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Merged in ezhulenev/eigen/tiling_3 (pull request PR-438)

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Tiled tensor executor
This commit is contained in:
Gael Guennebaud
2018-07-31 08:13:00 +00:00
parent 679eece876 966c2a7bb6
commit 678a0dcb12
33 changed files with 1989 additions and 246 deletions
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1
unsupported/test/CMakeLists.txt
View File

@@ -213,6 +213,7 @@ if(EIGEN_TEST_CXX11)
ei_add_test(cxx11_tensor_striding)
ei_add_test(cxx11_tensor_notification "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
ei_add_test(cxx11_tensor_thread_pool "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
ei_add_test(cxx11_tensor_executor "-pthread" "${CMAKE_THREAD_LIBS_INIT}")
ei_add_test(cxx11_tensor_ref)
ei_add_test(cxx11_tensor_random)
ei_add_test(cxx11_tensor_generator)

917
unsupported/test/cxx11_tensor_block_access.cpp
View File

@@ -19,11 +19,75 @@ using Eigen::Index;
using Eigen::RowMajor;
using Eigen::ColMajor;
using internal::TensorBlockShapeType;
template<typename T>
static const T& choose(int layout, const T& col, const T& row) {
return layout == ColMajor ? col : row;
}
static const TensorBlockShapeType RandomShape() {
return internal::random<bool>()
? internal::TensorBlockShapeType::kUniformAllDims
: internal::TensorBlockShapeType::kSkewedInnerDims;
}
template <int NumDims>
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];
for (int i = 0; i < size; ++i) {
data[i] = internal::random<T>();
}
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()
{
@@ -74,10 +138,8 @@ 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,
Index first_coeff_index,
int dim_index,
std::set<Index>* visited_coeffs) {
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();
@@ -94,89 +156,840 @@ 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);
auto total_coeffs = static_cast<int>(dims.TotalSize());
DSizes<Index, NumDims> dims = RandomDims<NumDims>();
// Keep track of elements indices available via block access.
std::set<Index> coeff_set;
// Try different combinations of block types and sizes.
auto block_shape_type =
internal::random<bool>()
? internal::TensorBlockShapeType::kUniformAllDims
: internal::TensorBlockShapeType::kSkewedInnerDims;
auto block_target_size = internal::random<int>(1, total_coeffs);
TensorBlockMapper block_mapper(dims, block_shape_type, block_target_size);
TensorBlockMapper block_mapper(dims, RandomShape(), RandomTargetSize(dims));
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.
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;
auto total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
// Try different combinations of block types and sizes.
auto block_shape_type =
internal::random<bool>()
? internal::TensorBlockShapeType::kUniformAllDims
: internal::TensorBlockShapeType::kSkewedInnerDims;
auto block_target_size = internal::random<int>(1, total_coeffs);
// 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, NULL);
UpdateCoeffSet<T, Layout, 4>(block, block.first_coeff_index(),
choose(Layout, 3, 0), &coeff_set);
TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
UpdateCoeffSet<T, Layout, NumDims>(block, block.first_coeff_index(),
choose(Layout, NumDims - 1, 0),
&coeff_set);
}
VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
}
EIGEN_DECLARE_TEST(cxx11_tensor_assign) {
CALL_SUBTEST(test_block_mapper_sanity<ColMajor>());
CALL_SUBTEST(test_block_mapper_sanity<RowMajor>());
CALL_SUBTEST(test_block_mapper_maps_every_element<ColMajor>());
CALL_SUBTEST(test_block_mapper_maps_every_element<RowMajor>());
CALL_SUBTEST(test_slice_block_mapper_maps_every_element<ColMajor>());
CALL_SUBTEST(test_slice_block_mapper_maps_every_element<RowMajor>());
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, NumDims, Layout>
TensorBlockReader;
typedef internal::TensorBlockWriter<T, Index, NumDims, Layout>
TensorBlockWriter;
DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
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()];
for (int i = 0; i < block_mapper.total_block_count(); ++i) {
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>
static int 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 int 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 int 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 <int Layout, int NumDims>
static array<Index, NumDims> ComputeStrides(
const array<Index, NumDims>& sizes) {
array<Index, NumDims> strides;
if (Layout == ColMajor) {
strides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
strides[i] = strides[i - 1] * sizes[i - 1];
}
} else {
strides[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * sizes[i + 1];
}
}
return strides;
}
template <typename T, int NumDims, int Layout>
static void test_block_io_copy_using_reordered_dimensions() {
typedef internal::TensorBlock<T, Index, NumDims, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, NumDims, Layout>
TensorBlockMapper;
typedef internal::TensorBlockReader<T, Index, NumDims, Layout>
TensorBlockReader;
typedef internal::TensorBlockWriter<T, Index, NumDims, Layout>
TensorBlockWriter;
DSizes<Index, NumDims> input_tensor_dims = RandomDims<NumDims>();
const auto input_tensor_size = input_tensor_dims.TotalSize();
// Create a random input tensor.
T* input_data = GenerateRandomData<T>(input_tensor_size);
// Create a random dimension re-ordering/shuffle.
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, 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;
}
// Random block shape and size.
TensorBlockMapper block_mapper(output_tensor_dims, RandomShape(),
RandomTargetSize(input_tensor_dims));
auto* block_data = new T[block_mapper.block_dims_total_size()];
auto* output_data = new T[input_tensor_size];
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, 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,
input_tensor_strides, input_data);
TensorBlockWriter::Run(block, first_coeff_index, input_to_output_dim_map,
input_tensor_strides, output_data);
}
for (int i = 0; i < input_tensor_size; ++i) {
VERIFY_IS_EQUAL(input_data[i], output_data[i]);
}
delete[] input_data;
delete[] block_data;
delete[] output_data;
}
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>
TensorBlockReader;
typedef internal::TensorBlockWriter<float, Index, 5, Layout>
TensorBlockWriter;
DSizes<Index, 5> rnd_dims = RandomDims<5>();
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 = rnd_dims;
DSizes<Index, 5> input_tensor_strides(
ComputeStrides<Layout, 5>(input_tensor_dims));
DSizes<Index, 5> output_tensor_strides(
ComputeStrides<Layout, 5>(output_tensor_dims));
DSizes<Index, 5> 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;
// Verify that data was correctly read/written from/into the block.
const auto verify_is_equal = [&](const float* output_data) {
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) {
const Index output_offset =
i * output_tensor_strides[0] + j * output_tensor_strides[1] +
k * output_tensor_strides[2] + l * output_tensor_strides[3] +
m * output_tensor_strides[4];
const Index input_offset =
i % input_tensor_dims[0] * input_tensor_strides[0] +
j % input_tensor_dims[1] * input_tensor_strides[1] +
k % input_tensor_dims[2] * input_tensor_strides[2] +
l % input_tensor_dims[3] * input_tensor_strides[3] +
m % input_tensor_dims[4] * input_tensor_strides[4];
VERIFY_IS_EQUAL(output_data[output_offset],
input_data[input_offset]);
}
}
}
}
}
};
{
auto* output_data = new float[output_tensor_dims.TotalSize()];
TensorBlock read_block(0, output_tensor_dims, output_tensor_strides,
input_tensor_strides_with_zeros, output_data);
TensorBlockReader::Run(&read_block, input_data);
verify_is_equal(output_data);
delete[] output_data;
}
{
auto* output_data = new float[output_tensor_dims.TotalSize()];
TensorBlock write_block(0, output_tensor_dims,
input_tensor_strides_with_zeros,
output_tensor_strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
verify_is_equal(output_data);
delete[] output_data;
}
delete[] input_data;
}
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>
TensorBlockReader;
typedef internal::TensorBlockWriter<float, Index, 5, Layout>
TensorBlockWriter;
// Total size > 1.
{
DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
const auto total_size = block_sizes.TotalSize();
// Create a random input tensor.
auto* input_data = GenerateRandomData<float>(total_size);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
{
auto* output_data = new float[block_sizes.TotalSize()];
TensorBlock read_block(0, block_sizes, strides, strides, output_data);
TensorBlockReader::Run(&read_block, input_data);
for (int i = 0; i < total_size; ++i) {
VERIFY_IS_EQUAL(output_data[i], input_data[i]);
}
delete[] output_data;
}
{
auto* output_data = new float[block_sizes.TotalSize()];
TensorBlock write_block(0, block_sizes, strides, strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
for (int i = 0; i < total_size; ++i) {
VERIFY_IS_EQUAL(output_data[i], input_data[i]);
}
delete[] output_data;
}
}
// Total size == 1.
{
DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
const auto total_size = block_sizes.TotalSize();
// Create a random input tensor.
auto* input_data = GenerateRandomData<float>(total_size);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(block_sizes));
{
auto* output_data = new float[block_sizes.TotalSize()];
TensorBlock read_block(0, block_sizes, strides, strides, output_data);
TensorBlockReader::Run(&read_block, input_data);
for (int i = 0; i < total_size; ++i) {
VERIFY_IS_EQUAL(output_data[i], input_data[i]);
}
delete[] output_data;
}
{
auto* output_data = new float[block_sizes.TotalSize()];
TensorBlock write_block(0, block_sizes, strides, strides, input_data);
TensorBlockWriter::Run(write_block, output_data);
for (int i = 0; i < total_size; ++i) {
VERIFY_IS_EQUAL(output_data[i], input_data[i]);
}
delete[] output_data;
}
}
}
template <typename T, int NumDims, int Layout>
static void test_block_cwise_binary_io_basic() {
typedef internal::scalar_sum_op<T> BinaryFunctor;
typedef internal::TensorBlockCwiseBinaryIO<BinaryFunctor, Index, T, NumDims,
Layout>
TensorBlockCwiseBinaryIO;
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.
T* left_data = GenerateRandomData<T>(total_size);
T* right_data = GenerateRandomData<T>(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);
for (int i = 0; i < total_size; ++i) {
VERIFY_IS_EQUAL(output_data[i], functor(left_data[i], right_data[i]));
}
delete[] left_data;
delete[] right_data;
delete[] output_data;
}
template <int Layout>
static void test_block_cwise_binary_io_squeeze_ones() {
typedef internal::scalar_sum_op<float> BinaryFunctor;
typedef internal::TensorBlockCwiseBinaryIO<BinaryFunctor, Index, float, 5,
Layout>
TensorBlockCwiseBinaryIO;
DSizes<Index, 5> block_sizes(1, 2, 1, 3, 1);
DSizes<Index, 5> strides(ComputeStrides<Layout, 5>(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);
auto* output_data = new float[total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, block_sizes, strides, output_data,
strides, left_data, strides, right_data);
for (int i = 0; i < total_size; ++i) {
VERIFY_IS_EQUAL(output_data[i], functor(left_data[i], right_data[i]));
}
delete[] left_data;
delete[] right_data;
delete[] output_data;
}
template <int Layout>
static void test_block_cwise_binary_io_zero_strides() {
typedef internal::scalar_sum_op<float> BinaryFunctor;
typedef internal::TensorBlockCwiseBinaryIO<BinaryFunctor, Index, float, 5,
Layout>
TensorBlockCwiseBinaryIO;
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 = 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;
// Generate random data.
auto* left_data = GenerateRandomData<float>(left_sizes.TotalSize());
auto* right_data = GenerateRandomData<float>(right_sizes.TotalSize());
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();
auto* output_data = new float[output_total_size];
BinaryFunctor functor;
TensorBlockCwiseBinaryIO::Run(functor, output_sizes, output_strides,
output_data, left_strides, left_data,
right_strides, right_data);
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];
Index left_index = i * left_strides[0] + j * left_strides[1] +
k * left_strides[2] + l * left_strides[3] +
m * left_strides[4];
Index right_index = i * right_strides[0] + j * right_strides[1] +
k * right_strides[2] + l * right_strides[3] +
m * right_strides[4];
VERIFY_IS_EQUAL(
output_data[output_index],
functor(left_data[left_index], right_data[right_index]));
}
}
}
}
}
delete[] left_data;
delete[] right_data;
delete[] output_data;
}
template <int Layout>
static void test_uniform_block_shape()
{
using T = int;
typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
{
// Test shape 'UniformAllDims' with uniform 'max_coeff count'.
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 5 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
for (int i = 0; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
// partially into first inner-most dimension.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 7 * 5 * 5 * 5 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 5 * 6;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(6, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
// fully into first inner-most dimension.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 5 * 5 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 5 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(5, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'UniformAllDims' with larger 'max_coeff count' which spills
// fully into first few inner-most dimensions.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(7, 5, 6, 17, 7);
const size_t max_coeff_count = 7 * 5 * 6 * 7 * 5;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
VERIFY_IS_EQUAL(7, block.block_sizes()[3]);
VERIFY_IS_EQUAL(5, block.block_sizes()[4]);
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(7, 5, 6, 9, 7);
const size_t max_coeff_count = 5 * 5 * 5 * 6 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(6, block.block_sizes()[3]);
VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(5, block.block_sizes()[0]);
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'UniformAllDims' with full allocation to all dims.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(7, 5, 6, 17, 7);
const size_t max_coeff_count = 7 * 5 * 6 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(7, 5, 6, 9, 7);
const size_t max_coeff_count = 7 * 5 * 6 * 9 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kUniformAllDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(9, block.block_sizes()[3]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(7, block.block_sizes()[0]);
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
}
template <int Layout>
static void test_skewed_inner_dim_block_shape()
{
using T = int;
typedef internal::TensorBlock<T, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, 5, Layout> TensorBlockMapper;
// Test shape 'SkewedInnerDims' with partial allocation to inner-most dim.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 10 * 1 * 1 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(10, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 1 * 1 * 6;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(6, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'SkewedInnerDims' with full allocation to inner-most dim.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 1 * 1 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
for (int i = 1; i < 5; ++i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 1 * 1 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
for (int i = 3; i >= 0; --i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'SkewedInnerDims' with full allocation to inner-most dim,
// and partial allocation to second inner-dim.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 3 * 1 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY_IS_EQUAL(3, block.block_sizes()[1]);
for (int i = 2; i < 5; ++i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 1 * 15 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(15, block.block_sizes()[3]);
for (int i = 2; i >= 0; --i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'SkewedInnerDims' with full allocation to inner-most dim,
// and partial allocation to third inner-dim.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 5 * 1 * 1;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
for (int i = 3; i < 5; ++i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 1 * 1 * 5 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
VERIFY_IS_EQUAL(5, block.block_sizes()[2]);
for (int i = 1; i >= 0; --i) {
VERIFY_IS_EQUAL(1, block.block_sizes()[i]);
}
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
// Test shape 'SkewedInnerDims' with full allocation to all dims.
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const size_t max_coeff_count = 11 * 5 * 6 * 17 * 7;
TensorBlockMapper block_mapper(dims, TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count);
TensorBlock block = block_mapper.GetBlockForIndex(0, nullptr);
VERIFY_IS_EQUAL(7, block.block_sizes()[4]);
VERIFY_IS_EQUAL(17, block.block_sizes()[3]);
VERIFY_IS_EQUAL(6, block.block_sizes()[2]);
VERIFY_IS_EQUAL(5, block.block_sizes()[1]);
VERIFY_IS_EQUAL(11, block.block_sizes()[0]);
VERIFY(block.block_sizes().TotalSize() <= max_coeff_count);
}
}
template <int Layout>
static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
{
using T = int;
// Test blocking of tensors with zero dimensions:
// - we must not crash on asserts and divisions by zero
// - we must not return block with zero dimensions
// (recipe for overflows/underflows, divisions by zero and NaNs later)
// - total block count must be zero
{
typedef internal::TensorBlockMapper<T, Index, 1, Layout> TensorBlockMapper;
DSizes<Index, 1> dims(0);
for (int max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
TensorBlockMapper block_mapper(dims, block_shape, max_coeff_count);
VERIFY_IS_EQUAL(block_mapper.total_block_count(), 0);
VERIFY(block_mapper.block_dims_total_size() >= 1);
}
}
{
typedef internal::TensorBlockMapper<T, Index, 2, Layout> TensorBlockMapper;
for (int dim1 = 0; dim1 < 3; ++dim1) {
for (int dim2 = 0; dim2 < 3; ++dim2) {
DSizes<Index, 2> dims(dim1, dim2);
for (int max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
TensorBlockMapper block_mapper(dims, block_shape, max_coeff_count);
if (dim1 * dim2 == 0) {
VERIFY_IS_EQUAL(block_mapper.total_block_count(), 0);
}
VERIFY(block_mapper.block_dims_total_size() >= 1);
}
}
}
}
}
#define TEST_LAYOUTS(NAME) \
CALL_SUBTEST(NAME<ColMajor>()); \
CALL_SUBTEST(NAME<RowMajor>())
#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) {
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 TEST_LAYOUTS
#undef TEST_LAYOUTS_WITH_ARG

2
unsupported/test/cxx11_tensor_complex_cwise_ops_gpu.cu
View File

@@ -93,7 +93,7 @@ void test_cuda_complex_cwise_ops() {
}
void test_cxx11_tensor_complex_cwise_ops()
EIGEN_DECLARE_TEST(test_cxx11_tensor_complex_cwise_ops)
{
CALL_SUBTEST(test_cuda_complex_cwise_ops<float>());
CALL_SUBTEST(test_cuda_complex_cwise_ops<double>());

2
unsupported/test/cxx11_tensor_complex_gpu.cu
View File

@@ -177,7 +177,7 @@ static void test_cuda_product_reductions() {
}
void test_cxx11_tensor_complex()
EIGEN_DECLARE_TEST(test_cxx11_tensor_complex)
{
CALL_SUBTEST(test_cuda_nullary());
CALL_SUBTEST(test_cuda_sum_reductions());

87
unsupported/test/cxx11_tensor_executor.cpp Normal file
View File

@@ -0,0 +1,87 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
//
// 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/.
#define EIGEN_USE_THREADS
#include "main.h"
#include <Eigen/CXX11/Tensor>
using Eigen::Tensor;
using Eigen::RowMajor;
using Eigen::ColMajor;
// A set of tests to verify that different TensorExecutor strategies yields the
// same results for all the ops, supporting tiled execution.
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.
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();
const auto expr = lhs + rhs;
using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
using Executor =
internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
Executor::run(Assign(dst, expr), d);
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));
}
}
}
}
#define CALL_SUBTEST_COMBINATIONS(NAME) \
CALL_SUBTEST((NAME<DefaultDevice, false, false, ColMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, false, true, ColMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, true, false, ColMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, true, true, ColMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, false, false, RowMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, false, true, RowMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, true, false, RowMajor>(default_device))); \
CALL_SUBTEST((NAME<DefaultDevice, true, true, RowMajor>(default_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, false, false, ColMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, false, true, ColMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, true, false, ColMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, true, true, ColMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, false, false, RowMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, false, true, RowMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, true, false, RowMajor>(tp_device))); \
CALL_SUBTEST((NAME<ThreadPoolDevice, true, true, RowMajor>(tp_device)))
EIGEN_DECLARE_TEST(cxx11_tensor_executor) {
Eigen::DefaultDevice default_device;
const auto num_threads = internal::random<int>(1, 24);
Eigen::ThreadPool tp(num_threads);
Eigen::ThreadPoolDevice tp_device(&tp, num_threads);
CALL_SUBTEST_COMBINATIONS(test_execute_binary_expr);
}
#undef CALL_SUBTEST_COMBINATIONS