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#include <vector>
#include "caffe/filler.hpp"
#include "caffe/layers/embed_layer.hpp"
#include "caffe/util/gpu_util.cuh"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
__global__ void EmbedForward(const int nthreads, const Dtype* bottom_data,
const Dtype* weight, const int M, const int N, const int K,
Dtype* top_data) {
CUDA_KERNEL_LOOP(top_index, nthreads) {
const int n = top_index / N;
const int d = top_index % N;
const int index = static_cast<int>(bottom_data[n]);
const int weight_index = index * N + d;
top_data[top_index] = weight[weight_index];
}
}
template <typename Dtype>
__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
const Dtype* top_diff, const int M, const int N, const int K,
Dtype* weight_diff);
template <typename Dtype>
__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
const Dtype* top_diff, const int M, const int N, const int K,
Dtype* weight_diff) {
CUDA_KERNEL_LOOP(top_index, nthreads) {
const int n = top_index / N;
const int d = top_index % N;
const int index = static_cast<int>(bottom_data[n]);
const int weight_index = index * N + d;
caffe_gpu_atomic_add(top_diff[top_index], weight_diff + weight_index);
}
}
template <typename Dtype>
void EmbedLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
const Dtype* weight = this->blobs_[0]->gpu_data();
const int count = top[0]->count();
EmbedForward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
<<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, weight, M_, N_, K_, top_data);
if (bias_term_) {
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
bias_multiplier_.gpu_data(),
this->blobs_[1]->gpu_data(), Dtype(1), top_data);
}
}
template <typename Dtype>
void EmbedLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
if (this->param_propagate_down_[0]) {
const int top_count = top[0]->count();
const Dtype* top_diff = top[0]->gpu_diff();
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
EmbedBackward<Dtype> // NOLINT_NEXT_LINE(whitespace/operators)
<<<CAFFE_GET_BLOCKS(top_count), CAFFE_CUDA_NUM_THREADS>>>(
top_count, bottom_data, top_diff, M_, N_, K_, weight_diff);
}
if (bias_term_ && this->param_propagate_down_[1]) {
const Dtype* top_diff = top[0]->gpu_diff();
Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
bias_multiplier_.gpu_data(), Dtype(1), bias_diff);
}
}
INSTANTIATE_LAYER_GPU_FUNCS(EmbedLayer);
} // namespace caffe
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