src/neuralnet/connection_layer/concate.cc

/************************************************************
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements.  See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership.  The ASF licenses this file
* to you 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 "singa/neuralnet/connection_layer.h"
#include "singa/utils/singleton.h"
#include "singa/utils/context.h"

namespace singa {

void ConcateLayer::Setup(const LayerProto& conf,
                         const vector<Layer*>& srclayers) {
  CHECK_GT(srclayers.size(), 1);
  Layer::Setup(conf, srclayers);
  vector<int> shape = srclayers[0]->data(this).shape();
  concate_dim_ = conf.concate_conf().concate_dim();
  num_concates_ = conf.concate_conf().num_concates();
  CHECK_GE(concate_dim_, 0);
  CHECK_LT(concate_dim_, shape.size());
  CHECK_EQ(num_concates_, srclayers.size());
  for (size_t i = 1; i < srclayers.size(); i++) {
    const vector<int>& src_shape = srclayers[i]->data(this).shape();
    for (size_t j = 0; j < shape.size(); j++)
      if (static_cast<int>(j) == concate_dim_)
        shape[j] += src_shape[j];
      else
        CHECK_EQ(shape[j], src_shape[j]);
  }
  data_.Reshape(shape);
  grad_.Reshape(shape);
}

void ConcateLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
  CHECK_GT(srclayers.size(), 1);
  CHECK_EQ(num_concates_, srclayers.size());
  // calculate step for each memcpy
  int step = srclayers[0]->data(this).shape()[concate_dim_];
  for (unsigned i = concate_dim_ + 1; i < data_.shape().size(); ++i)
    step *= data_.shape()[i];
  int srclayer_offset = 0;
  int concate_offset = 0;
  auto context = Singleton<Context>::Instance();
  int device = context->device_id(std::this_thread::get_id());
  while (concate_offset < data_.count()) {
    for (size_t i = 0; i < srclayers.size(); ++i) {
      if (device < 0) {
        const float* src = srclayers[i]->data(this).cpu_data()
          + srclayer_offset;
        float* dst = data_.mutable_cpu_data() + concate_offset;
        memcpy(dst, src, step * sizeof(float));
      } else {
#ifdef USE_GPU
        const float* src = srclayers[i]->data(this).gpu_data()
          + srclayer_offset;
        float* dst = data_.mutable_gpu_data() + concate_offset;
        cudaMemcpy(dst, src, step * sizeof(float), cudaMemcpyDefault);
#else
        LOG(FATAL) << "GPU is not supported";
#endif
      }
      concate_offset += step;
    }
    srclayer_offset += step;
  }
}

void ConcateLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers) {
  CHECK_GT(srclayers.size(), 1);
  CHECK_EQ(num_concates_, srclayers.size());
  // calculate step for each memcpy
  int step = srclayers[0]->grad(this).shape()[concate_dim_];
  for (unsigned i = concate_dim_ + 1; i < grad_.shape().size(); ++i)
    step *= grad_.shape()[i];
  int srclayer_offset = 0;
  int concate_offset = 0;
  auto context = Singleton<Context>::Instance();
  int device = context->device_id(std::this_thread::get_id());
  while (concate_offset < grad_.count()) {
    for (size_t i = 0; i < srclayers.size(); ++i) {
      if (device < 0) {
        const float* src = grad_.cpu_data() + concate_offset;
        float* dst = srclayers[i]->mutable_grad(this)->mutable_cpu_data()
          + srclayer_offset;
        memcpy(dst, src, step * sizeof(float));
      } else {
#ifdef USE_GPU
        const float* src = grad_.gpu_data() + concate_offset;
        float* dst = srclayers[i]->mutable_grad(this)->mutable_gpu_data()
          + srclayer_offset;
        cudaMemcpy(dst, src, step * sizeof(float), cudaMemcpyDefault);
#else
        LOG(FATAL) << "GPU is not supported";
#endif
      }
      concate_offset += step;
    }
    srclayer_offset += step;
  }
}

}  // namespace singa