id: 227736336 Pull Request resolved: #3744
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D57745489 |
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) ghstack-source-id: 228038402 Pull Request resolved: #3762
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/) [ghstack-poisoned]
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Summary: Pull Request resolved: #3762 ## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: #3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` ghstack-source-id: 228201965 Reviewed By: copyrightly Differential Revision: D57880520 fbshipit-source-id: 67da04bcbb2b36ce2c1ec2c8f7ccf59ed512547c
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Pull Request resolved: pytorch/executorch#3762 ## The Operator `nn.Module` invocations on the embedding returned by [`torch.nn.Embedding`](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) get compiled to `aten.embedding.default` in the Edge Dialect, which carries the following signature. ``` - func: embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor ``` ## Implementation This is a C-packing-only implementation. Interestingly, the 1D-`indices` case is equivalent to the `dim=0` case of the preceding `aten.index_select`: pytorch/executorch#3744 ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` I naïvely thought the rest of the operator would be similarly easy but it wasn't. The 2D and 3D-`indices` cases are more involved to the extent that we require a standalone `cpp`/`glsl` file. ## Codegen We add support for making 2D and 3D index tensors. This requires new generation functions as well as renaming of the `case_name` string to recursively handle list `pylist`s. ``` // 1D Test(weight=[10, 9], indices=[0, 2]), // 2D Test(weight=[10, 9], indices=[[0, 2], [1, 4], [7, 7]]), // 3D Test(weight=[10, 9], indices=[[[3, 1, 4], [1, 5, 9]], [[2, 6, 5], [3, 5, 8]]]), ``` ghstack-source-id: 228201965 Differential Revision: [D57880520](https://our.internmc.facebook.com/intern/diff/D57880520/)
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Pull Request resolved: pytorch/executorch#3744 ## The Operator `nn.Module` invocations of [`torch.index_select`](https://pytorch.org/docs/stable/generated/torch.index_select.html) get compiled to `aten.index_select.default` in the Edge Dialect, which carries the following signature. ``` - func: index_select(Tensor self, int dim, Tensor index) -> Tensor ``` ## Implementation This is a C-packing-only implementation. It is very similar to `aten.slice`: pytorch/executorch#3171 ``` - func: slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) ``` It features a similar split between a shader for N,H,W and a shader for C, because copying from the C-dimension is more difficult due to C-packing. Both `index_select` and `slice` copy specific indices across 1 dimension. The difference is in the way these indices are specified. - `slice` uses `start=1`/`end=5`/`step=2` as three scalars for indices `1,3`. - `index_select` lists the exact indices inside a tensor e.g. `index=torch.tensor([1,3])`. Hence, `slice` uses a `offset=1` and `step=2` to compute input position. In `index_select`, we read the index tensor to compute input position. Differential Revision: [D57745489](https://our.internmc.facebook.com/intern/diff/D57745489/) ghstack-source-id: 227954599
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Stack from ghstack (oldest at bottom):
The Operator
nn.Moduleinvocations oftorch.index_selectget compiled toaten.index_select.defaultin the Edge Dialect, which carries the following signature.Implementation
This is a C-packing-only implementation. It is very similar to
aten.slice: #3171It features a similar split between a shader for N,H,W and a shader for C, because copying from the C-dimension is more difficult due to C-packing.
Both
index_selectandslicecopy specific indices across 1 dimension. The difference is in the way these indices are specified.sliceusesstart=1/end=5/step=2as three scalars for indices1,3.index_selectlists the exact indices inside a tensor e.g.index=torch.tensor([1,3]).Hence,
sliceuses aoffset=1andstep=2to compute input position. Inindex_select, we read the index tensor to compute input position.Differential Revision: D57745489