mirror of https://github.com/llvm/torch-mlir
102 lines
3.7 KiB
C++
102 lines
3.7 KiB
C++
//===- MaximizeValueSemantics.cpp --------------------------------*- C++-*-===//
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//
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// This file is licensed 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 "PassDetail.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "npcomp/Dialect/Torch/IR/TorchOps.h"
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#include "npcomp/Dialect/Torch/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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using namespace mlir::NPCOMP::Torch;
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class AbstractlyInterpretCopyToNonValueTensorOpUsersWithinABlock
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: public OpRewritePattern<CopyToNonValueTensorOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(CopyToNonValueTensorOp copy,
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PatternRewriter &rewriter) const override {
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SmallVector<Operation *> users;
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// See if our limited form of analysis is even applicatble.
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for (Operation *user : copy.getResult().getUsers()) {
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// We can only analyze within a single basic block.
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if (user->getBlock() != copy->getBlock())
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return failure();
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// We can only analyze these ops.
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if (!isa<CopyToValueTensorOp, OverwriteTensorOp>(user))
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return failure();
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users.push_back(user);
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}
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// Sort by order in the block, so we can abstractly interpret the ops.
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llvm::sort(users, [](Operation *lhs, Operation *rhs) {
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return lhs->isBeforeInBlock(rhs);
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});
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// Do an abstract interpretation within the block.
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// We track the current value tensor that holds the same contents as the
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// non-value tensor at each program point as we walk forward.
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Value currentlyHeldValueTensor = copy.getOperand();
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for (Operation *user : users) {
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if (auto copyToValueTensor = dyn_cast<CopyToValueTensorOp>(user)) {
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rewriter.replaceOp(copyToValueTensor, {currentlyHeldValueTensor});
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} else if (auto overwriteTensor = dyn_cast<OverwriteTensorOp>(user)) {
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currentlyHeldValueTensor = overwriteTensor.value();
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rewriter.eraseOp(overwriteTensor);
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} else {
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llvm_unreachable("only those ops supported!");
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}
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}
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rewriter.eraseOp(copy);
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return success();
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}
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};
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class RewriteNonValueTensorNeverMutatedOrAliased
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: public OpRewritePattern<CopyToNonValueTensorOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(CopyToNonValueTensorOp copy,
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PatternRewriter &rewriter) const override {
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SmallVector<Operation *> users;
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// See if our limited form of analysis is even applicatble.
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for (Operation *user : copy.getResult().getUsers()) {
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if (!isa<CopyToValueTensorOp>(user))
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return failure();
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users.push_back(user);
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}
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for (Operation *user : users)
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rewriter.replaceOp(user, copy.getOperand());
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return success();
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}
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};
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namespace {
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class MaximizeValueSemanticsPass
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: public MaximizeValueSemanticsBase<MaximizeValueSemanticsPass> {
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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auto func = getOperation();
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RewritePatternSet patterns(context);
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patterns.insert<AbstractlyInterpretCopyToNonValueTensorOpUsersWithinABlock,
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RewriteNonValueTensorNeverMutatedOrAliased>(context);
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(void)applyPatternsAndFoldGreedily(func, std::move(patterns));
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<FuncOp>>
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mlir::NPCOMP::Torch::createMaximizeValueSemanticsPass() {
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return std::make_unique<MaximizeValueSemanticsPass>();
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}
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