87c770bbd0
The TensorCopyInsertion pass resolves out-of-place bufferization decisions by inserting explicit `bufferization.alloc_tensor` ops. This change moves that functionality into a new BufferizableOpInterface method, so that it can be overridden by op implementations. Some op bufferizations must insert additional `alloc_tensor` ops to make sure that certain aliasing invariants are not violated (e.g., scf::ForOp). This will be addressed in a subsequent change. Differential Revision: https://reviews.llvm.org/D126817
113 lines
3.9 KiB
C++
113 lines
3.9 KiB
C++
//===- TensorCopyInsertion.cpp - Resolve Bufferization Conflicts w/ Copies ===//
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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/Bufferization/Transforms/TensorCopyInsertion.h"
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#include "PassDetail.h"
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#include "mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h"
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#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
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#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
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#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
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#include "mlir/Dialect/Bufferization/Transforms/OneShotModuleBufferize.h"
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#include "mlir/Dialect/Bufferization/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::bufferization;
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LogicalResult mlir::bufferization::insertTensorCopies(
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Operation *op, const OneShotBufferizationOptions &options) {
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OneShotAnalysisState state(op, options);
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// Run normal One-Shot Bufferize analysis or One-Shot Module Bufferize
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// analysis depending on whether function boundary bufferization is enabled or
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// not.
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if (options.bufferizeFunctionBoundaries) {
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if (failed(analyzeModuleOp(cast<ModuleOp>(op), state)))
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return failure();
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} else {
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if (failed(analyzeOp(op, state)))
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return failure();
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}
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if (options.testAnalysisOnly)
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return success();
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return insertTensorCopies(op, state);
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}
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LogicalResult
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mlir::bufferization::insertTensorCopies(Operation *op,
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const AnalysisState &state) {
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IRRewriter rewriter(op->getContext());
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WalkResult result = op->walk([&](Operation *op) {
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auto bufferizableOp = state.getOptions().dynCastBufferizableOp(op);
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if (!bufferizableOp)
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return WalkResult::skip();
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// Find AllocTensorOps without an `escape` attribute and add the attribute
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// based on analysis results.
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if (auto allocTensorOp = dyn_cast<AllocTensorOp>(op)) {
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if (allocTensorOp.escape())
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return WalkResult::advance();
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bool escape = state.isTensorYielded(allocTensorOp.result());
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allocTensorOp.escapeAttr(rewriter.getBoolAttr(escape));
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return WalkResult::advance();
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}
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// Find inplacability conflicts and resolve them. (Typically with explicit
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// tensor copies in the form of AllocTensorOps.)
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rewriter.setInsertionPoint(op);
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if (failed(bufferizableOp.resolveConflicts(rewriter, state)))
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return WalkResult::interrupt();
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return WalkResult::advance();
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});
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return failure(result.wasInterrupted());
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}
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namespace {
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struct TensorCopyInsertionPass
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: TensorCopyInsertionBase<TensorCopyInsertionPass> {
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TensorCopyInsertionPass()
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: TensorCopyInsertionBase<TensorCopyInsertionPass>(),
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options(llvm::None) {}
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TensorCopyInsertionPass(const OneShotBufferizationOptions &options)
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: TensorCopyInsertionBase<TensorCopyInsertionPass>(), options(options) {}
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<bufferization::BufferizationDialect>();
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}
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void runOnOperation() override {
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if (options.hasValue()) {
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if (failed(insertTensorCopies(getOperation(), *options)))
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signalPassFailure();
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} else {
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OneShotBufferizationOptions options;
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options.allowReturnAllocs = allowReturnAllocs;
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options.bufferizeFunctionBoundaries = bufferizeFunctionBoundaries;
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if (failed(insertTensorCopies(getOperation(), options)))
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signalPassFailure();
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}
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}
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private:
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Optional<OneShotBufferizationOptions> options;
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};
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} // namespace
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std::unique_ptr<Pass> mlir::bufferization::createTensorCopyInsertionPass() {
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return std::make_unique<TensorCopyInsertionPass>();
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}
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std::unique_ptr<Pass> mlir::bufferization::createTensorCopyInsertionPass(
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const OneShotBufferizationOptions &options) {
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return std::make_unique<TensorCopyInsertionPass>(options);
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}
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