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#include "caffe2/operators/batch_moments_op.h"
#include "caffe2/utils/cub_namespace.cuh"
#include <cub/block/block_reduce.cuh>
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
namespace {
template <typename T>
using BlockReduce = cub::BlockReduce<T, CAFFE_CUDA_NUM_THREADS>;
template <typename T, StorageOrder kOrder>
__global__ void BatchMomentsCUDAKernel(
const int N,
const int C,
const int HxW,
const T* X,
T* mu,
T* var) {
const int outer_size = C;
const int inner_size = N * HxW;
__shared__ typename BlockReduce<T>::TempStorage m_storage;
__shared__ typename BlockReduce<T>::TempStorage v_storage;
for (int i = blockIdx.x; i < outer_size; i += gridDim.x) {
T m_sum = 0;
T v_sum = 0;
for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
const int index = kOrder == StorageOrder::NCHW
? (j / HxW * C + i) * HxW + j % HxW
: j * outer_size + i;
#if __CUDA_ARCH__ >= 350
m_sum += __ldg(X + index);
v_sum += __ldg(X + index) * __ldg(X + index);
#else
m_sum += X[index];
v_sum += X[index] * X[index];
#endif
}
m_sum = BlockReduce<T>(m_storage).Reduce(m_sum, cub::Sum());
v_sum = BlockReduce<T>(v_storage).Reduce(v_sum, cub::Sum());
if (threadIdx.x == 0) {
mu[i] = m_sum / static_cast<T>(N * HxW);
var[i] = v_sum / static_cast<T>(N * HxW);
}
__syncthreads();
}
}
template <typename T, StorageOrder kOrder>
__global__ void BatchMomentsGradientCUDAKernel(
const int N,
const int C,
const int HxW,
const T* dmu,
const T* dvar,
const T* X,
T* dX) {
const int size = N * C * HxW;
const T scale = T(1) / static_cast<T>(N * HxW);
CUDA_1D_KERNEL_LOOP(i, size) {
const int i_mu = kOrder == StorageOrder::NCHW ? i / (HxW) % C : i % C;
#if __CUDA_ARCH__ >= 350
dX[i] =
(__ldg(dmu + i_mu) + __ldg(dvar + i_mu) * T(2) * __ldg(X + i)) * scale;
#else
dX[i] = (dmu[i_mu] + dvar[i_mu] * T(2) * X[i]) * scale;
#endif
}
}
} // namespace
template <>
bool BatchMomentsOp<float, CUDAContext>::ComputeBatchMomentsNCHW(
const int N,
const int C,
const int HxW,
const float* X,
float* mu,
float* var) {
const int outer_size = N * HxW;
BatchMomentsCUDAKernel<float, StorageOrder::NCHW>
<<<std::min(outer_size, CAFFE_MAXIMUM_NUM_BLOCKS),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(N, C, HxW, X, mu, var);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template <>
bool BatchMomentsOp<float, CUDAContext>::ComputeBatchMomentsNHWC(
const int N,
const int C,
const int HxW,
const float* X,
float* mu,
float* var) {
const int outer_size = N * HxW;
BatchMomentsCUDAKernel<float, StorageOrder::NHWC>
<<<std::min(outer_size, CAFFE_MAXIMUM_NUM_BLOCKS),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(N, C, HxW, X, mu, var);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template <>
bool BatchMomentsGradientOp<float, CUDAContext>::
ComputeBatchMomentsGradientNCHW(
const int N,
const int C,
const int HxW,
const float* dmu,
const float* dvar,
const float* X,
float* dX) {
const int size = N * C * HxW;
BatchMomentsGradientCUDAKernel<float, StorageOrder::NCHW>
<<<CAFFE_GET_BLOCKS(size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(N, C, HxW, dmu, dvar, X, dX);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
template <>
bool BatchMomentsGradientOp<float, CUDAContext>::
ComputeBatchMomentsGradientNHWC(
const int N,
const int C,
const int HxW,
const float* dmu,
const float* dvar,
const float* X,
float* dX) {
const int size = N * C * HxW;
BatchMomentsGradientCUDAKernel<float, StorageOrder::NHWC>
<<<CAFFE_GET_BLOCKS(size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(N, C, HxW, dmu, dvar, X, dX);
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
REGISTER_CUDA_OPERATOR(BatchMoments, BatchMomentsOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(
BatchMomentsGradient,
BatchMomentsGradientOp<float, CUDAContext>);
} // namespace caffe2
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