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//===- SparsificationAndBufferizationPass.cpp - Tensor to Memref Lowering -===//
//
// 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/SparseTensor/Transforms/Passes.h"
#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
#include "mlir/Dialect/Bufferization/Transforms/OneShotModuleBufferize.h"
#include "mlir/Dialect/Bufferization/Transforms/Passes.h"
#include "mlir/Dialect/Bufferization/Transforms/Transforms.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/GPU/IR/GPUDialect.h"
#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Transforms/Passes.h"
using namespace mlir;
using namespace mlir::func;
namespace mlir {
namespace sparse_tensor {
/// Return `true` if one of the given types is a sparse tensor type.
static bool containsSparseTensor(TypeRange types) {
for (Type t : types)
if (getSparseTensorEncoding(t))
return true;
return false;
}
/// A pass that lowers tensor ops to memref ops, regardless of whether they are
/// dense or sparse.
///
/// One-Shot Analysis is used to detect RaW conflicts and to insert buffer
/// copies of the tensor level (`insertTensorCopies`). Afterwards, the lowering
/// of tensor ops to memref ops follows a different code path depending on
/// whether the op is sparse or dense:
///
/// * Sparse tensor ops are lowered through Sparsification and follow-up pass
/// that lowers sparse_tensor dialect ops.
/// * Dense tensor ops are lowered through BufferizableOpInterface
/// implementations.
class SparsificationAndBufferizationPass
: public PassWrapper<SparsificationAndBufferizationPass,
OperationPass<ModuleOp>> {
public:
SparsificationAndBufferizationPass(
const bufferization::OneShotBufferizationOptions &bufferizationOptions,
const SparsificationOptions &sparsificationOptions,
const SparseTensorConversionOptions &sparseTensorConversionOptions,
bool createSparseDeallocs, bool enableRuntimeLibrary,
bool enableBufferInitialization, unsigned vectorLength,
bool enableVLAVectorization, bool enableSIMDIndex32)
: bufferizationOptions(bufferizationOptions),
sparsificationOptions(sparsificationOptions),
sparseTensorConversionOptions(sparseTensorConversionOptions),
createSparseDeallocs(createSparseDeallocs),
enableRuntimeLibrary(enableRuntimeLibrary),
enableBufferInitialization(enableBufferInitialization),
vectorLength(vectorLength),
enableVLAVectorization(enableVLAVectorization),
enableSIMDIndex32(enableSIMDIndex32) {}
/// Bufferize all dense ops. This assumes that no further analysis is needed
/// and that all required buffer copies were already inserted by
/// `insertTensorCopies` in the form of `bufferization.alloc_tensor` ops.
LogicalResult runDenseBufferization() {
bufferization::OpFilter denseOpFilter;
denseOpFilter.allowOperation([&](Operation *op) {
if (containsSparseTensor(TypeRange(op->getResults())) ||
containsSparseTensor(TypeRange(op->getOperands())))
return false;
if (auto funcOp = dyn_cast<func::FuncOp>(op)) {
FunctionType funcType = funcOp.getFunctionType();
if (containsSparseTensor(funcType.getInputs()) ||
containsSparseTensor(funcType.getResults()))
return false;
}
return true;
});
if (failed(bufferization::bufferizeOp(getOperation(), bufferizationOptions,
/*copyBeforeWrite=*/false,
&denseOpFilter)))
return failure();
bufferization::removeBufferizationAttributesInModule(getOperation());
return success();
}
void getDependentDialects(::mlir::DialectRegistry ®istry) const override {
registry.insert<bufferization::BufferizationDialect>();
registry.insert<gpu::GPUDialect>();
registry.insert<LLVM::LLVMDialect>();
}
void runOnOperation() override {
{
// Run enabling transformations.
OpPassManager pm("builtin.module");
pm.addPass(createPreSparsificationRewritePass());
pm.addNestedPass<func::FuncOp>(
bufferization::createEmptyTensorToAllocTensorPass());
if (failed(runPipeline(pm, getOperation())))
return signalPassFailure();
}
// Insert tensor copies. This step runs One-Shot Analysis (which analyzes
// SSA use-def chains of tensor IR) and decides where buffer copies are
// needed and where buffers can be written to in-place. These decisions are
// materialized in the IR in the form of `bufferization.alloc_tensor` ops.
//
// Note: All following steps in this pass must be careful not to modify the
// structure of the IR (i.e., tensor use-def chains), as that could
// invalidate the results of the analysis. From now on, only small and
// localized rewrites are allowed, such as replacing a tensor op with its
// memref equivalent.
if (failed(bufferization::insertTensorCopies(getOperation(),
bufferizationOptions)))
return signalPassFailure();
// `testAnalysisOnly` is a debug/testing flag. If set, the results of
// OneShotAnalysis are added to the IR via attributes. In that case, do not
// continue with the remaining pipeline.
if (bufferizationOptions.testAnalysisOnly)
return;
// Bufferize all sparse ops. No further analysis is needed. All required
// buffer copies were already inserted by `insertTensorCopies` in the form
// of `bufferization.alloc_tensor` ops.
{
OpPassManager pm("builtin.module");
pm.addPass(createSparsificationPass(sparsificationOptions));
pm.addPass(createPostSparsificationRewritePass(enableRuntimeLibrary));
if (vectorLength > 0) {
pm.addPass(mlir::createLoopInvariantCodeMotionPass());
pm.addPass(createSparseVectorizationPass(
vectorLength, enableVLAVectorization, enableSIMDIndex32));
}
if (enableRuntimeLibrary) {
pm.addPass(
createSparseTensorConversionPass(sparseTensorConversionOptions));
} else {
pm.addPass(createSparseTensorCodegenPass(createSparseDeallocs,
enableBufferInitialization));
pm.addPass(createSparseBufferRewritePass(enableBufferInitialization));
pm.addPass(createStorageSpecifierToLLVMPass());
}
if (failed(runPipeline(pm, getOperation())))
return signalPassFailure();
}
// Bufferize all dense ops.
if (failed(runDenseBufferization()))
signalPassFailure();
}
private:
bufferization::OneShotBufferizationOptions bufferizationOptions;
SparsificationOptions sparsificationOptions;
SparseTensorConversionOptions sparseTensorConversionOptions;
bool createSparseDeallocs;
bool enableRuntimeLibrary;
bool enableBufferInitialization;
unsigned vectorLength;
bool enableVLAVectorization;
bool enableSIMDIndex32;
};
} // namespace sparse_tensor
} // namespace mlir
std::unique_ptr<Pass> mlir::createSparsificationAndBufferizationPass(
const bufferization::OneShotBufferizationOptions &bufferizationOptions,
const SparsificationOptions &sparsificationOptions,
const SparseTensorConversionOptions &sparseTensorConversionOptions,
bool createSparseDeallocs, bool enableRuntimeLibrary,
bool enableBufferInitialization, unsigned vectorLength,
bool enableVLAVectorization, bool enableSIMDIndex32) {
return std::make_unique<
mlir::sparse_tensor::SparsificationAndBufferizationPass>(
bufferizationOptions, sparsificationOptions,
sparseTensorConversionOptions, createSparseDeallocs, enableRuntimeLibrary,
enableBufferInitialization, vectorLength, enableVLAVectorization,
enableSIMDIndex32);
}
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