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torch-mlir/lib/Dialect/Torch/IR/CMakeLists.txt

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[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_mlir_library(TorchMLIRTorchDialect
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
TorchDialect.cpp
TorchOps.cpp
Make TorchOps.cpp faster to iterate on. The ODS-generated code included via the `TorchOps.cpp.inc` file takes a very long time to compile. This PR isolates it into its own file so that the build system can cache it. This PR creates a new file `TorchOpsODSGenerated.cpp` just to include the `TorchOps.cpp.inc` file. Doing so required moving to the "new" way to define verifiers, since the static `verify` free functions in TorchOps.cpp weren't accessible from the .inc file after it was moved to `TorchOpsODSGenerated.cpp`. On my machine, this drops the build time of TorchOps.cpp (such as when iterating on a canonicalizer) from >40 seconds to <10 seconds. 10 seconds still isn't great though, but at least it isn't "go get a coffee" type of waiting.
2022-03-16 08:54:57 +08:00
TorchOpsODSGenerated.cpp
Move Torch type implementation code into TorchTypes.cpp
2021-06-11 07:46:20 +08:00
TorchTypes.cpp
Reduce compilation time for TorchOps.cpp.inc The `assemblyFormat` stuff (which generates unrolled, per-op C++ code) was taking up a lot of compile time, and all the ops are essentially printed with the same logic. So this PR makes them all call the same helper function. This is done by using `let hasCustomAssemblyFormat = 1` and then implementing `FooOp::parse` and `FooOp::print`. Additionally, the `Generated*Ops.td` files are all collapsed into just `GeneratedTorchOps.td` (there is no reason to have the files separate, since the files are very large anyway so one is always having to search within them -- editors don't care that the file to search is now a bit bigger :) ). This reduces TorchOpsODSGenerated.cpp compile time (which is now GeneratedTorchOps.cpp) from 39 to 31 seconds on my machine. This is actually less than I expected, but this PR is an overall cleanup to the code anyway. The next step will be to introduce (better) functionality upstream for sharding the TorchOps.cpp.inc file, so that we can truly parallelize the O(#ops) costs. This is also necessary, because after this PR, TorchDialect.cpp is now the slowest file to compile, due to the `addOperations<... all the ops ...>` call, which needs to be shareded too.
2022-03-19 05:04:47 +08:00
UtilsForODSGenerated.cpp
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
ADDITIONAL_HEADER_DIRS
[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
${PROJECT_SOURCE_DIR}/include/torch-mlir/Dialect/Torch
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
DEPENDS
MLIRTorchOpsIncGen
Add initial TorchScript module importer It turns out that this was easiest to structure as a general IValue importer, since torch module are just one of the possible IValue's. We import the IValue object graph in a braindead fashion into basicpy ops and a new `torch.nn_module` op that is used to model the attributes/methods of a torch::jit::Module IValue. See `Torch/ops.mlir` for an example, and also check out the .py import tests in `frontends/pytorch/test/module_import`. As part of this change, a few housekeeping tasks: - extract some helpers from graph_importer.cpp - more helpers around the C API - misc touchups
2021-01-28 08:35:44 +08:00
MLIRTorchTypesIncGen
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
LINK_LIBS PUBLIC
Integrate llvm-project and mlir-hlo. (#2454) Corresponding commits: * mlir-hlo: 16886a108eff5197f816ca0f1950cc5ff1b078d9 * stablehlo: 77a59815a82b34f7b08ed2d42a711d9920682d0e * llvm-project: 4acc3ffbb0af5631bc7916aeff3570f448899647 * Adapt to ByteCodeOpInterface changes. * Adapt to RegionBranchPoint changes: https://reviews.llvm.org/D159116 * Adapt inferReturnTypes to get the value from properties. * Adapt invalid.mlir to properties syntax * [TOSA] Align with custom assembly format change. * [TOSA] handle change of axis to int32 type * [TOSA] Restore improper convert to i32 Landing with Windows broken (it cannot be fixed because of the way the mlir-hlo dep is inserted). Will followup with an untangling. --------- Co-authored-by: TatWai Chong <tatwai.chong@arm.com> Co-authored-by: Eric Kunze <eric.kunze@arm.com>
2023-09-13 06:09:57 +08:00
MLIRBytecodeOpInterface
MLIRBytecodeReader
MLIRBytecodeWriter
Add CMake dep to Func dialect (#1196) The Torch dialect has an include to `mlir/Dialect/Func/IR/FuncOps.h` and should therefore have a CMake dependency to the MLIRFuncDialect. Otherwise, the build can fail since it may occur that `mlir/Dialect/Func/IR/FuncOps.h.inc` isn't generated yet.
2022-08-09 21:54:30 +08:00
MLIRFuncDialect
Add boilerplate for Torch dialect.
2020-09-29 03:02:35 +08:00
MLIRIR
MLIRSupport
Add support for prim::Loop op. This is a funny one. It combines a `for` and `while` loop in one op. We will need to write some conversions to `scf`.
2021-03-02 07:00:32 +08:00
MLIRControlFlowInterfaces
[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
MLIRInferTypeOpInterface
Properly model "derefinement". In terms of IR structure, TorchScript allows types to vary in many circumstances where MLIR requires pointer-identical types. In particular, it is valid to pass any subtype in place of a type. For example, if an `Optional[int]` is required somewhere in the IR, it is legal to pass a value of just `int` (but not the other way around; see `torch.prim.unchecked_cast`). In effect, every *use* can have a different type. We introduce a new op `torch.derefine` that models that impedance mismatch. This op allows casting a value from one type to a type that it is a subtype of to model this behavior. Recommended review order: - TorchOps.td for new torch.derefine (and updated docs for `torch.prim.unchecked_cast`) - new test code in if.py, loop.py, function-derefine.py - new code in node_importer.cpp for handling derefinement insertion - function_importer.cpp and utils changes in torch_to_mlir_utils.cpp Properly handling derefinement on function boundaries required relayering the code so that graph_importer.cpp/.h is now function_importer.cpp/.h because only the `torch::jit::Function` (actually the `c10::FunctionSchema` it holds) knows the derefined types that are actually needed at the boundary (see `function-derefine.py` for a test). Annoyingly, this churns all the functions which are now prefixed with `__torch__.` but that is more correct anyway (that is their linkage name in the `torch::jit::CompilationUnit`; the previous `mb.import_function` was actually buggy in the case of functions calling each other as it would reference their unqualified name). With this change, we can import `resnet18` from `torchvision` :) IR: https://gist.github.com/silvasean/6426a5272d8a6c7caae533fce05ab704
2021-03-02 09:24:15 +08:00
MLIRSideEffectInterfaces
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
torch_mlir_target_includes(TorchMLIRTorchDialect)