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438 lines
15 KiB
C++
438 lines
15 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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// clang-format off
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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// clang-format on
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// -------------------------------------------------------------------------- //
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// A set of tests for TensorBlockIO: copying data between tensor blocks.
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template <int NumDims>
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static DSizes<Index, NumDims> RandomDims(Index min, Index max) {
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DSizes<Index, NumDims> dims;
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for (int i = 0; i < NumDims; ++i) {
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dims[i] = internal::random<Index>(min, max);
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}
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return DSizes<Index, NumDims>(dims);
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}
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static internal::TensorBlockShapeType RandomBlockShape() {
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return internal::random<bool>()
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? internal::TensorBlockShapeType::kUniformAllDims
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: internal::TensorBlockShapeType::kSkewedInnerDims;
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}
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template <int NumDims>
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static size_t RandomTargetBlockSize(const DSizes<Index, NumDims>& dims) {
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return internal::random<size_t>(1, dims.TotalSize());
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}
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template <int Layout, int NumDims>
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static Index GetInputIndex(Index output_index,
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const array<Index, NumDims>& output_to_input_dim_map,
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const array<Index, NumDims>& input_strides,
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const array<Index, NumDims>& output_strides) {
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int input_index = 0;
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if (Layout == ColMajor) {
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx = output_index / output_strides[i];
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input_index += idx * input_strides[output_to_input_dim_map[i]];
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output_index -= idx * output_strides[i];
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}
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return input_index +
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output_index * input_strides[output_to_input_dim_map[0]];
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = output_index / output_strides[i];
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input_index += idx * input_strides[output_to_input_dim_map[i]];
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output_index -= idx * output_strides[i];
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}
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return input_index +
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output_index * input_strides[output_to_input_dim_map[NumDims - 1]];
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}
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}
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template <typename T, int NumDims, int Layout>
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static void test_block_io_copy_data_from_source_to_target() {
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using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
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using IODst = typename TensorBlockIO::Dst;
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using IOSrc = typename TensorBlockIO::Src;
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// Generate a random input Tensor.
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DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
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Tensor<T, NumDims, Layout> input(dims);
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input.setRandom();
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// Write data to an output Tensor.
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Tensor<T, NumDims, Layout> output(dims);
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// Construct a tensor block mapper.
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using TensorBlockMapper =
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internal::TensorBlockMapper<NumDims, Layout, Index>;
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TensorBlockMapper block_mapper(
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dims, {RandomBlockShape(), RandomTargetBlockSize(dims), {0, 0, 0}});
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// We will copy data from input to output through this buffer.
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Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
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// Precompute strides for TensorBlockIO::Copy.
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auto input_strides = internal::strides<Layout>(dims);
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auto output_strides = internal::strides<Layout>(dims);
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const T* input_data = input.data();
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T* output_data = output.data();
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T* block_data = block.data();
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for (int i = 0; i < block_mapper.blockCount(); ++i) {
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auto desc = block_mapper.blockDescriptor(i);
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auto blk_dims = desc.dimensions();
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auto blk_strides = internal::strides<Layout>(blk_dims);
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{
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// Read from input into a block buffer.
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IODst dst(blk_dims, blk_strides, block_data, 0);
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IOSrc src(input_strides, input_data, desc.offset());
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TensorBlockIO::Copy(dst, src);
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}
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{
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// Write from block buffer to output.
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IODst dst(blk_dims, output_strides, output_data, desc.offset());
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IOSrc src(blk_strides, block_data, 0);
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TensorBlockIO::Copy(dst, src);
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}
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}
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for (int i = 0; i < dims.TotalSize(); ++i) {
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VERIFY_IS_EQUAL(input_data[i], output_data[i]);
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}
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}
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template <typename T, int NumDims, int Layout>
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static void test_block_io_copy_using_reordered_dimensions() {
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// Generate a random input Tensor.
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DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 30);
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Tensor<T, NumDims, Layout> input(dims);
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input.setRandom();
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// Create a random dimension re-ordering/shuffle.
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std::vector<int> shuffle;
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for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
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std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937(g_seed));
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DSizes<Index, NumDims> output_tensor_dims;
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DSizes<Index, NumDims> input_to_output_dim_map;
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DSizes<Index, NumDims> output_to_input_dim_map;
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for (Index i = 0; i < NumDims; ++i) {
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output_tensor_dims[shuffle[i]] = dims[i];
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input_to_output_dim_map[i] = shuffle[i];
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output_to_input_dim_map[shuffle[i]] = i;
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}
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// Write data to an output Tensor.
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Tensor<T, NumDims, Layout> output(output_tensor_dims);
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// Construct a tensor block mapper.
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// NOTE: Tensor block mapper works with shuffled dimensions.
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using TensorBlockMapper =
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internal::TensorBlockMapper<NumDims, Layout, Index>;
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TensorBlockMapper block_mapper(output_tensor_dims,
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{RandomBlockShape(),
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RandomTargetBlockSize(output_tensor_dims),
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{0, 0, 0}});
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// We will copy data from input to output through this buffer.
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Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
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// Precompute strides for TensorBlockIO::Copy.
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auto input_strides = internal::strides<Layout>(dims);
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auto output_strides = internal::strides<Layout>(output_tensor_dims);
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const T* input_data = input.data();
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T* output_data = output.data();
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T* block_data = block.data();
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for (Index i = 0; i < block_mapper.blockCount(); ++i) {
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auto desc = block_mapper.blockDescriptor(i);
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const Index first_coeff_index = GetInputIndex<Layout, NumDims>(
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desc.offset(), output_to_input_dim_map, input_strides,
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output_strides);
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// NOTE: Block dimensions are in the same order as output dimensions.
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using TensorBlockIO = internal::TensorBlockIO<T, Index, NumDims, Layout>;
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using IODst = typename TensorBlockIO::Dst;
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using IOSrc = typename TensorBlockIO::Src;
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auto blk_dims = desc.dimensions();
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auto blk_strides = internal::strides<Layout>(blk_dims);
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{
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// Read from input into a block buffer.
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IODst dst(blk_dims, blk_strides, block_data, 0);
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IOSrc src(input_strides, input_data, first_coeff_index);
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// TODO(ezhulenev): Remove when fully switched to TensorBlock.
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DSizes<int, NumDims> dim_map;
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for (int j = 0; j < NumDims; ++j)
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dim_map[j] = static_cast<int>(output_to_input_dim_map[j]);
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TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
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}
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{
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// We need to convert block dimensions from output to input order.
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auto dst_dims = blk_dims;
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for (int out_dim = 0; out_dim < NumDims; ++out_dim) {
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dst_dims[output_to_input_dim_map[out_dim]] = blk_dims[out_dim];
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}
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// Write from block buffer to output.
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IODst dst(dst_dims, input_strides, output_data, first_coeff_index);
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IOSrc src(blk_strides, block_data, 0);
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// TODO(ezhulenev): Remove when fully switched to TensorBlock.
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DSizes<int, NumDims> dim_map;
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for (int j = 0; j < NumDims; ++j)
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dim_map[j] = static_cast<int>(input_to_output_dim_map[j]);
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TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/dim_map);
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}
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}
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for (Index i = 0; i < dims.TotalSize(); ++i) {
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VERIFY_IS_EQUAL(input_data[i], output_data[i]);
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}
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}
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// This is the special case for reading data with reordering, when dimensions
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// before/after reordering are the same. Squeezing reads along inner dimensions
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// in this case is illegal, because we reorder innermost dimension.
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template <int Layout>
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static void test_block_io_copy_using_reordered_dimensions_do_not_squeeze() {
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DSizes<Index, 3> tensor_dims(7, 9, 7);
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DSizes<Index, 3> block_dims = tensor_dims;
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DSizes<int, 3> block_to_tensor_dim;
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block_to_tensor_dim[0] = 2;
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block_to_tensor_dim[1] = 1;
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block_to_tensor_dim[2] = 0;
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auto tensor_strides = internal::strides<Layout>(tensor_dims);
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auto block_strides = internal::strides<Layout>(block_dims);
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Tensor<float, 3, Layout> block(block_dims);
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Tensor<float, 3, Layout> tensor(tensor_dims);
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tensor.setRandom();
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float* tensor_data = tensor.data();
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float* block_data = block.data();
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using TensorBlockIO = internal::TensorBlockIO<float, Index, 3, Layout>;
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using IODst = typename TensorBlockIO::Dst;
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using IOSrc = typename TensorBlockIO::Src;
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// Read from a tensor into a block.
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IODst dst(block_dims, block_strides, block_data, 0);
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IOSrc src(tensor_strides, tensor_data, 0);
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TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);
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TensorMap<Tensor<float, 3, Layout> > block_tensor(block_data, block_dims);
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TensorMap<Tensor<float, 3, Layout> > tensor_tensor(tensor_data, tensor_dims);
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for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
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for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
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for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
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float block_value = block_tensor(d2, d1, d0);
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float tensor_value = tensor_tensor(d0, d1, d2);
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VERIFY_IS_EQUAL(block_value, tensor_value);
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}
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}
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}
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}
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// This is the special case for reading data with reordering, when dimensions
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// before/after reordering are the same. Squeezing reads in this case is allowed
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// because we reorder outer dimensions.
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template <int Layout>
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static void test_block_io_copy_using_reordered_dimensions_squeeze() {
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DSizes<Index, 4> tensor_dims(7, 5, 9, 9);
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DSizes<Index, 4> block_dims = tensor_dims;
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DSizes<int, 4> block_to_tensor_dim;
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block_to_tensor_dim[0] = 0;
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block_to_tensor_dim[1] = 1;
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block_to_tensor_dim[2] = 3;
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block_to_tensor_dim[3] = 2;
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auto tensor_strides = internal::strides<Layout>(tensor_dims);
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auto block_strides = internal::strides<Layout>(block_dims);
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Tensor<float, 4, Layout> block(block_dims);
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Tensor<float, 4, Layout> tensor(tensor_dims);
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tensor.setRandom();
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float* tensor_data = tensor.data();
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float* block_data = block.data();
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using TensorBlockIO = internal::TensorBlockIO<float, Index, 4, Layout>;
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using IODst = typename TensorBlockIO::Dst;
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using IOSrc = typename TensorBlockIO::Src;
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// Read from a tensor into a block.
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IODst dst(block_dims, block_strides, block_data, 0);
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IOSrc src(tensor_strides, tensor_data, 0);
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TensorBlockIO::Copy(dst, src, /*dst_to_src_dim_map=*/block_to_tensor_dim);
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TensorMap<Tensor<float, 4, Layout> > block_tensor(block_data, block_dims);
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TensorMap<Tensor<float, 4, Layout> > tensor_tensor(tensor_data, tensor_dims);
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for (Index d0 = 0; d0 < tensor_dims[0]; ++d0) {
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for (Index d1 = 0; d1 < tensor_dims[1]; ++d1) {
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for (Index d2 = 0; d2 < tensor_dims[2]; ++d2) {
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for (Index d3 = 0; d3 < tensor_dims[3]; ++d3) {
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float block_value = block_tensor(d0, d1, d3, d2);
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float tensor_value = tensor_tensor(d0, d1, d2, d3);
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VERIFY_IS_EQUAL(block_value, tensor_value);
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}
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}
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}
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}
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}
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template <int Layout>
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static void test_block_io_zero_stride() {
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DSizes<Index, 5> rnd_dims = RandomDims<5>(1, 30);
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DSizes<Index, 5> input_tensor_dims = rnd_dims;
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input_tensor_dims[0] = 1;
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input_tensor_dims[2] = 1;
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input_tensor_dims[4] = 1;
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Tensor<float, 5, Layout> input(input_tensor_dims);
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input.setRandom();
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DSizes<Index, 5> output_tensor_dims = rnd_dims;
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auto input_tensor_strides = internal::strides<Layout>(input_tensor_dims);
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auto output_tensor_strides = internal::strides<Layout>(output_tensor_dims);
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auto input_tensor_strides_with_zeros = input_tensor_strides;
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input_tensor_strides_with_zeros[0] = 0;
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input_tensor_strides_with_zeros[2] = 0;
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input_tensor_strides_with_zeros[4] = 0;
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Tensor<float, 5, Layout> output(output_tensor_dims);
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output.setRandom();
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using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
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using IODst = typename TensorBlockIO::Dst;
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using IOSrc = typename TensorBlockIO::Src;
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// Write data from input to output with broadcasting in dims [0, 2, 4].
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IODst dst(output_tensor_dims, output_tensor_strides, output.data(), 0);
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IOSrc src(input_tensor_strides_with_zeros, input.data(), 0);
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TensorBlockIO::Copy(dst, src);
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for (int i = 0; i < output_tensor_dims[0]; ++i) {
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for (int j = 0; j < output_tensor_dims[1]; ++j) {
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for (int k = 0; k < output_tensor_dims[2]; ++k) {
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for (int l = 0; l < output_tensor_dims[3]; ++l) {
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for (int m = 0; m < output_tensor_dims[4]; ++m) {
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float input_value = input(0, j, 0, l, 0);
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float output_value = output(i, j, k, l, m);
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VERIFY_IS_EQUAL(input_value, output_value);
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}
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}
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}
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}
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}
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}
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template <int Layout>
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static void test_block_io_squeeze_ones() {
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using TensorBlockIO = internal::TensorBlockIO<float, Index, 5, Layout>;
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using IODst = typename TensorBlockIO::Dst;
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using IOSrc = typename TensorBlockIO::Src;
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// Total size > 1.
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{
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DSizes<Index, 5> block_sizes(1, 2, 1, 2, 1);
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auto strides = internal::strides<Layout>(block_sizes);
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// Create a random input tensor.
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Tensor<float, 5> input(block_sizes);
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input.setRandom();
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Tensor<float, 5> output(block_sizes);
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IODst dst(block_sizes, strides, output.data(), 0);
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IOSrc src(strides, input.data());
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TensorBlockIO::Copy(dst, src);
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for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
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VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
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}
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}
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// Total size == 1.
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{
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DSizes<Index, 5> block_sizes(1, 1, 1, 1, 1);
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auto strides = internal::strides<Layout>(block_sizes);
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// Create a random input tensor.
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Tensor<float, 5> input(block_sizes);
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input.setRandom();
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Tensor<float, 5> output(block_sizes);
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IODst dst(block_sizes, strides, output.data(), 0);
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IOSrc src(strides, input.data());
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TensorBlockIO::Copy(dst, src);
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for (Index i = 0; i < block_sizes.TotalSize(); ++i) {
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VERIFY_IS_EQUAL(output.data()[i], input.data()[i]);
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}
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}
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}
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#define CALL_SUBTESTS(NAME) \
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CALL_SUBTEST((NAME<float, 1, RowMajor>())); \
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CALL_SUBTEST((NAME<float, 2, RowMajor>())); \
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CALL_SUBTEST((NAME<float, 4, RowMajor>())); \
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CALL_SUBTEST((NAME<float, 5, RowMajor>())); \
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CALL_SUBTEST((NAME<float, 1, ColMajor>())); \
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CALL_SUBTEST((NAME<float, 2, ColMajor>())); \
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CALL_SUBTEST((NAME<float, 4, ColMajor>())); \
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CALL_SUBTEST((NAME<float, 5, ColMajor>()))
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EIGEN_DECLARE_TEST(cxx11_tensor_block_io) {
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// clang-format off
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CALL_SUBTESTS(test_block_io_copy_data_from_source_to_target);
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CALL_SUBTESTS(test_block_io_copy_using_reordered_dimensions);
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CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<RowMajor>());
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CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_do_not_squeeze<ColMajor>());
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CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<RowMajor>());
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CALL_SUBTEST(test_block_io_copy_using_reordered_dimensions_squeeze<ColMajor>());
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CALL_SUBTEST(test_block_io_zero_stride<RowMajor>());
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CALL_SUBTEST(test_block_io_zero_stride<ColMajor>());
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CALL_SUBTEST(test_block_io_squeeze_ones<RowMajor>());
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CALL_SUBTEST(test_block_io_squeeze_ones<ColMajor>());
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// clang-format on
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}
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