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https://github.com/saymrwulf/pytorch.git
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9409a3a39b
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52240 Test Plan: Sandcastle tests Reviewed By: xush6528 Differential Revision: D26408330 fbshipit-source-id: 60779ba0e38c8f90e0e341c8faa2661e631112dd
115 lines
2.9 KiB
Text
115 lines
2.9 KiB
Text
#include "caffe2/core/context_gpu.h"
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#include "caffe2/operators/batch_permutation_op.h"
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namespace caffe2 {
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namespace {
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template <bool forward>
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__global__ void BatchPermutationKernel(
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int N,
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int K,
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const float* src,
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const int* indices,
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float* dst) {
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if (forward) {
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CUDA_1D_KERNEL_LOOP(index, N * K) {
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int k = index % K;
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int n = index / K;
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int idx = indices[n];
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CUDA_KERNEL_ASSERT(idx >= 0);
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CUDA_KERNEL_ASSERT(idx < N);
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dst[index] = src[idx * K + k];
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}
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} else {
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CUDA_1D_KERNEL_LOOP(index, N * K) {
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int k = index % K;
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int n = index / K;
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// NOTE: an alternative implementation if we want to align the index with
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// the output tensor (rather than the input tensor).
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// int idx = -1;
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// for (size_t i = 0; i < N; ++i) {
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// if (indices[i] == n) {
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// idx = i;
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// }
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// }
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// CUDA_KERNEL_ASSERT(idx >= 0);
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// CUDA_KERNEL_ASSERT(idx < N);
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// dst[index] = src[idx * K + k];
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int idx = indices[n];
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CUDA_KERNEL_ASSERT(idx >= 0);
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CUDA_KERNEL_ASSERT(idx < N);
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dst[idx * K + k] = src[index];
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}
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}
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}
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} // namespace
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template <>
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bool BatchPermutationOp<float, CUDAContext>::RunOnDevice() {
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auto& X = Input(0);
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auto& indices = Input(1);
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CAFFE_ENFORCE(indices.dim() == 1, "indices must be 1-d");
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CAFFE_ENFORCE(
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X.dim32(0) == indices.dim32(0),
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"X.dim32(0) must be equal to indices.dim32(0)",
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"(",
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X.dim32(0),
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" vs. ",
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indices.dim32(0),
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")");
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auto* Y = Output(0, X.sizes(), at::dtype<float>());
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if (X.dim32(0) > 0) {
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BatchPermutationKernel<true>
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<<<CAFFE_GET_BLOCKS(X.numel()),
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CAFFE_CUDA_NUM_THREADS,
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0,
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context_.cuda_stream()>>>(
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X.dim32(0),
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X.numel() / X.dim32(0),
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X.data<float>(),
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indices.data<int>(),
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Y->mutable_data<float>());
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C10_CUDA_KERNEL_LAUNCH_CHECK();
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}
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return true;
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}
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template <>
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bool BatchPermutationGradientOp<float, CUDAContext>::RunOnDevice() {
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auto& indices = Input(0);
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auto& dY = Input(1);
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auto* dX = Output(0, dY.sizes(), at::dtype<float>());
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if (dY.dim32(0) > 0) {
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BatchPermutationKernel<false>
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<<<CAFFE_GET_BLOCKS(dY.numel()),
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CAFFE_CUDA_NUM_THREADS,
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0,
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context_.cuda_stream()>>>(
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dY.dim32(0),
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dY.numel() / dY.dim32(0),
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dY.data<float>(),
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indices.data<int>(),
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dX->mutable_data<float>());
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C10_CUDA_KERNEL_LAUNCH_CHECK();
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}
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return true;
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}
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REGISTER_CUDA_OPERATOR(
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BatchPermutation,
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BatchPermutationOp<float, CUDAContext>);
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REGISTER_CUDA_OPERATOR(
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BatchPermutationGradient,
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BatchPermutationGradientOp<float, CUDAContext>);
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} // namespace caffe2
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using BatchPermutationOpFloatCUDA =
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caffe2::BatchPermutationOp<float, caffe2::CUDAContext>;
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C10_EXPORT_CAFFE2_OP_TO_C10_CUDA(BatchPermutation, BatchPermutationOpFloatCUDA);
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