llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
torch-mlir/lib/InitAll.cpp

78 lines
3.2 KiB
C++
Raw Permalink Normal View History

[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
//===----------------------------------------------------------------------===//
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
//
// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
Dual license the torch-mlir project. This commit (with approval from all contributors) dual licenses the torch-mlir project under both the standard LLVM license and the standard PyTorch license. This will facilitate moving code between torch-mlir and the two upstream projects. The standard file comment is now: ``` // This file is licensed under the Apache License v2.0 with LLVM Exceptions. // See https://llvm.org/LICENSE.txt for license information. // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception // Also available under a BSD-style license. See LICENSE. ``` See `LICENSE` in the project root for the terms of both licenses.
2021-09-30 00:03:40 +08:00
// Also available under a BSD-style license. See LICENSE.
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
//
//===----------------------------------------------------------------------===//
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
#include "torch-mlir/InitAll.h"
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
2023-11-03 10:45:55 +08:00
#include "mlir/Dialect/Complex/IR/Complex.h"
Fixes Windows DLL crash (#2321) * explicit inliner extension * fixed import formatting
2023-07-19 10:12:46 +08:00
#include "mlir/Dialect/Func/Extensions/InlinerExtension.h"
Resolve CI testing failure for Lazy Tensor Core (#1088) * Xfail unsupported ops * Register FuncDialect * Include dynamic_ir in build * Code reformat * Enable LTC tests for macOS and Source Build
2022-07-26 07:40:25 +08:00
#include "mlir/Dialect/Func/IR/FuncOps.h"
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
2023-11-03 10:45:55 +08:00
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/MLProgram/IR/MLProgram.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
Link necessary op interface implementations (#3364) This patch adds two `memref` passes to `torch-mlir-opt`, which already occur in the pass pipeline `torch-backend-to-linalg-on-tensors-backend-pipeline`. Additionally, necessary op interface external models are included to address issue #3352.
2024-06-04 08:43:28 +08:00
#include "mlir/Dialect/Tensor/IR/TensorInferTypeOpInterfaceImpl.h"
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
2023-11-03 10:45:55 +08:00
#include "mlir/Dialect/Tosa/IR/TosaOps.h"
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
#include "mlir/IR/Dialect.h"
Add TMTensor dialect to torch-mlir This is intended to explore support for non-structured ops that can't be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter` are added as the first such ops. The dialect should aim to be upstreamed in the future.
2022-02-03 07:01:38 +08:00
#include "torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorDialect.h"
#include "torch-mlir-dialects/Dialect/TMTensor/Transforms/Passes.h"
Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
#include "torch-mlir/Conversion/Passes.h"
Initial TorchOnnxToTorch conversion pipeline. (#2585) Adds a pipeline to convert custom ops and metadata represented as `torch.operator` custom ops to corresponding `torch` ops where possible. This is part of a multi-part approach for building ONNX import in as a regular feature of torch-mlir. It is focused on the conversions vs the infra. We will end up maintaining a [pure-python importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py) to go with this in torch-mlir, and we will also maintain test case generation utilities derived from it. I have left substantial documentation in the README of the conversion directory, including the recommended approach that we will take to keep building this out. (note that this organizes the code to coincide with the refactoring in #2442 versus the current flat arrangement)
2023-11-22 13:02:55 +08:00
#include "torch-mlir/Conversion/TorchOnnxToTorch/Passes.h"
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionDialect.h"
#include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h"
Bring up new RefBackend. `tools/torchscript_e2e_test.sh` is all green. This needs a few passes I put into torch-mlir/lib/RefBackend (not to be confused with `npcomp/lib/RefBackend`, which will soon be deleted). For the sake of review, since this brings together a lot of things, I split this into its own commit. I temporarily commented out some "list" stuff that we are going to remove as part of the torch-mlir refocus.
2021-09-23 00:55:09 +08:00
#include "torch-mlir/RefBackend/Passes.h"
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
[Stablehlo] add refbackend (#2712)
2024-02-16 01:08:48 +08:00
#ifdef TORCH_MLIR_ENABLE_STABLEHLO
#include "stablehlo/conversions/linalg/transforms/Passes.h"
[Stablehlo] add stablehlo-aggressive-simplification in e2e test (#3109) * so that more stablehlo e2e testcases would pass.
2024-04-07 10:48:11 +08:00
#include "stablehlo/transforms/Passes.h"
[Stablehlo] add refbackend (#2712)
2024-02-16 01:08:48 +08:00
#endif
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
void mlir::torch::registerAllDialects(mlir::DialectRegistry &registry) {
Resolve CI testing failure for Lazy Tensor Core (#1088) * Xfail unsupported ops * Register FuncDialect * Include dynamic_ir in build * Code reformat * Enable LTC tests for macOS and Source Build
2022-07-26 07:40:25 +08:00
registry.insert<mlir::func::FuncDialect>();
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
registry.insert<mlir::torch::Torch::TorchDialect>();
Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
registry.insert<mlir::torch::TorchConversion::TorchConversionDialect>();
Add TMTensor dialect to torch-mlir This is intended to explore support for non-structured ops that can't be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter` are added as the first such ops. The dialect should aim to be upstreamed in the future.
2022-02-03 07:01:38 +08:00
registry.insert<mlir::torch::TMTensor::TMTensorDialect>();
Link necessary op interface implementations (#3364) This patch adds two `memref` passes to `torch-mlir-opt`, which already occur in the pass pipeline `torch-backend-to-linalg-on-tensors-backend-pipeline`. Additionally, necessary op interface external models are included to address issue #3352.
2024-06-04 08:43:28 +08:00
}
void mlir::torch::registerAllExtensions(mlir::DialectRegistry &registry) {
Fixes Windows DLL crash (#2321) * explicit inliner extension * fixed import formatting
2023-07-19 10:12:46 +08:00
mlir::func::registerInlinerExtension(registry);
Link necessary op interface implementations (#3364) This patch adds two `memref` passes to `torch-mlir-opt`, which already occur in the pass pipeline `torch-backend-to-linalg-on-tensors-backend-pipeline`. Additionally, necessary op interface external models are included to address issue #3352.
2024-06-04 08:43:28 +08:00
tensor::registerInferTypeOpInterfaceExternalModels(registry);
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
}
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
2023-11-03 10:45:55 +08:00
// TODO: Break this up when backends are separated.
void mlir::torch::registerOptionalInputDialects(
mlir::DialectRegistry &registry) {
registry.insert<complex::ComplexDialect, linalg::LinalgDialect,
memref::MemRefDialect, ml_program::MLProgramDialect,
scf::SCFDialect, tensor::TensorDialect, tosa::TosaDialect>();
}
Bring up new RefBackend. `tools/torchscript_e2e_test.sh` is all green. This needs a few passes I put into torch-mlir/lib/RefBackend (not to be confused with `npcomp/lib/RefBackend`, which will soon be deleted). For the sake of review, since this brings together a lot of things, I split this into its own commit. I temporarily commented out some "list" stuff that we are going to remove as part of the torch-mlir refocus.
2021-09-23 00:55:09 +08:00
void mlir::torch::registerAllPasses() {
mlir::torch::registerTorchPasses();
Move TorchConversion dialect and TorchTo* into torch-mlir
2021-09-24 11:59:12 +08:00
mlir::torch::registerTorchConversionPasses();
mlir::torch::registerConversionPasses();
Initial TorchOnnxToTorch conversion pipeline. (#2585) Adds a pipeline to convert custom ops and metadata represented as `torch.operator` custom ops to corresponding `torch` ops where possible. This is part of a multi-part approach for building ONNX import in as a regular feature of torch-mlir. It is focused on the conversions vs the infra. We will end up maintaining a [pure-python importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py) to go with this in torch-mlir, and we will also maintain test case generation utilities derived from it. I have left substantial documentation in the README of the conversion directory, including the recommended approach that we will take to keep building this out. (note that this organizes the code to coincide with the refactoring in #2442 versus the current flat arrangement)
2023-11-22 13:02:55 +08:00
mlir::torch::onnx_c::registerTorchOnnxToTorchPasses();
Add TMTensor dialect to torch-mlir This is intended to explore support for non-structured ops that can't be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter` are added as the first such ops. The dialect should aim to be upstreamed in the future.
2022-02-03 07:01:38 +08:00
mlir::torch::TMTensor::registerPasses();
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
2023-11-03 10:45:55 +08:00
[Stablehlo] add refbackend (#2712)
2024-02-16 01:08:48 +08:00
#ifdef TORCH_MLIR_ENABLE_STABLEHLO
mlir::stablehlo::registerStablehloLegalizeToLinalgPass();
[Stablehlo] add stablehlo-aggressive-simplification in e2e test (#3109) * so that more stablehlo e2e testcases would pass.
2024-04-07 10:48:11 +08:00
mlir::stablehlo::registerStablehloAggressiveSimplificationPass();
mlir::stablehlo::registerStablehloRefineShapesPass();
[Stablehlo] support uint8 (#3367) Support lowering unsigned integer type to stablehlo as discussed in https://github.com/llvm/torch-mlir/pull/2184. The things I do in this PR: 1. create `setupBackendTypeConversionForStablehlo()`, `createFuncBackendTypeConversionForStablehloPass` and `createFinalizingBackendTypeConversionForStablehloPass`. 2. remove `InferTypeOpInterface` from `torch_c.to_builtin_tensor`, because it's different result type between linalg backend and stablehlo backend: ``` // linalg backend func.func @forward(%arg0: !torch.vtensor<[3],ui8>) -> tensor<3xf32> { %c = torch_c.to_builtin_tensor %arg0 : (!torch.vtensor<[3], ui8> -> tensor<3xi8> %0 = tensor.empty() : tensor<3xf32> %1 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%arg0 : tensor<3xi8>) outs(%0 : tensor<3xf32>) { ^bb0(%in: i8, %out: f32): %2 = arith.uitofp %in : i8 to f32 linalg.yield %2 : f32 } -> tensor<3xf32> return %1 : tensor<3xf32> } // stablehlo backend func.func @forward(%arg0: !torch.vtensor<[3],ui8>) -> tensor<3xf32> { %c = torch_c.to_builtin_tensor %arg0 : (!torch.vtensor<[3], ui8> -> tensor<3xui8> %0 = stablehlo.convert %arg0 : (tensor<3xui8> -> tensor<3xf32> return %0 : tensor<3xf32> } ``` 3. fix stablehlo and linalg's conversion
2024-06-04 09:04:59 +08:00
mlir::stablehlo::registerStablehloConvertToSignlessPass();
[Stablehlo] legalize deprecated ops to stablehlo ops (#3543)
2024-07-17 00:05:11 +08:00
mlir::stablehlo::registerShapeLegalizeToStablehloPass();
mlir::stablehlo::registerStablehloLegalizeDeprecatedOpsPass();
[Stablehlo] add refbackend (#2712)
2024-02-16 01:08:48 +08:00
#endif
Re-organize project structure to separate PyTorch dependencies from core project. (#2542) This is a first step towards the structure we discussed here: https://gist.github.com/stellaraccident/931b068aaf7fa56f34069426740ebf20 There are two primary goals: 1. Separate the core project (C++ dialects and conversions) from the hard PyTorch dependencies. We move all such things into projects/pt1 as a starting point since they are presently entangled with PT1-era APIs. Additional work can be done to disentangle components from that (specifically LTC is identified as likely ultimately living in a `projects/ltc`). 2. Create space for native PyTorch2 Dynamo-based infra to be upstreamed without needing to co-exist with the original TorchScript path. Very little changes in this path with respect to build layering or options. These can be updated in a followup without commingling directory structure changes. This also takes steps toward a couple of other layering enhancements: * Removes the llvm-external-projects/torch-mlir-dialects sub-project, collapsing it into the main tree. * Audits and fixes up the core C++ build to account for issues found while moving things. This is just an opportunistic pass through but roughly ~halves the number of build actions for the project from the high 4000's to the low 2000's. It deviates from the discussed plan by having a `projects/` tree instead of `compat/`. As I was thinking about it, this will better accommodate the follow-on code movement. Once things are roughly in place and the CI passing, followups will focus on more in-situ fixes and cleanups.
2023-11-03 10:45:55 +08:00
#ifdef TORCH_MLIR_ENABLE_REFBACKEND
mlir::torch::RefBackend::registerRefBackendPasses();
#endif
Bring up new RefBackend. `tools/torchscript_e2e_test.sh` is all green. This needs a few passes I put into torch-mlir/lib/RefBackend (not to be confused with `npcomp/lib/RefBackend`, which will soon be deleted). For the sake of review, since this brings together a lot of things, I split this into its own commit. I temporarily commented out some "list" stuff that we are going to remove as part of the torch-mlir refocus.
2021-09-23 00:55:09 +08:00
}