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#include "caffe2/operators/log1p_op.h"
#include <algorithm>
#include <functional>
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
namespace {
template <typename T>
__global__ void
Log1pGradientCUDAKernel(const int N, const T* dY, const T* X, T* dX) {
CUDA_1D_KERNEL_LOOP(i, N) {
#if __CUDA_ARCH__ >= 350
dX[i] = __ldg(dY + i) / (__ldg(X + i) + T(1));
#else
dX[i] = dY[i] / (X[i] + T(1));
#endif
}
}
} // namespace
template <>
template <typename T>
bool Log1pGradientFunctor<CUDAContext>::Forward(
const std::vector<int>& X_dims,
const std::vector<int>& /* dY_dims */,
const T* X,
const T* dY,
T* dX,
CUDAContext* context) const {
const int size = std::accumulate(
X_dims.cbegin(), X_dims.cend(), 1, std::multiplies<int>());
Log1pGradientCUDAKernel<T>
<<<CAFFE_GET_BLOCKS(size),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(size, dY, X, dX);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
REGISTER_CUDA_OPERATOR(
Log1p,
UnaryElementwiseOp<
TensorTypes<float>,
CUDAContext,
Log1pFunctor<CUDAContext>>);
REGISTER_CUDA_OPERATOR(
Log1pGradient,
BinaryElementwiseOp<
TensorTypes<float>,
CUDAContext,
Log1pGradientFunctor<CUDAContext>>);
} // namespace caffe2
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