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https://gitlab.com/libeigen/eigen.git
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Initial support of TensorBlock
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@@ -130,6 +130,7 @@ if (NOT CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
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ei_add_test(cxx11_tensor_dimension)
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ei_add_test(cxx11_tensor_map)
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ei_add_test(cxx11_tensor_assign)
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ei_add_test(cxx11_tensor_block_access)
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ei_add_test(cxx11_tensor_comparisons)
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ei_add_test(cxx11_tensor_forced_eval)
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ei_add_test(cxx11_tensor_math)
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@@ -291,14 +292,14 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
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unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
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endif()
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# Add HIP specific tests
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# Add HIP specific tests
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if (EIGEN_TEST_HIP)
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set(HIP_PATH "/opt/rocm/hip" CACHE STRING "Path to the HIP installation.")
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if (EXISTS ${HIP_PATH})
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list(APPEND CMAKE_MODULE_PATH ${HIP_PATH}/cmake)
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list(APPEND CMAKE_MODULE_PATH ${HIP_PATH}/cmake)
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find_package(HIP REQUIRED)
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if (HIP_FOUND)
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@@ -328,22 +329,22 @@ if (EIGEN_TEST_HIP)
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ei_add_test(cxx11_tensor_contract_gpu)
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ei_add_test(cxx11_tensor_of_float16_gpu)
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ei_add_test(cxx11_tensor_random_gpu)
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unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
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elseif (${HIP_PLATFORM} STREQUAL "nvcc")
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message(FATAL_ERROR "HIP_PLATFORM = nvcc is not supported within Eigen")
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else ()
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message(FATAL_ERROR "Unknown HIP_PLATFORM = ${HIP_PLATFORM}")
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endif()
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endif(HIP_FOUND)
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else ()
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message(FATAL_ERROR "EIGEN_TEST_HIP is ON, but the specified HIP_PATH (${HIP_PATH}) does not exist")
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endif()
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endif(EIGEN_TEST_HIP)
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182
unsupported/test/cxx11_tensor_block_access.cpp
Normal file
182
unsupported/test/cxx11_tensor_block_access.cpp
Normal file
@@ -0,0 +1,182 @@
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// 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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// Copyright (C) 2018 Andy Davis <andydavis@google.com>
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// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
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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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#include "main.h"
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#include <set>
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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using Eigen::Index;
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using Eigen::RowMajor;
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using Eigen::ColMajor;
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template<typename T>
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static const T& choose(int layout, const T& col, const T& row) {
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return layout == ColMajor ? col : row;
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}
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template <int Layout>
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static void test_block_mapper_sanity()
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{
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using T = int;
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using TensorBlock = internal::TensorBlock<T, Index, 2, Layout>;
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using TensorBlockMapper = internal::TensorBlockMapper<T, Index, 2, Layout>;
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DSizes<Index, 2> tensor_dims(100, 100);
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// Test uniform blocks.
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TensorBlockMapper uniform_block_mapper(
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tensor_dims, internal::TensorBlockShapeType::kUniformAllDims, 100);
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VERIFY_IS_EQUAL(uniform_block_mapper.total_block_count(), 100);
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VERIFY_IS_EQUAL(uniform_block_mapper.block_dims_total_size(), 100);
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// 10x10 blocks
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auto uniform_b0 = uniform_block_mapper.GetBlockForIndex(0, nullptr);
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VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(0), 10);
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VERIFY_IS_EQUAL(uniform_b0.block_sizes().at(1), 10);
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// Depending on a layout we stride by cols rows.
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VERIFY_IS_EQUAL(uniform_b0.block_strides().at(0), choose(Layout, 1, 10));
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VERIFY_IS_EQUAL(uniform_b0.block_strides().at(1), choose(Layout, 10, 1));
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// Tensor strides depend only on a layout and not on the block size.
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VERIFY_IS_EQUAL(uniform_b0.tensor_strides().at(0), choose(Layout, 1, 100));
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VERIFY_IS_EQUAL(uniform_b0.tensor_strides().at(1), choose(Layout, 100, 1));
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// Test skewed to inner dims blocks.
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TensorBlockMapper skewed_block_mapper(
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tensor_dims, internal::TensorBlockShapeType::kSkewedInnerDims, 100);
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VERIFY_IS_EQUAL(skewed_block_mapper.total_block_count(), 100);
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VERIFY_IS_EQUAL(skewed_block_mapper.block_dims_total_size(), 100);
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// 1x100 (100x1) rows/cols depending on a tensor layout.
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auto skewed_b0 = skewed_block_mapper.GetBlockForIndex(0, nullptr);
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VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(0), choose(Layout, 100, 1));
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VERIFY_IS_EQUAL(skewed_b0.block_sizes().at(1), choose(Layout, 1, 100));
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// Depending on a layout we stride by cols rows.
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VERIFY_IS_EQUAL(skewed_b0.block_strides().at(0), choose(Layout, 1, 100));
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VERIFY_IS_EQUAL(skewed_b0.block_strides().at(1), choose(Layout, 100, 1));
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// Tensor strides depend only on a layout and not on the block size.
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VERIFY_IS_EQUAL(skewed_b0.tensor_strides().at(0), choose(Layout, 1, 100));
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VERIFY_IS_EQUAL(skewed_b0.tensor_strides().at(1), choose(Layout, 100, 1));
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}
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// Given a TensorBlock "visit" every element accessible though it, and a keep an
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// index in the visited set. Verify that every coeff accessed only once.
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template <typename T, int Layout, int NumDims>
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static void UpdateCoeffSet(
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const internal::TensorBlock<T, Index, 4, Layout>& block,
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Index first_coeff_index,
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int dim_index,
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std::set<Index>* visited_coeffs) {
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const DSizes<Index, NumDims> block_sizes = block.block_sizes();
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const DSizes<Index, NumDims> tensor_strides = block.tensor_strides();
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for (int i = 0; i < block_sizes[dim_index]; ++i) {
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if (tensor_strides[dim_index] == 1) {
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auto inserted = visited_coeffs->insert(first_coeff_index + i);
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VERIFY_IS_EQUAL(inserted.second, true);
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} else {
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int next_dim_index = dim_index + choose(Layout, -1, 1);
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UpdateCoeffSet<T, Layout, NumDims>(block, first_coeff_index,
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next_dim_index, visited_coeffs);
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first_coeff_index += tensor_strides[dim_index];
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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_mapper_maps_every_element()
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{
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using T = int;
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using TensorBlock = internal::TensorBlock<T, Index, 4, Layout>;
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using TensorBlockMapper = internal::TensorBlockMapper<T, Index, 4, Layout>;
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DSizes<Index, 4> dims(5, 7, 11, 17);
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auto total_coeffs = static_cast<int>(dims.TotalSize());
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// Keep track of elements indices available via block access.
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std::set<Index> coeff_set;
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// Try different combinations of block types and sizes.
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auto block_shape_type =
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internal::random<bool>()
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? internal::TensorBlockShapeType::kUniformAllDims
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: internal::TensorBlockShapeType::kSkewedInnerDims;
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auto block_target_size = internal::random<int>(1, total_coeffs);
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TensorBlockMapper block_mapper(dims, block_shape_type, block_target_size);
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for (int i = 0; i < block_mapper.total_block_count(); ++i) {
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TensorBlock block = block_mapper.GetBlockForIndex(i, nullptr);
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UpdateCoeffSet<T, Layout, 4>(block, block.first_coeff_index(),
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choose(Layout, 3, 0), &coeff_set);
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}
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// Verify that every coefficient in the original Tensor is accessible through
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// TensorBlock only once.
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VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
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VERIFY_IS_EQUAL(*coeff_set.begin(), static_cast<Index>(0));
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VERIFY_IS_EQUAL(*coeff_set.rbegin(), static_cast<Index>(total_coeffs - 1));
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}
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template <int Layout>
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static void test_slice_block_mapper_maps_every_element()
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{
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using T = int;
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using TensorBlock = internal::TensorBlock<T, Index, 4, Layout>;
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using TensorSliceBlockMapper =
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internal::TensorSliceBlockMapper<T, Index, 4, Layout>;
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DSizes<Index, 4> tensor_dims(5,7,11,17);
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DSizes<Index, 4> tensor_slice_offsets(1,3,5,7);
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DSizes<Index, 4> tensor_slice_extents(3,2,4,5);
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// Keep track of elements indices available via block access.
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std::set<Index> coeff_set;
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auto total_coeffs = static_cast<int>(tensor_slice_extents.TotalSize());
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// Try different combinations of block types and sizes.
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auto block_shape_type =
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internal::random<bool>()
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? internal::TensorBlockShapeType::kUniformAllDims
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: internal::TensorBlockShapeType::kSkewedInnerDims;
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auto block_target_size = internal::random<int>(1, total_coeffs);
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// Pick a random dimension sizes for the tensor blocks.
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DSizes<Index, 4> block_sizes;
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for (int i = 0; i < 4; ++i) {
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block_sizes[i] = internal::random<int>(1, tensor_slice_extents[i]);
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}
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TensorSliceBlockMapper block_mapper(tensor_dims, tensor_slice_offsets,
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tensor_slice_extents, block_sizes,
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DimensionList<Index, 4>());
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for (int i = 0; i < block_mapper.total_block_count(); ++i) {
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TensorBlock block = block_mapper.GetBlockForIndex(i, NULL);
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UpdateCoeffSet<T, Layout, 4>(block, block.first_coeff_index(),
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choose(Layout, 3, 0), &coeff_set);
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}
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VERIFY_IS_EQUAL(coeff_set.size(), total_coeffs);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_assign) {
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CALL_SUBTEST(test_block_mapper_sanity<ColMajor>());
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CALL_SUBTEST(test_block_mapper_sanity<RowMajor>());
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CALL_SUBTEST(test_block_mapper_maps_every_element<ColMajor>());
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CALL_SUBTEST(test_block_mapper_maps_every_element<RowMajor>());
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CALL_SUBTEST(test_slice_block_mapper_maps_every_element<ColMajor>());
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CALL_SUBTEST(test_slice_block_mapper_maps_every_element<RowMajor>());
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}
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