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torch-mlir/test/Dialect/Torch/reify-shape-calculations.mlir

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Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// RUN: torch-mlir-opt -torch-reify-shape-calculations -split-input-file %s | FileCheck %s
// CHECK: module {
// CHECK: func private @__torch_mlir_shape_fn.aten.tanh(
// CHECK-LABEL: func @basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor) -> !torch.vtensor {
// CHECK: %[[RESULT:.*]] = torch.shape.calculate {
// CHECK: %[[TANH:.*]] = torch.aten.tanh %[[ARG]] : !torch.vtensor -> !torch.vtensor
// CHECK: torch.shape.calculate.yield %[[TANH]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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// CHECK: %[[SHAPE:.*]] = torch.aten.size %[[ARG]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[RESULT_SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.tanh(%[[SHAPE]]) : (!torch.list<int>) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[RESULT_SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: } : !torch.vtensor
// CHECK: return %[[RESULT:.*]] : !torch.vtensor
func @basic(%arg0: !torch.vtensor) -> !torch.vtensor {
%0 = torch.aten.tanh %arg0 : !torch.vtensor -> !torch.vtensor
return %0 : !torch.vtensor
}
// -----
// CHECK: module {
// CHECK: func private @__torch_mlir_shape_fn.aten.fill.Scalar(
Improve "pseudo" op terminology. The term "pseudo" is very vague and was getting confusing (I felt I had to explain it in every comment referencing it). Instead, rework the "pseudo" ops to instead be named: - MLIR Syntax: `torch.valsem.*` - C++ / ODS: `ValsemVariant*Op` This makes it clear what the concept is, and avoids confusion with other things that might be called "pseudo", since these are very specific and should be 100% consistently named w.r.t. the non-valsem-variant ops that they correspond to.
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// CHECK-LABEL: func @valsem_ops(
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor,
// CHECK-SAME: %[[ARG1:.*]]: !torch.int) -> !torch.vtensor {
// CHECK: %[[RESULT:.*]] = torch.shape.calculate {
Improve "pseudo" op terminology. The term "pseudo" is very vague and was getting confusing (I felt I had to explain it in every comment referencing it). Instead, rework the "pseudo" ops to instead be named: - MLIR Syntax: `torch.valsem.*` - C++ / ODS: `ValsemVariant*Op` This makes it clear what the concept is, and avoids confusion with other things that might be called "pseudo", since these are very specific and should be 100% consistently named w.r.t. the non-valsem-variant ops that they correspond to.
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// CHECK: %[[VALUE:.*]] = torch.valsem.aten.fill.Scalar %[[ARG0]], %[[ARG1]] : !torch.vtensor, !torch.int -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: torch.shape.calculate.yield %[[VALUE]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[SHAPE:.*]] = torch.aten.size %[[ARG0]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[RESULT_SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.fill.Scalar(%[[SHAPE]], %{{.*}}) : (!torch.list<int>, !torch.float) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[RESULT_SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: } : !torch.vtensor
// CHECK: return %[[RESULT:.*]] : !torch.vtensor
Improve "pseudo" op terminology. The term "pseudo" is very vague and was getting confusing (I felt I had to explain it in every comment referencing it). Instead, rework the "pseudo" ops to instead be named: - MLIR Syntax: `torch.valsem.*` - C++ / ODS: `ValsemVariant*Op` This makes it clear what the concept is, and avoids confusion with other things that might be called "pseudo", since these are very specific and should be 100% consistently named w.r.t. the non-valsem-variant ops that they correspond to.
2022-03-16 07:57:33 +08:00
func @valsem_ops(%arg0: !torch.vtensor, %arg1: !torch.int) -> !torch.vtensor {
%0 = torch.valsem.aten.fill.Scalar %arg0, %arg1 : !torch.vtensor, !torch.int -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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return %0 : !torch.vtensor
}
// -----
// CHECK: module {
// CHECK-LABEL: func private @__torch_mlir_shape_fn.aten.uniform(
// CHECK-SAME: {{.*}}!torch.any)
// CHECK-LABEL: func @adjust_shape_function_arg$torch.any(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor,
// CHECK-SAME: %[[ARG1:.*]]: !torch.float) -> !torch.vtensor {
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[RESULT:.*]] = torch.shape.calculate {
Improve "pseudo" op terminology. The term "pseudo" is very vague and was getting confusing (I felt I had to explain it in every comment referencing it). Instead, rework the "pseudo" ops to instead be named: - MLIR Syntax: `torch.valsem.*` - C++ / ODS: `ValsemVariant*Op` This makes it clear what the concept is, and avoids confusion with other things that might be called "pseudo", since these are very specific and should be 100% consistently named w.r.t. the non-valsem-variant ops that they correspond to.
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// CHECK: %[[UNIFORM:.*]] = torch.valsem.aten.uniform %[[ARG0]], %[[ARG1]], %[[ARG1]], %[[NONE]] : !torch.vtensor, !torch.float, !torch.float, !torch.none -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: torch.shape.calculate.yield %[[UNIFORM]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[ARG0_SHAPE:.*]] = torch.aten.size %[[ARG0]] : !torch.vtensor -> !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: %[[ANY:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.any
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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// CHECK: %[[SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.uniform(%[[ARG0_SHAPE]], %[[ARG1]], %[[ARG1]], %[[ANY]]) : (!torch.list<int>, !torch.float, !torch.float, !torch.any) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: } : !torch.vtensor
// CHECK: return %[[RESULT:.*]] : !torch.vtensor
func @adjust_shape_function_arg$torch.any(%arg0: !torch.vtensor, %arg1: !torch.float) -> !torch.vtensor {
%none = torch.constant.none
Improve "pseudo" op terminology. The term "pseudo" is very vague and was getting confusing (I felt I had to explain it in every comment referencing it). Instead, rework the "pseudo" ops to instead be named: - MLIR Syntax: `torch.valsem.*` - C++ / ODS: `ValsemVariant*Op` This makes it clear what the concept is, and avoids confusion with other things that might be called "pseudo", since these are very specific and should be 100% consistently named w.r.t. the non-valsem-variant ops that they correspond to.
2022-03-16 07:57:33 +08:00
%0 = torch.valsem.aten.uniform %arg0, %arg1, %arg1, %none : !torch.vtensor, !torch.float, !torch.float, !torch.none -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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return %0 : !torch.vtensor
}
// -----
// torch.aten.add.Tensor also has a shape function that calls a "broadcast"
// helper, so this test also checks our logic for transitively pulling in
// callees of the shape functions.
// CHECK: module {
// CHECK: func private @__torch_mlir_shape_fn.aten.add.Tensor(
// CHECK-LABEL: func @adjust_shape_function_arg$scalar(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor) -> !torch.vtensor {
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[RESULT:.*]] = torch.shape.calculate {
// CHECK: %[[ADD:.*]] = torch.aten.add.Tensor %[[ARG0]], %[[ARG1]], %[[INT1]] : !torch.vtensor, !torch.vtensor, !torch.int -> !torch.vtensor
// CHECK: torch.shape.calculate.yield %[[ADD]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[ARG0_SHAPE:.*]] = torch.aten.size %[[ARG0]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[ARG1_SHAPE:.*]] = torch.aten.size %[[ARG1]] : !torch.vtensor -> !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: %[[SCALAR_CONVERTED:.*]] = torch.aten.Float.Scalar %[[INT1]] : !torch.int -> !torch.float
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[RESULT_SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.add.Tensor(%[[ARG0_SHAPE]], %[[ARG1_SHAPE]], %[[SCALAR_CONVERTED]]) : (!torch.list<int>, !torch.list<int>, !torch.float) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[RESULT_SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: } : !torch.vtensor
// CHECK: return %[[RESULT:.*]] : !torch.vtensor
func @adjust_shape_function_arg$scalar(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> !torch.vtensor {
%int1 = torch.constant.int 1
%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor, !torch.vtensor, !torch.int -> !torch.vtensor
return %0 : !torch.vtensor
}
// -----
// CHECK: module {
// CHECK: func private @__torch_mlir_shape_fn.aten.topk(
// CHECK-LABEL: func @multiple_results(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> (!torch.tensor, !torch.tensor) {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: %[[INT3:.*]] = torch.constant.int 3
// CHECK: %[[INT1:.*]] = torch.constant.int 1
// CHECK: %[[RESULTS:.*]]:2 = torch.shape.calculate {
// CHECK: %[[TOP_VALUES:.*]], %[[TOPK_INDICES:.*]] = torch.aten.topk %[[ARG]], %[[INT3]], %[[INT1]], %[[TRUE]], %[[TRUE]] : !torch.tensor, !torch.int, !torch.int, !torch.bool, !torch.bool -> !torch.tensor, !torch.tensor
// CHECK: torch.shape.calculate.yield %[[TOP_VALUES]], %[[TOPK_INDICES]] : !torch.tensor, !torch.tensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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// CHECK: %[[ARG_SHAPE:.*]] = torch.aten.size %[[ARG]] : !torch.tensor -> !torch.list<int>
// CHECK: %[[TOPK_SHAPE_TUPLE:.*]] = call @__torch_mlir_shape_fn.aten.topk(%[[ARG_SHAPE]], %[[INT3]], %[[INT1]], %[[TRUE]], %[[TRUE]]) : (!torch.list<int>, !torch.int, !torch.int, !torch.bool, !torch.bool) -> !torch.tuple<list<int>, list<int>>
// CHECK: %[[TOPK_SHAPE:.*]]:2 = torch.prim.TupleUnpack %[[TOPK_SHAPE_TUPLE]] : !torch.tuple<list<int>, list<int>> -> !torch.list<int>, !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[TOPK_SHAPE]]#0, %[[TOPK_SHAPE]]#1 : !torch.list<int>, !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: } : !torch.tensor, !torch.tensor
// CHECK: return %[[RESULTS:.*]]#0, %[[RESULTS]]#1 : !torch.tensor, !torch.tensor
func @multiple_results(%arg0: !torch.tensor) -> (!torch.tensor, !torch.tensor) {
%true = torch.constant.bool true
%int3 = torch.constant.int 3
%int1 = torch.constant.int 1
%values, %indices = torch.aten.topk %arg0, %int3, %int1, %true, %true : !torch.tensor, !torch.int, !torch.int, !torch.bool, !torch.bool -> !torch.tensor, !torch.tensor
return %values, %indices : !torch.tensor, !torch.tensor
}
// -----
// CHECK-LABEL: func @adjust_shape_function_arg$optional(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor) -> !torch.vtensor {
// CHECK: %[[RESULT:.*]] = torch.shape.calculate {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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// CHECK: %[[CONV:.*]] = torch.aten.conv2d %[[ARG0]], %[[ARG1]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}} : !torch.vtensor, !torch.vtensor, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.int -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: torch.shape.calculate.yield %[[CONV]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[SHAPE0:.*]] = torch.aten.size %[[ARG0]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[SHAPE1:.*]] = torch.aten.size %[[ARG1]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[DEREFINED:.*]] = torch.derefine %{{.*}} : !torch.none to !torch.optional<list<int>>
// CHECK: %[[SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.conv2d(%[[SHAPE0]], %[[SHAPE1]], %[[DEREFINED]], %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}) : (!torch.list<int>, !torch.list<int>, !torch.optional<list<int>>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.int) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: } : !torch.vtensor
// CHECK: return %[[RESULT:.*]] : !torch.vtensor
func @adjust_shape_function_arg$optional(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> !torch.vtensor {
%int3 = torch.constant.int 3
%int4 = torch.constant.int 4
%int2 = torch.constant.int 2
%int1 = torch.constant.int 1
%none = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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%24 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%25 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
%26 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%29 = torch.aten.conv2d %arg0, %arg1, %none, %24, %25, %26, %int1 : !torch.vtensor, !torch.vtensor, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.int -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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return %29 : !torch.vtensor
}
// -----
// CHECK-LABEL: func @adjust_shape_function_arg$optional_tensor(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor) -> !torch.vtensor {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: %[[C1EM5:.*]] = torch.constant.float 1.000000e-05
// CHECK: %[[C1EM1:.*]] = torch.constant.float 1.000000e-01
// CHECK: %[[NONE:.*]] = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[DEREFINED:.*]] = torch.derefine %[[ARG]] : !torch.vtensor to !torch.optional<vtensor>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: %[[RESULT:.*]] = torch.shape.calculate {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[BN:.*]] = torch.aten.batch_norm %[[ARG]], %[[DEREFINED]], %[[NONE]], %[[NONE]], %[[NONE]], %[[FALSE]], %[[C1EM1]], %[[C1EM5]], %[[TRUE]] : !torch.vtensor, !torch.optional<vtensor>, !torch.none, !torch.none, !torch.none, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: torch.shape.calculate.yield %[[BN]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[ARG_SIZE:.*]] = torch.aten.size %[[ARG]] : !torch.vtensor -> !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: %[[NONE2:.*]] = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[IS:.*]] = torch.aten.__is__ %[[DEREFINED]], %[[NONE2]] : !torch.optional<vtensor>, !torch.none -> !torch.bool
// CHECK: %[[DEREFINED_OPTIONAL_SIZE:.*]] = torch.prim.If %[[IS]] -> (!torch.optional<list<int>>) {
// CHECK: %[[DEREFINE_NONE:.*]] = torch.derefine %[[NONE2]] : !torch.none to !torch.optional<list<int>>
// CHECK: torch.prim.If.yield %[[DEREFINE_NONE]] : !torch.optional<list<int>>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: } else {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[DOWNCASTED:.*]] = torch.prim.unchecked_cast %[[DEREFINED]] : !torch.optional<vtensor> -> !torch.vtensor
// CHECK: %[[DOWNCASTED_SIZE:.*]] = torch.aten.size %[[DOWNCASTED]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[DEREFINE_DOWNCASTED_SIZE:.*]] = torch.derefine %[[DOWNCASTED_SIZE]] : !torch.list<int> to !torch.optional<list<int>>
// CHECK: torch.prim.If.yield %[[DEREFINE_DOWNCASTED_SIZE]] : !torch.optional<list<int>>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: }
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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// CHECK: %[[DEREFINED_NONE1:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.optional<list<int>>
// CHECK: %[[DEREFINED_NONE2:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.optional<list<int>>
// CHECK: %[[DEREFINED_NONE3:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.optional<list<int>>
// CHECK: %[[BN_SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.batch_norm(%[[ARG_SIZE]], %[[DEREFINED_OPTIONAL_SIZE:.*]], %[[DEREFINED_NONE1]], %[[DEREFINED_NONE2]], %[[DEREFINED_NONE3]], %[[FALSE]], %[[C1EM1]], %[[C1EM5]], %[[TRUE]]) : (!torch.list<int>, !torch.optional<list<int>>, !torch.optional<list<int>>, !torch.optional<list<int>>, !torch.optional<list<int>>, !torch.bool, !torch.float, !torch.float, !torch.bool) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[BN_SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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// CHECK: } : !torch.vtensor
// CHECK: return %[[RESULT:.*]] : !torch.vtensor
func @adjust_shape_function_arg$optional_tensor(%arg0: !torch.vtensor) -> !torch.vtensor {
%false = torch.constant.bool false
%true = torch.constant.bool true
%float1.000000e-05 = torch.constant.float 1.000000e-05
%float1.000000e-01 = torch.constant.float 1.000000e-01
%none = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
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%derefined_tensor = torch.derefine %arg0 : !torch.vtensor to !torch.optional<vtensor>
%0 = torch.aten.batch_norm %arg0, %derefined_tensor, %none, %none, %none, %false, %float1.000000e-01, %float1.000000e-05, %true : !torch.vtensor, !torch.optional<vtensor>, !torch.none, !torch.none, !torch.none, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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return %0 : !torch.vtensor
}
// -----
// CHECK-LABEL: func @adjust_shape_function_arg$list(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor) -> !torch.vtensor {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[LIST:.*]] = torch.prim.ListConstruct %[[ARG1]] : (!torch.vtensor) -> !torch.list<vtensor>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: %[[VAL_3:.*]] = torch.shape.calculate {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[VAL_4:.*]] = torch.aten.index.Tensor %[[ARG0]], %[[LIST]] : !torch.vtensor, !torch.list<vtensor> -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: torch.shape.calculate.yield %[[VAL_4]] : !torch.vtensor
// CHECK: } shapes {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[ARG0_SHAPE:.*]] = torch.aten.size %[[ARG0]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[ADJUSTED_LIST:.*]] = torch.prim.ListConstruct : () -> !torch.list<optional<list<int>>>
// CHECK: %[[LIST_SIZE:.*]] = torch.aten.len.t %[[LIST]] : !torch.list<vtensor> -> !torch.int
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: %[[CTRUE:.*]] = torch.constant.bool true
// CHECK: torch.prim.Loop %[[LIST_SIZE]], %[[CTRUE]], init() {
// CHECK: ^bb0(%[[ITER_NUM:.*]]: !torch.int):
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[UNADJUSTED_ELEMENT:.*]] = torch.aten.__getitem__.t %[[LIST]], %[[ITER_NUM]] : !torch.list<vtensor>, !torch.int -> !torch.vtensor
// CHECK: %[[UNADJUSTED_ELEMENT_SHAPE:.*]] = torch.aten.size %[[UNADJUSTED_ELEMENT]] : !torch.vtensor -> !torch.list<int>
// CHECK: %[[ADJUSTED_ELEMENT:.*]] = torch.derefine %[[UNADJUSTED_ELEMENT_SHAPE]] : !torch.list<int> to !torch.optional<list<int>>
// CHECK: %{{.*}} = torch.aten.append.t %[[ADJUSTED_LIST]], %[[ADJUSTED_ELEMENT]] : !torch.list<optional<list<int>>>, !torch.optional<list<int>> -> !torch.list<optional<list<int>>>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: torch.prim.Loop.condition %[[CTRUE]], iter()
// CHECK: } : (!torch.int, !torch.bool) -> ()
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[RESULT_SHAPE:.*]] = call @__torch_mlir_shape_fn.aten.index.Tensor(%[[ARG0_SHAPE]], %[[ADJUSTED_LIST]]) : (!torch.list<int>, !torch.list<optional<list<int>>>) -> !torch.list<int>
// CHECK: torch.shape.calculate.yield.shapes %[[RESULT_SHAPE]] : !torch.list<int>
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-10 08:44:22 +08:00
// CHECK: } : !torch.vtensor
// CHECK: return %[[VAL_15:.*]] : !torch.vtensor
func @adjust_shape_function_arg$list(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> !torch.vtensor {
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.prim.ListConstruct %arg1 : (!torch.vtensor) -> !torch.list<vtensor>
%1 = torch.aten.index.Tensor %arg0, %0 : !torch.vtensor, !torch.list<vtensor> -> !torch.vtensor
Introduce new shape library design. See the documentation in `docs/shape_lib.md` and `docs/adding_a_shape_function.md` for an overview of the system. This completely overhauls how we represent shape functions. In particular, RefineTypes does not infer shapes anymore (only dtypes). Shape functions are now written in (TorchScript'able) Python. Recommended review order: 1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`. 1. Code and tests for ReifyShapeCalculations, DropShapeCalculations. 1. Code and tests for SimplifyShapeCalculations. 1. shape_lib_gen.py 1. Code and tests for new RefineTypes pass. 1. Random folders/canonicalizers in TorchOps.cpp and associated test in `canonicalize.mlir`. 1. New ReadOnly trait inferred from the registry. 1. Any miscellaneous remaining stuff. Example `-print-ir-after-all` for ElementwiseUnaryModule: [IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212). Example `-print-ir-after-all` for ElementwiseBinaryModule: [IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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return %1 : !torch.vtensor
}