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//===- 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/Arithmetic/IR/Arithmetic.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/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
using namespace mlir;
using namespace mlir::sparse_tensor;
namespace {
//===----------------------------------------------------------------------===//
// Passes declaration.
//===----------------------------------------------------------------------===//
#define GEN_PASS_CLASSES
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h.inc"
//===----------------------------------------------------------------------===//
// Passes implementation.
//===----------------------------------------------------------------------===//
struct SparsificationPass : public SparsificationBase<SparsificationPass> {
SparsificationPass() = default;
SparsificationPass(const SparsificationPass &pass) = default;
SparsificationPass(const SparsificationOptions &options) {
parallelization = static_cast<int32_t>(options.parallelizationStrategy);
vectorization = static_cast<int32_t>(options.vectorizationStrategy);
vectorLength = options.vectorLength;
enableSIMDIndex32 = options.enableSIMDIndex32;
enableVLAVectorization = options.enableVLAVectorization;
}
void runOnOperation() override {
auto *ctx = &getContext();
RewritePatternSet patterns(ctx);
// Translate strategy flags to strategy options.
SparsificationOptions options(
sparseParallelizationStrategy(parallelization),
sparseVectorizationStrategy(vectorization), vectorLength,
enableSIMDIndex32, enableVLAVectorization);
// Apply rewriting.
populateSparsificationPatterns(patterns, options);
vector::populateVectorToVectorCanonicalizationPatterns(patterns);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
}
};
class SparseTensorTypeConverter : public TypeConverter {
public:
SparseTensorTypeConverter() {
addConversion([](Type type) { return type; });
addConversion(convertSparseTensorTypes);
}
// Maps each sparse tensor type to an opaque pointer.
static Optional<Type> convertSparseTensorTypes(Type type) {
if (getSparseTensorEncoding(type) != nullptr)
return LLVM::LLVMPointerType::get(IntegerType::get(type.getContext(), 8));
return llvm::None;
}
};
struct SparseTensorConversionPass
: public SparseTensorConversionBase<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);
SparseTensorTypeConverter 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());
});
// The following operations and dialects may be introduced by the
// rewriting rules, and are therefore marked as legal.
target.addLegalOp<arith::CmpFOp, arith::CmpIOp, arith::ConstantOp,
arith::IndexCastOp, complex::ConstantOp,
complex::NotEqualOp, linalg::FillOp, linalg::YieldOp,
tensor::ExtractOp>();
target
.addLegalDialect<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);
populateSparseTensorConversionPatterns(converter, patterns, options);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
signalPassFailure();
}
};
} // namespace
SparseParallelizationStrategy
mlir::sparseParallelizationStrategy(int32_t flag) {
switch (flag) {
default:
return SparseParallelizationStrategy::kNone;
case 1:
return SparseParallelizationStrategy::kDenseOuterLoop;
case 2:
return SparseParallelizationStrategy::kAnyStorageOuterLoop;
case 3:
return SparseParallelizationStrategy::kDenseAnyLoop;
case 4:
return SparseParallelizationStrategy::kAnyStorageAnyLoop;
}
}
SparseVectorizationStrategy mlir::sparseVectorizationStrategy(int32_t flag) {
switch (flag) {
default:
return SparseVectorizationStrategy::kNone;
case 1:
return SparseVectorizationStrategy::kDenseInnerLoop;
case 2:
return SparseVectorizationStrategy::kAnyStorageInnerLoop;
}
}
SparseToSparseConversionStrategy
mlir::sparseToSparseConversionStrategy(int32_t flag) {
switch (flag) {
default:
return SparseToSparseConversionStrategy::kAuto;
case 1:
return SparseToSparseConversionStrategy::kViaCOO;
case 2:
return SparseToSparseConversionStrategy::kDirect;
}
}
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);
}
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