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

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This commit is contained in:
Eugene Zhulenev
2018-07-27 12:45:17 -07:00
parent 6913221c43
commit 966c2a7bb6
6 changed files with 447 additions and 478 deletions
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292
unsupported/test/cxx11_tensor_block_access.cpp
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@@ -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;
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::TensorBlock<T, Index, 5, Layout> TensorBlock;
typedef internal::TensorBlockMapper<T, Index, 5, 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
#undef TEST_LAYOUTS
#undef TEST_LAYOUTS_WITH_ARG