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reduction_ops.cu
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reduction_ops.cu
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#include "caffe2/core/context_gpu.h"
#include "caffe2/operators/reduction_ops.h"
#include "caffe2/utils/conversions.h"
#include <cub/cub.cuh>
namespace caffe2 {
REGISTER_CUDA_OPERATOR(SumElements, SumElementsOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(SumElementsInt, SumElementsIntOp<int, CUDAContext>);
REGISTER_CUDA_OPERATOR(SumSqrElements, SumSqrElementsOp<CUDAContext>);
REGISTER_CUDA_OPERATOR(RowwiseMax, MaxReductionOp<float, CUDAContext, true>);
REGISTER_CUDA_OPERATOR(ColwiseMax, MaxReductionOp<float, CUDAContext, false>);
REGISTER_CUDA_OPERATOR(
RowwiseMaxGradient,
MaxReductionGradientOp<float, CUDAContext, true>)
REGISTER_CUDA_OPERATOR(
ColwiseMaxGradient,
MaxReductionGradientOp<float, CUDAContext, false>)
REGISTER_CUDA_OPERATOR(
SumElementsGradient,
SumElementsGradientOp<float, CUDAContext>);
template <typename T>
__global__ void
SumElementsGradientKernel(bool average, const int N, const T* dY, T* dX) {
const T value = average ? (*dY) / N : *dY;
CUDA_1D_KERNEL_LOOP(i, N) {
dX[i] = value;
}
}
__global__ void rowwise_max_gradient_kernel(
const int batch_size,
const int M,
const int N,
const float* X,
const float* Y,
const float* dY,
float* dX) {
const int input_size = M * N;
CUDA_1D_KERNEL_LOOP(i, batch_size * M * N) {
const int b_i = i / input_size;
const int b_n = i / input_size / N;
const int y_index = b_i * M + b_n;
if (X[i] == Y[y_index]) {
dX[i] = dY[y_index];
} else {
dX[i] = 0.0;
}
}
}
template <>
bool SumSqrElementsOp<CUDAContext>::RunOnDevice() {
return DispatchHelper<TensorTypes<float, at::Half>>::call(this, Input(0));
}
__global__ void colwise_max_gradient_kernel(
const int batch_size,
const int M,
const int N,
const float* X,
const float* Y,
const float* dY,
float* dX) {
const int input_size = M * N;
CUDA_1D_KERNEL_LOOP(i, batch_size * M * N) {
const int b_i = i / input_size;
const int b_n = i % input_size % N;
const int y_index = b_i * N + b_n;
if (X[i] == Y[y_index]) {
dX[i] = dY[y_index];
} else {
dX[i] = 0.0;
}
}
}
template <>
bool SumElementsGradientOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0);
auto& dY = Input(1);
DCHECK_EQ(dY.numel(), 1);
auto* dX = Output(0, X.sizes(), at::dtype<float>());
SumElementsGradientKernel<float>
<<<CAFFE_GET_BLOCKS(X.numel()),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
average_,
X.numel(),
dY.data<float>(),
dX->template mutable_data<float>());
return true;
}
template <typename T, class Context, bool ROWWISE>
bool MaxReductionGradientOp<T, Context, ROWWISE>::RunOnDevice() {
auto& X = Input(0);
auto& Y = Input(1);
auto& dY = Input(2);
auto* dX = Output(0, X.sizes(), at::dtype<T>());
CAFFE_ENFORCE_EQ(X.dim(), 3);
const int batch_size = X.dim32(0);
const int M = X.dim32(1);
const int N = X.dim32(2);
const T* Xdata = X.template data<T>();
const T* Ydata = Y.template data<T>();
const T* dYdata = dY.template data<T>();
T* dXdata = dX->template mutable_data<T>();
const int input_size = M * N;
if (ROWWISE) {
rowwise_max_gradient_kernel<<<
CAFFE_GET_BLOCKS(batch_size * input_size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
batch_size, M, N, Xdata, Ydata, dYdata, dXdata);
} else {
colwise_max_gradient_kernel<<<
CAFFE_GET_BLOCKS(batch_size * input_size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
batch_size, M, N, Xdata, Ydata, dYdata, dXdata);
}
return true;
}
} // namespace caffe2