209 lines
7.4 KiB
C++
209 lines
7.4 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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// This file implements logic for lowering MHLO dialect to Standard dialect.
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#include "llvm/ADT/StringSwitch.h"
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#include "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/passes.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/IR/BuiltinOps.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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namespace mlir {
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namespace {
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#include "generated_legalize_to_standard.inc"
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} // end anonymous namespace
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namespace mhlo {
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namespace {
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class CompareIConvert : public OpRewritePattern<mhlo::CompareOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(mhlo::CompareOp op,
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PatternRewriter &rewriter) const override {
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auto lhs = op.lhs();
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auto rhs = op.rhs();
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auto lhs_type = lhs.getType().cast<TensorType>();
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auto rhs_type = rhs.getType().cast<TensorType>();
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// Broadcasting not supported by this rewrite.
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if (lhs_type.getShape() != rhs_type.getShape()) return failure();
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if (!lhs_type.getElementType().isSignlessInteger() ||
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!rhs_type.getElementType().isSignlessInteger())
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return failure();
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auto comparison_direction = op.comparison_direction();
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auto compare_predicate =
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llvm::StringSwitch<Optional<CmpIPredicate>>(comparison_direction)
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.Case("EQ", CmpIPredicate::eq)
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.Case("NE", CmpIPredicate::ne)
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.Case("LT", CmpIPredicate::slt)
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.Case("LE", CmpIPredicate::sle)
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.Case("GT", CmpIPredicate::sgt)
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.Case("GE", CmpIPredicate::sge)
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.Default(llvm::None);
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if (!compare_predicate.hasValue()) return failure();
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rewriter.replaceOpWithNewOp<CmpIOp>(op, compare_predicate.getValue(), lhs,
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rhs);
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return success();
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}
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};
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class CompareFConvert : public OpRewritePattern<mhlo::CompareOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(mhlo::CompareOp op,
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PatternRewriter &rewriter) const override {
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auto lhs = op.lhs();
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auto rhs = op.rhs();
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auto lhs_type = lhs.getType().cast<TensorType>();
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auto rhs_type = rhs.getType().cast<TensorType>();
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// Broadcasting not supported by this rewrite.
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if (lhs_type.getShape() != rhs_type.getShape()) return failure();
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if (!lhs_type.getElementType().isa<FloatType>() ||
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!rhs_type.getElementType().isa<FloatType>())
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return failure();
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auto comparison_direction = op.comparison_direction();
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auto compare_predicate =
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llvm::StringSwitch<Optional<CmpFPredicate>>(comparison_direction)
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.Case("EQ", CmpFPredicate::OEQ)
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.Case("NE", CmpFPredicate::UNE)
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.Case("LT", CmpFPredicate::OLT)
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.Case("LE", CmpFPredicate::OLE)
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.Case("GT", CmpFPredicate::OGT)
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.Case("GE", CmpFPredicate::OGE)
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.Default(llvm::None);
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if (!compare_predicate.hasValue()) return failure();
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rewriter.replaceOpWithNewOp<CmpFOp>(op, compare_predicate.getValue(), lhs,
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rhs);
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return success();
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}
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};
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// Replace IotaOp with an integer constant. A ConvertOp is added to
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// convert the integer constant to iota result type. For complex types, the real
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// part is replaced with the generated constant and the imaginary part is
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// replaced with zero tensor.
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class ConvertIotaOp : public OpRewritePattern<mhlo::IotaOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(mhlo::IotaOp op,
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PatternRewriter &rewriter) const override {
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auto output_type = op.getType().cast<ShapedType>();
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auto output_size = output_type.getNumElements();
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auto dimension = op.iota_dimension();
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auto max_dim_size = output_type.getDimSize(dimension);
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auto element_type = output_type.getElementType();
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int bitwidth;
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auto complex_ty = element_type.dyn_cast<ComplexType>();
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Type int_or_float_ty = element_type;
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if (complex_ty) int_or_float_ty = complex_ty.getElementType();
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bitwidth = int_or_float_ty.getIntOrFloatBitWidth();
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llvm::SmallVector<APInt, 10> values;
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values.reserve(output_size);
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int64_t increase_stride = output_size;
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for (int i = 0; i <= dimension; i++) {
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increase_stride /= output_type.getDimSize(i);
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}
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int64_t current_value = 0;
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for (int i = 0; i < output_size; i++) {
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int64_t value = (current_value / increase_stride) % max_dim_size;
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values.push_back(APInt(bitwidth, value));
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++current_value;
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}
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auto int_shape_type = RankedTensorType::get(
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output_type.getShape(),
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IntegerType::get(rewriter.getContext(), bitwidth));
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auto loc = op.getLoc();
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auto integer_const = rewriter.create<mlir::ConstantOp>(
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loc, DenseIntElementsAttr::get(int_shape_type, values));
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auto int_or_float_shape_ty =
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RankedTensorType::get(output_type.getShape(), int_or_float_ty);
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auto iota_const =
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rewriter.create<ConvertOp>(loc, int_or_float_shape_ty, integer_const);
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// For int/float types we are done, replace op and return.
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if (!complex_ty) {
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rewriter.replaceOp(op, iota_const.getResult());
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return success();
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}
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// For complex types, generate a constant tensor of zeroes for the imaginary
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// part and use iota_const for real part.
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auto zeroes = rewriter.create<mlir::ConstantOp>(
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loc, DenseIntElementsAttr::get(int_shape_type, APInt(bitwidth, 0)));
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auto imag_zeroes =
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rewriter.create<ConvertOp>(loc, int_or_float_shape_ty, zeroes);
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rewriter.replaceOpWithNewOp<mhlo::ComplexOp>(op, iota_const, imag_zeroes);
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return success();
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}
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};
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} // end anonymous namespace
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namespace {
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struct LegalizeToStandardPass
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: public PassWrapper<LegalizeToStandardPass, FunctionPass> {
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<StandardOpsDialect>();
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}
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/// Perform the lowering to Standard dialect.
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void runOnFunction() override;
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};
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} // end anonymous namespace
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std::unique_ptr<mlir::OperationPass<mlir::FuncOp>> createLegalizeToStdPass() {
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return std::make_unique<LegalizeToStandardPass>();
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}
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void PopulateMhloToStdPatterns(OwningRewritePatternList *patterns,
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mlir::MLIRContext *ctx) {
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mlir::populateWithGenerated(*patterns);
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patterns->insert<CompareFConvert, CompareIConvert, ConvertIotaOp>(ctx);
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}
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/// Perform the lowering to standard dialect.
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void LegalizeToStandardPass::runOnFunction() {
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OwningRewritePatternList patterns(&getContext());
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mlir::mhlo::PopulateMhloToStdPatterns(&patterns, &getContext());
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(void)applyPatternsAndFoldGreedily(getFunction(), std::move(patterns));
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}
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} // end namespace mhlo
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} // end namespace mlir
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