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Merged in ezhulenev/eigen-02 (pull request PR-528)

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[TensorBlockIO] Check if it's allowed to squeeze inner dimensions

Approved-by: Rasmus Munk Larsen <rmlarsen@google.com>
This commit is contained in:
Rasmus Munk Larsen 2018-10-16 15:39:40 +00:00
commit d52763bb4f
2 changed files with 144 additions and 5 deletions
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36
unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
View File

@ -246,6 +246,8 @@ class TensorBlockIO {
typedef TensorBlockCopyOp<Scalar, StorageIndex> BlockCopyOp;
protected:
typedef array<StorageIndex, NumDims> Dimensions;
struct BlockIteratorState {
StorageIndex input_stride;
StorageIndex output_stride;
@ -262,22 +264,46 @@ class TensorBlockIO {
count(0) {}
};
// Compute how many inner dimensions it's allowed to squeeze when doing IO
// between a tensor and a block. It's safe to squeeze inner dimensions, only
// if they are not reordered.
static int NumSqueezableInnerDims(const Dimensions& tensor_to_block_dim_map) {
int num_squeezable_dims = 0;
if (Layout == ColMajor) {
for (int i = 0; i < NumDims; ++i) {
if (tensor_to_block_dim_map[i] == i) num_squeezable_dims++;
else break;
}
} else {
for (int i = NumDims - 1; i >= 0; --i) {
if (tensor_to_block_dim_map[i] == i) num_squeezable_dims++;
else break;
}
}
return num_squeezable_dims;
}
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Copy(
const Block& block, StorageIndex first_coeff_index,
const array<StorageIndex, NumDims>& tensor_to_block_dim_map,
const array<StorageIndex, NumDims>& tensor_strides, const Scalar* src_data,
const Dimensions& tensor_to_block_dim_map,
const Dimensions& tensor_strides,
const Scalar* src_data,
Scalar* dst_data) {
// Do not squeeze reordered inner dimensions.
int num_squeezable_dims = NumSqueezableInnerDims(tensor_to_block_dim_map);
// 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.
StorageIndex num_size_one_inner_dims = 0;
for (int i = 0; i < NumDims; ++i) {
for (int i = 0; i < num_squeezable_dims; ++i) {
const int dim = cond<Layout>()(i, NumDims - i - 1);
if (block.block_sizes()[tensor_to_block_dim_map[dim]] != 1) {
num_size_one_inner_dims = i;
break;
}
}
// Calculate strides and dimensions.
const StorageIndex tensor_stride1_dim = cond<Layout>()(
num_size_one_inner_dims, NumDims - num_size_one_inner_dims - 1);
@ -286,7 +312,9 @@ class TensorBlockIO {
StorageIndex block_inner_dim_size =
NumDims == 0 ? 1
: block.block_sizes()[block_dim_for_tensor_stride1_dim];
for (Index i = num_size_one_inner_dims + 1; i < NumDims; ++i) {
// Squeeze multiple inner dims into one for larger inner dim size.
for (Index i = num_size_one_inner_dims + 1; i < num_squeezable_dims; ++i) {
const Index dim = cond<Layout>()(i, NumDims - i - 1);
const StorageIndex block_stride =
block.block_strides()[tensor_to_block_dim_map[dim]];

113
unsupported/test/cxx11_tensor_block_access.cpp
View File

@ -367,6 +367,116 @@ static void test_block_io_copy_using_reordered_dimensions() {
delete[] output_data;
}
// This is the special case for reading data with reordering, when dimensions
// before/after reordering are the same. Squeezing reads along inner dimensions
// in this case is illegal, because we reorder innermost dimension.
template <int Layout>
static void test_block_io_copy_using_reordered_dimensions_do_not_squeeze()
{
typedef internal::TensorBlock<float, Index, 3, Layout> TensorBlock;
typedef internal::TensorBlockReader<float, Index, 3, Layout>
TensorBlockReader;
DSizes<Index, 3> tensor_dims;
tensor_dims[0] = 7;
tensor_dims[1] = 9;
tensor_dims[2] = 7;
DSizes<Index, 3> block_dims = tensor_dims;
DSizes<Index, 3> tensor_to_block_dim_map;
tensor_to_block_dim_map[0] = 2;
tensor_to_block_dim_map[1] = 1;
tensor_to_block_dim_map[2] = 0;
DSizes<Index, 3> tensor_strides(ComputeStrides<Layout, 3>(tensor_dims));
DSizes<Index, 3> block_strides(ComputeStrides<Layout, 3>(block_dims));
const Index tensor_size = tensor_dims.TotalSize();
float* tensor_data = GenerateRandomData<float>(tensor_size);
float* block_data = new float[tensor_size];
TensorBlock block(0, block_dims, block_strides, tensor_strides, block_data);
TensorBlockReader::Run(&block,
0,
tensor_to_block_dim_map,
tensor_strides,
tensor_data);
TensorMap<Tensor<float, 3, Layout> > block_tensor(block_data, block_dims);
TensorMap<Tensor<float, 3, Layout> > tensor_tensor(tensor_data, tensor_dims);
for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
float block_value = block_tensor(d2, d1, d0);
float tensor_value = tensor_tensor(d0, d1, d2);
VERIFY_IS_EQUAL(block_value, tensor_value);
}
}
}
delete[] block_data;
delete[] tensor_data;
}
// This is the special case for reading data with reordering, when dimensions
// before/after reordering are the same. Squeezing reads in this case is allowed
// because we reorder outer dimensions.
template <int Layout>
static void test_block_io_copy_using_reordered_dimensions_squeeze()
{
typedef internal::TensorBlock<float, Index, 4, Layout> TensorBlock;
typedef internal::TensorBlockReader<float, Index, 4, Layout>
TensorBlockReader;
DSizes<Index, 4> tensor_dims;
tensor_dims[0] = 7;
tensor_dims[1] = 5;
tensor_dims[2] = 9;
tensor_dims[3] = 9;
DSizes<Index, 4> block_dims = tensor_dims;
DSizes<Index, 4> tensor_to_block_dim_map;
tensor_to_block_dim_map[0] = 0;
tensor_to_block_dim_map[1] = 1;
tensor_to_block_dim_map[2] = 3;
tensor_to_block_dim_map[3] = 2;
DSizes<Index, 4> tensor_strides(ComputeStrides<Layout, 4>(tensor_dims));
DSizes<Index, 4> block_strides(ComputeStrides<Layout, 4>(block_dims));
const Index tensor_size = tensor_dims.TotalSize();
float* tensor_data = GenerateRandomData<float>(tensor_size);
float* block_data = new float[tensor_size];
TensorBlock block(0, block_dims, block_strides, tensor_strides, block_data);
TensorBlockReader::Run(&block,
0,
tensor_to_block_dim_map,
tensor_strides,
tensor_data);
TensorMap<Tensor<float, 4, Layout> > block_tensor(block_data, block_dims);
TensorMap<Tensor<float, 4, Layout> > tensor_tensor(tensor_data, tensor_dims);
for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
for (Index d3 = 0; d3 < tensor_dims[3]; ++d3) {
float block_value = block_tensor(d0, d1, d3, d2);
float tensor_value = tensor_tensor(d0, d1, d2, d3);
VERIFY_IS_EQUAL(block_value, tensor_value);
}
}
}
}
delete[] block_data;
delete[] tensor_data;
}
template<typename Scalar, typename StorageIndex, int Dim>
class EqualityChecker
{
@ -400,7 +510,6 @@ public:
}
};
template <int Layout>
static void test_block_io_zero_stride()
{
@ -1092,6 +1201,8 @@ EIGEN_DECLARE_TEST(cxx11_tensor_block_access) {
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_copy_using_reordered_dimensions_do_not_squeeze);
TEST_LAYOUTS(test_block_io_copy_using_reordered_dimensions_squeeze);
TEST_LAYOUTS(test_block_io_zero_stride);
TEST_LAYOUTS(test_block_io_squeeze_ones);
TEST_LAYOUTS_AND_DIMS(float, test_block_cwise_unary_io_basic);