elementwise_compute.cc

// Copyright (c) 2019 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/lite/kernels/arm/elementwise_compute.h"
#include <string>
#include "paddle/fluid/lite/arm/math/funcs.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace arm {

inline bool is_broadcast(const DDim& x_dims, const DDim& y_dims, int axis,
                         int* pre, int* n, int* post) {
  if (axis < 0) {
    axis = x_dims.size() - y_dims.size();
  }
  if (x_dims.size() == y_dims.size()) {
    return false;
  }
  *pre = 1;
  *n = 1;
  *post = 1;
  for (int i = 0; i < axis; ++i) {
    (*pre) *= x_dims[i];
  }
  for (int i = 0; i < y_dims.size(); ++i) {
    CHECK_EQ(x_dims[i + axis], y_dims[i]) << "Broadcast dimension mismatch.";
    (*n) *= y_dims[i];
  }
  for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
    (*post) *= x_dims[i];
  }
  return true;
}

void ElementwiseAddCompute::Run() {
  auto& param = Param<operators::ElementwiseParam>();
  const float* x_data = param.X->data<float>();
  const float* y_data = param.Y->data<float>();
  float* out_data = param.Out->mutable_data<float>();
  int axis = param.axis;
  auto x_dims = param.X->dims();
  auto y_dims = param.Y->dims();
  int pre, n, post;
  if (is_broadcast(x_dims, y_dims, axis, &pre, &n, &post)) {
    lite::arm::math::elementwise_add_broadcast(x_data, y_data, out_data, pre, n,
                                               post);
  } else {
    lite::arm::math::elementwise_add(x_data, y_data, out_data,
                                     x_dims.production());
  }
}

void ElementwiseAddActivationCompute::Run() {
  auto& param = Param<operators::FusionElementwiseActivationParam>();
  const float* x_data = param.X->data<float>();
  const float* y_data = param.Y->data<float>();
  float* out_data = param.Out->mutable_data<float>();
  int axis = param.axis;
  std::string act_type = param.act_type;
  auto x_dims = param.X->dims();
  auto y_dims = param.Y->dims();
  int pre, n, post;
  if (is_broadcast(x_dims, y_dims, axis, &pre, &n, &post)) {
    if (act_type == "relu") {
      lite::arm::math::elementwise_add_relu_broadcast(x_data, y_data, out_data,
                                                      pre, n, post);
    } else {
      LOG(FATAL) << "unsupported Activation type: " << act_type;
    }
  } else {
    if (act_type == "relu") {
      lite::arm::math::elementwise_add_relu(x_data, y_data, out_data,
                                            x_dims.production());
    } else {
      LOG(FATAL) << "unsupported Activation type: " << act_type;
    }
  }
}

}  // namespace arm
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(elementwise_add, kARM, kFloat, kNCHW,
                     paddle::lite::kernels::arm::ElementwiseAddCompute, def)
    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
    .Finalize();

REGISTER_LITE_KERNEL(
    fusion_elementwise_add_activation, kARM, kFloat, kNCHW,
    paddle::lite::kernels::arm::ElementwiseAddActivationCompute, def)
    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindInput("Y", {LiteType::GetTensorTy(TARGET(kARM))})
    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
    .Finalize();