pd_ops.cc

// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "paddle/infrt/dialect/pd_ops.h"

#include <mlir/IR/Matchers.h>
#include <mlir/IR/PatternMatch.h>
#include "paddle/infrt/dialect/infrt_base.h"

#define GET_OP_CLASSES
#include "paddle/infrt/dialect/pd_ops.cpp.inc"  // NOLINT

#include "paddle/infrt/dialect/rewrite.hpp.inc"  // NOLINT

namespace mlir {
namespace pd {
PaddleDialect::PaddleDialect(MLIRContext *context)
    : Dialect("pd", context, TypeID::get<PaddleDialect>()) {
  addOperations<
#define GET_OP_LIST
#include "paddle/infrt/dialect/pd_ops.cpp.inc"  // NOLINT
      >();
#undef GET_OP_LIST
}

mlir::Operation *PaddleDialect::materializeConstant(mlir::OpBuilder &builder,
                                                    mlir::Attribute value,
                                                    mlir::Type type,
                                                    mlir::Location loc) {
  return builder.create<ConstantOp>(loc, value);
}

void ConstantOp::build(OpBuilder &builder,
                       OperationState &state,
                       Attribute value) {
  if (auto elem_attr = value.dyn_cast<ElementsAttr>()) {
    return ConstantOp::build(builder, state, elem_attr);
  } else if (value.isa<BoolAttr, FloatAttr, IntegerAttr>()) {
    ShapedType type = RankedTensorType::get(/*shape=*/{}, value.getType());
    state.addAttribute("value", DenseElementsAttr::get(type, value));
    state.addTypes(type);
    return;
  }
  llvm_unreachable("unsupported attribute type for building pd.constant");
}

LogicalResult ConstantOp::inferReturnTypes(
    MLIRContext *context,
    Optional<Location> location,
    ValueRange operands,
    DictionaryAttr attributes,
    RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.push_back(attributes.get("value").getType());
  return success();
}
mlir::OpFoldResult ConstantOp::fold(
    ::llvm::ArrayRef<mlir::Attribute> operands) {
  return value();
}

LogicalResult ElementwiseAdd::inferReturnTypes(
    MLIRContext *context,
    Optional<Location> location,
    ValueRange operands,
    DictionaryAttr attributes,
    RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.push_back(operands[0].getType());
  return success();
}
void ElementwiseAdd::getCanonicalizationPatterns(
    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
  results.insert<FuseMulAdd>(context);
}

mlir::OpFoldResult ElementwiseAdd::fold(
    llvm::ArrayRef<mlir::Attribute> operands) {
  if (getElementTypeOrSelf(getType()).isa<FloatType>()) {
    if (!operands[0] || !operands[1]) return {};
    DenseElementsAttr lhs = operands[0].dyn_cast<DenseElementsAttr>();
    DenseElementsAttr rhs = operands[1].dyn_cast<DenseElementsAttr>();
    if (!lhs || !rhs) return {};
    ShapedType type = getType().template cast<ShapedType>();
    if (!type.hasStaticShape()) return {};
    Type etype = type.getElementType();
    if (!etype.isa<FloatType>()) return {};
    SmallVector<APFloat, 6> values;
    values.reserve(lhs.getNumElements());
    for (const auto zip :
         llvm::zip(lhs.getValues<APFloat>(), rhs.getValues<APFloat>())) {
      values.push_back(
          std::plus<APFloat>()(std::get<0>(zip), std::get<1>(zip)));
    }
    return DenseElementsAttr::get(type, values);
  }
  return {};
}

LogicalResult ElementwiseDiv::inferReturnTypes(
    MLIRContext *context,
    Optional<Location> location,
    ValueRange operands,
    DictionaryAttr attributes,
    RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.push_back(operands[0].getType());
  return success();
}

LogicalResult ElementwiseMul::inferReturnTypes(
    MLIRContext *context,
    Optional<Location> location,
    ValueRange operands,
    DictionaryAttr attributes,
    RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.push_back(operands[0].getType());
  return success();
}

LogicalResult ElementwiseSub::inferReturnTypes(
    MLIRContext *context,
    Optional<Location> location,
    ValueRange operands,
    DictionaryAttr attributes,
    RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.push_back(operands[0].getType());
  return success();
}

LogicalResult MulOp::inferReturnTypes(
    MLIRContext *context,
    Optional<Location> location,
    ValueRange operands,
    DictionaryAttr attributes,
    RegionRange regions,
    SmallVectorImpl<Type> &inferredReturnTypes) {
  inferredReturnTypes.push_back(operands[0].getType());
  return success();
}

void ReluOp::getCanonicalizationPatterns(
    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
  results.insert<FuseFCRelu>(context);
}

void FusedRepeatedFCRelu::getCanonicalizationPatterns(
    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
  results.insert<FuseRepeatedFCRelu2>(context);
}

void BatchNormOp::getCanonicalizationPatterns(
    mlir::OwningRewritePatternList &results, mlir::MLIRContext *context) {
  results.insert<FuseBatchNormWithConvPattern>(context);
}

}  // namespace pd
}  // namespace mlir