src/neuralnet/connection_layer/slice.cc

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#include "singa/neuralnet/connection_layer.h"
#include "singa/utils/math_blob.h"
#include "singa/utils/singleton.h"
#include "singa/utils/context.h"

namespace singa {

using std::vector;

SliceLayer::~SliceLayer() {
  for (size_t i = 1; i < datavec_.size(); ++i) {
    if (datavec_[i] != nullptr) delete datavec_[i];
    if (gradvec_[i] != nullptr) delete gradvec_[i];
  }
}

void SliceLayer::Setup(const LayerProto& conf,
                       const vector<Layer*>& srclayers) {
  CHECK_EQ(srclayers.size(), 1);
  Layer::Setup(conf, srclayers);
  vector<int> shape = srclayers[0]->data(this).shape();
  slice_dim_ = conf.slice_conf().slice_dim();
  num_slices_ = conf.slice_conf().num_slices();
  CHECK_GE(slice_dim_, 0);
  CHECK_LT(slice_dim_, shape.size());
  CHECK_GT(num_slices_, 0);
  // add num_slices-1 more blobs
  for (int i = 1; i < num_slices_; ++i) {
    datavec_.push_back(new Blob<float>());
    gradvec_.push_back(new Blob<float>());
  }
  // TODO(wangsh): remove equal-size restrict later
  CHECK_EQ(shape[slice_dim_] % num_slices_, 0);
  shape[slice_dim_] /= num_slices_;
  for (int i = 0; i < num_slices_; ++i) {
    // if (i == slice_num - 1) shape[slice_dim] += remain;
    datavec_[i]->Reshape(shape);
    gradvec_[i]->Reshape(shape);
  }
}

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

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

const Blob<float>& SliceLayer::data(const Layer* from) {
  int idx = from ? layer_idx_.Get(from) : 0;
  CHECK_LT(idx, num_slices_);
  return *datavec_[idx];
}

const Blob<float>& SliceLayer::grad(const Layer* from) {
  int idx = from ? layer_idx_.Get(from) : 0;
  CHECK_LT(idx, num_slices_);
  return *gradvec_[idx];
}

Blob<float>* SliceLayer::mutable_data(const Layer* from) {
  CHECK(from);
  int idx = layer_idx_.Get(from);
  CHECK_LT(idx, num_slices_);
  return datavec_[idx];
}

Blob<float>* SliceLayer::mutable_grad(const Layer* from) {
  CHECK(from);
  int idx = layer_idx_.Get(from);
  CHECK_LT(idx, num_slices_);
  return gradvec_[idx];
}
const std::string SliceLayer::ToString(bool debug, int flag) {
  if (!debug)
    return "";
  string ret = "";
  if ((flag & kForward) == kForward && data_.count() !=0) {
    for (unsigned k = 0; k < datavec_.size(); k++)
      ret += StringPrintf("data-%u :%e ", k, Asum(*datavec_.at(k)));
  }
  if ((flag & kBackward) == kBackward && grad_.count() != 0) {
    for (unsigned k = 0; k < gradvec_.size(); k++)
    ret += StringPrintf("grad-%u:%e ", k, Asum(*gradvec_.at(k)));
  }
  return ret;
}
}  // namespace singa