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#include "caffe2/core/context_gpu.h"
#include "caffe2/operators/cosine_embedding_criterion_op.h"
namespace caffe2 {
namespace {
__global__ void CECKernel(
const int N, const float* S, const int* Y, const float margin,
float* output) {
CUDA_1D_KERNEL_LOOP(i, N) {
output[i] = Y[i] == 1 ? (1. - S[i]) : fmaxf(0.f, S[i] - margin);
}
}
__global__ void CECGradientKernel(
const int N, const float* S, const int* Y, const float* dOutput,
const float margin, float* dS) {
CUDA_1D_KERNEL_LOOP(i, N) {
dS[i] = dOutput[i] * (Y[i] == 1 ? -1 : static_cast<float>(S[i] >= margin));
}
}
} // namespace
template <>
bool CosineEmbeddingCriterionOp<CUDAContext>::RunOnDevice() {
auto& S = Input(0);
auto& Y = Input(1);
CAFFE_ENFORCE(S.numel() == Y.numel(),
"The embedding and label should have the same size.");
auto* output = Output(0, S.sizes(), at::dtype<float>());
const float* Sdata = S.data<float>();
const int* Ydata = Y.data<int>();
float* output_data = output->template mutable_data<float>();
CECKernel<<<CAFFE_GET_BLOCKS(S.numel()), CAFFE_CUDA_NUM_THREADS,
0, context_.cuda_stream()>>>(
S.numel(), Sdata, Ydata, margin_, output_data);
return true;
}
template <>
bool CosineEmbeddingCriterionGradientOp<CUDAContext>::RunOnDevice() {
auto& S = Input(0);
auto& Y = Input(1);
auto& dOutput = Input(2);
auto* dS = Output(0, S.sizes(), at::dtype<float>());
const float* Sdata = S.data<float>();
const int* Ydata = Y.data<int>();
const float* dOutput_data = dOutput.data<float>();
float* dSdata = dS->template mutable_data<float>();
CECGradientKernel<<<CAFFE_GET_BLOCKS(S.numel()), CAFFE_CUDA_NUM_THREADS,
0, context_.cuda_stream()>>>(
S.numel(), Sdata, Ydata, dOutput_data, margin_, dSdata);
return true;
}
REGISTER_CUDA_OPERATOR(
CosineEmbeddingCriterion,
CosineEmbeddingCriterionOp<CUDAContext>);
REGISTER_CUDA_OPERATOR(
CosineEmbeddingCriterionGradient,
CosineEmbeddingCriterionGradientOp<CUDAContext>);
} // namespace caffe2
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