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#include "caffe2/operators/elu_op.h"
#include <algorithm>
#include <functional>
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
namespace {
template <typename T>
__global__ void EluCUDAKernel(const int N, const T alpha, const T* X, T* Y);
template <>
__global__ void
EluCUDAKernel<float>(const int N, const float alpha, const float* X, float* Y) {
CUDA_1D_KERNEL_LOOP(i, N) {
#if __CUDA_ARCH__ >= 350
Y[i] =
__ldg(X + i) < 0 ? alpha * (expf(__ldg(X + i)) - 1.0f) : __ldg(X + i);
#else
Y[i] = X[i] < 0 ? alpha * (expf(X[i]) - 1.0f) : X[i];
#endif
}
}
template <typename T>
__global__ void EluGradientCUDAKernel(
const int N,
const T alpha,
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(dY + i) * (__ldg(Y + i) + alpha)
: __ldg(dY + i);
#else
dX[i] = Y[i] < 0 ? dY[i] * (Y[i] + alpha) : dY[i];
#endif
}
}
} // namespace
template <>
template <typename T>
bool EluFunctor<CUDAContext>::
operator()(const int N, const T* X, T* Y, CUDAContext* context) const {
EluCUDAKernel<T>
<<<CAFFE_GET_BLOCKS(N),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(N, alpha, X, Y);
return true;
}
template <>
template <typename T>
bool EluGradientFunctor<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>());
EluGradientCUDAKernel<T>
<<<CAFFE_GET_BLOCKS(size),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(size, alpha, dY, Y, dX);
return true;
}
REGISTER_CUDA_OPERATOR(
Elu,
UnaryElementwiseWithArgsOp<
TensorTypes<float>,
CUDAContext,
EluFunctor<CUDAContext>>);
REGISTER_CUDA_OPERATOR(
EluGradient,
BinaryElementwiseWithArgsOp<
TensorTypes<float>,
CUDAContext,
EluGradientFunctor<CUDAContext>>);
} // namespace caffe2
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