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#include "caffe2/operators/mod_op.h"
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
namespace {
template <typename T>
__global__ void ModOpSimpleKernel(const int N, const int64_t divisor_,
const T* data_ptr, T* output_ptr) {
CUDA_1D_KERNEL_LOOP(i, N) {
output_ptr[i] = data_ptr[i] % divisor_;
}
}
template <typename T>
__global__ void ModOpKernel(const int N, const int64_t divisor_,
const T* data_ptr, T* output_ptr) {
CUDA_1D_KERNEL_LOOP(i, N) {
output_ptr[i] = data_ptr[i] % divisor_;
if (output_ptr[i] && ((output_ptr[i] > 0) != (divisor_ > 0))) {
output_ptr[i] += divisor_;
}
}
}
} // namespace
template <>
template <typename T>
bool ModOp<CUDAContext>::DoRunWithType() {
auto& data = Input(DATA);
auto N = data.numel();
const auto* data_ptr = data.template data<T>();
auto* output = Output(0, data.sizes(), at::dtype<T>());
auto* output_ptr = output->template mutable_data<T>();
if (sign_follow_divisor_) {
ModOpKernel<<<
CAFFE_GET_BLOCKS(N),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
N, divisor_, data_ptr, output_ptr);
C10_CUDA_KERNEL_LAUNCH_CHECK();
} else {
ModOpSimpleKernel<<<
CAFFE_GET_BLOCKS(N),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
N, divisor_, data_ptr, output_ptr);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
return true;
}
REGISTER_CUDA_OPERATOR(Mod, ModOp<CUDAContext>);
} // namespace caffe2
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