elementwise.cc

// Copyright (c) 2018 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/fluid/inference/anakin/convert/elementwise.h"
#include <algorithm>
#include <string>
#include <vector>

using anakin::graph::GraphGlobalMem;
using anakin::AK_FLOAT;
using anakin::Precision;
using anakin::saber::NV;
using anakin::saber::X86;
using anakin::saber::Shape;
using anakin::PBlock;
using anakin::PTuple;

namespace paddle {
namespace inference {
namespace anakin {

void ElementwiseAddOpConverter::operator()(
    const framework::proto::OpDesc &op, const framework::BlockDesc &block_desc,
    const framework::Scope &scope, bool test_mode) {
  framework::OpDesc op_desc(op, nullptr);
  PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
  PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);
  PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);

  auto x_name = op_desc.Input("X").front();
  auto y_name = op_desc.Input("Y").front();
  auto out_name = op_desc.Output("Out").front();
  auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front();

  engine_->AddOp(op_name, "Eltwise", {x_name, y_name}, {out_name});
  std::string elementwise_type = "Add";
  engine_->AddOpAttr<std::string>(op_name, "type", elementwise_type);
  std::vector<float> coeff = {1.0, 1.0};
  engine_->AddOpAttr<PTuple<float>>(op_name, "coeff", coeff);
}

void ElementwiseMulOpConverter::operator()(
    const framework::proto::OpDesc &op, const framework::BlockDesc &block_desc,
    const framework::Scope &scope, bool test_mode) {
  framework::OpDesc op_desc(op, nullptr);
  PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
  PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);
  PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);

  auto x_name = op_desc.Input("X").front();
  auto y_name = op_desc.Input("Y").front();
  auto out_name = op_desc.Output("Out").front();
  auto op_name = op_desc.Type() + ":" + op_desc.Output("Out").front();

  engine_->AddOp(op_name, "Scale", {x_name, y_name}, {out_name});
  // Fill a number to weight_1 as a placeholder.
  Shape shape1(std::vector<int>({1, 1, 1, 1}));
  auto *weight1 =
      GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape1);
  auto *placeholder_data =
      static_cast<float *>(weight1->h_tensor().mutable_data());
  float weight1_data[] = {1};
  std::copy(std::begin(weight1_data), std::end(weight1_data), placeholder_data);
  engine_->AddOpAttr(op_name, "weight_1", *weight1);

  auto axis = boost::get<int>(op_desc.GetAttr("axis"));
  engine_->AddOpAttr(op_name, "axis", axis);
  engine_->AddOpAttr(op_name, "num_axes", 1);
  engine_->AddOpAttr(op_name, "bias_term", false);
}

}  // namespace anakin
}  // namespace inference
}  // namespace paddle

REGISTER_ANAKIN_OP_CONVERTER(elementwise_add, ElementwiseAddOpConverter);
REGISTER_ANAKIN_OP_CONVERTER(elementwise_mul, ElementwiseMulOpConverter);