All Classes Namespaces Functions Variables Typedefs Enumerations Enumerator Friends
Transpose.cuh
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the BSD+Patents license found in the
* LICENSE file in the root directory of this source tree.
*/
// Copyright 2004-present Facebook. All Rights Reserved.
#pragma once
#include "../../FaissAssert.h"
#include "Tensor.cuh"
#include "DeviceUtils.h"
#include <cuda.h>
#include <stdio.h>
namespace faiss { namespace gpu {
template <typename T, typename IndexT>
struct TensorInfo {
static constexpr int kMaxDims = 8;
T* data;
IndexT sizes[kMaxDims];
IndexT strides[kMaxDims];
int dims;
};
template <typename T, typename IndexT, int Dim>
struct TensorInfoOffset {
__device__ inline static unsigned int get(const TensorInfo<T, IndexT>& info,
IndexT linearId) {
IndexT offset = 0;
#pragma unroll
for (int i = Dim - 1; i >= 0; --i) {
IndexT curDimIndex = linearId % info.sizes[i];
IndexT curDimOffset = curDimIndex * info.strides[i];
offset += curDimOffset;
if (i > 0) {
linearId /= info.sizes[i];
}
}
return offset;
}
};
template <typename T, typename IndexT>
struct TensorInfoOffset<T, IndexT, -1> {
__device__ inline static unsigned int get(const TensorInfo<T, IndexT>& info,
IndexT linearId) {
return linearId;
}
};
template <typename T, typename IndexT, int Dim>
TensorInfo<T, IndexT> getTensorInfo(const Tensor<T, Dim, true>& t) {
TensorInfo<T, IndexT> info;
for (int i = 0; i < Dim; ++i) {
info.sizes[i] = (IndexT) t.getSize(i);
info.strides[i] = (IndexT) t.getStride(i);
}
info.data = t.data();
info.dims = Dim;
return info;
}
template <typename T, typename IndexT, int DimInput, int DimOutput>
__global__ void transposeAny(TensorInfo<T, IndexT> input,
TensorInfo<T, IndexT> output,
IndexT totalSize) {
for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x;
i < totalSize;
i += gridDim.x + blockDim.x) {
auto inputOffset = TensorInfoOffset<T, IndexT, DimInput>::get(input, i);
auto outputOffset = TensorInfoOffset<T, IndexT, DimOutput>::get(output, i);
#if __CUDA_ARCH__ >= 350
output.data[outputOffset] = __ldg(&input.data[inputOffset]);
#else
output.data[outputOffset] = input.data[inputOffset];
#endif
}
}
/// Performs an out-of-place transposition between any two dimensions.
/// Best performance is if the transposed dimensions are not
/// innermost, since the reads and writes will be coalesced.
/// Could include a shared memory transposition if the dimensions
/// being transposed are innermost, but would require support for
/// arbitrary rectangular matrices.
/// This linearized implementation seems to perform well enough,
/// especially for cases that we care about (outer dimension
/// transpositions).
template <typename T, int Dim>
void runTransposeAny(Tensor<T, Dim, true>& in,
int dim1, int dim2,
Tensor<T, Dim, true>& out,
cudaStream_t stream) {
static_assert(Dim <= TensorInfo<T, unsigned int>::kMaxDims,
"too many dimensions");
FAISS_ASSERT(dim1 != dim2);
FAISS_ASSERT(dim1 < Dim && dim2 < Dim);
int outSize[Dim];
for (int i = 0; i < Dim; ++i) {
outSize[i] = in.getSize(i);
}
std::swap(outSize[dim1], outSize[dim2]);
for (int i = 0; i < Dim; ++i) {
FAISS_ASSERT(out.getSize(i) == outSize[i]);
}
size_t totalSize = in.numElements();
size_t block = std::min((size_t) getMaxThreadsCurrentDevice(), totalSize);
if (totalSize <= (size_t) std::numeric_limits<int>::max()) {
// div/mod seems faster with unsigned types
auto inInfo = getTensorInfo<T, unsigned int, Dim>(in);
auto outInfo = getTensorInfo<T, unsigned int, Dim>(out);
std::swap(inInfo.sizes[dim1], inInfo.sizes[dim2]);
std::swap(inInfo.strides[dim1], inInfo.strides[dim2]);
auto grid = std::min(utils::divUp(totalSize, block), (size_t) 4096);
transposeAny<T, unsigned int, Dim, -1>
<<<grid, block, 0, stream>>>(inInfo, outInfo, totalSize);
} else {
auto inInfo = getTensorInfo<T, unsigned long, Dim>(in);
auto outInfo = getTensorInfo<T, unsigned long, Dim>(out);
std::swap(inInfo.sizes[dim1], inInfo.sizes[dim2]);
std::swap(inInfo.strides[dim1], inInfo.strides[dim2]);
auto grid = std::min(utils::divUp(totalSize, block), (size_t) 4096);
transposeAny<T, unsigned long, Dim, -1>
<<<grid, block, 0, stream>>>(inInfo, outInfo, totalSize);
}
CUDA_TEST_ERROR();
}
} } // namespace
faiss::gpu::TensorInfo
Definition: Transpose.cuh:23
faiss::gpu::Tensor::getSize
__host__ __device__ IndexT getSize(int i) const
Definition: Tensor.cuh:224
faiss::gpu::Tensor::data
__host__ __device__ DataPtrType data()
Returns a raw pointer to the start of our data.
Definition: Tensor.cuh:176
faiss::gpu::Tensor
Our tensor type.
Definition: Tensor.cuh:30
faiss::gpu::Tensor::getStride
__host__ __device__ IndexT getStride(int i) const
Definition: Tensor.cuh:230
faiss::gpu::TensorInfoOffset
Definition: Transpose.cuh:33