Files
clang-p2996/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp
Peiming Liu 26eb2c6b42 [mlir][sparse] remove vector support in sparsification
Sparse compiler used to generate vectorized code for sparse tensors computation, but it should really be delegated to other vectorization passes for better progressive lowering.

 https://discourse.llvm.org/t/rfc-structured-codegen-beyond-rectangular-arrays/64707

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D136183
2022-10-19 18:11:29 +00:00

285 lines
12 KiB
C++

//===- SparseTensorPasses.cpp - Pass for autogen sparse tensor code -------===//
//
// Part of the LLVM Project, 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
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Func/Transforms/FuncConversions.h"
#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/SCF/Transforms/Transforms.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
namespace mlir {
#define GEN_PASS_DEF_SPARSETENSORREWRITE
#define GEN_PASS_DEF_SPARSIFICATIONPASS
#define GEN_PASS_DEF_SPARSETENSORCONVERSIONPASS
#define GEN_PASS_DEF_SPARSETENSORCODEGEN
#define GEN_PASS_DEF_SPARSEBUFFERREWRITE
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h.inc"
} // namespace mlir
using namespace mlir;
using namespace mlir::sparse_tensor;
namespace {
//===----------------------------------------------------------------------===//
// Passes implementation.
//===----------------------------------------------------------------------===//
struct SparseTensorRewritePass
: public impl::SparseTensorRewriteBase<SparseTensorRewritePass> {
SparseTensorRewritePass() = default;
SparseTensorRewritePass(const SparseTensorRewritePass &pass) = default;
SparseTensorRewritePass(bool enableRT, bool foreach, bool convert) {
enableRuntimeLibrary = enableRT;
enableForeach = foreach;
enableConvert = convert;
}
void runOnOperation() override {
auto *ctx = &getContext();
RewritePatternSet patterns(ctx);
populateSparseTensorRewriting(patterns, enableRuntimeLibrary, enableForeach,
enableConvert);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
}
};
struct SparsificationPass
: public impl::SparsificationPassBase<SparsificationPass> {
SparsificationPass() = default;
SparsificationPass(const SparsificationPass &pass) = default;
SparsificationPass(const SparsificationOptions &options) {
parallelization = options.parallelizationStrategy;
}
void runOnOperation() override {
auto *ctx = &getContext();
// Translate strategy flags to strategy options.
SparsificationOptions options(parallelization);
// Apply sparsification and vector cleanup rewriting.
RewritePatternSet patterns(ctx);
populateSparsificationPatterns(patterns, options);
vector::populateVectorToVectorCanonicalizationPatterns(patterns);
scf::ForOp::getCanonicalizationPatterns(patterns, ctx);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
}
};
struct SparseTensorConversionPass
: public impl::SparseTensorConversionPassBase<SparseTensorConversionPass> {
SparseTensorConversionPass() = default;
SparseTensorConversionPass(const SparseTensorConversionPass &pass) = default;
SparseTensorConversionPass(const SparseTensorConversionOptions &options) {
sparseToSparse = static_cast<int32_t>(options.sparseToSparseStrategy);
}
void runOnOperation() override {
auto *ctx = &getContext();
RewritePatternSet patterns(ctx);
SparseTensorTypeToPtrConverter converter;
ConversionTarget target(*ctx);
// Everything in the sparse dialect must go!
target.addIllegalDialect<SparseTensorDialect>();
// All dynamic rules below accept new function, call, return, and various
// tensor and bufferization operations as legal output of the rewriting
// provided that all sparse tensor types have been fully rewritten.
target.addDynamicallyLegalOp<func::FuncOp>([&](func::FuncOp op) {
return converter.isSignatureLegal(op.getFunctionType());
});
target.addDynamicallyLegalOp<func::CallOp>([&](func::CallOp op) {
return converter.isSignatureLegal(op.getCalleeType());
});
target.addDynamicallyLegalOp<func::ReturnOp>([&](func::ReturnOp op) {
return converter.isLegal(op.getOperandTypes());
});
target.addDynamicallyLegalOp<tensor::DimOp>([&](tensor::DimOp op) {
return converter.isLegal(op.getOperandTypes());
});
target.addDynamicallyLegalOp<tensor::CastOp>([&](tensor::CastOp op) {
return converter.isLegal(op.getSource().getType()) &&
converter.isLegal(op.getDest().getType());
});
target.addDynamicallyLegalOp<tensor::ExpandShapeOp>(
[&](tensor::ExpandShapeOp op) {
return converter.isLegal(op.getSrc().getType()) &&
converter.isLegal(op.getResult().getType());
});
target.addDynamicallyLegalOp<tensor::CollapseShapeOp>(
[&](tensor::CollapseShapeOp op) {
return converter.isLegal(op.getSrc().getType()) &&
converter.isLegal(op.getResult().getType());
});
target.addDynamicallyLegalOp<bufferization::AllocTensorOp>(
[&](bufferization::AllocTensorOp op) {
return converter.isLegal(op.getType());
});
target.addDynamicallyLegalOp<bufferization::DeallocTensorOp>(
[&](bufferization::DeallocTensorOp op) {
return converter.isLegal(op.getTensor().getType());
});
// The following operations and dialects may be introduced by the
// rewriting rules, and are therefore marked as legal.
target.addLegalOp<complex::ConstantOp, complex::NotEqualOp, linalg::FillOp,
linalg::YieldOp, tensor::ExtractOp>();
target.addLegalDialect<
arith::ArithDialect, bufferization::BufferizationDialect,
LLVM::LLVMDialect, memref::MemRefDialect, scf::SCFDialect>();
// Translate strategy flags to strategy options.
SparseTensorConversionOptions options(
sparseToSparseConversionStrategy(sparseToSparse));
// Populate with rules and apply rewriting rules.
populateFunctionOpInterfaceTypeConversionPattern<func::FuncOp>(patterns,
converter);
populateCallOpTypeConversionPattern(patterns, converter);
scf::populateSCFStructuralTypeConversionsAndLegality(converter, patterns,
target);
populateSparseTensorConversionPatterns(converter, patterns, options);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
signalPassFailure();
}
};
struct SparseTensorCodegenPass
: public impl::SparseTensorCodegenBase<SparseTensorCodegenPass> {
SparseTensorCodegenPass() = default;
SparseTensorCodegenPass(const SparseTensorCodegenPass &pass) = default;
void runOnOperation() override {
auto *ctx = &getContext();
RewritePatternSet patterns(ctx);
SparseTensorTypeToBufferConverter converter;
ConversionTarget target(*ctx);
// Most ops in the sparse dialect must go!
target.addIllegalDialect<SparseTensorDialect>();
target.addLegalOp<SortOp>();
target.addLegalOp<PushBackOp>();
// All dynamic rules below accept new function, call, return, and various
// tensor and bufferization operations as legal output of the rewriting
// provided that all sparse tensor types have been fully rewritten.
target.addDynamicallyLegalOp<func::FuncOp>([&](func::FuncOp op) {
return converter.isSignatureLegal(op.getFunctionType());
});
target.addDynamicallyLegalOp<func::CallOp>([&](func::CallOp op) {
return converter.isSignatureLegal(op.getCalleeType());
});
target.addDynamicallyLegalOp<func::ReturnOp>([&](func::ReturnOp op) {
return converter.isLegal(op.getOperandTypes());
});
target.addDynamicallyLegalOp<bufferization::AllocTensorOp>(
[&](bufferization::AllocTensorOp op) {
return converter.isLegal(op.getType());
});
target.addDynamicallyLegalOp<bufferization::DeallocTensorOp>(
[&](bufferization::DeallocTensorOp op) {
return converter.isLegal(op.getTensor().getType());
});
// The following operations and dialects may be introduced by the
// codegen rules, and are therefore marked as legal.
target.addLegalOp<linalg::FillOp>();
target.addLegalDialect<arith::ArithDialect,
bufferization::BufferizationDialect,
memref::MemRefDialect, scf::SCFDialect>();
target.addLegalOp<UnrealizedConversionCastOp>();
// Populate with rules and apply rewriting rules.
populateFunctionOpInterfaceTypeConversionPattern<func::FuncOp>(patterns,
converter);
scf::populateSCFStructuralTypeConversionsAndLegality(converter, patterns,
target);
populateSparseTensorCodegenPatterns(converter, patterns);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
signalPassFailure();
}
};
struct SparseBufferRewritePass
: public impl::SparseBufferRewriteBase<SparseBufferRewritePass> {
SparseBufferRewritePass() = default;
SparseBufferRewritePass(const SparseBufferRewritePass &pass) = default;
void runOnOperation() override {
auto *ctx = &getContext();
RewritePatternSet patterns(ctx);
populateSparseBufferRewriting(patterns);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
}
};
} // namespace
//===----------------------------------------------------------------------===//
// Strategy flag methods.
//===----------------------------------------------------------------------===//
SparseToSparseConversionStrategy
mlir::sparseToSparseConversionStrategy(int32_t flag) {
switch (flag) {
default:
return SparseToSparseConversionStrategy::kAuto;
case 1:
return SparseToSparseConversionStrategy::kViaCOO;
case 2:
return SparseToSparseConversionStrategy::kDirect;
}
}
//===----------------------------------------------------------------------===//
// Pass creation methods.
//===----------------------------------------------------------------------===//
std::unique_ptr<Pass> mlir::createSparseTensorRewritePass() {
return std::make_unique<SparseTensorRewritePass>();
}
std::unique_ptr<Pass> mlir::createSparseTensorRewritePass(bool enableRT,
bool enableForeach,
bool enableConvert) {
return std::make_unique<SparseTensorRewritePass>(enableRT, enableForeach,
enableConvert);
}
std::unique_ptr<Pass> mlir::createSparsificationPass() {
return std::make_unique<SparsificationPass>();
}
std::unique_ptr<Pass>
mlir::createSparsificationPass(const SparsificationOptions &options) {
return std::make_unique<SparsificationPass>(options);
}
std::unique_ptr<Pass> mlir::createSparseTensorConversionPass() {
return std::make_unique<SparseTensorConversionPass>();
}
std::unique_ptr<Pass> mlir::createSparseTensorConversionPass(
const SparseTensorConversionOptions &options) {
return std::make_unique<SparseTensorConversionPass>(options);
}
std::unique_ptr<Pass> mlir::createSparseTensorCodegenPass() {
return std::make_unique<SparseTensorCodegenPass>();
}
std::unique_ptr<Pass> mlir::createSparseBufferRewritePass() {
return std::make_unique<SparseBufferRewritePass>();
}