aboutsummaryrefslogtreecommitdiffhomepage
diff options
context:
space:
mode:
Diffstat
-rw-r--r--unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h418
-rw-r--r--unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h1
-rw-r--r--unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h9
-rw-r--r--unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h185
-rw-r--r--unsupported/test/cxx11_tensor_block_access.cpp292
-rw-r--r--unsupported/test/cxx11_tensor_executor.cpp20
6 files changed, 447 insertions, 478 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
index 5321acecf..84cf6d216 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
@@ -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
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
index 7ff0d323b..343ab6269 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
@@ -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>
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
index ba02802d2..f9a1bd68c 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
@@ -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:
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
index 024de3696..ac5afd891 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
@@ -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;
- 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;
+ using Evaluator = TensorEvaluator<Expression, ThreadPoolDevice>;
+ using StorageIndex = typename traits<Expression>::Index;
- static const int NumDims = traits<Expression>::NumDimensions;
+ static const int NumDims = traits<Expression>::NumDimensions;
- typedef TensorBlock<Scalar, Index, NumDims, Evaluator::Layout> TensorBlock;
- typedef TensorBlockMapper<Scalar, Index, NumDims, Evaluator::Layout>
- TensorBlockMapper;
+ static inline void run(const Expression& expr,
+ const ThreadPoolDevice& device) {
+ 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();
diff --git a/unsupported/test/cxx11_tensor_block_access.cpp b/unsupported/test/cxx11_tensor_block_access.cpp
index 416b686e4..6feeff231 100644
--- a/unsupported/test/cxx11_tensor_block_access.cpp
+++ b/unsupported/test/cxx11_tensor_block_access.cpp
@@ -37,6 +37,31 @@ static std::size_t RandomTargetSize(const DSizes<Index, NumDims>& dims) {
return internal::random<int>(1, dims.TotalSize());
}
+template <int NumDims>
+static DSizes<Index, NumDims> RandomDims() {
+ array<Index, NumDims> dims;
+ for (int i = 0; i < NumDims; ++i) {
+ dims[i] = internal::random<int>(1, 20);
+ }
+ return DSizes<Index, NumDims>(dims);
+};
+
+/** Dummy data type to test TensorBlock copy ops. */
+struct Data {
+ Data() : Data(0) {}
+ explicit Data(int v) { value = v; }
+ int value;
+};
+
+bool operator==(const Data& lhs, const Data& rhs) {
+ return lhs.value == rhs.value;
+}
+
+std::ostream& operator<<(std::ostream& os, const Data& d) {
+ os << "Data: value=" << d.value;
+ return os;
+}
+
template <typename T>
static T* GenerateRandomData(const Index& size) {
T* data = new T[size];
@@ -46,6 +71,23 @@ static T* GenerateRandomData(const Index& size) {
return data;
}
+template <>
+Data* GenerateRandomData(const Index& size) {
+ Data* data = new Data[size];
+ for (int i = 0; i < size; ++i) {
+ data[i] = Data(internal::random<int>(1, 100));
+ }
+ return data;
+}
+
+template <int NumDims>
+static void Debug(DSizes<Index, NumDims> dims) {
+ for (int i = 0; i < NumDims; ++i) {
+ std::cout << dims[i] << "; ";
+ }
+ std::cout << std::endl;
+}
+
template <int Layout>
static void test_block_mapper_sanity()
{
@@ -96,7 +138,7 @@ static void test_block_mapper_sanity()
// index in the visited set. Verify that every coeff accessed only once.
template <typename T, int Layout, int NumDims>
static void UpdateCoeffSet(
- const internal::TensorBlock<T, Index, 4, Layout>& block,
+ const internal::TensorBlock<T, Index, NumDims, Layout>& block,
Index first_coeff_index, int dim_index, std::set<Index>* visited_coeffs) {
const DSizes<Index, NumDims> block_sizes = block.block_sizes();
const DSizes<Index, NumDims> tensor_strides = block.tensor_strides();
@@ -114,14 +156,13 @@ static void UpdateCoeffSet(
}
}
-template <int Layout>
-static void test_block_mapper_maps_every_element()
-{
- using T = int;
- using TensorBlock = internal::TensorBlock<T, Index, 4, Layout>;
- using TensorBlockMapper = internal::TensorBlockMapper<T, Index, 4, Layout>;
+template <typename T, int NumDims, int Layout>
+static void test_block_mapper_maps_every_element() {
+ using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>;
+ using TensorBlockMapper =
+ internal::TensorBlockMapper<T, Index, NumDims, Layout>;
- DSizes<Index, 4> dims(5, 7, 11, 17);
+ DSizes<Index, NumDims> dims = RandomDims<NumDims>();
// Keep track of elements indices available via block access.
std::set<Index> coeff_set;
@@ -131,29 +172,36 @@ static void test_block_mapper_maps_every_element()
for (int i = 0; i < block_mapper.total_block_count(); ++i) {
TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
- UpdateCoeffSet<T, Layout, 4>(block, block.first_coeff_index(),
- choose(Layout, 3, 0), &coeff_set);
+ UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
+ choose(Layout, NumDims - 1, 0),
+ &coeff_set);
}
// Verify that every coefficient in the original Tensor is accessible through
// TensorBlock only once.
- auto total_coeffs = static_cast<int>(dims.TotalSize());
+ Index total_coeffs = dims.TotalSize();
VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
- VERIFY_IS_EQUAL(*coeff_set.begin(), static_cast<Index>(0));
- VERIFY_IS_EQUAL(*coeff_set.rbegin(), static_cast<Index>(total_coeffs - 1));
+ VERIFY_IS_EQUAL(*coeff_set.begin(), 0);
+ VERIFY_IS_EQUAL(*coeff_set.rbegin(), total_coeffs - 1);
}
-template <int Layout>
-static void test_slice_block_mapper_maps_every_element()
-{
- using T = int;
- using TensorBlock = internal::TensorBlock<T, Index, 4, Layout>;
+template <typename T, int NumDims, int Layout>
+static void test_slice_block_mapper_maps_every_element() {
+ using TensorBlock = internal::TensorBlock<T, Index, NumDims, Layout>;
using TensorSliceBlockMapper =
- internal::TensorSliceBlockMapper<T, Index, 4, Layout>;
+ internal::TensorSliceBlockMapper<T, Index, NumDims, Layout>;
- DSizes<Index, 4> tensor_dims(5,7,11,17);
- DSizes<Index, 4> tensor_slice_offsets(1,3,5,7);
- DSizes<Index, 4> tensor_slice_extents(3,2,4,5);
+ DSizes<Index, NumDims> tensor_dims = RandomDims<NumDims>();
+ DSizes<Index, NumDims> tensor_slice_offsets = RandomDims<NumDims>();
+ DSizes<Index, NumDims> tensor_slice_extents = RandomDims<NumDims>();
+
+ // Make sure that tensor offsets + extents do not overflow.
+ for (int i = 0; i < NumDims; ++i) {
+ tensor_slice_offsets[i] =
+ numext::mini(tensor_dims[i] - 1, tensor_slice_offsets[i]);
+ tensor_slice_extents[i] = numext::mini(
+ tensor_slice_extents[i], tensor_dims[i] - tensor_slice_offsets[i]);
+ }
// Keep track of elements indices available via block access.
std::set<Index> coeff_set;
@@ -161,61 +209,59 @@ static void test_slice_block_mapper_maps_every_element()
auto total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
// Pick a random dimension sizes for the tensor blocks.
- DSizes<Index, 4> block_sizes;
- for (int i = 0; i < 4; ++i) {
+ DSizes<Index, NumDims> block_sizes;
+ for (int i = 0; i < NumDims; ++i) {
block_sizes[i] = internal::random<int>(1, tensor_slice_extents[i]);
}
TensorSliceBlockMapper block_mapper(tensor_dims, tensor_slice_offsets,
tensor_slice_extents, block_sizes,
- DimensionList<Index, 4>());
+ DimensionList<Index, NumDims>());
for (int i = 0; i < block_mapper.total_block_count(); ++i) {
TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
- UpdateCoeffSet<T, Layout, 4>(block, block.first_coeff_index(),
- choose(Layout, 3, 0), &coeff_set);
+ UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
+ choose(Layout, NumDims - 1, 0),
+ &coeff_set);
}
VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
}
-template <int Layout>
-static void test_block_io_copy_data_from_source_to_target()
-{
- using T = float;
-
- typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
- typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
+template <typename T, int NumDims, int Layout>
+static void test_block_io_copy_data_from_source_to_target() {
+ typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
+ typedef internal::TensorBlockMapper<T, Index, NumDims, Layout>
+ TensorBlockMapper;
- typedef internal::TensorBlockReader<T, Index, 5, Layout, true>
+ typedef internal::TensorBlockReader<T, Index, NumDims, Layout>
TensorBlockReader;
- typedef internal::TensorBlockWriter<T, Index, 5, Layout, true>
+ typedef internal::TensorBlockWriter<T, Index, NumDims, Layout>
TensorBlockWriter;
- typedef std::vector<T, aligned_allocator<T>> DataVector;
-
- DSizes<Index, 5> input_tensor_dims(5, 7, 11, 17, 3);
+ DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
- DataVector input_data(input_tensor_size, 0);
- for (int i = 0; i < input_tensor_size; ++i) {
- input_data[i] = internal::random<T>();
- }
- DataVector output_data(input_tensor_size, 0);
+ T* input_data = GenerateRandomData<T>(input_tensor_size);
+ T* output_data = new T[input_tensor_size];
TensorBlockMapper block_mapper(input_tensor_dims, RandomShape(),
RandomTargetSize(input_tensor_dims));
+ T* block_data = new T[block_mapper.block_dims_total_size()];
- DataVector block_data(block_mapper.block_dims_total_size(), 0);
for (int i = 0; i < block_mapper.total_block_count(); ++i) {
- TensorBlock block = block_mapper.GetBlockForIndex(i, block_data.data());
- TensorBlockReader::Run(&block, input_data.data());
- TensorBlockWriter::Run(block, output_data.data());
+ TensorBlock block = block_mapper.GetBlockForIndex(i, block_data);
+ TensorBlockReader::Run(&block, input_data);
+ TensorBlockWriter::Run(block, output_data);
}
for (int i = 0; i < input_tensor_size; ++i) {
VERIFY_IS_EQUAL(input_data[i], output_data[i]);
}
+
+ delete[] input_data;
+ delete[] output_data;
+ delete[] block_data;
}
template <int Layout, int NumDims>
@@ -261,31 +307,32 @@ static array<Index, NumDims> ComputeStrides(
return strides;
}
-template <int Layout>
+template <typename T, int NumDims, int Layout>
static void test_block_io_copy_using_reordered_dimensions() {
- typedef internal::TensorBlock<float, Index, 5, Layout> TensorBlock;
- typedef internal::TensorBlockMapper<float, Index, 5, Layout>
+ typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
+ typedef internal::TensorBlockMapper<T, Index, NumDims, Layout>
TensorBlockMapper;
- typedef internal::TensorBlockReader<float, Index, 5, Layout, false>
+ typedef internal::TensorBlockReader<T, Index, NumDims, Layout>
TensorBlockReader;
- typedef internal::TensorBlockWriter<float, Index, 5, Layout, false>
+ typedef internal::TensorBlockWriter<T, Index, NumDims, Layout>
TensorBlockWriter;
- DSizes<Index, 5> input_tensor_dims(5, 7, 11, 17, 3);
+ DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
// Create a random input tensor.
- auto* input_data = GenerateRandomData<float>(input_tensor_size);
+ T* input_data = GenerateRandomData<T>(input_tensor_size);
// Create a random dimension re-ordering/shuffle.
- std::vector<Index> shuffle = {0, 1, 2, 3, 4};
+ std::vector<Index> shuffle;
+ for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937());
- DSizes<Index, 5> output_tensor_dims;
- array<Index, 5> input_to_output_dim_map;
- array<Index, 5> output_to_input_dim_map;
- for (Index i = 0; i < 5; ++i) {
+ DSizes<Index, NumDims> output_tensor_dims;
+ array<Index, NumDims> input_to_output_dim_map;
+ array<Index, NumDims> output_to_input_dim_map;
+ for (Index i = 0; i < NumDims; ++i) {
output_tensor_dims[shuffle[i]] = input_tensor_dims[i];
input_to_output_dim_map[i] = shuffle[i];
output_to_input_dim_map[shuffle[i]] = i;
@@ -295,17 +342,17 @@ static void test_block_io_copy_using_reordered_dimensions() {
TensorBlockMapper block_mapper(output_tensor_dims, RandomShape(),
RandomTargetSize(input_tensor_dims));
- auto* block_data = new float[block_mapper.block_dims_total_size()];
- auto* output_data = new float[input_tensor_size];
+ auto* block_data = new T[block_mapper.block_dims_total_size()];
+ auto* output_data = new T[input_tensor_size];
- array<Index, 5> input_tensor_strides =
- ComputeStrides<Layout, 5>(input_tensor_dims);
- array<Index, 5> output_tensor_strides =
- ComputeStrides<Layout, 5>(output_tensor_dims);
+ array<Index, NumDims> input_tensor_strides =
+ ComputeStrides<Layout, NumDims>(input_tensor_dims);
+ array<Index, NumDims> output_tensor_strides =
+ ComputeStrides<Layout, NumDims>(output_tensor_dims);
for (Index i = 0; i < block_mapper.total_block_count(); ++i) {
TensorBlock block = block_mapper.GetBlockForIndex(i, block_data);
- const Index first_coeff_index = GetInputIndex<Layout, 5>(
+ const Index first_coeff_index = GetInputIndex<Layout, NumDims>(
block.first_coeff_index(), output_to_input_dim_map,
input_tensor_strides, output_tensor_strides);
TensorBlockReader::Run(&block, first_coeff_index, input_to_output_dim_map,
@@ -327,18 +374,21 @@ template <int Layout>
static void test_block_io_zero_stride()
{
typedef internal::TensorBlock<float, Index, 5, Layout> TensorBlock;
- typedef internal::TensorBlockReader<float, Index, 5, Layout, true>
+ typedef internal::TensorBlockReader<float, Index, 5, Layout>
TensorBlockReader;
- typedef internal::TensorBlockWriter<float, Index, 5, Layout, true>
+ typedef internal::TensorBlockWriter<float, Index, 5, Layout>
TensorBlockWriter;
- DSizes<Index, 5> input_tensor_dims(1, 2, 1, 3, 1);
- const auto input_tensor_size = input_tensor_dims.TotalSize();
+ DSizes<Index, 5> rnd_dims = RandomDims<5>();
- // Create a random input tensor.
+ DSizes<Index, 5> input_tensor_dims = rnd_dims;
+ input_tensor_dims[0] = 1;
+ input_tensor_dims[2] = 1;
+ input_tensor_dims[4] = 1;
+ const auto input_tensor_size = input_tensor_dims.TotalSize();
auto* input_data = GenerateRandomData<float>(input_tensor_size);
- DSizes<Index, 5> output_tensor_dims(3, 2, 3, 3, 2);
+ DSizes<Index, 5> output_tensor_dims = rnd_dims;
DSizes<Index, 5> input_tensor_strides(
ComputeStrides<Layout, 5>(input_tensor_dims));
@@ -401,9 +451,9 @@ static void test_block_io_zero_stride()
template <int Layout>
static void test_block_io_squeeze_ones() {
typedef internal::TensorBlock<float, Index, 5, Layout> TensorBlock;
- typedef internal::TensorBlockReader<float, Index, 5, Layout, true>
+ typedef internal::TensorBlockReader<float, Index, 5, Layout>
TensorBlockReader;
- typedef internal::TensorBlockWriter<float, Index, 5, Layout, true>
+ typedef internal::TensorBlockWriter<float, Index, 5, Layout>
TensorBlockWriter;
// Total size > 1.
@@ -467,23 +517,23 @@ static void test_block_io_squeeze_ones() {
}
}
-template <int Layout>
+template <typename T, int NumDims, int Layout>
static void test_block_cwise_binary_io_basic() {
- typedef internal::scalar_sum_op<float> BinaryFunctor;
- typedef internal::TensorBlockCwiseBinaryIO<BinaryFunctor, Index, float, 5,
+ typedef internal::scalar_sum_op<T> BinaryFunctor;
+ typedef internal::TensorBlockCwiseBinaryIO<BinaryFunctor, Index, T, NumDims,
Layout>
TensorBlockCwiseBinaryIO;
- DSizes<Index, 5> block_sizes(2, 3, 5, 7, 11);
- DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
+ DSizes<Index, NumDims> block_sizes = RandomDims<NumDims>();
+ DSizes<Index, NumDims> strides(ComputeStrides<Layout, NumDims>(block_sizes));
const auto total_size = block_sizes.TotalSize();
// Create a random input tensors.
- auto* left_data = GenerateRandomData<float>(total_size);
- auto* right_data = GenerateRandomData<float>(total_size);
+ T* left_data = GenerateRandomData<T>(total_size);
+ T* right_data = GenerateRandomData<T>(total_size);
- auto* output_data = new float[total_size];
+ T* output_data = new T[total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, block_sizes, strides, output_data,
strides, left_data, strides, right_data);
@@ -532,13 +582,22 @@ static void test_block_cwise_binary_io_zero_strides() {
Layout>
TensorBlockCwiseBinaryIO;
- DSizes<Index, 5> left_sizes(1, 3, 1, 7, 1);
+ DSizes<Index, 5> rnd_dims = RandomDims<5>();
+
+ DSizes<Index, 5> left_sizes = rnd_dims;
+ left_sizes[0] = 1;
+ left_sizes[2] = 1;
+ left_sizes[4] = 1;
+
DSizes<Index, 5> left_strides(ComputeStrides<Layout, 5>(left_sizes));
left_strides[0] = 0;
left_strides[2] = 0;
left_strides[4] = 0;
- DSizes<Index, 5> right_sizes(2, 1, 5, 1, 11);
+ DSizes<Index, 5> right_sizes = rnd_dims;
+ right_sizes[1] = 0;
+ right_sizes[3] = 0;
+
DSizes<Index, 5> right_strides(ComputeStrides<Layout, 5>(right_sizes));
right_strides[1] = 0;
right_strides[3] = 0;
@@ -547,7 +606,7 @@ static void test_block_cwise_binary_io_zero_strides() {
auto* left_data = GenerateRandomData<float>(left_sizes.TotalSize());
auto* right_data = GenerateRandomData<float>(right_sizes.TotalSize());
- DSizes<Index, 5> output_sizes(2, 3, 5, 7, 11);
+ DSizes<Index, 5> output_sizes = rnd_dims;
DSizes<Index, 5> output_strides(ComputeStrides<Layout, 5>(output_sizes));
const auto output_total_size = output_sizes.TotalSize();
@@ -557,11 +616,11 @@ static void test_block_cwise_binary_io_zero_strides() {
TensorBlockCwiseBinaryIO::Run(functor, output_sizes, output_strides,
output_data, left_strides, left_data,
right_strides, right_data);
- for (int i = 0; i < 2; ++i) {
- for (int j = 0; j < 3; ++j) {
- for (int k = 0; k < 5; ++k) {
- for (int l = 0; l < 7; ++l) {
- for (int m = 0; m < 11; ++m) {
+ for (int i = 0; i < rnd_dims[0]; ++i) {
+ for (int j = 0; j < rnd_dims[1]; ++j) {
+ for (int k = 0; k < rnd_dims[2]; ++k) {
+ for (int l = 0; l < rnd_dims[3]; ++l) {
+ for (int m = 0; m < rnd_dims[4]; ++m) {
Index output_index = i * output_strides[0] + j * output_strides[1] +
k * output_strides[2] + l * output_strides[3] +
m * output_strides[4];
@@ -893,31 +952,44 @@ static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
}
}
-#define CALL_SUBTEST_LAYOUTS(NAME) \
+#define TEST_LAYOUTS(NAME) \
CALL_SUBTEST(NAME<ColMajor>()); \
CALL_SUBTEST(NAME<RowMajor>())
-#define CALL_SUBTEST_LAYOUTS_WITH_ARG(NAME, ARG) \
+#define TEST_LAYOUTS_AND_DIMS(TYPE, NAME) \
+ CALL_SUBTEST((NAME<TYPE, 1, ColMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 1, RowMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 2, ColMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 2, RowMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 3, ColMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 3, RowMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 4, ColMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 4, RowMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 5, ColMajor>())); \
+ CALL_SUBTEST((NAME<TYPE, 5, RowMajor>()))
+
+#define TEST_LAYOUTS_WITH_ARG(NAME, ARG) \
CALL_SUBTEST(NAME<ColMajor>(ARG)); \
CALL_SUBTEST(NAME<RowMajor>(ARG))
EIGEN_DECLARE_TEST(cxx11_tensor_block_access) {
- CALL_SUBTEST_LAYOUTS(test_block_mapper_sanity);
- CALL_SUBTEST_LAYOUTS(test_block_mapper_maps_every_element);
- CALL_SUBTEST_LAYOUTS(test_slice_block_mapper_maps_every_element);
- CALL_SUBTEST_LAYOUTS(test_block_io_copy_data_from_source_to_target);
- CALL_SUBTEST_LAYOUTS(test_block_io_copy_using_reordered_dimensions);
- CALL_SUBTEST_LAYOUTS(test_block_io_zero_stride);
- CALL_SUBTEST_LAYOUTS(test_block_io_squeeze_ones);
- CALL_SUBTEST_LAYOUTS(test_block_cwise_binary_io_basic);
- CALL_SUBTEST_LAYOUTS(test_block_cwise_binary_io_squeeze_ones);
- CALL_SUBTEST_LAYOUTS(test_block_cwise_binary_io_zero_strides);
- CALL_SUBTEST_LAYOUTS(test_uniform_block_shape);
- CALL_SUBTEST_LAYOUTS(test_skewed_inner_dim_block_shape);
-
- CALL_SUBTEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims);
- CALL_SUBTEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims);
+ TEST_LAYOUTS(test_block_mapper_sanity);
+ TEST_LAYOUTS_AND_DIMS(float, test_block_mapper_maps_every_element);
+ TEST_LAYOUTS_AND_DIMS(float, test_slice_block_mapper_maps_every_element);
+ TEST_LAYOUTS_AND_DIMS(float, test_block_io_copy_data_from_source_to_target);
+ TEST_LAYOUTS_AND_DIMS(Data, test_block_io_copy_data_from_source_to_target);
+ TEST_LAYOUTS_AND_DIMS(float, test_block_io_copy_using_reordered_dimensions);
+ TEST_LAYOUTS_AND_DIMS(Data, test_block_io_copy_using_reordered_dimensions);
+ TEST_LAYOUTS(test_block_io_zero_stride);
+ TEST_LAYOUTS(test_block_io_squeeze_ones);
+ TEST_LAYOUTS_AND_DIMS(float, test_block_cwise_binary_io_basic);
+ TEST_LAYOUTS(test_block_cwise_binary_io_squeeze_ones);
+ TEST_LAYOUTS(test_block_cwise_binary_io_zero_strides);
+ TEST_LAYOUTS(test_uniform_block_shape);
+ TEST_LAYOUTS(test_skewed_inner_dim_block_shape);
+ TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kUniformAllDims);
+ TEST_LAYOUTS_WITH_ARG(test_empty_dims, TensorBlockShapeType::kSkewedInnerDims);
}
-#undef CALL_SUBTEST_LAYOUTS
-#undef CALL_SUBTEST_LAYOUTS_WITH_ARG \ No newline at end of file
+#undef TEST_LAYOUTS
+#undef TEST_LAYOUTS_WITH_ARG \ No newline at end of file
diff --git a/unsupported/test/cxx11_tensor_executor.cpp b/unsupported/test/cxx11_tensor_executor.cpp
index 5ae45ac5b..274f901ce 100644
--- a/unsupported/test/cxx11_tensor_executor.cpp
+++ b/unsupported/test/cxx11_tensor_executor.cpp
@@ -13,7 +13,6 @@
#include <Eigen/CXX11/Tensor>
-using Eigen::Index;
using Eigen::Tensor;
using Eigen::RowMajor;
using Eigen::ColMajor;
@@ -25,9 +24,16 @@ template <typename Device, bool Vectorizable, bool Tileable, int Layout>
static void test_execute_binary_expr(Device d) {
// Pick a large enough tensor size to bypass small tensor block evaluation
// optimization.
- Tensor<float, 3> lhs(840, 390, 37);
- Tensor<float, 3> rhs(840, 390, 37);
- Tensor<float, 3> dst(840, 390, 37);
+ int d0 = internal::random<int>(100, 200);
+ int d1 = internal::random<int>(100, 200);
+ int d2 = internal::random<int>(100, 200);
+
+ static constexpr int Options = 0;
+ using IndexType = int;
+
+ Tensor<float, 3, Options, IndexType> lhs(d0, d1, d2);
+ Tensor<float, 3, Options, IndexType> rhs(d0, d1, d2);
+ Tensor<float, 3, Options, IndexType> dst(d0, d1, d2);
lhs.setRandom();
rhs.setRandom();
@@ -40,9 +46,9 @@ static void test_execute_binary_expr(Device d) {
Executor::run(Assign(dst, expr), d);
- for (int i = 0; i < 840; ++i) {
- for (int j = 0; j < 390; ++j) {
- for (int k = 0; k < 37; ++k) {
+ for (int i = 0; i < d0; ++i) {
+ for (int j = 0; j < d1; ++j) {
+ for (int k = 0; k < d2; ++k) {
float sum = lhs(i, j, k) + rhs(i, j, k);
VERIFY_IS_EQUAL(sum, dst(i, j, k));
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-31 09:04:31 +0000