3655069234
This commit moves FuncOp out of the builtin dialect, and into the Func dialect. This move has been planned in some capacity from the moment we made FuncOp an operation (years ago). This commit handles the functional aspects of the move, but various aspects are left untouched to ease migration: func::FuncOp is re-exported into mlir to reduce the actual API churn, the assembly format still accepts the unqualified `func`. These temporary measures will remain for a little while to simplify migration before being removed. Differential Revision: https://reviews.llvm.org/D121266
67 lines
3.0 KiB
C++
67 lines
3.0 KiB
C++
//===- SparseTensorPipelines.cpp - Pipelines for sparse tensor code -------===//
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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 "mlir/Dialect/SparseTensor/Pipelines/Passes.h"
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#include "mlir/Conversion/Passes.h"
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#include "mlir/Dialect/Arithmetic/Transforms/Passes.h"
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#include "mlir/Dialect/Bufferization/Transforms/Passes.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Func/Transforms/Passes.h"
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#include "mlir/Dialect/Linalg/Passes.h"
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#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
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#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
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#include "mlir/Dialect/Tensor/Transforms/Passes.h"
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#include "mlir/Dialect/Vector/Transforms/Passes.h"
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#include "mlir/Pass/PassManager.h"
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using namespace mlir;
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using namespace mlir::sparse_tensor;
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//===----------------------------------------------------------------------===//
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// Pipeline implementation.
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//===----------------------------------------------------------------------===//
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void mlir::sparse_tensor::buildSparseCompiler(
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OpPassManager &pm, const SparseCompilerOptions &options) {
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// TODO(wrengr): ensure the original `pm` is for ModuleOp
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pm.addNestedPass<FuncOp>(createLinalgGeneralizationPass());
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pm.addPass(createLinalgElementwiseOpFusionPass());
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pm.addPass(createSparsificationPass(options.sparsificationOptions()));
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pm.addPass(createSparseTensorConversionPass());
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pm.addNestedPass<FuncOp>(createLinalgBufferizePass());
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pm.addNestedPass<FuncOp>(vector::createVectorBufferizePass());
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pm.addNestedPass<FuncOp>(createConvertLinalgToLoopsPass());
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pm.addNestedPass<FuncOp>(createConvertVectorToSCFPass());
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pm.addNestedPass<FuncOp>(createConvertSCFToCFPass());
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pm.addPass(func::createFuncBufferizePass());
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pm.addPass(arith::createConstantBufferizePass());
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pm.addNestedPass<FuncOp>(createTensorBufferizePass());
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pm.addNestedPass<FuncOp>(
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mlir::bufferization::createFinalizingBufferizePass());
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pm.addPass(createLowerAffinePass());
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pm.addPass(createConvertVectorToLLVMPass(options.lowerVectorToLLVMOptions()));
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pm.addPass(createMemRefToLLVMPass());
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pm.addNestedPass<FuncOp>(createConvertMathToLLVMPass());
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pm.addPass(createConvertFuncToLLVMPass());
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pm.addPass(createReconcileUnrealizedCastsPass());
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}
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//===----------------------------------------------------------------------===//
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// Pipeline registration.
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//===----------------------------------------------------------------------===//
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void mlir::sparse_tensor::registerSparseTensorPipelines() {
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PassPipelineRegistration<SparseCompilerOptions>(
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"sparse-compiler",
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"The standard pipeline for taking sparsity-agnostic IR using the"
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" sparse-tensor type, and lowering it to LLVM IR with concrete"
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" representations and algorithms for sparse tensors.",
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buildSparseCompiler);
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}
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