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torch-mlir/test/Conversion/TorchToLinalg/flatten.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-linalg -split-input-file -verify-diagnostics | FileCheck %s
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +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.flatten.using_ints$basic(
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK-SAME: %[[TENSOR:.*]]: !torch.vtensor<[3,3,2,2,3,3,5],f32>) -> !torch.vtensor<[3,3,?,3,5],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: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[TENSOR]] : !torch.vtensor<[3,3,2,2,3,3,5],f32> -> tensor<3x3x2x2x3x3x5xf32>
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: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1], [2, 3, 4], [5], [6]] : tensor<3x3x2x2x3x3x5xf32> into tensor<3x3x12x3x5xf32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: %[[DYNAMIC:.*]] = tensor.cast %[[COLLAPSED]] : tensor<3x3x12x3x5xf32> to tensor<3x3x?x3x5xf32>
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: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[DYNAMIC]] : tensor<3x3x?x3x5xf32> -> !torch.vtensor<[3,3,?,3,5],f32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: return %[[RESULT]] : !torch.vtensor<[3,3,?,3,5],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.aten.flatten.using_ints$basic(%arg0: !torch.vtensor<[3,3,2,2,3,3,5],f32>) -> !torch.vtensor<[3,3,?,3,5],f32> {
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
%int2 = torch.constant.int 2
%int4 = torch.constant.int 4
%0 = torch.aten.flatten.using_ints %arg0, %int2, %int4 : !torch.vtensor<[3,3,2,2,3,3,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,3,?,3,5],f32>
return %0 : !torch.vtensor<[3,3,?,3,5],f32>
}
// -----
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @torch.aten.flatten.using_ints$basic_negative(
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK-SAME: %[[TENSOR:.*]]: !torch.vtensor<[3,3,2,2,3,3,5],f32>) -> !torch.vtensor<[3,3,?,3,5],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: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[TENSOR]] : !torch.vtensor<[3,3,2,2,3,3,5],f32> -> tensor<3x3x2x2x3x3x5xf32>
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: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1], [2, 3, 4], [5], [6]] : tensor<3x3x2x2x3x3x5xf32> into tensor<3x3x12x3x5xf32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: %[[DYNAMIC:.*]] = tensor.cast %[[COLLAPSED]] : tensor<3x3x12x3x5xf32> to tensor<3x3x?x3x5xf32>
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: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[DYNAMIC]] : tensor<3x3x?x3x5xf32> -> !torch.vtensor<[3,3,?,3,5],f32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: return %[[RESULT]] : !torch.vtensor<[3,3,?,3,5],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.aten.flatten.using_ints$basic_negative(%arg0: !torch.vtensor<[3,3,2,2,3,3,5],f32>) -> !torch.vtensor<[3,3,?,3,5],f32> {
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
%int-5 = torch.constant.int -5
%int-3 = torch.constant.int -3
%0 = torch.aten.flatten.using_ints %arg0, %int-5, %int-3 : !torch.vtensor<[3,3,2,2,3,3,5],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,3,?,3,5],f32>
return %0 : !torch.vtensor<[3,3,?,3,5],f32>
}
// -----
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @torch.aten.flatten.using_ints$flatten_front(
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK-SAME: %[[TENSOR:.*]]: !torch.vtensor<[3,3,2,2],f32>) -> !torch.vtensor<[?,?],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: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[TENSOR]] : !torch.vtensor<[3,3,2,2],f32> -> tensor<3x3x2x2xf32>
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: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0, 1, 2], [3]] : tensor<3x3x2x2xf32> into tensor<18x2xf32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: %[[DYNAMIC:.*]] = tensor.cast %[[COLLAPSED]] : tensor<18x2xf32> to tensor<?x?xf32>
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: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[DYNAMIC]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: return %[[RESULT]] : !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.aten.flatten.using_ints$flatten_front(%arg0: !torch.vtensor<[3,3,2,2],f32>) -> !torch.vtensor<[?,?],f32> {
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%0 = torch.aten.flatten.using_ints %arg0, %int0, %int2 : !torch.vtensor<[3,3,2,2],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @torch.aten.flatten.using_ints$flatten_back(
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK-SAME: %[[TENSOR:.*]]: !torch.vtensor<[3,3,2,2],f32>) -> !torch.vtensor<[?,12],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: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[TENSOR]] : !torch.vtensor<[3,3,2,2],f32> -> tensor<3x3x2x2xf32>
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: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[BUILTIN_TENSOR]] {{\[\[}}0], [1, 2, 3]] : tensor<3x3x2x2xf32> into tensor<3x12xf32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: %[[DYNAMIC:.*]] = tensor.cast %[[COLLAPSED]] : tensor<3x12xf32> to tensor<?x12xf32>
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: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[DYNAMIC]] : tensor<?x12xf32> -> !torch.vtensor<[?,12],f32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,12],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.aten.flatten.using_ints$flatten_back(%arg0: !torch.vtensor<[3,3,2,2],f32>) -> !torch.vtensor<[?,12],f32> {
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
%int1 = torch.constant.int 1
%int-1 = torch.constant.int -1
%0 = torch.aten.flatten.using_ints %arg0, %int1, %int-1 : !torch.vtensor<[3,3,2,2],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,12],f32>
return %0 : !torch.vtensor<[?,12],f32>
}
// -----
mlir: bump llvm tag to 5380e3 (#856) In addition to updating the llvm-project submodule, this patch also: 1. updates shape functions and tests so that `func` and `call` operations refer to the `func` dialect 2. avoid duplicate registration of dialects
2022-05-17 03:54:35 +08:00
// CHECK-LABEL: func.func @torch.aten.flatten.using_ints$rank0(
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK-SAME: %[[TENSOR:.*]]: !torch.vtensor<[],f32>) -> !torch.vtensor<[1],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: %[[BUILTIN_TENSOR:.*]] = torch_c.to_builtin_tensor %[[TENSOR]] : !torch.vtensor<[],f32> -> tensor<f32>
Integrate llvm-project at dabdec1001dc368373dd581cf72f37a440873ce3 (#3300) Co-authored-by: Jacques Pienaar <jpienaar@google.com>
2024-05-09 02:43:06 +08:00
// CHECK: %[[COLLAPSED:.*]] = tensor.expand_shape %[[BUILTIN_TENSOR]] [] output_shape [1] : tensor<f32> into tensor<1xf32>
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: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[COLLAPSED]] : tensor<1xf32> -> !torch.vtensor<[1],f32>
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
// CHECK: return %[[RESULT]] : !torch.vtensor<[1],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.aten.flatten.using_ints$rank0(%arg0: !torch.vtensor<[],f32>) -> !torch.vtensor<[1],f32> {
Add torch.aten.flatten.using_ints and aten.MaxPool2d linalg lowering - torch.aten.flatten.using_ints to linalg lowering - torch.aten.max_pool2d to linalg lowering - Support torch.aten.conv2d for more flexible dilation and strides values
2021-07-08 04:59:47 +08:00
%int0 = torch.constant.int 0
%0 = torch.aten.flatten.using_ints %arg0, %int0, %int0 : !torch.vtensor<[],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}