mirror of https://github.com/llvm/torch-mlir
82 lines
3.5 KiB
C++
82 lines
3.5 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "npcomp/Dialect/TorchConversion/Transforms/Passes.h"
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#include "mlir/Dialect/Linalg/Passes.h"
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#include "mlir/Dialect/MemRef/Transforms/Passes.h"
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#include "mlir/Dialect/StandardOps/Transforms/Passes.h"
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#include "mlir/Pass/PassManager.h"
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#include "mlir/Transforms/Passes.h"
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#include "npcomp/Conversion/TorchToLinalg/TorchToLinalg.h"
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#include "npcomp/Conversion/TorchToSCF/TorchToSCF.h"
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#include "npcomp/Conversion/TorchToStd/TorchToStd.h"
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#include "torch-mlir/Dialect/Torch/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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using namespace mlir::torch;
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//===----------------------------------------------------------------------===//
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// Pass registration
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//===----------------------------------------------------------------------===//
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namespace {
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#define GEN_PASS_REGISTRATION
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#include "npcomp/Dialect/TorchConversion/Transforms/Passes.h.inc"
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} // end namespace
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void mlir::NPCOMP::registerTorchConversionPasses() {
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::registerPasses();
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mlir::PassPipelineRegistration<Torch::TorchLoweringPipelineOptions>(
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"torchscript-to-npcomp-backend-pipeline",
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"Pipeline lowering torch object graph to npcomp backend format.",
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mlir::NPCOMP::TorchConversion::createTorchScriptToNpcompBackendPipeline);
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}
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void mlir::NPCOMP::TorchConversion::createTorchScriptToNpcompBackendPipeline(
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OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
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// Conversion to the npcomp backend contract starts from the Torch backend
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// contract.
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Torch::createTorchScriptToTorchBackendPipeline(pm, options);
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// Check some invariants to catch errors in a clear way.
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pm.addPass(
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TorchConversion::createVerifyInvariantsBeforeBackendLoweringPass());
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// Lower to linalg + guards which is the input to codegen backends.
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// We do this first as it tends to involve pattern-matching against constants,
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// (e.g. dimensions which must be constant in a ranked programming model)
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// and those constants get somewhat obscured by TorchToStd.
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pm.addNestedPass<FuncOp>(createConvertTorchToLinalgPass());
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pm.addNestedPass<FuncOp>(createConvertTorchToStdPass());
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pm.addNestedPass<FuncOp>(createConvertTorchToSCFPass());
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pm.addNestedPass<FuncOp>(createStdExpandOpsPass());
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if (options.optimize) {
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// Clean up any non-canonical code introduced above..
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pm.addNestedPass<FuncOp>(createCanonicalizerPass());
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// Resolve `dim` ops on tensors (which currently live in the `memref`
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// dialect for some reason -- we don't have memrefs at this level).
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pm.addNestedPass<FuncOp>(memref::createResolveShapedTypeResultDimsPass());
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// The resolution of `dim` ops tends to create identical ops. CSE them.
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pm.addNestedPass<FuncOp>(createCSEPass());
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}
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// Finish the type conversion from `torch` types to the types of the npcomp
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// backend contract.
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pm.addPass(TorchConversion::createFuncBackendTypeConversionPass());
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pm.addNestedPass<FuncOp>(
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TorchConversion::createFinalizingBackendTypeConversionPass());
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// Verify that we have lowered to the form that linalg on tensors backends
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// expect. This fails compilation (signalPassFailure) if the IR is not in the
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// correct form.
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pm.addPass(TorchConversion::createVerifyLinalgOnTensorsBackendContractPass());
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}
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