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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// clang-format off
#include "main.h"
#include <Eigen/CXX11/Tensor>
// clang-format on
// -------------------------------------------------------------------------- //
// A set of tests for TensorBlockIO: copying data between tensor blocks.
template <int NumDims>
static DSizes<Index, NumDims> RandomDims(Index min, Index max) {
DSizes<Index, NumDims> dims;
for (int i = 0; i < NumDims; ++i) {
dims[i] = internal::random<Index>(min, max);
}
return DSizes<Index, NumDims>(dims);
}
static internal::TensorBlockShapeType RandomBlockShape() {
return internal::random<bool>() ? internal::TensorBlockShapeType::kUniformAllDims
: internal::TensorBlockShapeType::kSkewedInnerDims;
}
template <int NumDims>
static size_t RandomTargetBlockSize(const DSizes<Index, NumDims>& dims) {
return internal::random<size_t>(1, dims.TotalSize());
}
template <int Layout, int NumDims>
static Index GetInputIndex(Index output_index, const array<Index, NumDims>& output_to_input_dim_map,
const array<Index, NumDims>& input_strides, const array<Index, NumDims>& output_strides) {
int input_index = 0;
if (Layout == ColMajor) {
for (int i = NumDims - 1; i > 0; --i) {
const Index idx = output_index / output_strides[i];
input_index += idx * input_strides[output_to_input_dim_map[i]];
output_index -= idx * output_strides[i];
}
return input_index + output_index * input_strides[output_to_input_dim_map[0]];
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = output_index / output_strides[i];
input_index += idx * input_strides[output_to_input_dim_map[i]];
output_index -= idx * output_strides[i];
}
return input_index + output_index * input_strides[output_to_input_dim_map[NumDims - 1]];
}
}
template <typename T, int NumDims, int Layout>
static void test_block_io_copy_data_from_source_to_target() {
using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
using IODst = typename TensorBlockIO::Dst;
using IOSrc = typename TensorBlockIO::Src;
// Generate a random input Tensor.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
// Write data to an output Tensor.
Tensor<T, NumDims, Layout> output(dims);
// Construct a tensor block mapper.
using TensorBlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
TensorBlockMapper block_mapper(dims, {RandomBlockShape(), RandomTargetBlockSize(dims), {0, 0, 0}});
// We will copy data from input to output through this buffer.
Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
// Precompute strides for TensorBlockIO::Copy.
auto input_strides = internal::strides<Layout>(dims);
auto output_strides = internal::strides<Layout>(dims);
const T* input_data = input.data();
T* output_data = output.data();
T* block_data = block.data();
for (int i = 0; i < block_mapper.blockCount(); ++i) {
auto desc = block_mapper.blockDescriptor(i);
auto blk_dims = desc.dimensions();
auto blk_strides = internal::strides<Layout>(blk_dims);
{
// Read from input into a block buffer.
IODst dst(blk_dims, blk_strides, block_data, 0);
IOSrc src(input_strides, input_data, desc.offset());
TensorBlockIO::Copy(dst, src);
}
{
// Write from block buffer to output.
IODst dst(blk_dims, output_strides, output_data, desc.offset());
IOSrc src(blk_strides, block_data, 0);
TensorBlockIO::Copy(dst, src);
}
}
for (int i = 0; i < dims.TotalSize(); ++i) {
VERIFY_IS_EQUAL(input_data[i], output_data[i]);
}
}
template <typename T, int NumDims, int Layout>
static void test_block_io_copy_using_reordered_dimensions() {
// Generate a random input Tensor.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
// Create a random dimension re-ordering/shuffle.
std::vector<int> shuffle;
for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937(g_seed));
DSizes<Index, NumDims> output_tensor_dims;
DSizes<Index, NumDims> input_to_output_dim_map;
DSizes<Index, NumDims> output_to_input_dim_map;
for (Index i = 0; i < NumDims; ++i) {
output_tensor_dims[shuffle[i]] = dims[i];
input_to_output_dim_map[i] = shuffle[i];
output_to_input_dim_map[shuffle[i]] = i;
}
// Write data to an output Tensor.
Tensor<T, NumDims, Layout> output(output_tensor_dims);
// Construct a tensor block mapper.
// NOTE: Tensor block mapper works with shuffled dimensions.
using TensorBlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
TensorBlockMapper block_mapper(output_tensor_dims,
{RandomBlockShape(), RandomTargetBlockSize(output_tensor_dims), {0, 0, 0}});
// We will copy data from input to output through this buffer.
Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
// Precompute strides for TensorBlockIO::Copy.
auto input_strides = internal::strides<Layout>(dims);
auto output_strides = internal::strides<Layout>(output_tensor_dims);
const T* input_data = input.data();
T* output_data = output.data();
T* block_data = block.data();
for (Index i = 0; i < block_mapper.blockCount(); ++i) {
auto desc = block_mapper.blockDescriptor(i);
const Index first_coeff_index =
GetInputIndex<Layout, NumDims>(desc.offset(), output_to_input_dim_map, input_strides, output_strides);
// NOTE: Block dimensions are in the same order as output dimensions.
using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
using IODst = typename TensorBlockIO::Dst;
using IOSrc = typename TensorBlockIO::Src;
auto blk_dims = desc.dimensions();
auto blk_strides = internal::strides<Layout>(blk_dims);
{
// Read from input into a block buffer.
IODst dst(blk_dims, blk_strides, block_data, 0);
IOSrc src(input_strides, input_data, first_coeff_index);
// TODO(ezhulenev): Remove when fully switched to TensorBlock.
DSizes<int, NumDims> dim_map;
for (int j = 0; j < NumDims; ++j) dim_map[j] = static_cast<int>(output_to_input_dim_map[j]);
TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
}
{
// We need to convert block dimensions from output to input order.
auto dst_dims = blk_dims;
for (int out_dim = 0; out_dim < NumDims; ++out_dim) {
dst_dims[output_to_input_dim_map[out_dim]] = blk_dims[out_dim];
}
// Write from block buffer to output.
IODst dst(dst_dims, input_strides, output_data, first_coeff_index);
IOSrc src(blk_strides, block_data, 0);
// TODO(ezhulenev): Remove when fully switched to TensorBlock.
DSizes<int, NumDims> dim_map;
for (int j = 0; j < NumDims; ++j) dim_map[j] = static_cast<int>(input_to_output_dim_map[j]);
TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
}
}
for (Index i = 0; i < dims.TotalSize(); ++i) {
VERIFY_IS_EQUAL(input_data[i], output_data[i]);
}
}
// 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() {
DSizes<Index, 3> tensor_dims(7, 9, 7);
DSizes<Index, 3> block_dims = tensor_dims;
DSizes<int, 3> block_to_tensor_dim;
block_to_tensor_dim[0] = 2;
block_to_tensor_dim[1] = 1;
block_to_tensor_dim[2] = 0;
auto tensor_strides = internal::strides<Layout>(tensor_dims);
auto block_strides = internal::strides<Layout>(block_dims);
Tensor<float, 3, Layout> block(block_dims);
Tensor<float, 3, Layout> tensor(tensor_dims);
tensor.setRandom();
float* tensor_data = tensor.data();
float* block_data = block.data();
using TensorBlockIO = internal::TensorBlockIO<float, Index, 3, Layout>;
using IODst = typename TensorBlockIO::Dst;
using IOSrc = typename TensorBlockIO::Src;
// Read from a tensor into a block.
IODst dst(block_dims, block_strides, block_data, 0);
IOSrc src(tensor_strides, tensor_data, 0);
TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);
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);
}
}
}
}
// 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() {
DSizes<Index, 4> tensor_dims(7, 5, 9, 9);
DSizes<Index, 4> block_dims = tensor_dims;
DSizes<int, 4> block_to_tensor_dim;
block_to_tensor_dim[0] = 0;
block_to_tensor_dim[1] = 1;
block_to_tensor_dim[2] = 3;
block_to_tensor_dim[3] = 2;
auto tensor_strides = internal::strides<Layout>(tensor_dims);
auto block_strides = internal::strides<Layout>(block_dims);
Tensor<float, 4, Layout> block(block_dims);
Tensor<float, 4, Layout> tensor(tensor_dims);
tensor.setRandom();
float* tensor_data = tensor.data();
float* block_data = block.data();
using TensorBlockIO = internal::TensorBlockIO<float, Index, 4, Layout>;
using IODst = typename TensorBlockIO::Dst;
using IOSrc = typename TensorBlockIO::Src;
// Read from a tensor into a block.
IODst dst(block_dims, block_strides, block_data, 0);
IOSrc src(tensor_strides, tensor_data, 0);
TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);
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);
}
}
}
}
}
template <int Layout>
static void test_block_io_zero_stride() {
DSizes<Index, 5> rnd_dims = RandomDims<5>(1, 30);
DSizes<Index, 5> input_tensor_dims = rnd_dims;
input_tensor_dims[0] = 1;
input_tensor_dims[2] = 1;
input_tensor_dims[4] = 1;
Tensor<float, 5, Layout> input(input_tensor_dims);
input.setRandom();
DSizes<Index, 5> output_tensor_dims = rnd_dims;
auto input_tensor_strides = internal::strides<Layout>(input_tensor_dims);
auto output_tensor_strides = internal::strides<Layout>(output_tensor_dims);
auto input_tensor_strides_with_zeros = input_tensor_strides;
input_tensor_strides_with_zeros[0] = 0;
input_tensor_strides_with_zeros[2] = 0;
input_tensor_strides_with_zeros[4] = 0;
Tensor<float, 5, Layout> output(output_tensor_dims);
output.setRandom();
using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
using IODst = typename TensorBlockIO::Dst;
using IOSrc = typename TensorBlockIO::Src;
// Write data from input to output with broadcasting in dims [0, 2, 4].
IODst dst(output_tensor_dims, output_tensor_strides, output.data(), 0);
IOSrc src(input_tensor_strides_with_zeros, input.data(), 0);
TensorBlockIO::Copy(dst, src);
for (int i = 0; i < output_tensor_dims[0]; ++i) {
for (int j = 0; j < output_tensor_dims[1]; ++j) {
for (int k = 0; k < output_tensor_dims[2]; ++k) {
for (int l = 0; l < output_tensor_dims[3]; ++l) {
for (int m = 0; m < output_tensor_dims[4]; ++m) {
float input_value = input(0, j, 0, l, 0);
float output_value = output(i, j, k, l, m);
VERIFY_IS_EQUAL(input_value, output_value);
}
}
}
}
}
}
template <int Layout>
static void test_block_io_squeeze_ones() {
using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
using IODst = typename TensorBlockIO::Dst;
using IOSrc = typename TensorBlockIO::Src;
// Total size > 1.
{
DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
auto strides = internal::strides<Layout>(block_sizes);
// Create a random input tensor.
Tensor<float, 5> input(block_sizes);
input.setRandom();
Tensor<float, 5> output(block_sizes);
IODst dst(block_sizes, strides, output.data(), 0);
IOSrc src(strides, input.data());
TensorBlockIO::Copy(dst, src);
for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
}
}
// Total size == 1.
{
DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
auto strides = internal::strides<Layout>(block_sizes);
// Create a random input tensor.
Tensor<float, 5> input(block_sizes);
input.setRandom();
Tensor<float, 5> output(block_sizes);
IODst dst(block_sizes, strides, output.data(), 0);
IOSrc src(strides, input.data());
TensorBlockIO::Copy(dst, src);
for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
}
}
}
#define CALL_SUBTESTS(NAME) \
CALL_SUBTEST((NAME<float, 1, RowMajor>())); \
CALL_SUBTEST((NAME<float, 2, RowMajor>())); \
CALL_SUBTEST((NAME<float, 4, RowMajor>())); \
CALL_SUBTEST((NAME<float, 5, RowMajor>())); \
CALL_SUBTEST((NAME<float, 1, ColMajor>())); \
CALL_SUBTEST((NAME<float, 2, ColMajor>())); \
CALL_SUBTEST((NAME<float, 4, ColMajor>())); \
CALL_SUBTEST((NAME<float, 5, ColMajor>())); \
CALL_SUBTEST((NAME<bool, 1, RowMajor>())); \
CALL_SUBTEST((NAME<bool, 2, RowMajor>())); \
CALL_SUBTEST((NAME<bool, 4, RowMajor>())); \
CALL_SUBTEST((NAME<bool, 5, RowMajor>())); \
CALL_SUBTEST((NAME<bool, 1, ColMajor>())); \
CALL_SUBTEST((NAME<bool, 2, ColMajor>())); \
CALL_SUBTEST((NAME<bool, 4, ColMajor>())); \
CALL_SUBTEST((NAME<bool, 5, ColMajor>()))
EIGEN_DECLARE_TEST(cxx11_tensor_block_io) {
// clang-format off
CALL_SUBTESTS(test_block_io_copy_data_from_source_to_target);
CALL_SUBTESTS(test_block_io_copy_using_reordered_dimensions);
CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<RowMajor>());
CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<ColMajor>());
CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<RowMajor>());
CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<ColMajor>());
CALL_SUBTEST(test_block_io_zero_stride<RowMajor>());
CALL_SUBTEST(test_block_io_zero_stride<ColMajor>());
CALL_SUBTEST(test_block_io_squeeze_ones<RowMajor>());
CALL_SUBTEST(test_block_io_squeeze_ones<ColMajor>());
// clang-format on
}