devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Rename Index to StorageIndex + use Eigen::Array and Eigen::Map when possible

Browse Source
This commit is contained in:
Eugene Zhulenev
2018-07-27 12:45:17 -07:00
parent 6913221c43
commit 966c2a7bb6
6 changed files with 447 additions and 478 deletions
Download Patch File Download Diff File Expand all files Collapse all files

418
unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
View File

@@ -67,21 +67,21 @@ enum class TensorBlockShapeType {
struct TensorOpResourceRequirements {
TensorBlockShapeType block_shape;
std::size_t block_total_size;
Index block_total_size;
// TODO(andydavis) Add 'target_num_threads' to support communication of
// thread-resource requirements. This will allow ops deep in the
// expression tree (like reductions) to communicate resources
// requirements based on local state (like the total number of reductions
// to be computed).
TensorOpResourceRequirements(internal::TensorBlockShapeType shape,
const std::size_t size)
const Index size)
: block_shape(shape), block_total_size(size) {}
};
// Tries to merge multiple resource requirements.
EIGEN_STRONG_INLINE void MergeResourceRequirements(
const std::vector<TensorOpResourceRequirements>& resources,
TensorBlockShapeType* block_shape, std::size_t* block_total_size) {
TensorBlockShapeType* block_shape, Index* block_total_size) {
if (resources.empty()) {
return;
}
@@ -108,12 +108,12 @@ EIGEN_STRONG_INLINE void MergeResourceRequirements(
* This class represents a tensor block specified by the index of the
* first block coefficient, and the size of the block in each dimension.
*/
template <typename Scalar, typename Index, int NumDims, int Layout>
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlock {
public:
typedef DSizes<Index, NumDims> Dimensions;
typedef DSizes<StorageIndex, NumDims> Dimensions;
TensorBlock(const Index first_coeff_index, const Dimensions& block_sizes,
TensorBlock(const StorageIndex first_coeff_index, const Dimensions& block_sizes,
const Dimensions& block_strides, const Dimensions& tensor_strides,
Scalar* data)
: m_first_coeff_index(first_coeff_index),
@@ -122,7 +122,7 @@ class TensorBlock {
m_tensor_strides(tensor_strides),
m_data(data) {}
Index first_coeff_index() const { return m_first_coeff_index; }
StorageIndex first_coeff_index() const { return m_first_coeff_index; }
const Dimensions& block_sizes() const { return m_block_sizes; }
@@ -135,108 +135,33 @@ class TensorBlock {
const Scalar* data() const { return m_data; }
private:
Index m_first_coeff_index;
StorageIndex m_first_coeff_index;
Dimensions m_block_sizes;
Dimensions m_block_strides;
Dimensions m_tensor_strides;
Scalar* m_data; // Not owned.
};
template <typename Scalar, typename Index, bool Vectorizable>
template <typename Scalar, typename StorageIndex>
struct TensorBlockCopyOp {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const Index num_coeff_to_copy, const Index dst_index,
const Index dst_stride, Scalar* EIGEN_RESTRICT dst_data,
const Index src_index, const Index src_stride,
const StorageIndex num_coeff_to_copy, const StorageIndex dst_index,
const StorageIndex dst_stride, Scalar* EIGEN_RESTRICT dst_data,
const StorageIndex src_index, const StorageIndex src_stride,
const Scalar* EIGEN_RESTRICT src_data) {
for (Index i = 0; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i * dst_stride] =
src_data[src_index + i * src_stride];
}
}
};
const Scalar* src_base = &src_data[src_index];
Scalar* dst_base = &dst_data[dst_index];
// NOTE: Benchmarks run on an implementation of this that broke each of the
// loops in these conditionals into it's own template specialization (to
// avoid conditionals in the caller's loop) did not show an improvement.
template <typename Scalar, typename Index>
struct TensorBlockCopyOp<Scalar, Index, true> {
typedef typename packet_traits<Scalar>::type Packet;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const Index num_coeff_to_copy, const Index dst_index,
const Index dst_stride, Scalar* EIGEN_RESTRICT dst_data,
const Index src_index, const Index src_stride,
const Scalar* EIGEN_RESTRICT src_data) {
if (src_stride == 1) {
const Index packet_size = internal::unpacket_traits<Packet>::size;
const Index vectorized_size =
(num_coeff_to_copy / packet_size) * packet_size;
if (dst_stride == 1) {
// LINEAR
for (Index i = 0; i < vectorized_size; i += packet_size) {
Packet p = internal::ploadu<Packet>(src_data + src_index + i);
internal::pstoreu<Scalar, Packet>(dst_data + dst_index + i, p);
}
for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i] = src_data[src_index + i];
}
} else {
// SCATTER
for (Index i = 0; i < vectorized_size; i += packet_size) {
Packet p = internal::ploadu<Packet>(src_data + src_index + i);
internal::pscatter<Scalar, Packet>(
dst_data + dst_index + i * dst_stride, p, dst_stride);
}
for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i * dst_stride] = src_data[src_index + i];
}
}
} else if (src_stride == 0) {
const Index packet_size = internal::unpacket_traits<Packet>::size;
const Index vectorized_size =
(num_coeff_to_copy / packet_size) * packet_size;
if (dst_stride == 1) {
// LINEAR
for (Index i = 0; i < vectorized_size; i += packet_size) {
Packet p = internal::pload1<Packet>(src_data + src_index);
internal::pstoreu<Scalar, Packet>(dst_data + dst_index + i, p);
}
for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i] = src_data[src_index];
}
} else {
// SCATTER
for (Index i = 0; i < vectorized_size; i += packet_size) {
Packet p = internal::pload1<Packet>(src_data + src_index);
internal::pscatter<Scalar, Packet>(
dst_data + dst_index + i * dst_stride, p, dst_stride);
}
for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i * dst_stride] = src_data[src_index];
}
}
} else {
if (dst_stride == 1) {
// GATHER
const Index packet_size = internal::unpacket_traits<Packet>::size;
const Index vectorized_size =
(num_coeff_to_copy / packet_size) * packet_size;
for (Index i = 0; i < vectorized_size; i += packet_size) {
Packet p = internal::pgather<Scalar, Packet>(
src_data + src_index + i * src_stride, src_stride);
internal::pstoreu<Scalar, Packet>(dst_data + dst_index + i, p);
}
for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i] = src_data[src_index + i * src_stride];
}
} else {
// RANDOM
for (Index i = 0; i < num_coeff_to_copy; ++i) {
dst_data[dst_index + i * dst_stride] =
src_data[src_index + i * src_stride];
}
}
}
using Src = const Eigen::Array<Scalar, Dynamic, 1>;
using Dst = Eigen::Array<Scalar, Dynamic, 1>;
using SrcMap = Eigen::Map<Src, 0, InnerStride<>>;
using DstMap = Eigen::Map<Dst, 0, InnerStride<>>;
const SrcMap src(src_base, num_coeff_to_copy, InnerStride<>(src_stride));
DstMap dst(dst_base, num_coeff_to_copy, InnerStride<>(dst_stride));
dst = src;
}
};
@@ -249,34 +174,34 @@ struct TensorBlockCopyOp<Scalar, Index, true> {
* This class is responsible for copying data between a tensor and a tensor
* block.
*/
template <typename Scalar, typename Index, int NumDims, int Layout,
bool Vectorizable, bool BlockRead>
template <typename Scalar, typename StorageIndex, int NumDims, int Layout,
bool BlockRead>
class TensorBlockIO {
public:
typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef typename internal::TensorBlockCopyOp<Scalar, Index, Vectorizable>
typedef typename internal::TensorBlockCopyOp<Scalar, StorageIndex>
TensorBlockCopyOp;
protected:
struct BlockIteratorState {
Index input_stride;
Index output_stride;
Index input_span;
Index output_span;
Index size;
Index count;
StorageIndex input_stride;
StorageIndex output_stride;
StorageIndex input_span;
StorageIndex output_span;
StorageIndex size;
StorageIndex count;
};
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Copy(
const TensorBlock& block, Index first_coeff_index,
const array<Index, NumDims>& tensor_to_block_dim_map,
const array<Index, NumDims>& tensor_strides, const Scalar* src_data,
const TensorBlock& block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data,
Scalar* dst_data) {
// Find the innermost tensor dimension whose size is not 1. This is the
// effective inner dim. If all dimensions are of size 1, then fallback to
// using the actual innermost dim to avoid out-of-bound access.
Index num_size_one_inner_dims = 0;
StorageIndex num_size_one_inner_dims = 0;
for (int i = 0; i < NumDims; ++i) {
const int dim = cond<Layout>()(i, NumDims - i - 1);
if (block.block_sizes()[tensor_to_block_dim_map[dim]] != 1) {
@@ -285,16 +210,16 @@ class TensorBlockIO {
}
}
// Calculate strides and dimensions.
const Index tensor_stride1_dim = cond<Layout>()(
const StorageIndex tensor_stride1_dim = cond<Layout>()(
num_size_one_inner_dims, NumDims - num_size_one_inner_dims - 1);
const Index block_dim_for_tensor_stride1_dim =
const StorageIndex block_dim_for_tensor_stride1_dim =
NumDims == 0 ? 1 : tensor_to_block_dim_map[tensor_stride1_dim];
size_t block_inner_dim_size =
NumDims == 0 ? 1
: block.block_sizes()[block_dim_for_tensor_stride1_dim];
for (int i = num_size_one_inner_dims + 1; i < NumDims; ++i) {
const int dim = cond<Layout>()(i, NumDims - i - 1);
const Index block_stride =
const StorageIndex block_stride =
block.block_strides()[tensor_to_block_dim_map[dim]];
if (block_inner_dim_size == block_stride &&
block_stride == tensor_strides[dim]) {
@@ -306,10 +231,10 @@ class TensorBlockIO {
}
}
Index inputIndex;
Index outputIndex;
Index input_stride;
Index output_stride;
StorageIndex inputIndex;
StorageIndex outputIndex;
StorageIndex input_stride;
StorageIndex output_stride;
// Setup strides to read/write along the tensor's stride1 dimension.
if (BlockRead) {
@@ -337,7 +262,7 @@ class TensorBlockIO {
int num_squeezed_dims = 0;
for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
const int dim = cond<Layout>()(i + 1, NumDims - i - 2);
const Index size = block.block_sizes()[tensor_to_block_dim_map[dim]];
const StorageIndex size = block.block_sizes()[tensor_to_block_dim_map[dim]];
if (size == 1) {
continue;
}
@@ -362,9 +287,9 @@ class TensorBlockIO {
}
// Iterate copying data from src to dst.
const Index block_total_size =
const StorageIndex block_total_size =
NumDims == 0 ? 1 : block.block_sizes().TotalSize();
for (Index i = 0; i < block_total_size; i += block_inner_dim_size) {
for (StorageIndex i = 0; i < block_total_size; i += block_inner_dim_size) {
TensorBlockCopyOp::Run(block_inner_dim_size, outputIndex, output_stride,
dst_data, inputIndex, input_stride, src_data);
// Update index.
@@ -391,19 +316,18 @@ class TensorBlockIO {
* This class is responsible for reading a tensor block.
*
*/
template <typename Scalar, typename Index, int NumDims, int Layout,
bool Vectorizable>
class TensorBlockReader
: public TensorBlockIO<Scalar, Index, NumDims, Layout, Vectorizable, true> {
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlockReader : public TensorBlockIO<Scalar, StorageIndex, NumDims,
Layout, /*BlockRead=*/true> {
public:
typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef TensorBlockIO<Scalar, Index, NumDims, Layout, Vectorizable, true>
typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/true>
Base;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
TensorBlock* block, const Scalar* src_data) {
array<Index, NumDims> tensor_to_block_dim_map;
array<StorageIndex, NumDims> tensor_to_block_dim_map;
for (int i = 0; i < NumDims; ++i) {
tensor_to_block_dim_map[i] = i;
}
@@ -412,9 +336,9 @@ class TensorBlockReader
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
TensorBlock* block, Index first_coeff_index,
const array<Index, NumDims>& tensor_to_block_dim_map,
const array<Index, NumDims>& tensor_strides, const Scalar* src_data) {
TensorBlock* block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data) {
Base::Copy(*block, first_coeff_index, tensor_to_block_dim_map,
tensor_strides, src_data, block->data());
}
@@ -429,19 +353,18 @@ class TensorBlockReader
* This class is responsible for writing a tensor block.
*
*/
template <typename Scalar, typename Index, int NumDims, int Layout,
bool Vectorizable>
class TensorBlockWriter : public TensorBlockIO<Scalar, Index, NumDims, Layout,
Vectorizable, false> {
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlockWriter : public TensorBlockIO<Scalar, StorageIndex, NumDims,
Layout, /*BlockRead=*/false> {
public:
typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef TensorBlockIO<Scalar, Index, NumDims, Layout, Vectorizable, false>
typedef TensorBlockIO<Scalar, StorageIndex, NumDims, Layout, /*BlockRead=*/false>
Base;
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const TensorBlock& block, Scalar* dst_data) {
array<Index, NumDims> tensor_to_block_dim_map;
array<StorageIndex, NumDims> tensor_to_block_dim_map;
for (int i = 0; i < NumDims; ++i) {
tensor_to_block_dim_map[i] = i;
}
@@ -450,9 +373,9 @@ class TensorBlockWriter : public TensorBlockIO<Scalar, Index, NumDims, Layout,
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const TensorBlock& block, Index first_coeff_index,
const array<Index, NumDims>& tensor_to_block_dim_map,
const array<Index, NumDims>& tensor_strides, Scalar* dst_data) {
const TensorBlock& block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, Scalar* dst_data) {
Base::Copy(block, first_coeff_index, tensor_to_block_dim_map,
tensor_strides, block.data(), dst_data);
}
@@ -468,67 +391,34 @@ class TensorBlockWriter : public TensorBlockIO<Scalar, Index, NumDims, Layout,
* result of the cwise binary op to the strided output array.
*
*/
template <bool Vectorizable>
struct TensorBlockCwiseBinaryOp {
template <typename Index, typename BinaryFunctor, typename OutputScalar,
template <typename StorageIndex, typename BinaryFunctor, typename OutputScalar,
typename LeftScalar, typename RightScalar>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const BinaryFunctor& functor, const Index num_coeff,
const Index output_index, const Index output_stride,
OutputScalar* output_data, const Index left_index,
const Index left_stride, const LeftScalar* left_data,
const Index right_index, const Index right_stride,
const BinaryFunctor& functor, const StorageIndex num_coeff,
const StorageIndex output_index, const StorageIndex output_stride,
OutputScalar* output_data, const StorageIndex left_index,
const StorageIndex left_stride, const LeftScalar* left_data,
const StorageIndex right_index, const StorageIndex right_stride,
const RightScalar* right_data) {
for (Index i = 0; i < num_coeff; ++i) {
output_data[output_index + i * output_stride] =
functor(left_data[left_index + i * left_stride],
right_data[right_index + i * right_stride]);
}
}
};
using Lhs = const Eigen::Array<LeftScalar, Dynamic, 1>;
using Rhs = const Eigen::Array<RightScalar, Dynamic, 1>;
using Out = Eigen::Array<OutputScalar, Dynamic, 1>;
template <>
struct TensorBlockCwiseBinaryOp<true> {
template <typename Index, typename BinaryFunctor, typename OutputScalar,
typename LeftScalar, typename RightScalar>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const BinaryFunctor& functor, const Index num_coeff,
const Index output_index, const Index output_stride,
OutputScalar* output_data, const Index left_index,
const Index left_stride, const LeftScalar* left_data,
const Index right_index, const Index right_stride,
const RightScalar* right_data) {
EIGEN_STATIC_ASSERT(functor_traits<BinaryFunctor>::PacketAccess,
YOU_MADE_A_PROGRAMMING_MISTAKE);
typedef typename packet_traits<OutputScalar>::type OutputPacket;
typedef typename packet_traits<LeftScalar>::type LeftPacket;
typedef typename packet_traits<RightScalar>::type RightPacket;
const Index packet_size = unpacket_traits<OutputPacket>::size;
EIGEN_STATIC_ASSERT(unpacket_traits<LeftPacket>::size == packet_size,
YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(unpacket_traits<RightPacket>::size == packet_size,
YOU_MADE_A_PROGRAMMING_MISTAKE);
const Index vectorized_size = (num_coeff / packet_size) * packet_size;
if (output_stride != 1 || left_stride != 1 || right_stride != 1) {
TensorBlockCwiseBinaryOp<false>::Run(
functor, num_coeff, output_index, output_stride, output_data,
left_index, left_stride, left_data, right_index, right_stride,
right_data);
return;
}
// Vectorization for the most common case.
for (Index i = 0; i < vectorized_size; i += packet_size) {
LeftPacket l = internal::ploadu<LeftPacket>(left_data + left_index + i);
RightPacket r =
internal::ploadu<RightPacket>(right_data + right_index + i);
OutputPacket p = functor.packetOp(l, r);
internal::pstoreu<OutputScalar, OutputPacket>(
output_data + output_index + i, p);
}
for (Index i = vectorized_size; i < num_coeff; ++i) {
output_data[output_index + i] =
functor(left_data[left_index + i], right_data[right_index + i]);
}
using LhsMap = Eigen::Map<Lhs, 0, InnerStride<>>;
using RhsMap = Eigen::Map<Rhs, 0, InnerStride<>>;
using OutMap = Eigen::Map<Out, 0, InnerStride<>>;
const LeftScalar* lhs_base = &left_data[left_index];
const RightScalar* rhs_base = &right_data[right_index];
OutputScalar* out_base = &output_data[output_index];
const LhsMap lhs(lhs_base, num_coeff, InnerStride<>(left_stride));
const RhsMap rhs(rhs_base, num_coeff, InnerStride<>(right_stride));
OutMap out(out_base, num_coeff, InnerStride<>(output_stride));
out =
Eigen::CwiseBinaryOp<BinaryFunctor, LhsMap, RhsMap>(lhs, rhs, functor);
}
};
@@ -541,28 +431,26 @@ struct TensorBlockCwiseBinaryOp<true> {
* This class carries out the binary op on given blocks.
*
*/
template <typename BinaryFunctor, typename Index, typename OutputScalar,
template <typename BinaryFunctor, typename StorageIndex, typename OutputScalar,
int NumDims, int Layout>
struct TensorBlockCwiseBinaryIO {
typedef typename internal::TensorBlock<OutputScalar, Index, NumDims,
typedef typename internal::TensorBlock<OutputScalar, StorageIndex, NumDims,
Layout>::Dimensions Dimensions;
typedef internal::TensorBlockCwiseBinaryOp<
functor_traits<BinaryFunctor>::PacketAccess>
TensorBlockCwiseBinaryOp;
struct BlockIteratorState {
Index output_stride, output_span;
Index left_stride, left_span;
Index right_stride, right_span;
Index size, count;
StorageIndex output_stride, output_span;
StorageIndex left_stride, left_span;
StorageIndex right_stride, right_span;
StorageIndex size, count;
};
template <typename LeftScalar, typename RightScalar>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
const BinaryFunctor& functor, const Dimensions& block_sizes,
const Dimensions& block_strides, OutputScalar* output_data,
const array<Index, NumDims>& left_strides, const LeftScalar* left_data,
const array<Index, NumDims>& right_strides,
const array<StorageIndex, NumDims>& left_strides,
const LeftScalar* left_data,
const array<StorageIndex, NumDims>& right_strides,
const RightScalar* right_data) {
// Find the innermost dimension whose size is not 1. This is the effective
// inner dim. If all dimensions are of size 1, fallback to using the actual
@@ -580,7 +468,7 @@ struct TensorBlockCwiseBinaryIO {
NumDims == 0 ? 1
: cond<Layout>()(num_size_one_inner_dims,
NumDims - num_size_one_inner_dims - 1);
Index inner_dim_size = NumDims == 0 ? 1 : block_sizes[inner_dim];
StorageIndex inner_dim_size = NumDims == 0 ? 1 : block_sizes[inner_dim];
for (int i = num_size_one_inner_dims + 1; i < NumDims; ++i) {
const int dim = cond<Layout>()(i, NumDims - i - 1);
// Merge multiple inner dims into one for larger inner dim size (i.e.
@@ -595,10 +483,12 @@ struct TensorBlockCwiseBinaryIO {
}
}
Index output_index = 0, left_index = 0, right_index = 0;
const Index output_stride = NumDims == 0 ? 1 : block_strides[inner_dim];
const Index left_stride = NumDims == 0 ? 1 : left_strides[inner_dim];
const Index right_stride = NumDims == 0 ? 1 : right_strides[inner_dim];
StorageIndex output_index = 0, left_index = 0, right_index = 0;
const StorageIndex output_stride =
NumDims == 0 ? 1 : block_strides[inner_dim];
const StorageIndex left_stride = NumDims == 0 ? 1 : left_strides[inner_dim];
const StorageIndex right_stride =
NumDims == 0 ? 1 : right_strides[inner_dim];
const int at_least_1_dim = NumDims <= 1 ? 1 : NumDims - 1;
array<BlockIteratorState, at_least_1_dim> block_iter_state;
@@ -607,7 +497,7 @@ struct TensorBlockCwiseBinaryIO {
int num_squeezed_dims = 0;
for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
const int dim = cond<Layout>()(i + 1, NumDims - i - 2);
const Index size = block_sizes[dim];
const StorageIndex size = block_sizes[dim];
if (size == 1) {
continue;
}
@@ -624,8 +514,9 @@ struct TensorBlockCwiseBinaryIO {
}
// Compute cwise binary op.
const Index block_total_size = NumDims == 0 ? 1 : block_sizes.TotalSize();
for (Index i = 0; i < block_total_size; i += inner_dim_size) {
const StorageIndex block_total_size =
NumDims == 0 ? 1 : block_sizes.TotalSize();
for (StorageIndex i = 0; i < block_total_size; i += inner_dim_size) {
TensorBlockCwiseBinaryOp::Run(functor, inner_dim_size, output_index,
output_stride, output_data, left_index,
left_stride, left_data, right_index,
@@ -661,10 +552,10 @@ struct TensorBlockCwiseBinaryIO {
template <class ArgType, class Device>
struct TensorBlockView {
typedef TensorEvaluator<ArgType, Device> Impl;
typedef typename Impl::Index Index;
typedef typename Impl::Index StorageIndex;
typedef typename remove_const<typename Impl::Scalar>::type Scalar;
static const int NumDims = array_size<typename Impl::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef DSizes<StorageIndex, NumDims> Dimensions;
// Constructs a TensorBlockView for `impl`. `block` is only used for for
// specifying the start offset, shape, and strides of the block.
@@ -701,7 +592,7 @@ struct TensorBlockView {
}
}
}
TensorBlock<Scalar, Index, NumDims, Impl::Layout> input_block(
TensorBlock<Scalar, StorageIndex, NumDims, Impl::Layout> input_block(
block.first_coeff_index(), m_block_sizes, m_block_strides,
block.tensor_strides(), m_allocated_data);
impl.block(&input_block);
@@ -733,21 +624,21 @@ struct TensorBlockView {
*
* This class is responsible for iterating over the blocks of a tensor.
*/
template <typename Scalar, typename Index, int NumDims, int Layout>
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorBlockMapper {
public:
typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef DSizes<Index, NumDims> Dimensions;
typedef DSizes<StorageIndex, NumDims> Dimensions;
TensorBlockMapper(const Dimensions& dims,
const TensorBlockShapeType block_shape,
size_t min_target_size)
Index min_target_size)
: m_dimensions(dims),
m_block_dim_sizes(BlockDimensions(dims, block_shape, min_target_size)) {
// Calculate block counts by dimension and total block count.
DSizes<Index, NumDims> block_count;
for (size_t i = 0; i < block_count.rank(); ++i) {
DSizes<StorageIndex, NumDims> block_count;
for (Index i = 0; i < block_count.rank(); ++i) {
block_count[i] = divup(m_dimensions[i], m_block_dim_sizes[i]);
}
m_total_block_count = array_prod(block_count);
@@ -773,15 +664,15 @@ class TensorBlockMapper {
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
GetBlockForIndex(Index block_index, Scalar* data) const {
Index first_coeff_index = 0;
DSizes<Index, NumDims> coords;
DSizes<Index, NumDims> sizes;
DSizes<Index, NumDims> strides;
GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
StorageIndex first_coeff_index = 0;
DSizes<StorageIndex, NumDims> coords;
DSizes<StorageIndex, NumDims> sizes;
DSizes<StorageIndex, NumDims> strides;
if (NumDims > 0) {
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = block_index / m_block_strides[i];
const StorageIndex idx = block_index / m_block_strides[i];
coords[i] = idx * m_block_dim_sizes[i];
sizes[i] =
numext::mini((m_dimensions[i] - coords[i]), m_block_dim_sizes[i]);
@@ -799,7 +690,7 @@ class TensorBlockMapper {
}
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = block_index / m_block_strides[i];
const StorageIndex idx = block_index / m_block_strides[i];
coords[i] = idx * m_block_dim_sizes[i];
sizes[i] =
numext::mini((m_dimensions[i] - coords[i]), m_block_dim_sizes[i]);
@@ -824,19 +715,20 @@ class TensorBlockMapper {
data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index total_block_count() const {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
return m_total_block_count;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index block_dims_total_size() const {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex
block_dims_total_size() const {
return m_block_dim_sizes.TotalSize();
}
private:
static Dimensions BlockDimensions(const Dimensions& tensor_dims,
const TensorBlockShapeType block_shape,
size_t min_target_size) {
min_target_size = numext::maxi<size_t>(1, min_target_size);
Index min_target_size) {
min_target_size = numext::maxi<Index>(1, min_target_size);
// If tensor fully fits into the target size, we'll treat it a single block.
Dimensions block_dim_sizes = tensor_dims;
@@ -865,14 +757,14 @@ class TensorBlockMapper {
dim_size_target, static_cast<size_t>(tensor_dims[i]));
}
// Add any un-allocated coefficients to inner dimension(s).
Index total_size = block_dim_sizes.TotalSize();
StorageIndex total_size = block_dim_sizes.TotalSize();
for (int i = 0; i < NumDims; ++i) {
const int dim = cond<Layout>()(i, NumDims - i - 1);
if (block_dim_sizes[dim] < tensor_dims[dim]) {
const Index total_size_other_dims =
const StorageIndex total_size_other_dims =
total_size / block_dim_sizes[dim];
const Index alloc_avail =
divup<Index>(min_target_size, total_size_other_dims);
const StorageIndex alloc_avail =
divup<StorageIndex>(min_target_size, total_size_other_dims);
if (alloc_avail == block_dim_sizes[dim]) {
// Insufficient excess coefficients to allocate.
break;
@@ -882,14 +774,14 @@ class TensorBlockMapper {
}
}
} else if (block_shape == TensorBlockShapeType::kSkewedInnerDims) {
Index coeff_to_allocate = min_target_size;
StorageIndex coeff_to_allocate = min_target_size;
for (int i = 0; i < NumDims; ++i) {
const int dim = cond<Layout>()(i, NumDims - i - 1);
block_dim_sizes[dim] =
numext::mini(coeff_to_allocate, tensor_dims[dim]);
coeff_to_allocate =
divup(coeff_to_allocate,
numext::maxi(static_cast<Index>(1), block_dim_sizes[dim]));
coeff_to_allocate = divup(
coeff_to_allocate,
numext::maxi(static_cast<StorageIndex>(1), block_dim_sizes[dim]));
}
eigen_assert(coeff_to_allocate == 1);
} else {
@@ -908,7 +800,7 @@ class TensorBlockMapper {
Dimensions m_block_dim_sizes;
Dimensions m_block_strides;
Dimensions m_tensor_strides;
Index m_total_block_count;
StorageIndex m_total_block_count;
};
/**
@@ -923,12 +815,12 @@ class TensorBlockMapper {
* processed together.
*
*/
template <typename Scalar, typename Index, int NumDims, int Layout>
template <typename Scalar, typename StorageIndex, int NumDims, int Layout>
class TensorSliceBlockMapper {
public:
typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
typedef typename internal::TensorBlock<Scalar, StorageIndex, NumDims, Layout>
TensorBlock;
typedef DSizes<Index, NumDims> Dimensions;
typedef DSizes<StorageIndex, NumDims> Dimensions;
TensorSliceBlockMapper(const Dimensions& tensor_dims,
const Dimensions& tensor_slice_offsets,
@@ -942,7 +834,7 @@ class TensorSliceBlockMapper {
m_block_stride_order(block_stride_order),
m_total_block_count(1) {
// Calculate block counts by dimension and total block count.
DSizes<Index, NumDims> block_count;
DSizes<StorageIndex, NumDims> block_count;
for (size_t i = 0; i < block_count.rank(); ++i) {
block_count[i] = divup(m_tensor_slice_extents[i], m_block_dim_sizes[i]);
}
@@ -969,11 +861,11 @@ class TensorSliceBlockMapper {
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
GetBlockForIndex(Index block_index, Scalar* data) const {
Index first_coeff_index = 0;
DSizes<Index, NumDims> coords;
DSizes<Index, NumDims> sizes;
DSizes<Index, NumDims> strides;
GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
StorageIndex first_coeff_index = 0;
DSizes<StorageIndex, NumDims> coords;
DSizes<StorageIndex, NumDims> sizes;
DSizes<StorageIndex, NumDims> strides;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = block_index / m_block_strides[i];
@@ -991,16 +883,16 @@ class TensorSliceBlockMapper {
m_block_dim_sizes[0]);
first_coeff_index += coords[0] * m_tensor_strides[0];
Index prev_dim = m_block_stride_order[0];
StorageIndex prev_dim = m_block_stride_order[0];
strides[prev_dim] = 1;
for (int i = 1; i < NumDims; ++i) {
const Index curr_dim = m_block_stride_order[i];
const StorageIndex curr_dim = m_block_stride_order[i];
strides[curr_dim] = strides[prev_dim] * sizes[prev_dim];
prev_dim = curr_dim;
}
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = block_index / m_block_strides[i];
const StorageIndex idx = block_index / m_block_strides[i];
coords[i] = m_tensor_slice_offsets[i] + idx * m_block_dim_sizes[i];
sizes[i] = numext::mini(
m_tensor_slice_offsets[i] + m_tensor_slice_extents[i] - coords[i],
@@ -1016,10 +908,10 @@ class TensorSliceBlockMapper {
m_block_dim_sizes[NumDims - 1]);
first_coeff_index += coords[NumDims - 1] * m_tensor_strides[NumDims - 1];
Index prev_dim = m_block_stride_order[NumDims - 1];
StorageIndex prev_dim = m_block_stride_order[NumDims - 1];
strides[prev_dim] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
const Index curr_dim = m_block_stride_order[i];
const StorageIndex curr_dim = m_block_stride_order[i];
strides[curr_dim] = strides[prev_dim] * sizes[prev_dim];
prev_dim = curr_dim;
}
@@ -1029,7 +921,7 @@ class TensorSliceBlockMapper {
data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index total_block_count() const {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
return m_total_block_count;
}
@@ -1041,7 +933,7 @@ class TensorSliceBlockMapper {
Dimensions m_block_dim_sizes;
Dimensions m_block_stride_order;
Dimensions m_block_strides;
Index m_total_block_count;
StorageIndex m_total_block_count;
};
} // namespace internal

1
unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
View File

@@ -1,5 +1,4 @@
// This file is part of Eigen, a lightweight C++ template library
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>

9
unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
View File

@@ -51,12 +51,10 @@ struct TensorEvaluator
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout>
TensorBlock;
typedef typename internal::TensorBlockReader<
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout,
PacketAccess>
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout>
TensorBlockReader;
typedef typename internal::TensorBlockWriter<
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout,
PacketAccess>
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout>
TensorBlockWriter;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
@@ -204,8 +202,7 @@ struct TensorEvaluator<const Derived, Device>
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout>
TensorBlock;
typedef typename internal::TensorBlockReader<
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout,
PacketAccess>
typename internal::remove_const<Scalar>::type, Index, NumCoords, Layout>
TensorBlockReader;
// Used for accessor extraction in SYCL Managed TensorMap:

185
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
View File

@@ -36,15 +36,16 @@ template <typename Expression, typename Device, bool Vectorizable,
bool Tileable>
class TensorExecutor {
public:
typedef typename Expression::Index Index;
using StorageIndex = typename Expression::Index;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr,
const Device& device = Device()) {
TensorEvaluator<Expression, Device> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
const Index size = array_prod(evaluator.dimensions());
for (Index i = 0; i < size; ++i) {
const StorageIndex size = array_prod(evaluator.dimensions());
for (StorageIndex i = 0; i < size; ++i) {
evaluator.evalScalar(i);
}
}
@@ -56,35 +57,36 @@ class TensorExecutor {
* Process all the data with a single cpu thread, using vectorized instructions.
*/
template <typename Expression>
class TensorExecutor<Expression, DefaultDevice, /*Vectorizable*/ true, /*Tilable*/ false> {
class TensorExecutor<Expression, DefaultDevice, /*Vectorizable*/ true,
/*Tileable*/ false> {
public:
typedef typename Expression::Index Index;
using StorageIndex = typename Expression::Index;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr, const DefaultDevice& device = DefaultDevice())
{
static inline void run(const Expression& expr,
const DefaultDevice& device = DefaultDevice()) {
TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign)
{
const Index size = array_prod(evaluator.dimensions());
if (needs_assign) {
const StorageIndex size = array_prod(evaluator.dimensions());
const int PacketSize = unpacket_traits<typename TensorEvaluator<
Expression, DefaultDevice>::PacketReturnType>::size;
// Give compiler a strong possibility to unroll the loop. But don't insist
// on unrolling, because if the function is expensive compiler should not
// unroll the loop at the expense of inlining.
const Index UnrolledSize = (size / (4 * PacketSize)) * 4 * PacketSize;
for (Index i = 0; i < UnrolledSize; i += 4*PacketSize) {
for (Index j = 0; j < 4; j++) {
const StorageIndex UnrolledSize =
(size / (4 * PacketSize)) * 4 * PacketSize;
for (StorageIndex i = 0; i < UnrolledSize; i += 4 * PacketSize) {
for (StorageIndex j = 0; j < 4; j++) {
evaluator.evalPacket(i + j * PacketSize);
}
}
const Index VectorizedSize = (size / PacketSize) * PacketSize;
for (Index i = UnrolledSize; i < VectorizedSize; i += PacketSize) {
const StorageIndex VectorizedSize = (size / PacketSize) * PacketSize;
for (StorageIndex i = UnrolledSize; i < VectorizedSize; i += PacketSize) {
evaluator.evalPacket(i);
}
for (Index i = VectorizedSize; i < size; ++i) {
for (StorageIndex i = VectorizedSize; i < size; ++i) {
evaluator.evalScalar(i);
}
}
@@ -97,42 +99,41 @@ class TensorExecutor<Expression, DefaultDevice, /*Vectorizable*/ true, /*Tilable
* sizing a block to fit L1 cache we get better cache performance.
*/
template <typename Expression, bool Vectorizable>
class TensorExecutor<Expression, DefaultDevice, Vectorizable, /*Tilable*/ true> {
class TensorExecutor<Expression, DefaultDevice, Vectorizable,
/*Tileable*/ true> {
public:
typedef typename Expression::Index Index;
using Scalar = typename traits<Expression>::Scalar;
using ScalarNoConst = typename remove_const<Scalar>::type;
using Evaluator = TensorEvaluator<Expression, DefaultDevice>;
using StorageIndex = typename traits<Expression>::Index;
static const int NumDims = traits<Expression>::NumDimensions;
EIGEN_DEVICE_FUNC
static inline void run(const Expression& expr,
const DefaultDevice& device = DefaultDevice()) {
using Evaluator = TensorEvaluator<Expression, DefaultDevice>;
using Index = typename traits<Expression>::Index;
const int NumDims = traits<Expression>::NumDimensions;
using Scalar = typename traits<Expression>::Scalar;
using ScalarNoConst = typename remove_const<Scalar>::type;
using TensorBlock =
TensorBlock<ScalarNoConst, Index, NumDims, Evaluator::Layout>;
using TensorBlockMapper =
TensorBlockMapper<ScalarNoConst, Index, NumDims, Evaluator::Layout>;
TensorBlock<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout>;
using TensorBlockMapper = TensorBlockMapper<ScalarNoConst, StorageIndex,
NumDims, Evaluator::Layout>;
Evaluator evaluator(expr, device);
std::size_t total_size = array_prod(evaluator.dimensions());
std::size_t cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
Index total_size = array_prod(evaluator.dimensions());
Index cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
if (total_size < cache_size) {
// TODO(andydavis) Reduce block management overhead for small tensors.
// TODO(wuke) Do not do this when evaluating TensorBroadcastingOp.
internal::TensorExecutor<Expression, DefaultDevice, Vectorizable,
false>::run(expr, device);
/*Tileable*/ false>::run(expr, device);
return;
}
const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
if (needs_assign) {
// Size tensor blocks to fit in cache (or requested target block size).
size_t block_total_size = numext::mini(cache_size, total_size);
Index block_total_size = numext::mini(cache_size, total_size);
TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
// Query expression tree for desired block size/shape.
std::vector<TensorOpResourceRequirements> resources;
@@ -146,8 +147,8 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable, /*Tilable*/ true>
Scalar* data = static_cast<Scalar*>(
device.allocate(block_total_size * sizeof(Scalar)));
const Index total_block_count = block_mapper.total_block_count();
for (Index i = 0; i < total_block_count; ++i) {
const StorageIndex total_block_count = block_mapper.total_block_count();
for (StorageIndex i = 0; i < total_block_count; ++i) {
TensorBlock block = block_mapper.GetBlockForIndex(i, data);
evaluator.evalBlock(&block);
}
@@ -162,37 +163,38 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable, /*Tilable*/ true>
* executed on a single core.
*/
#ifdef EIGEN_USE_THREADS
template <typename Evaluator, typename Index, bool Vectorizable>
template <typename Evaluator, typename StorageIndex, bool Vectorizable>
struct EvalRange {
static void run(Evaluator* evaluator_in, const Index first, const Index last) {
static void run(Evaluator* evaluator_in, const StorageIndex first,
const StorageIndex last) {
Evaluator evaluator = *evaluator_in;
eigen_assert(last >= first);
for (Index i = first; i < last; ++i) {
for (StorageIndex i = first; i < last; ++i) {
evaluator.evalScalar(i);
}
}
static Index alignBlockSize(Index size) {
return size;
}
static StorageIndex alignBlockSize(StorageIndex size) { return size; }
};
template <typename Evaluator, typename Index>
struct EvalRange<Evaluator, Index, /*Vectorizable*/ true> {
static const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
template <typename Evaluator, typename StorageIndex>
struct EvalRange<Evaluator, StorageIndex, /*Vectorizable*/ true> {
static const int PacketSize =
unpacket_traits<typename Evaluator::PacketReturnType>::size;
static void run(Evaluator* evaluator_in, const Index first, const Index last) {
static void run(Evaluator* evaluator_in, const StorageIndex first,
const StorageIndex last) {
Evaluator evaluator = *evaluator_in;
eigen_assert(last >= first);
Index i = first;
StorageIndex i = first;
if (last - first >= PacketSize) {
eigen_assert(first % PacketSize == 0);
Index last_chunk_offset = last - 4 * PacketSize;
StorageIndex last_chunk_offset = last - 4 * PacketSize;
// Give compiler a strong possibility to unroll the loop. But don't insist
// on unrolling, because if the function is expensive compiler should not
// unroll the loop at the expense of inlining.
for (; i <= last_chunk_offset; i += 4*PacketSize) {
for (Index j = 0; j < 4; j++) {
for (; i <= last_chunk_offset; i += 4 * PacketSize) {
for (StorageIndex j = 0; j < 4; j++) {
evaluator.evalPacket(i + j * PacketSize);
}
}
@@ -206,7 +208,7 @@ struct EvalRange<Evaluator, Index, /*Vectorizable*/ true> {
}
}
static Index alignBlockSize(Index size) {
static StorageIndex alignBlockSize(StorageIndex size) {
// Align block size to packet size and account for unrolling in run above.
if (size >= 16 * PacketSize) {
return (size + 4 * PacketSize - 1) & ~(4 * PacketSize - 1);
@@ -219,24 +221,24 @@ struct EvalRange<Evaluator, Index, /*Vectorizable*/ true> {
template <typename Expression, bool Vectorizable, bool Tileable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
public:
typedef typename Expression::Index Index;
using StorageIndex = typename Expression::Index;
static inline void run(const Expression& expr,
const ThreadPoolDevice& device) {
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
typedef EvalRange<Evaluator, Index, Vectorizable> EvalRange;
typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
Evaluator evaluator(expr, device);
const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
if (needs_assign) {
const Index PacketSize =
const StorageIndex PacketSize =
Vectorizable
? unpacket_traits<typename Evaluator::PacketReturnType>::size
: 1;
const Index size = array_prod(evaluator.dimensions());
const StorageIndex size = array_prod(evaluator.dimensions());
device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
EvalRange::alignBlockSize,
[&evaluator](Index first, Index last) {
[&evaluator](StorageIndex first, StorageIndex last) {
EvalRange::run(&evaluator, first, last);
});
}
@@ -247,24 +249,24 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tileable> {
template <typename Expression, bool Vectorizable>
class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ true> {
public:
typedef typename Expression::Index Index;
using Scalar = typename traits<Expression>::Scalar;
using ScalarNoConst = typename remove_const<Scalar>::type;
using Evaluator = TensorEvaluator<Expression, ThreadPoolDevice>;
using StorageIndex = typename traits<Expression>::Index;
static const int NumDims = traits<Expression>::NumDimensions;
static inline void run(const Expression& expr,
const ThreadPoolDevice& device) {
typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
typedef typename internal::remove_const<
typename traits<Expression>::Scalar>::type Scalar;
typedef typename traits<Expression>::Index Index;
static const int NumDims = traits<Expression>::NumDimensions;
typedef TensorBlock<Scalar, Index, NumDims, Evaluator::Layout> TensorBlock;
typedef TensorBlockMapper<Scalar, Index, NumDims, Evaluator::Layout>
TensorBlockMapper;
using TensorBlock =
TensorBlock<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout>;
using TensorBlockMapper =
TensorBlockMapper<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout>;
Evaluator evaluator(expr, device);
std::size_t total_size = array_prod(evaluator.dimensions());
std::size_t cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
StorageIndex total_size = array_prod(evaluator.dimensions());
StorageIndex cache_size = device.firstLevelCacheSize() / sizeof(Scalar);
if (total_size < cache_size) {
// TODO(andydavis) Reduce block management overhead for small tensors.
internal::TensorExecutor<Expression, ThreadPoolDevice, Vectorizable,
@@ -276,7 +278,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
if (needs_assign) {
TensorBlockShapeType block_shape = TensorBlockShapeType::kSkewedInnerDims;
size_t block_total_size = 0;
Index block_total_size = 0;
// Query expression tree for desired block size/shape.
std::vector<internal::TensorOpResourceRequirements> resources;
evaluator.getResourceRequirements(&resources);
@@ -296,15 +298,16 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
void* buf = device.allocate((num_threads + 1) * aligned_blocksize);
device.parallelFor(
block_mapper.total_block_count(), cost * block_size,
[=, &device, &evaluator, &block_mapper](Index first, Index last) {
[=, &device, &evaluator, &block_mapper](StorageIndex first,
StorageIndex last) {
// currentThreadId() returns -1 if called from a thread not in the
// threadpool, such as the main thread dispatching Eigen
// thread pool, such as the main thread dispatching Eigen
// expressions.
const int thread_idx = device.currentThreadId();
eigen_assert(thread_idx >= -1 && thread_idx < num_threads);
Scalar* thread_buf = reinterpret_cast<Scalar*>(
static_cast<char*>(buf) + aligned_blocksize * (thread_idx + 1));
for (Index i = first; i < last; ++i) {
for (StorageIndex i = first; i < last; ++i) {
auto block = block_mapper.GetBlockForIndex(i, thread_buf);
evaluator.evalBlock(&block);
}
@@ -324,51 +327,51 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, /*Tileable*/ tr
template <typename Expression, bool Vectorizable, bool Tileable>
class TensorExecutor<Expression, GpuDevice, Vectorizable, Tileable> {
public:
typedef typename Expression::Index Index;
typedef typename Expression::Index StorageIndex;
static void run(const Expression& expr, const GpuDevice& device);
};
#if defined(EIGEN_GPUCC)
template <typename Evaluator, typename Index, bool Vectorizable>
template <typename Evaluator, typename StorageIndex, bool Vectorizable>
struct EigenMetaKernelEval {
static __device__ EIGEN_ALWAYS_INLINE
void run(Evaluator& eval, Index first, Index last, Index step_size) {
for (Index i = first; i < last; i += step_size) {
void run(Evaluator& eval, StorageIndex first, StorageIndex last, StorageIndex step_size) {
for (StorageIndex i = first; i < last; i += step_size) {
eval.evalScalar(i);
}
}
};
template <typename Evaluator, typename Index>
struct EigenMetaKernelEval<Evaluator, Index, true> {
template <typename Evaluator, typename StorageIndex>
struct EigenMetaKernelEval<Evaluator, StorageIndex, true> {
static __device__ EIGEN_ALWAYS_INLINE
void run(Evaluator& eval, Index first, Index last, Index step_size) {
const Index PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
const Index vectorized_size = (last / PacketSize) * PacketSize;
const Index vectorized_step_size = step_size * PacketSize;
void run(Evaluator& eval, StorageIndex first, StorageIndex last, StorageIndex step_size) {
const StorageIndex PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
const StorageIndex vectorized_size = (last / PacketSize) * PacketSize;
const StorageIndex vectorized_step_size = step_size * PacketSize;
// Use the vector path
for (Index i = first * PacketSize; i < vectorized_size;
for (StorageIndex i = first * PacketSize; i < vectorized_size;
i += vectorized_step_size) {
eval.evalPacket(i);
}
for (Index i = vectorized_size + first; i < last; i += step_size) {
for (StorageIndex i = vectorized_size + first; i < last; i += step_size) {
eval.evalScalar(i);
}
}
};
template <typename Evaluator, typename Index>
template <typename Evaluator, typename StorageIndex>
__global__ void
__launch_bounds__(1024)
EigenMetaKernel(Evaluator eval, Index size) {
EigenMetaKernel(Evaluator eval, StorageIndex size) {
const Index first_index = blockIdx.x * blockDim.x + threadIdx.x;
const Index step_size = blockDim.x * gridDim.x;
const StorageIndex first_index = blockIdx.x * blockDim.x + threadIdx.x;
const StorageIndex step_size = blockDim.x * gridDim.x;
const bool vectorizable = Evaluator::PacketAccess & Evaluator::IsAligned;
EigenMetaKernelEval<Evaluator, Index, vectorizable>::run(eval, first_index, size, step_size);
EigenMetaKernelEval<Evaluator, StorageIndex, vectorizable>::run(eval, first_index, size, step_size);
}
/*static*/
@@ -382,12 +385,12 @@ inline void TensorExecutor<Expression, GpuDevice, Vectorizable, Tileable>::run(
const int block_size = device.maxGpuThreadsPerBlock();
const int max_blocks = device.getNumGpuMultiProcessors() *
device.maxGpuThreadsPerMultiProcessor() / block_size;
const Index size = array_prod(evaluator.dimensions());
const StorageIndex size = array_prod(evaluator.dimensions());
// Create a least one block to ensure we won't crash when tensorflow calls with tensors of size 0.
const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, divup<int>(size, block_size)), 1);
LAUNCH_GPU_KERNEL(
(EigenMetaKernel<TensorEvaluator<Expression, GpuDevice>, Index>),
(EigenMetaKernel<TensorEvaluator<Expression, GpuDevice>, StorageIndex>),
num_blocks, block_size, 0, device, evaluator, size);
}
evaluator.cleanup();