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Move single-tensor-tuple-return test to mlir unit test. Also, add multiple return test.
2022-11-10 22:23:53 +08:00
// RUN: torch-mlir-opt -torch-adjust-calling-conventions -allow-unregistered-dialect -split-input-file -verify-diagnostics %s | FileCheck %s
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @basic(
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3,?],f32>) -> !torch.tensor {
// CHECK: %[[ERASED:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.vtensor<[2,3,?],f32> to !torch.vtensor
Make MaximizeValueSemantics a bit smarter. This adds a pattern to MaximizeValueSemantics which does a simple abstract interpretation within a block, which handles simple cases of `torch.overwrite_tensor`, enough to remove all the unnecessary uses of non-value tensors in ResNet right now. Before/after IR: [gist](https://gist.github.com/silvasean/a3e1ef625b19dfc63579f73cd3b543b6) Also, - Split `torch.copy.tensor` into `torch.copy.to_tensor` and `torch.copy.to_vtensor` which convert between value and non-value semantic tensors. This is a much cleaner factorization as they have very separate use cases and properties (e.g. different side effects) - Remove the various canonicalization patterns they had, which were confusing because they resulted in limited forms of maximizing value semantics throughout the pipeline. We should structure our compilation pipeline such that only MaximizeValueSemantics should be maximizing value semantics. - Adjust pass pipeline to only run MaximizeValueSemantics once. - Make OverwriteTensorOp `$value` always be a value tensor and `$overwritten` be a non-value tensor.
2021-06-19 04:47:47 +08:00
// CHECK: %[[NONVAL_TENSOR:.*]] = torch.copy.to_tensor %[[ERASED]] : !torch.tensor
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK: return %[[NONVAL_TENSOR]] : !torch.tensor
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @basic(%arg0: !torch.tensor {torch.type_bound = !torch.vtensor<[2,3,?],f32>}) -> !torch.tensor {
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
return %arg0 : !torch.tensor
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
}
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @no_type_bound(
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: return %[[ARG]] : !torch.tensor
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @no_type_bound(%arg0: !torch.tensor) -> !torch.tensor {
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
return %arg0 : !torch.tensor
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
}
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @call(
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3,?],f32>) -> !torch.tensor {
// CHECK: %[[ARG_ERASED:.*]] = torch.tensor_static_info_cast %[[ARG]] : !torch.vtensor<[2,3,?],f32> to !torch.vtensor
Make MaximizeValueSemantics a bit smarter. This adds a pattern to MaximizeValueSemantics which does a simple abstract interpretation within a block, which handles simple cases of `torch.overwrite_tensor`, enough to remove all the unnecessary uses of non-value tensors in ResNet right now. Before/after IR: [gist](https://gist.github.com/silvasean/a3e1ef625b19dfc63579f73cd3b543b6) Also, - Split `torch.copy.tensor` into `torch.copy.to_tensor` and `torch.copy.to_vtensor` which convert between value and non-value semantic tensors. This is a much cleaner factorization as they have very separate use cases and properties (e.g. different side effects) - Remove the various canonicalization patterns they had, which were confusing because they resulted in limited forms of maximizing value semantics throughout the pipeline. We should structure our compilation pipeline such that only MaximizeValueSemantics should be maximizing value semantics. - Adjust pass pipeline to only run MaximizeValueSemantics once. - Make OverwriteTensorOp `$value` always be a value tensor and `$overwritten` be a non-value tensor.
2021-06-19 04:47:47 +08:00
// CHECK: %[[ARG_NONVAL:.*]] = torch.copy.to_tensor %[[ARG_ERASED]] : !torch.tensor
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK: %[[INFO_ADDED:.*]] = torch.tensor_static_info_cast %[[ARG_NONVAL]] : !torch.tensor to !torch.tensor<[2,3,?],f32>
Make MaximizeValueSemantics a bit smarter. This adds a pattern to MaximizeValueSemantics which does a simple abstract interpretation within a block, which handles simple cases of `torch.overwrite_tensor`, enough to remove all the unnecessary uses of non-value tensors in ResNet right now. Before/after IR: [gist](https://gist.github.com/silvasean/a3e1ef625b19dfc63579f73cd3b543b6) Also, - Split `torch.copy.tensor` into `torch.copy.to_tensor` and `torch.copy.to_vtensor` which convert between value and non-value semantic tensors. This is a much cleaner factorization as they have very separate use cases and properties (e.g. different side effects) - Remove the various canonicalization patterns they had, which were confusing because they resulted in limited forms of maximizing value semantics throughout the pipeline. We should structure our compilation pipeline such that only MaximizeValueSemantics should be maximizing value semantics. - Adjust pass pipeline to only run MaximizeValueSemantics once. - Make OverwriteTensorOp `$value` always be a value tensor and `$overwritten` be a non-value tensor.
2021-06-19 04:47:47 +08:00
// CHECK: %[[CALL_ARG:.*]] = torch.copy.to_vtensor %[[INFO_ADDED]] : !torch.vtensor<[2,3,?],f32>
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
// CHECK: %[[CALL_RES:.*]] = call @call(%[[CALL_ARG]]) : (!torch.vtensor<[2,3,?],f32>) -> !torch.tensor
// CHECK: return %[[ARG_NONVAL]] : !torch.tensor
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @call(%arg0: !torch.tensor {torch.type_bound = !torch.vtensor<[2,3,?],f32>}) -> !torch.tensor {
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
%0 = call @call(%arg0) : (!torch.tensor) -> !torch.tensor
return %arg0 : !torch.tensor
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
}
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @none_return() {
Introduce native `!torch.none` type. - Add `torch.constant.none` op to construct it (naming is chosen to be analogous to Torch's representation of a prim::Constant with NoneType, rather than using the "singleton" terminology of Basicpy).
2021-06-15 02:36:10 +08:00
// CHECK: %[[NONE:.*]] = torch.constant.none
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
// CHECK: return
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @none_return() -> !torch.none {
Introduce native `!torch.none` type. - Add `torch.constant.none` op to construct it (naming is chosen to be analogous to Torch's representation of a prim::Constant with NoneType, rather than using the "singleton" terminology of Basicpy).
2021-06-15 02:36:10 +08:00
%1 = torch.constant.none
return %1 : !torch.none
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
}
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @none_call_return() {
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
// CHECK: call @none_return() : () -> ()
Introduce native `!torch.none` type. - Add `torch.constant.none` op to construct it (naming is chosen to be analogous to Torch's representation of a prim::Constant with NoneType, rather than using the "singleton" terminology of Basicpy).
2021-06-15 02:36:10 +08:00
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: "test.use"(%[[NONE]]) : (!torch.none) -> ()
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
// CHECK: return
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @none_call_return() {
Introduce native `!torch.none` type. - Add `torch.constant.none` op to construct it (naming is chosen to be analogous to Torch's representation of a prim::Constant with NoneType, rather than using the "singleton" terminology of Basicpy).
2021-06-15 02:36:10 +08:00
%0 = call @none_return() : () -> !torch.none
"test.use"(%0) : (!torch.none) -> ()
Add torch-adjust-calling-conventions pass. This pass incorporates torch.type_bound info and also removes NoneType returns (eventually it will rewrite tuple types too, but can't yet because !basicpy.TupleType doesn't track element types). Recommend looking at adjust-calling-conventions.mlir first to see what it is doing, and holding your nose for the implementation of the pass. I decided to implement this with the conversion framework, because it gives us *some* goodies for type conversion -- mainly avoiding large amounts of tricky RAUW dances. Unfortunately, the conversion framework isn't a perfect fit for a couple reasons: - the incorporation of torch.type_bound is a context-sensitive rewrite (requires looking at the arg attr, not just the type). - NoneType conversion is 1->0, which requires some special handling - (not implemented yet) 1->N tuple type conversions require special handling. It's a little bit scary, but on balance doing it the other way would have its own downsides.
2021-04-02 08:36:18 +08:00
return
}
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @tuple_return(
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[?],f32>) -> (!torch.tensor, !torch.tensor) {
[torch-mlir] bump stablehlo/llvm version (#3471) Update to llvm/llvm-project@5207632f8698a2fab0c4cdcdf2f7ad9aaf96e06f Update to openxla/stablehlo@d41390c3a731ba038e6363f75fcd135e6f727039
2024-06-19 07:59:53 +08:00
// CHECK-DAG: %[[ARG0_ERASED:.*]] = torch.tensor_static_info_cast %[[ARG0]] : !torch.vtensor<[?],f32> to !torch.vtensor
// CHECK-DAG: %[[ARG0_NONVAL:.*]] = torch.copy.to_tensor %[[ARG0_ERASED]] : !torch.tensor
// CHECK-DAG: %[[ARG1_ERASED:.*]] = torch.tensor_static_info_cast %[[ARG1]] : !torch.vtensor<[?],f32> to !torch.vtensor
// CHECK-DAG: %[[ARG1_NONVAL:.*]] = torch.copy.to_tensor %[[ARG1_ERASED]] : !torch.tensor
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: %[[TUPLE:.*]] = torch.prim.TupleConstruct %[[ARG0_NONVAL]], %[[ARG1_NONVAL]] :
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-SAME: !torch.tensor, !torch.tensor -> !torch.tuple<tensor, tensor>
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: %[[CST0:.*]] = torch.constant.int 0
// CHECK: %[[RET0:.*]] = torch.prim.TupleIndex %[[TUPLE]], %[[CST0]] :
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-SAME: !torch.tuple<tensor, tensor>, !torch.int -> !torch.tensor
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: %[[CST1:.*]] = torch.constant.int 1
// CHECK: %[[RET1:.*]] = torch.prim.TupleIndex %[[TUPLE]], %[[CST1]] :
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-SAME: !torch.tuple<tensor, tensor>, !torch.int -> !torch.tensor
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: return %[[RET0]], %[[RET1]] : !torch.tensor, !torch.tensor
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @tuple_return(%arg0: !torch.tensor {torch.type_bound = !torch.vtensor<[?],f32>},
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
%arg1: !torch.tensor {torch.type_bound = !torch.vtensor<[?],f32>}) -> !torch.tuple<tensor, tensor> {
%1 = torch.prim.TupleConstruct %arg0, %arg1 : !torch.tensor, !torch.tensor -> !torch.tuple<tensor, tensor>
return %1 : !torch.tuple<tensor, tensor>
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
}
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @call_tuple_return(
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[?],f32>) -> (!torch.tensor, !torch.tensor) {
[torch-mlir] bump stablehlo/llvm version (#3471) Update to llvm/llvm-project@5207632f8698a2fab0c4cdcdf2f7ad9aaf96e06f Update to openxla/stablehlo@d41390c3a731ba038e6363f75fcd135e6f727039
2024-06-19 07:59:53 +08:00
// CHECK-DAG: %[[ARG0_ERASED:.*]] = torch.tensor_static_info_cast %[[ARG0]] : !torch.vtensor<[?],f32> to !torch.vtensor
// CHECK-DAG: %[[ARG0_NONVAL:.*]] = torch.copy.to_tensor %[[ARG0_ERASED]] : !torch.tensor
// CHECK-DAG: %[[ARG1_ERASED:.*]] = torch.tensor_static_info_cast %[[ARG1]] : !torch.vtensor<[?],f32> to !torch.vtensor
// CHECK-DAG: %[[ARG1_NONVAL:.*]] = torch.copy.to_tensor %[[ARG1_ERASED]] : !torch.tensor
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: %[[ARG0_NONVAL_SHAPED:.*]] = torch.tensor_static_info_cast %[[ARG0_NONVAL]] : !torch.tensor to !torch.tensor<[?],f32>
// CHECK: %[[ARG0_VAL_SHAPED:.*]] = torch.copy.to_vtensor %[[ARG0_NONVAL_SHAPED]] : !torch.vtensor<[?],f32>
// CHECK: %[[ARG1_NONVAL_SHAPED:.*]] = torch.tensor_static_info_cast %[[ARG1_NONVAL]] : !torch.tensor to !torch.tensor<[?],f32>
// CHECK: %[[ARG1_VAL_SHAPED:.*]] = torch.copy.to_vtensor %[[ARG1_NONVAL_SHAPED]] : !torch.vtensor<[?],f32>
// CHECK: %[[RETS:.*]]:2 = call @tuple_return(%[[ARG0_VAL_SHAPED]], %[[ARG1_VAL_SHAPED]]) :
// CHECK-SAME: (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> (!torch.tensor, !torch.tensor)
// CHECK: %[[TUPLE:.*]] = torch.prim.TupleConstruct %[[RETS]]#0, %[[RETS]]#1 :
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-SAME: !torch.tensor, !torch.tensor -> !torch.tuple<tensor, tensor>
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: %[[CST0:.*]] = torch.constant.int 0
// CHECK: %[[RET0:.*]] = torch.prim.TupleIndex %[[TUPLE]], %[[CST0]] :
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-SAME: !torch.tuple<tensor, tensor>, !torch.int -> !torch.tensor
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: %[[CST1:.*]] = torch.constant.int 1
// CHECK: %[[RET1:.*]] = torch.prim.TupleIndex %[[TUPLE]], %[[CST1]] :
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-SAME: !torch.tuple<tensor, tensor>, !torch.int -> !torch.tensor
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
// CHECK: return %[[RET0]], %[[RET1]] : !torch.tensor, !torch.tensor
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
func.func @call_tuple_return(%arg0: !torch.tensor {torch.type_bound = !torch.vtensor<[?],f32>},
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
%arg1: !torch.tensor {torch.type_bound = !torch.vtensor<[?],f32>}) -> !torch.tuple<tensor, tensor> {
%0 = call @tuple_return(%arg0, %arg1) : (!torch.tensor, !torch.tensor) -> !torch.tuple<tensor, tensor>
return %0 : !torch.tuple<tensor, tensor>
Add support for multiple return values This change is to unblock the work of some backprop ops returning more than one tensors. We will need to think of a more scalable approach in the future if more flexible return types combinations are needed.
2021-11-08 23:56:40 +08:00
}