ab47418df6
The bufferization of arith.constant ops is also switched over to BufferizableOpInterface-based bufferization. The old implementation is deleted. Both implementations utilize GlobalCreator, now renamed to just `getGlobalFor`. GlobalCreator no longer maintains a set of all created allocations to avoid duplicate allocations of the same constant. Instead, `getGlobalFor` scans the module to see if there is already a global allocation with the same constant value. For compatibility reasons, it is still possible to create a pass that bufferizes only `arith.constant`. This pass (createConstantBufferizePass) could be deleted once all users were switched over to One-Shot bufferization. Differential Revision: https://reviews.llvm.org/D118483
61 lines
2.6 KiB
C++
61 lines
2.6 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/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/StandardOps/Transforms/Passes.h"
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#include "mlir/Dialect/Tensor/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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pm.addPass(createSparsificationPass(options.sparsificationOptions()));
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pm.addPass(createSparseTensorConversionPass());
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pm.addPass(createLinalgBufferizePass());
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pm.addPass(createConvertLinalgToLoopsPass());
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pm.addPass(createConvertVectorToSCFPass());
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pm.addPass(createLowerToCFGPass()); // --convert-scf-to-std
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pm.addPass(createFuncBufferizePass());
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pm.addPass(arith::createConstantBufferizePass());
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pm.addPass(createTensorBufferizePass());
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pm.addPass(createStdBufferizePass());
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pm.addPass(mlir::bufferization::createFinalizingBufferizePass());
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pm.addPass(createLowerAffinePass());
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pm.addPass(createConvertVectorToLLVMPass());
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pm.addPass(createMemRefToLLVMPass());
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pm.addPass(createConvertMathToLLVMPass());
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pm.addPass(createLowerToLLVMPass()); // --convert-std-to-llvm
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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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