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#include "caffe2/operators/relu_n_op.h"
#include <algorithm>
#include <functional>
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
namespace {
template <typename T>
__global__ void
ReluNCUDAKernel(const int N, const T threshold, const T* X, T* Y) {
CUDA_1D_KERNEL_LOOP(i, N) {
#if __CUDA_ARCH__ >= 350
Y[i] = __ldg(X + i) > 0
? (__ldg(X + i) < threshold ? __ldg(X + i) : threshold)
: T(0);
#else
Y[i] = X[i] > 0 ? (X[i] < threshold ? X[i] : threshold) : T(0);
#endif
}
}
template <typename T>
__global__ void ReluNGradientCUDAKernel(
const int N,
const T threshold,
const T* dY,
const T* Y,
T* dX) {
CUDA_1D_KERNEL_LOOP(i, N) {
#if __CUDA_ARCH__ >= 350
dX[i] = (__ldg(Y + i) > 0 && __ldg(Y + i) < threshold) ? dY[i] : T(0);
#else
dX[i] = (Y[i] > 0 && Y[i] < threshold) ? dY[i] : T(0);
#endif
}
}
} // namespace
template <>
template <typename T>
bool ReluNFunctor<CUDAContext>::
operator()(const int N, const T* X, T* Y, CUDAContext* context) const {
ReluNCUDAKernel<T>
<<<CAFFE_GET_BLOCKS(N),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(N, n, X, Y);
return true;
}
template <>
template <typename T>
bool ReluNGradientFunctor<CUDAContext>::Forward(
const std::vector<int>& Y_dims,
const std::vector<int>& /* dY_dims */,
const T* Y,
const T* dY,
T* dX,
CUDAContext* context) const {
const int size = std::accumulate(
Y_dims.cbegin(), Y_dims.cend(), 1, std::multiplies<int>());
ReluNGradientCUDAKernel<T>
<<<CAFFE_GET_BLOCKS(size),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(size, n, dY, Y, dX);
return true;
}
REGISTER_CUDA_OPERATOR(
ReluN,
UnaryElementwiseWithArgsOp<
TensorTypes<float>,
CUDAContext,
ReluNFunctor<CUDAContext>>);
REGISTER_CUDA_OPERATOR(
ReluNGradient,
BinaryElementwiseWithArgsOp<
TensorTypes<float>,
CUDAContext,
ReluNGradientFunctor<CUDAContext>>);
} // namespace caffe2
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