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//===- ToyCombine.cpp - Toy High Level Optimizer --------------------------===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
//
// This file implements a simple combiner for optimizing pattern in the Toy
// dialect.
//
//===----------------------------------------------------------------------===//
#include "toy/Dialect.h"
#include "mlir/IR/Operation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/StandardTypes.h"
#include <numeric>
namespace toy {
namespace {
/// Fold transpose(transpose(x)) -> transpose(x)
struct SimplifyRedundantTranspose : public mlir::RewritePattern {
/// We register this pattern to match every toy.transpose in the IR.
/// The "benefit" is used by the framework to order the patterns and process
/// them in order of profitability.
SimplifyRedundantTranspose(mlir::MLIRContext *context)
: RewritePattern(TransposeOp::getOperationName(), /* benefit = */ 1,
context) {}
/// This method is attempting to match a pattern and rewrite it. The rewriter
/// argument is the orchestrator of the sequence of rewrites. It is expected
/// to interact with it to perform any changes to the IR from here.
mlir::PatternMatchResult
matchAndRewrite(mlir::Operation *op,
mlir::PatternRewriter &rewriter) const override {
// We can directly cast the current operation as this will only get invoked
// on TransposeOp.
TransposeOp transpose = op->cast<TransposeOp>();
// look through the input to the current transpose
mlir::Value *transposeInput = transpose.getOperand();
mlir::Operation *transposeInputInst = transposeInput->getDefiningOp();
// If the input is defined by another Transpose, bingo!
TransposeOp transposeInputOp =
mlir::dyn_cast_or_null<TransposeOp>(transposeInputInst);
if (!transposeInputOp)
return matchFailure();
// Use the rewriter to perform the replacement
rewriter.replaceOp(op, {transposeInputOp.getOperand()}, {transposeInputOp});
return matchSuccess();
}
};
/// Fold reshape(constant(x)) -> constant(x'), with x' being reshaped in place.
struct SimplifyReshapeConstant : public mlir::RewritePattern {
SimplifyReshapeConstant(mlir::MLIRContext *context)
: RewritePattern(ReshapeOp::getOperationName(), /* benefit = */ 1,
context) {}
mlir::PatternMatchResult
matchAndRewrite(mlir::Operation *op,
mlir::PatternRewriter &rewriter) const override {
ReshapeOp reshape = op->cast<ReshapeOp>();
// look through the input to the current reshape
mlir::Value *reshapeInput = reshape.getOperand();
mlir::Operation *reshapeInputInst = reshapeInput->getDefiningOp();
// If the input is defined by another reshape, bingo!
ConstantOp constantOp =
mlir::dyn_cast_or_null<ConstantOp>(reshapeInputInst);
if (!constantOp)
return matchFailure();
auto reshapeType = op->getResult(0)->getType().cast<ToyArrayType>();
if (auto valueAttr =
constantOp.getAttrOfType<mlir::DenseElementsAttr>("value")) {
// FIXME Check matching of element count!
// auto oldType = constantOp.getType();
auto newType = rewriter.getTensorType(
reshapeType.getShape(), valueAttr.getType().getElementType());
auto newAttr =
mlir::DenseElementsAttr::get(newType, valueAttr.getRawData());
auto newConstant = rewriter.create<ConstantOp>(
constantOp.getLoc(), reshapeType.getShape(), newAttr);
rewriter.replaceOp(op, {newConstant});
} else if (auto valueAttr =
constantOp.getAttrOfType<mlir::FloatAttr>("value")) {
// Broadcast
auto dataSize = std::accumulate(reshapeType.getShape().begin(),
reshapeType.getShape().end(), 1,
std::multiplies<int>());
std::vector<mlir::Attribute> data(dataSize, valueAttr);
auto tensorTy = rewriter.getTensorType(reshapeType.getShape(),
reshapeType.getElementType());
auto newAttr = mlir::DenseElementsAttr::get(tensorTy, data);
auto newConstant = rewriter.create<ConstantOp>(
constantOp.getLoc(), reshapeType.getShape(), newAttr);
rewriter.replaceOp(op, {newConstant});
} else {
llvm_unreachable("Unsupported Constant format");
}
return matchSuccess();
}
};
/// Fold reshape(reshape(x)) -> reshape(x)
struct SimplifyReshapeReshape : public mlir::RewritePattern {
SimplifyReshapeReshape(mlir::MLIRContext *context)
: RewritePattern(ReshapeOp::getOperationName(), /* benefit = */ 1,
context) {}
mlir::PatternMatchResult
matchAndRewrite(mlir::Operation *op,
mlir::PatternRewriter &rewriter) const override {
ReshapeOp reshape = op->cast<ReshapeOp>();
// look through the input to the current reshape
mlir::Value *reshapeInput = reshape.getOperand();
mlir::Operation *reshapeInputInst = reshapeInput->getDefiningOp();
// If the input is defined by another reshape, bingo!
ReshapeOp reshapeInputOp =
mlir::dyn_cast_or_null<ReshapeOp>(reshapeInputInst);
if (!reshapeInputOp)
return matchFailure();
// Use the rewriter to perform the replacement
rewriter.replaceOp(op, {reshapeInputOp});
return matchSuccess();
}
};
/// Fold reshape(x)) -> x, when input type matches output type
struct SimplifyNullReshape : public mlir::RewritePattern {
SimplifyNullReshape(mlir::MLIRContext *context)
: RewritePattern(ReshapeOp::getOperationName(), /* benefit = */ 1,
context) {}
mlir::PatternMatchResult
matchAndRewrite(mlir::Operation *op,
mlir::PatternRewriter &rewriter) const override {
ReshapeOp reshape = op->cast<ReshapeOp>();
if (reshape.getOperand()->getType() != reshape.getResult()->getType())
return matchFailure();
rewriter.replaceOp(reshape, {reshape.getOperand()});
return matchSuccess();
}
};
} // end anonymous namespace.
// Register our patterns for rewrite by the Canonicalization framework.
void TransposeOp::getCanonicalizationPatterns(
mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
results.push_back(llvm::make_unique<SimplifyRedundantTranspose>(context));
}
// Register our patterns for rewrite by the Canonicalization framework.
void ReshapeOp::getCanonicalizationPatterns(
mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
results.push_back(llvm::make_unique<SimplifyReshapeConstant>(context));
results.push_back(llvm::make_unique<SimplifyReshapeReshape>(context));
results.push_back(llvm::make_unique<SimplifyNullReshape>(context));
}
namespace {
/// Fold type.cast(x) -> x, when input type matches output type
struct SimplifyIdentityTypeCast : public mlir::RewritePattern {
SimplifyIdentityTypeCast(mlir::MLIRContext *context)
: RewritePattern(TypeCastOp::getOperationName(), /* benefit = */ 1,
context) {}
mlir::PatternMatchResult
matchAndRewrite(mlir::Operation *op,
mlir::PatternRewriter &rewriter) const override {
TypeCastOp typeCast = op->cast<TypeCastOp>();
auto resTy = typeCast.getResult()->getType();
auto *candidateOp = op;
while (candidateOp && candidateOp->isa<TypeCastOp>()) {
if (resTy == candidateOp->getOperand(0)->getType()) {
rewriter.replaceOp(typeCast, {candidateOp->getOperand(0)});
return matchSuccess();
}
candidateOp = candidateOp->getOperand(0)->getDefiningOp();
}
return matchFailure();
}
};
} // end anonymous namespace.
void TypeCastOp::getCanonicalizationPatterns(
mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
results.push_back(llvm::make_unique<SimplifyIdentityTypeCast>(context));
}
} // namespace toy
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