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torch-mlir/test/Conversion/TorchToStd/basic.mlir

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Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-std | FileCheck %s
Add recognition/folder/lowering for aten::__is__, aten::ne.int, and aten::dim Interestingly, TorchScript has its own op (`torch::jit::Operator`) registry separate from the dispatcher (it is a superset of the dispatcher). This is where the "prim" ops and some "aten" ops (that should probably be renamed to "prim") live. In particular, `aten::__is__` is in that latter category of "aten but really prim". This registry is also the source of truth for what the TorchScript interpreter calls into when it executes. The bulk of the "not part of the dispatcher" ops live in https://github.com/pytorch/pytorch/blob/09feb5f579d40b550559360870907963e20c6954/torch/csrc/jit/runtime/register_prim_ops.cpp#L82 And the registry itself lives in: https://github.com/pytorch/pytorch/blob/09feb5f579d40b550559360870907963e20c6954/torch/csrc/jit/runtime/operator.cpp#L196 This fold further reduces the IR of ResNet by folding away some more not-taken branches. These not-taken branches in ResNet require first-class handling of the list type which we don't yet have on any backend.
2021-04-28 06:15:50 +08:00
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +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 @torch.aten.dim(
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<*,f32>) -> !torch.int {
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<*,f32> -> tensor<*xf32>
Update external/llvm-project - Add `qualified` to ods because of https://reviews.llvm.org/D113873 and https://reviews.llvm.org/D116905 - Needed to revert https://github.com/llvm/torch-mlir/pull/520 as it was based on an old torch version. https://github.com/llvm/torch-mlir/pull/527 will bring this back with a better design. - Change ConvertAtenCatOp to use more accurate tensor shape info and as much static info as possible to pass `tensor.insert_slice` verification code added by https://reviews.llvm.org/D114715 - Other minor fixes
2022-01-05 00:00:48 +08:00
// CHECK: %[[RANK:.*]] = tensor.rank %[[BUILTIN_TENSOR]] : tensor<*xf32>
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[RANK_I64:.*]] = arith.index_cast %[[RANK]] : index to i64
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[RANK_TORCH_INT:.*]] = torch_c.from_i64 %[[RANK_I64]]
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
// CHECK: return %[[RANK_TORCH_INT]] : !torch.int
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 @torch.aten.dim(%arg0: !torch.vtensor<*,f32>) -> !torch.int {
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
%0 = torch.aten.dim %arg0 : !torch.vtensor<*,f32> -> !torch.int
return %0 : !torch.int
Add recognition/folder/lowering for aten::__is__, aten::ne.int, and aten::dim Interestingly, TorchScript has its own op (`torch::jit::Operator`) registry separate from the dispatcher (it is a superset of the dispatcher). This is where the "prim" ops and some "aten" ops (that should probably be renamed to "prim") live. In particular, `aten::__is__` is in that latter category of "aten but really prim". This registry is also the source of truth for what the TorchScript interpreter calls into when it executes. The bulk of the "not part of the dispatcher" ops live in https://github.com/pytorch/pytorch/blob/09feb5f579d40b550559360870907963e20c6954/torch/csrc/jit/runtime/register_prim_ops.cpp#L82 And the registry itself lives in: https://github.com/pytorch/pytorch/blob/09feb5f579d40b550559360870907963e20c6954/torch/csrc/jit/runtime/operator.cpp#L196 This fold further reduces the IR of ResNet by folding away some more not-taken branches. These not-taken branches in ResNet require first-class handling of the list type which we don't yet have on any backend.
2021-04-28 06:15:50 +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 @torch.runtime.assert(
[TORCH][MLIR] Add run-time assert support in Torch-dialect - This commit adds `aten.assert` op in the Torch dialect. - The `aten.assert` op is lowered to `mlir::Assert` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-08 22:18:08 +08:00
// CHECK-SAME: %[[X:.*]]: !torch.int,
// CHECK-SAME: %[[Y:.*]]: !torch.int) {
// CHECK: %[[X_I64:.*]] = torch_c.to_i64 %[[X]]
// CHECK: %[[Y_I64:.*]] = torch_c.to_i64 %[[Y]]
// CHECK: %[[CMP:.*]] = arith.cmpi ne, %[[X_I64]], %[[Y_I64]] : i64
// CHECK: assert %[[CMP]], "x must not be equal to y"
// 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 @torch.runtime.assert(%arg0: !torch.int, %arg1: !torch.int) {
[TORCH][MLIR] Add run-time assert support in Torch-dialect - This commit adds `aten.assert` op in the Torch dialect. - The `aten.assert` op is lowered to `mlir::Assert` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-08 22:18:08 +08:00
%0 = torch.aten.ne.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.bool
torch.runtime.assert %0, "x must not be equal to y"
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
// CHECK-LABEL: func.func @torch.aten.ne.int(
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[CMP:.*]] = arith.cmpi ne, %[[LHS_I64]], %[[RHS_I64]] : i64
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.ne.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.bool {
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
%0 = torch.aten.ne.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.bool
Add `!torch.bool` type. This finishes removing the dependence on the basicpy dialect! Changes: - Add `!torch.bool` type and replace use of `!basicpy.BoolType` in Torch-related code. - Rename BuiltinTensorize to BackendTypeConversion since now it handles bool conversions (and, when we add !torch.int and !torch.float, it will handle those as well), and generalize the related utilities (I also moved them to Torch/Transforms since they aren't really part of Torch/IR). - Add `torch.to_i1` and `torch.from_i1` ops for materializations - [cleanup] Reorganize `torch.constant.*` ops in TorchOps.td - Remove dependency of `torch` dialect on `basicpy` dialect and also `std` dialect. For `std`, we use some call related ops, but the `torch` dialect itself never produces them (we have passes that do though). This is fairly mechanical. Recommended review order: - New stuff in Torch/IR - New BuiltinTypeConversion files. - Mechnical fixups elsewhere.
2021-06-16 07:47:53 +08:00
return %0 : !torch.bool
[LINALG] Add E2E support for `aten.eq.int` op - This commit adds lowering of `aten.eq.int` op as a part of `convert-torch-to-std` pass. - It also refactors the code for binary comparison ops lowering. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-12 05:39:30 +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 @torch.aten.eq.int(
[LINALG] Add E2E support for `aten.eq.int` op - This commit adds lowering of `aten.eq.int` op as a part of `convert-torch-to-std` pass. - It also refactors the code for binary comparison ops lowering. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-12 05:39:30 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[CMP:.*]] = arith.cmpi eq, %[[LHS_I64]], %[[RHS_I64]] : i64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.eq.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.bool {
[LINALG] Add E2E support for `aten.eq.int` op - This commit adds lowering of `aten.eq.int` op as a part of `convert-torch-to-std` pass. - It also refactors the code for binary comparison ops lowering. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-12 05:39:30 +08:00
%0 = torch.aten.eq.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
Add recognition/folder/lowering for aten::__is__, aten::ne.int, and aten::dim Interestingly, TorchScript has its own op (`torch::jit::Operator`) registry separate from the dispatcher (it is a superset of the dispatcher). This is where the "prim" ops and some "aten" ops (that should probably be renamed to "prim") live. In particular, `aten::__is__` is in that latter category of "aten but really prim". This registry is also the source of truth for what the TorchScript interpreter calls into when it executes. The bulk of the "not part of the dispatcher" ops live in https://github.com/pytorch/pytorch/blob/09feb5f579d40b550559360870907963e20c6954/torch/csrc/jit/runtime/register_prim_ops.cpp#L82 And the registry itself lives in: https://github.com/pytorch/pytorch/blob/09feb5f579d40b550559360870907963e20c6954/torch/csrc/jit/runtime/operator.cpp#L196 This fold further reduces the IR of ResNet by folding away some more not-taken branches. These not-taken branches in ResNet require first-class handling of the list type which we don't yet have on any backend.
2021-04-28 06:15:50 +08:00
}
Add aten::len.t, aten::size, and aten::gt.int primitive ops Also add some canonicalizations that finally reduce ResNet down to a single block.
2021-04-30 06:38:11 +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 @torch.aten.gt.int(
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[CMP:.*]] = arith.cmpi sgt, %[[LHS_I64]], %[[RHS_I64]] : i64
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.gt.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.bool {
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
%0 = torch.aten.gt.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.bool
Add `!torch.bool` type. This finishes removing the dependence on the basicpy dialect! Changes: - Add `!torch.bool` type and replace use of `!basicpy.BoolType` in Torch-related code. - Rename BuiltinTensorize to BackendTypeConversion since now it handles bool conversions (and, when we add !torch.int and !torch.float, it will handle those as well), and generalize the related utilities (I also moved them to Torch/Transforms since they aren't really part of Torch/IR). - Add `torch.to_i1` and `torch.from_i1` ops for materializations - [cleanup] Reorganize `torch.constant.*` ops in TorchOps.td - Remove dependency of `torch` dialect on `basicpy` dialect and also `std` dialect. For `std`, we use some call related ops, but the `torch` dialect itself never produces them (we have passes that do though). This is fairly mechanical. Recommended review order: - New stuff in Torch/IR - New BuiltinTypeConversion files. - Mechnical fixups elsewhere.
2021-06-16 07:47:53 +08:00
return %0 : !torch.bool
[MLIR][TORCH] Add E2E support for aten.ge.int op This commit adds lowering of `aten.ge.int` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-07-22 20:51:26 +08:00
}
// CHECK-LABEL: func.func @torch.aten.ge.int(
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[CMP:.*]] = arith.cmpi sge, %[[LHS_I64]], %[[RHS_I64]] : i64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
func.func @torch.aten.ge.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.bool {
%0 = torch.aten.ge.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
Add aten::len.t, aten::size, and aten::gt.int primitive ops Also add some canonicalizations that finally reduce ResNet down to a single block.
2021-04-30 06:38:11 +08:00
}
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
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 @torch.vtensor.literal() -> !torch.vtensor<[],f32> {
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[CST:.*]] = arith.constant dense<0.000000e+00> : tensor<f32>
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[VTENSOR:.*]] = torch_c.from_builtin_tensor %[[CST]] : tensor<f32> -> !torch.vtensor<[],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: return %[[VTENSOR]] : !torch.vtensor<[],f32>
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 @torch.vtensor.literal() -> !torch.vtensor<[],f32> {
Add `torch.vtensor.literal` op. This op is much better behaved than the `torch.tensor.literal` op (which is the new name of the `torch.tensor` op). In particular `torch.tensor.literal`: - always has a maximally refined type. - always has value semantics. - can be constant folded / CSE'd. ReduceOpVariants is changed to perform the transformation from `torch.tensor.literal` to `torch.vtensor.literal` (which in general involves static information casts and copies. This new op also allowed tightening up `torch.tensor.literal` to only accept NonValueTensorType (instead of any tensor type). This new ".literal" name is more descriptive. It was getting too confusing seeing an op called just `torch.tensor` (we originally called it that because that's the name of the similar function in the Torch Python API, but it just doesn't fit here).
2021-06-17 23:52:13 +08:00
%0 = torch.vtensor.literal(dense<0.0> : tensor<f32>) : !torch.vtensor<[],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
return %0 : !torch.vtensor<[],f32>
}
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +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 @torch.constant.bool() -> !torch.bool {
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[CST:.*]] = arith.constant true
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[BOOL:.*]] = torch_c.from_i1 %[[CST]]
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +08:00
// CHECK: return %[[BOOL]] : !torch.bool
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 @torch.constant.bool() -> !torch.bool {
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +08:00
%true = torch.constant.bool true
return %true : !torch.bool
}
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 @torch.constant.float() -> !torch.float {
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[CST:.*]] = arith.constant 1.000000e+00 : f64
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[FLOAT:.*]] = torch_c.from_f64 %[[CST]]
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +08:00
// CHECK: return %[[FLOAT]] : !torch.float
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 @torch.constant.float() -> !torch.float {
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +08:00
%float = torch.constant.float 1.000000e+00
return %float : !torch.float
}
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 @torch.constant.int() -> !torch.int {
Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa This brings in https://reviews.llvm.org/D110797. PRs that are in progress will need to use scripts provided by https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-16 02:34:29 +08:00
// CHECK: %[[CST:.*]] = arith.constant 1 : i64
Add TorchToIREE and factor out TorchConversion dialect. This converts a basic list op (torch.prim.ListConstruct) to the IREE dialect. ``` def forward(self, x: float): return [x, x] ``` turns into: ``` builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> { %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %0 : !torch.list<!torch.float> } ``` which turns into: ``` builtin.func @forward(%arg0: f64) -> !iree.list<f64> { %c1 = constant 1 : index %c0 = constant 0 : index %c2 = constant 2 : index %0 = iree.list.create %c2 : !iree.list<f64> iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64 iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64 return %0 : !iree.list<f64> } ``` As part of doing this, I realized that it was time to formalize the IR form that we reach right before running TorchTo{Linalg,Std,...}. We now call it the "Torch backend contract". We then lower the "Torch backend contract" to the "npcomp backend contract", which involves the new TorchConversion (`torch_c`) dialect, which holds ops that need to operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE list, etc.) and the `!torch` types. This made more sense, as I realized that if I didn't factor out `torch_c` then the Torch dialect would have a dependency on IREE dialect (we previously didn't notice this was an issue because we only depended on `builtin` types), which seemed wrong to me. Recommended review order: - TorchToIREE.cpp / `TorchToIREE/basic.mlir` - Look at the new structure of createTorchScriptToNpcompBackendPipeline. It now lives in TorchConversion/Transforms/Passes.cpp and cleanly calls into `Torch::createTorchScriptToTorchBackendPipeline` for the frontend lowering to the Torch backend contract. - Mechanical change extracting `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new TorchConversion dialect, and a few passes specific to the lowering from the Torch backend contract to the npcomp backend contract. - Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that we convert lists as part of operand materialization, we need to use the original operands). Also added test for AtenMaxPool2dOp and fixed m_TorchConstantIntList. - TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that are created as part of operand materialization for conv/max pool/avg pool ops in TorchToLinalg.
2021-08-12 05:40:08 +08:00
// CHECK: %[[INT:.*]] = torch_c.from_i64 %[[CST]]
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +08:00
// CHECK: return %[[INT]] : !torch.int
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 @torch.constant.int() -> !torch.int {
Convert Torch constant ops to std.constant
2021-06-19 01:44:43 +08:00
%int1 = torch.constant.int 1
return %int1 : !torch.int
}
add aten.add.int lowering in TorchToStd
2021-11-24 08:16:32 +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 @torch.aten.add.int(
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.int {
add aten.add.int lowering in TorchToStd
2021-11-24 08:16:32 +08:00
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK: %[[ADD:.*]] = arith.addi %[[LHS_I64:.*]], [[RHS_I64:.*]] : i64
// CHECK: %[[OUT:.*]] = torch_c.from_i64 %[[INT:.*]]
// CHECK: return %[[OUT:.*]] : !torch.int
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 @torch.aten.add.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.int {
add aten.sub.int/aten.mul.int lowering in TorchToStd
2021-12-17 23:24:56 +08:00
%0 = torch.aten.add.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.int
return %0 : !torch.int
}
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 @torch.aten.sub.int(
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.int {
add aten.sub.int/aten.mul.int lowering in TorchToStd
2021-12-17 23:24:56 +08:00
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK: %[[SUB:.*]] = arith.subi %[[LHS_I64:.*]], [[RHS_I64:.*]] : i64
// CHECK: %[[OUT:.*]] = torch_c.from_i64 %[[INT:.*]]
// CHECK: return %[[OUT:.*]] : !torch.int
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 @torch.aten.sub.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.int {
add aten.sub.int/aten.mul.int lowering in TorchToStd
2021-12-17 23:24:56 +08:00
%0 = torch.aten.sub.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.int
return %0 : !torch.int
}
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 @torch.aten.sub.float(
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.float) -> !torch.float {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_F64:.*]] = torch_c.to_f64 %[[RHS]]
// CHECK: %[[SUB:.*]] = arith.subf %[[LHS_F64:.*]], [[RHS_F64:.*]] : f64
// CHECK: %[[OUT:.*]] = torch_c.from_f64 %[[SUB:.*]]
// CHECK: return %[[OUT:.*]] : !torch.float
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 @torch.aten.sub.float(%arg0: !torch.float, %arg1: !torch.float) -> !torch.float {
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
%0 = torch.aten.sub.float %arg0, %arg1 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
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 @torch.aten.mul.int(
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.int,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.int {
add aten.sub.int/aten.mul.int lowering in TorchToStd
2021-12-17 23:24:56 +08:00
// CHECK: %[[LHS_I64:.*]] = torch_c.to_i64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
[LINALG] Add decomposition of `aten.dropout` op - This commit adds decomposition of `aten.dropout` op. It also covers the training mode of the same op. - It also adds lowering of `aten.sub.float` op. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-17 00:34:03 +08:00
// CHECK: %[[MUL:.*]] = arith.muli %[[LHS_I64:.*]], [[RHS_I64:.*]] : i64
// CHECK: %[[OUT:.*]] = torch_c.from_i64 %[[MUL:.*]]
// CHECK: return %[[OUT:.*]] : !torch.int
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 @torch.aten.mul.int(%arg0: !torch.int, %arg1: !torch.int) -> !torch.int {
add aten.sub.int/aten.mul.int lowering in TorchToStd
2021-12-17 23:24:56 +08:00
%0 = torch.aten.mul.int %arg0, %arg1 : !torch.int, !torch.int -> !torch.int
add aten.add.int lowering in TorchToStd
2021-11-24 08:16:32 +08:00
return %0 : !torch.int
}
[MLIR][TORCH] Add E2E support for aten.div.float op This commit adds lowering of `aten.div.float` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 20:06:41 +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 @torch.aten.div.float(
[MLIR][TORCH] Add E2E support for aten.div.float op This commit adds lowering of `aten.div.float` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 20:06:41 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.float) -> !torch.float {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_F64:.*]] = torch_c.to_f64 %[[RHS]]
// CHECK: %[[SUB:.*]] = arith.divf %[[LHS_F64:.*]], [[RHS_F64:.*]] : f64
// CHECK: %[[OUT:.*]] = torch_c.from_f64 %[[SUB:.*]]
// CHECK: return %[[OUT:.*]] : !torch.float
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 @torch.aten.div.float(%arg0: !torch.float, %arg1: !torch.float) -> !torch.float {
[MLIR][TORCH] Add E2E support for aten.div.float op This commit adds lowering of `aten.div.float` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 20:06:41 +08:00
%0 = torch.aten.div.float %arg0, %arg1 : !torch.float, !torch.float -> !torch.float
return %0 : !torch.float
}
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +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 @torch.aten.ge.float(
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.float) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_F64:.*]] = torch_c.to_f64 %[[RHS]]
// CHECK: %[[CMP:.*]] = arith.cmpf uge, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.ge.float(%arg0: !torch.float, %arg1: !torch.float) -> !torch.bool {
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
%0 = torch.aten.ge.float %arg0, %arg1 : !torch.float, !torch.float -> !torch.bool
return %0 : !torch.bool
}
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 @torch.aten.ge.float_int(
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[RHS_F64:.*]] = arith.sitofp %[[RHS_I64]] : i64 to f64
// CHECK: %[[CMP:.*]] = arith.cmpf uge, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.ge.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.bool {
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
%0 = torch.aten.ge.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}
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 @torch.aten.ne.float_int(
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[RHS_F64:.*]] = arith.sitofp %[[RHS_I64]] : i64 to f64
// CHECK: %[[CMP:.*]] = arith.cmpf une, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.ne.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.bool {
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
%0 = torch.aten.ne.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}
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 @torch.aten.ceil.float(
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.int {
// CHECK: %[[ARG_F64:.*]] = torch_c.to_f64 %[[ARG]]
// CHECK: %[[CEIL:.*]] = math.ceil %[[ARG_F64]] : f64
// CHECK: %[[CEIL_I64:.*]] = arith.fptosi %[[CEIL]] : f64 to i64
// CHECK: %[[OUT:.*]] = torch_c.from_i64 %[[CEIL_I64]]
// CHECK: return %[[OUT]] : !torch.int
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 @torch.aten.ceil.float(%arg0: !torch.float) -> !torch.int {
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
%0 = torch.aten.ceil.float %arg0 : !torch.float -> !torch.int
return %0 : !torch.int
}
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 @torch.aten.gt.float_int(
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
// CHECK-SAME: %[[LHS:.*]]: !torch.float,
// CHECK-SAME: %[[RHS:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[LHS_F64:.*]] = torch_c.to_f64 %[[LHS]]
// CHECK: %[[RHS_I64:.*]] = torch_c.to_i64 %[[RHS]]
// CHECK: %[[RHS_F64:.*]] = arith.sitofp %[[RHS_I64]] : i64 to f64
// CHECK: %[[CMP:.*]] = arith.cmpf ugt, %[[LHS_F64]], %[[RHS_F64]] : f64
// CHECK: %[[CMP_TORCH_BOOL:.*]] = torch_c.from_i1 %[[CMP]]
// CHECK: return %[[CMP_TORCH_BOOL]] : !torch.bool
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 @torch.aten.gt.float_int(%arg0: !torch.float, %arg1: !torch.int) -> !torch.bool {
[MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`, `aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op. This commit also fixes formatting for the file scalar.py and scalar_comparison.py. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-25 21:12:45 +08:00
%0 = torch.aten.gt.float_int %arg0, %arg1 : !torch.float, !torch.int -> !torch.bool
return %0 : !torch.bool
}
[MLIR][TORCH] Add E2E support for aten.sqrt.int op This commit adds lowering of `aten.sqrt.int` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-19 22:54:16 +08:00
// CHECK-LABEL: func.func @torch.aten.sqrt.int(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.float {
// CHECK: %[[ARG_I64:.*]] = torch_c.to_i64 %[[ARG]]
// CHECK: %[[ARG_F64:.*]] = arith.sitofp %[[ARG_I64]] : i64 to f64
// CHECK: %[[SQRT:.*]] = math.sqrt %[[ARG_F64]] : f64
// CHECK: %[[SQRT_TORCH_FLOAT:.*]] = torch_c.from_f64 %[[SQRT]]
// CHECK: return %[[SQRT_TORCH_FLOAT]] : !torch.float
func.func @torch.aten.sqrt.int(%arg0: !torch.int) -> !torch.float {
%0 = torch.aten.sqrt.int %arg0 : !torch.int -> !torch.float
return %0 : !torch.float
}
[MLIR][TORCH] Add E2E support for aten.any.bool op This commit adds lowering of `aten.any.bool` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-23 23:26:02 +08:00
// CHECK-LABEL: func.func @torch.aten.any.bool() -> !torch.bool {
// CHECK: %[[CST_FALSE:.*]] = arith.constant false
// CHECK: %[[FALSE:.*]] = torch_c.from_i1 %[[CST_FALSE]]
// CHECK: %[[CST_TRUE:.*]] = arith.constant true
// CHECK: %[[TRUE:.*]] = torch_c.from_i1 %[[CST_TRUE]]
// CHECK: %[[INPUT:.*]] = torch.prim.ListConstruct %[[FALSE]], %[[TRUE]], %[[FALSE]] : (!torch.bool, !torch.bool, !torch.bool) -> !torch.list<bool>
// CHECK: %[[CST_RESULT:.*]] = arith.constant true
// CHECK: %[[RESULT:.*]] = torch_c.from_i1 %[[CST_RESULT]]
// CHECK: return %[[RESULT]] : !torch.bool
func.func @torch.aten.any.bool() -> !torch.bool {
%false = torch.constant.bool false
%true = torch.constant.bool true
%input = torch.prim.ListConstruct %false, %true, %false : (!torch.bool, !torch.bool, !torch.bool) -> !torch.list<bool>
%0 = torch.aten.any.bool %input : !torch.list<bool> -> !torch.bool
return %0 : !torch.bool
}
[MLIR][TORCH] Add E2E support for aten.Bool.[float|int] op This commit adds lowering of `aten.Bool.float` and `aten.Bool.int` op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-20 16:26:52 +08:00
// CHECK-LABEL: func.func @torch.aten.Bool.float(
// CHECK-SAME: %[[ARG:.*]]: !torch.float) -> !torch.bool {
// CHECK: %[[ARG_F64:.*]] = torch_c.to_f64 %[[ARG]]
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f64
// CHECK: %[[TRUE:.*]] = arith.constant true
// CHECK: %[[FALSE:.*]] = arith.constant false
// CHECK: %[[CMP:.*]] = arith.cmpf une, %[[ARG_F64]], %[[CST]] : f64
// CHECK: %[[SELECT:.*]] = arith.select %[[CMP]], %[[TRUE]], %[[FALSE]] : i1
// CHECK: %[[OUT:.*]] = torch_c.from_i1 %[[SELECT]]
// CHECK: return %[[OUT]] : !torch.bool
func.func @torch.aten.Bool.float(%arg0: !torch.float) -> !torch.bool {
%0 = torch.aten.Bool.float %arg0 : !torch.float -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func.func @torch.aten.Bool.int(
// CHECK-SAME: %[[ARG:.*]]: !torch.int) -> !torch.bool {
// CHECK: %[[ARG_I64:.*]] = torch_c.to_i64 %[[ARG]]
// CHECK: %[[CST:.*]] = arith.constant 0 : i64
// CHECK: %[[TRUE:.*]] = arith.constant true
// CHECK: %[[FALSE:.*]] = arith.constant false
// CHECK: %[[CMP:.*]] = arith.cmpi ne, %[[ARG_I64]], %[[CST]] : i64
// CHECK: %[[SELECT:.*]] = arith.select %[[CMP]], %[[TRUE]], %[[FALSE]] : i1
// CHECK: %[[OUT:.*]] = torch_c.from_i1 %[[SELECT]]
// CHECK: return %[[OUT]] : !torch.bool
func.func @torch.aten.Bool.int(%arg0: !torch.int) -> !torch.bool {
%0 = torch.aten.Bool.int %arg0 : !torch.int -> !torch.bool
return %0 : !torch.bool
}