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Tensor-inl.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.
#include "../GpuFaissAssert.h"
#include "DeviceUtils.h"
#include <limits>
namespace faiss { namespace gpu {
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::Tensor()
: data_(nullptr) {
static_assert(Dim > 0, "must have > 0 dimensions");
for (int i = 0; i < Dim; ++i) {
size_[i] = 0;
stride_[i] = (IndexT) 1;
}
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::Tensor(
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>& t) {
this->operator=(t);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::Tensor(
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>&& t) {
this->operator=(std::move(t));
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>&
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::operator=(
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>& t) {
data_ = t.data_;
for (int i = 0; i < Dim; ++i) {
size_[i] = t.size_[i];
stride_[i] = t.stride_[i];
}
return *this;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>&
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::operator=(
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>&& t) {
data_ = t.data_; t.data_ = nullptr;
for (int i = 0; i < Dim; ++i) {
stride_[i] = t.stride_[i]; t.stride_[i] = 0;
size_[i] = t.size_[i]; t.size_[i] = 0;
}
return *this;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::
Tensor(DataPtrType data, const IndexT sizes[Dim])
: data_(data) {
static_assert(Dim > 0, "must have > 0 dimensions");
for (int i = 0; i < Dim; ++i) {
size_[i] = sizes[i];
}
stride_[Dim - 1] = (IndexT) 1;
for (int i = Dim - 2; i >= 0; --i) {
stride_[i] = stride_[i + 1] * sizes[i + 1];
}
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::
Tensor(DataPtrType data, std::initializer_list<IndexT> sizes)
: data_(data) {
GPU_FAISS_ASSERT(sizes.size() == Dim);
static_assert(Dim > 0, "must have > 0 dimensions");
int i = 0;
for (auto s : sizes) {
size_[i++] = s;
}
stride_[Dim - 1] = (IndexT) 1;
for (int j = Dim - 2; j >= 0; --j) {
stride_[j] = stride_[j + 1] * size_[j + 1];
}
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::Tensor(
DataPtrType data, const IndexT sizes[Dim], const IndexT strides[Dim])
: data_(data) {
static_assert(Dim > 0, "must have > 0 dimensions");
for (int i = 0; i < Dim; ++i) {
size_[i] = sizes[i];
stride_[i] = strides[i];
}
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ void
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::copyFrom(
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>& t,
cudaStream_t stream) {
// The tensor must be fully contiguous
GPU_FAISS_ASSERT(this->isContiguous());
// Size must be the same (since dimensions are checked and
// continuity is assumed, we need only check total number of
// elements
GPU_FAISS_ASSERT(this->numElements() == t.numElements());
if (t.numElements() > 0) {
GPU_FAISS_ASSERT(this->data_);
GPU_FAISS_ASSERT(t.data());
int ourDev = getDeviceForAddress(this->data_);
int tDev = getDeviceForAddress(t.data());
if (tDev == -1) {
CUDA_VERIFY(cudaMemcpyAsync(this->data_,
t.data(),
this->getSizeInBytes(),
ourDev == -1 ? cudaMemcpyHostToHost :
cudaMemcpyHostToDevice,
stream));
} else {
CUDA_VERIFY(cudaMemcpyAsync(this->data_,
t.data(),
this->getSizeInBytes(),
ourDev == -1 ? cudaMemcpyDeviceToHost :
cudaMemcpyDeviceToDevice,
stream));
}
}
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ void
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::copyTo(
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>& t,
cudaStream_t stream) {
// The tensor must be fully contiguous
GPU_FAISS_ASSERT(this->isContiguous());
// Size must be the same (since dimensions are checked and
// continuity is assumed, we need only check total number of
// elements
GPU_FAISS_ASSERT(this->numElements() == t.numElements());
if (t.numElements() > 0) {
GPU_FAISS_ASSERT(this->data_);
GPU_FAISS_ASSERT(t.data());
int ourDev = getDeviceForAddress(this->data_);
int tDev = getDeviceForAddress(t.data());
if (tDev == -1) {
CUDA_VERIFY(cudaMemcpyAsync(t.data(),
this->data_,
this->getSizeInBytes(),
ourDev == -1 ? cudaMemcpyHostToHost :
cudaMemcpyDeviceToHost,
stream));
} else {
CUDA_VERIFY(cudaMemcpyAsync(t.data(),
this->data_,
this->getSizeInBytes(),
ourDev == -1 ? cudaMemcpyHostToDevice :
cudaMemcpyDeviceToDevice,
stream));
}
}
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename OtherT, int OtherDim>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isSame(
const Tensor<OtherT, OtherDim, InnerContig, IndexT, PtrTraits>& rhs) const {
if (Dim != OtherDim) {
return false;
}
for (int i = 0; i < Dim; ++i) {
if (this->getSize(i) != rhs.getSize(i)) {
return false;
}
if (this->getStride(i) != rhs.getStride(i)) {
return false;
}
}
return true;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename OtherT, int OtherDim>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isSameSize(
const Tensor<OtherT, OtherDim, InnerContig, IndexT, PtrTraits>& rhs) const {
if (Dim != OtherDim) {
return false;
}
for (int i = 0; i < Dim; ++i) {
if (this->getSize(i) != rhs.getSize(i)) {
return false;
}
}
return true;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename U>
__host__ __device__ Tensor<U, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::cast() {
static_assert(sizeof(U) == sizeof(T), "cast must be to same size object");
return Tensor<U, Dim, InnerContig, IndexT, PtrTraits>(
reinterpret_cast<U*>(data_), size_, stride_);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename U>
__host__ __device__ const Tensor<U, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::cast() const {
static_assert(sizeof(U) == sizeof(T), "cast must be to same size object");
return Tensor<U, Dim, InnerContig, IndexT, PtrTraits>(
reinterpret_cast<U*>(data_), size_, stride_);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename U>
__host__ __device__ Tensor<U, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::castResize() {
static_assert(sizeof(U) >= sizeof(T), "only handles greater sizes");
constexpr int kMultiple = sizeof(U) / sizeof(T);
GPU_FAISS_ASSERT(canCastResize<U>());
IndexT newSize[Dim];
IndexT newStride[Dim];
for (int i = 0; i < Dim - 1; ++i) {
newSize[i] = size_[i];
newStride[i] = stride_[i] / kMultiple;
}
newStride[Dim - 1] = 1; // this is the same as the old stride
newSize[Dim - 1] = size_[Dim - 1] / kMultiple;
return Tensor<U, Dim, InnerContig, IndexT, PtrTraits>(
reinterpret_cast<U*>(data_), newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename U>
__host__ __device__ const Tensor<U, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::castResize() const {
return const_cast<Tensor<T, Dim, InnerContig, IndexT, PtrTraits>*>(this)->
castResize<U>();
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename U>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::canCastResize() const {
static_assert(sizeof(U) >= sizeof(T), "only handles greater sizes");
constexpr int kMultiple = sizeof(U) / sizeof(T);
// Ensure that the base pointer is sizeof(U) aligned
if (((uintptr_t) data_) % sizeof(U) != 0) {
return false;
}
// Check all outer strides
for (int i = 0; i < Dim - 1; ++i) {
if (stride_[i] % kMultiple != 0) {
return false;
}
}
// Check inner size
if (size_[Dim - 1] % kMultiple != 0) {
return false;
}
if (stride_[Dim - 1] != 1) {
return false;
}
return true;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename NewIndexT>
__host__ Tensor<T, Dim, InnerContig, NewIndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::castIndexType() const {
if (sizeof(NewIndexT) < sizeof(IndexT)) {
GPU_FAISS_ASSERT(this->canUseIndexType<NewIndexT>());
}
NewIndexT newSize[Dim];
NewIndexT newStride[Dim];
for (int i = 0; i < Dim; ++i) {
newSize[i] = (NewIndexT) size_[i];
newStride[i] = (NewIndexT) stride_[i];
}
return Tensor<T, Dim, InnerContig, NewIndexT, PtrTraits>(
data_, newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <typename NewIndexT>
__host__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::canUseIndexType() const {
static_assert(sizeof(size_t) >= sizeof(IndexT),
"index size too large");
static_assert(sizeof(size_t) >= sizeof(NewIndexT),
"new index size too large");
// Find maximum offset that can be calculated
// FIXME: maybe also consider offset in bytes? multiply by sizeof(T)?
size_t maxOffset = 0;
for (int i = 0; i < Dim; ++i) {
size_t curMaxOffset = (size_t) size_[i] * (size_t) stride_[i];
if (curMaxOffset > maxOffset) {
maxOffset = curMaxOffset;
}
}
if (maxOffset > (size_t) std::numeric_limits<NewIndexT>::max()) {
return false;
}
return true;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ size_t
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::numElements() const {
size_t size = (size_t) getSize(0);
for (int i = 1; i < Dim; ++i) {
size *= (size_t) getSize(i);
}
return size;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isContiguous() const {
long prevSize = 1;
for (int i = Dim - 1; i >= 0; --i) {
if (getSize(i) != (IndexT) 1) {
if (getStride(i) == prevSize) {
prevSize *= getSize(i);
} else {
return false;
}
}
}
return true;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isConsistentlySized(int i) const {
if (i == 0 && getStride(i) > 0 && getSize(i) > 0) {
return true;
} else if ((i > 0) && (i < Dim) && (getStride(i) > 0) &&
((getStride(i - 1) / getStride(i)) >= getSize(i))) {
return true;
}
return false;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isConsistentlySized() const {
for (int i = 0; i < Dim; ++i) {
if (!isConsistentlySized(i)) {
return false;
}
}
return true;
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ bool
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::isContiguousDim(int i) const {
return (i == Dim - 1) || // just in case
((i < Dim - 1) &&
((getStride(i) / getStride(i + 1)) == getSize(i + 1)));
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ Tensor<T, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::transpose(int dim1,
int dim2) const {
GPU_FAISS_ASSERT(dim1 >= 0 && dim1 < Dim);
GPU_FAISS_ASSERT(dim1 >= 0 && dim2 < Dim);
// If a tensor is innermost contiguous, one cannot transpose the innermost
// dimension
if (InnerContig) {
GPU_FAISS_ASSERT(dim1 != Dim - 1 && dim2 != Dim - 1);
}
IndexT newSize[Dim];
IndexT newStride[Dim];
for (int i = 0; i < Dim; ++i) {
newSize[i] = size_[i];
newStride[i] = stride_[i];
}
IndexT tmp = newSize[dim1];
newSize[dim1] = newSize[dim2];
newSize[dim2] = tmp;
tmp = newStride[dim1];
newStride[dim1] = newStride[dim2];
newStride[dim2] = tmp;
return Tensor<T, Dim, InnerContig, IndexT, PtrTraits>(data_, newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int NewDim>
__host__ __device__ Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::upcastOuter() {
// Can only create tensors of greater dimension
static_assert(NewDim > Dim, "Can only upcast to greater dim");
IndexT newSize[NewDim];
IndexT newStride[NewDim];
int shift = NewDim - Dim;
for (int i = 0; i < NewDim; ++i) {
if (i < shift) {
// These are the extended dimensions
newSize[i] = (IndexT) 1;
newStride[i] = size_[0] * stride_[0];
} else {
// Shift the remaining dimensions
newSize[i] = size_[i - shift];
newStride[i] = stride_[i - shift];
}
}
return Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>(
data_, newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int NewDim>
__host__ __device__ Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::upcastInner() {
// Can only create tensors of greater dimension
static_assert(NewDim > Dim, "Can only upcast to greater dim");
IndexT newSize[NewDim];
IndexT newStride[NewDim];
for (int i = 0; i < NewDim; ++i) {
if (i < Dim) {
// Existing dimensions get copied over
newSize[i] = size_[i];
newStride[i] = stride_[i];
} else {
// Extended dimensions
newSize[i] = (IndexT) 1;
newStride[i] = (IndexT) 1;
}
}
return Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>(
data_, newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int NewDim>
__host__ __device__ Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::downcastOuter() {
// Can only create tensors of lesser dimension
static_assert(NewDim < Dim, "Can only downcast to lesser dim");
// We can't downcast non-contiguous tensors, since it leaves
// garbage data in the tensor. The tensor needs to be contiguous
// in all of the dimensions we are collapsing (no padding in
// them).
for (int i = 0; i < Dim - NewDim; ++i) {
bool cont = isContiguousDim(i);
GPU_FAISS_ASSERT(cont);
}
IndexT newSize[NewDim];
IndexT newStride[NewDim];
int ignoredDims = Dim - NewDim;
IndexT collapsedSize = 1;
for (int i = 0; i < Dim; ++i) {
if (i < ignoredDims) {
// Collapse these dimensions
collapsedSize *= getSize(i);
} else {
// Non-collapsed dimensions
if (i == ignoredDims) {
// This is the first non-collapsed dimension
newSize[i - ignoredDims] = collapsedSize * getSize(i);
} else {
// Subsequent non-collapsed dimensions
newSize[i - ignoredDims] = getSize(i);
}
newStride[i - ignoredDims] = getStride(i);
}
}
return Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>(
data_, newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int NewDim>
__host__ __device__ Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::downcastInner() {
// Can only create tensors of lesser dimension
static_assert(NewDim < Dim, "Can only downcast to lesser dim");
// We can't downcast non-contiguous tensors, since it leaves
// garbage data in the tensor. The tensor needs to be contiguous
// in all of the dimensions we are collapsing (no padding in
// them).
for (int i = NewDim; i < Dim; ++i) {
GPU_FAISS_ASSERT(isContiguousDim(i));
}
IndexT newSize[NewDim];
IndexT newStride[NewDim];
IndexT collapsedSize = 1;
for (int i = Dim - 1; i >= 0; --i) {
if (i >= NewDim) {
// Collapse these dimensions
collapsedSize *= getSize(i);
} else {
// Non-collapsed dimensions
if (i == NewDim - 1) {
// This is the first non-collapsed dimension
newSize[i] = collapsedSize * getSize(i);
newStride[i] = getStride(Dim - 1);
} else {
// Subsequent non-collapsed dimensions
newSize[i] = getSize(i);
newStride[i] = getStride(i);
}
}
}
return Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>(
data_, newSize, newStride);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int SubDim>
__host__ __device__ Tensor<T, SubDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::view(DataPtrType at) {
static_assert(SubDim >= 1 && SubDim < Dim,
"can only create view of lesser dim");
IndexT viewSizes[SubDim];
IndexT viewStrides[SubDim];
for (int i = 0; i < SubDim; ++i) {
viewSizes[i] = size_[Dim - SubDim + i];
viewStrides[i] = stride_[Dim - SubDim + i];
}
return Tensor<T, SubDim, InnerContig, IndexT, PtrTraits>(
at, viewSizes, viewStrides);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int SubDim>
__host__ __device__ Tensor<T, SubDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::view() {
return view<SubDim>(data_);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ Tensor<T, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::narrowOutermost(IndexT start,
IndexT size) {
return this->narrow(0, start, size);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
__host__ __device__ Tensor<T, Dim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::narrow(int dim,
IndexT start,
IndexT size) {
DataPtrType newData = data_;
GPU_FAISS_ASSERT(start >= 0 &&
start < size_[dim] &&
(start + size) <= size_[dim]);
if (start > 0) {
newData += (size_t) start * stride_[dim];
}
IndexT newSize[Dim];
for (int i = 0; i < Dim; ++i) {
if (i == dim) {
GPU_FAISS_ASSERT(start + size <= size_[dim]);
newSize[i] = size;
} else {
newSize[i] = size_[i];
}
}
// If we were innermost contiguous before, we are still innermost contiguous
return Tensor<T, Dim, InnerContig, IndexT, PtrTraits>(newData, newSize, stride_);
}
template <typename T, int Dim, bool InnerContig,
typename IndexT, template <typename U> class PtrTraits>
template <int NewDim>
__host__ __device__ Tensor<T, NewDim, InnerContig, IndexT, PtrTraits>
Tensor<T, Dim, InnerContig, IndexT, PtrTraits>::view(
std::initializer_list<IndexT> sizes) {
GPU_FAISS_ASSERT(this->isContiguous());
GPU_FAISS_ASSERT(sizes.size() == NewDim);
// The total size of the new view must be the same as the total size
// of the old view
size_t curSize = numElements();
size_t newSize = 1;
for (auto s : sizes) {
newSize *= s;
}
GPU_FAISS_ASSERT(curSize == newSize);
return Tensor<T, NewDim, true, IndexT, PtrTraits>(data(), sizes);
}
} } // namespace
faiss::gpu::Tensor::upcastOuter
__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > upcastOuter()
Definition: Tensor-inl.cuh:490
faiss::gpu::Tensor::castIndexType
__host__ Tensor< T, Dim, InnerContig, NewIndexT, PtrTraits > castIndexType() const
Definition: Tensor-inl.cuh:341
faiss::gpu::Tensor::isContiguousDim
__host__ __device__ bool isContiguousDim(int i) const
Returns true if the given dimension index has no padding.
Definition: Tensor-inl.cuh:447
faiss::gpu::Tensor::cast
__host__ __device__ Tensor< U, Dim, InnerContig, IndexT, PtrTraits > cast()
Definition: Tensor-inl.cuh:253
faiss::gpu::Tensor::numElements
__host__ __device__ size_t numElements() const
Definition: Tensor-inl.cuh:388
faiss::gpu::Tensor::downcastOuter
__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > downcastOuter()
Definition: Tensor-inl.cuh:546
faiss::gpu::Tensor::canCastResize
__host__ __device__ bool canCastResize() const
Returns true if we can castResize() this tensor to the new type.
Definition: Tensor-inl.cuh:309
faiss::gpu::Tensor::data_
DataPtrType data_
Raw pointer to where the tensor data begins.
Definition: Tensor.cuh:344
faiss::gpu::Tensor::Tensor
__host__ __device__ Tensor()
Default constructor.
Definition: Tensor-inl.cuh:20
faiss::gpu::Tensor::upcastInner
__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > upcastInner()
Definition: Tensor-inl.cuh:519
faiss::gpu::Tensor::narrowOutermost
__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > narrowOutermost(IndexT start, IndexT size)
Definition: Tensor-inl.cuh:661
faiss::gpu::Tensor::stride_
IndexT stride_[Dim]
Array of strides (in sizeof(T) terms) per each dimension.
Definition: Tensor.cuh:347
faiss::gpu::Tensor::isContiguous
__host__ __device__ bool isContiguous() const
Definition: Tensor-inl.cuh:401
faiss::gpu::Tensor::sizes
__host__ __device__ const IndexT * sizes() const
Returns the size array.
Definition: Tensor.cuh:245
faiss::gpu::Tensor::copyFrom
__host__ void copyFrom(Tensor< T, Dim, InnerContig, IndexT, PtrTraits > &t, cudaStream_t stream)
Copies a tensor into ourselves; sizes must match.
Definition: Tensor-inl.cuh:132
faiss::gpu::Tensor::size_
IndexT size_[Dim]
Size per each dimension.
Definition: Tensor.cuh:350
faiss::gpu::Tensor::operator=
__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > & operator=(Tensor< T, Dim, InnerContig, IndexT, PtrTraits > &t)
Assignment.
Definition: Tensor-inl.cuh:50
faiss::gpu::Tensor::strides
__host__ __device__ const IndexT * strides() const
Returns the stride array.
Definition: Tensor.cuh:250
faiss::gpu::Tensor::getSize
__host__ __device__ IndexT getSize(int i) const
Definition: Tensor.cuh:224
faiss::gpu::Tensor::isSameSize
__host__ __device__ bool isSameSize(const Tensor< OtherT, OtherDim, InnerContig, IndexT, PtrTraits > &rhs) const
Returns true if the two tensors are of the same dimensionality and size.
Definition: Tensor-inl.cuh:234
faiss::gpu::Tensor::downcastInner
__host__ __device__ Tensor< T, NewDim, InnerContig, IndexT, PtrTraits > downcastInner()
Definition: Tensor-inl.cuh:591
faiss::gpu::Tensor::narrow
__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > narrow(int dim, IndexT start, IndexT size)
Definition: Tensor-inl.cuh:669
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::copyTo
__host__ void copyTo(Tensor< T, Dim, InnerContig, IndexT, PtrTraits > &t, cudaStream_t stream)
Copies ourselves into a tensor; sizes must match.
Definition: Tensor-inl.cuh:171
faiss::gpu::Tensor
Our tensor type.
Definition: Tensor.cuh:30
faiss::gpu::Tensor::canUseIndexType
__host__ bool canUseIndexType() const
Definition: Tensor-inl.cuh:361
faiss::gpu::Tensor::transpose
__host__ __device__ Tensor< T, Dim, InnerContig, IndexT, PtrTraits > transpose(int dim1, int dim2) const
Definition: Tensor-inl.cuh:456
faiss::gpu::Tensor::getStride
__host__ __device__ IndexT getStride(int i) const
Definition: Tensor.cuh:230
faiss::gpu::Tensor::castResize
__host__ __device__ Tensor< U, Dim, InnerContig, IndexT, PtrTraits > castResize()
Definition: Tensor-inl.cuh:275
faiss::gpu::Tensor::view
__host__ __device__ Tensor< T, SubDim, InnerContig, IndexT, PtrTraits > view()
Definition: Tensor-inl.cuh:654
faiss::gpu::Tensor::isSame
__host__ __device__ bool isSame(const Tensor< OtherT, OtherDim, InnerContig, IndexT, PtrTraits > &rhs) const
Definition: Tensor-inl.cuh:211