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Diffstat (limited to 'unsupported/test/cxx11_tensor_block_eval.cpp')
| -rw-r--r-- | unsupported/test/cxx11_tensor_block_eval.cpp | 858 |
1 files changed, 858 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_block_eval.cpp b/unsupported/test/cxx11_tensor_block_eval.cpp new file mode 100644 index 000000000..b2e26ebb7 --- /dev/null +++ b/unsupported/test/cxx11_tensor_block_eval.cpp @@ -0,0 +1,858 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +// clang-format off +#include "main.h" +#include <Eigen/CXX11/Tensor> +// clang-format on + +using Eigen::internal::TensorBlockDescriptor; +using Eigen::internal::TensorExecutor; + +// -------------------------------------------------------------------------- // +// Utility functions to generate random tensors, blocks, and evaluate them. + +template <int NumDims> +static DSizes<Index, NumDims> RandomDims(Index min, Index max) { + DSizes<Index, NumDims> dims; + for (int i = 0; i < NumDims; ++i) { + dims[i] = internal::random<Index>(min, max); + } + return DSizes<Index, NumDims>(dims); +} + +// Block offsets and extents allows to construct a TensorSlicingOp corresponding +// to a TensorBlockDescriptor. +template <int NumDims> +struct TensorBlockParams { + DSizes<Index, NumDims> offsets; + DSizes<Index, NumDims> sizes; + TensorBlockDescriptor<NumDims, Index> desc; +}; + +template <int Layout, int NumDims> +static TensorBlockParams<NumDims> RandomBlock(DSizes<Index, NumDims> dims, + Index min, Index max) { + // Choose random offsets and sizes along all tensor dimensions. + DSizes<Index, NumDims> offsets(RandomDims<NumDims>(min, max)); + DSizes<Index, NumDims> sizes(RandomDims<NumDims>(min, max)); + + // Make sure that offset + size do not overflow dims. + for (int i = 0; i < NumDims; ++i) { + offsets[i] = numext::mini(dims[i] - 1, offsets[i]); + sizes[i] = numext::mini(sizes[i], dims[i] - offsets[i]); + } + + Index offset = 0; + DSizes<Index, NumDims> strides = Eigen::internal::strides<Layout>(dims); + for (int i = 0; i < NumDims; ++i) { + offset += strides[i] * offsets[i]; + } + + return {offsets, sizes, TensorBlockDescriptor<NumDims, Index>(offset, sizes)}; +} + +// Generate block with block sizes skewed towards inner dimensions. This type of +// block is required for evaluating broadcast expressions. +template <int Layout, int NumDims> +static TensorBlockParams<NumDims> SkewedInnerBlock( + DSizes<Index, NumDims> dims) { + using BlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>; + BlockMapper block_mapper(dims, + {internal::TensorBlockShapeType::kSkewedInnerDims, + internal::random<size_t>(1, dims.TotalSize()), + {0, 0, 0}}); + + Index total_blocks = block_mapper.blockCount(); + Index block_index = internal::random<Index>(0, total_blocks - 1); + auto block = block_mapper.blockDescriptor(block_index); + DSizes<Index, NumDims> sizes = block.dimensions(); + + auto strides = internal::strides<Layout>(dims); + DSizes<Index, NumDims> offsets; + + // Compute offsets for the first block coefficient. + Index index = block.offset(); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + for (int i = NumDims - 1; i > 0; --i) { + const Index idx = index / strides[i]; + index -= idx * strides[i]; + offsets[i] = idx; + } + if (NumDims > 0) offsets[0] = index; + } else { + for (int i = 0; i < NumDims - 1; ++i) { + const Index idx = index / strides[i]; + index -= idx * strides[i]; + offsets[i] = idx; + } + if (NumDims > 0) offsets[NumDims - 1] = index; + } + + return {offsets, sizes, block}; +} + +template <int NumDims> +static TensorBlockParams<NumDims> FixedSizeBlock(DSizes<Index, NumDims> dims) { + DSizes<Index, NumDims> offsets; + for (int i = 0; i < NumDims; ++i) offsets[i] = 0; + + return {offsets, dims, TensorBlockDescriptor<NumDims, Index>(0, dims)}; +} + +inline Eigen::IndexList<Index, Eigen::type2index<1>> NByOne(Index n) { + Eigen::IndexList<Index, Eigen::type2index<1>> ret; + ret.set(0, n); + return ret; +} +inline Eigen::IndexList<Eigen::type2index<1>, Index> OneByM(Index m) { + Eigen::IndexList<Eigen::type2index<1>, Index> ret; + ret.set(1, m); + return ret; +} + +// -------------------------------------------------------------------------- // +// Verify that block expression evaluation produces the same result as a +// TensorSliceOp (reading a tensor block is same to taking a tensor slice). + +template <typename T, int NumDims, int Layout, typename Expression, + typename GenBlockParams> +static void VerifyBlockEvaluator(Expression expr, GenBlockParams gen_block) { + using Device = DefaultDevice; + auto d = Device(); + + // Scratch memory allocator for block evaluation. + typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; + TensorBlockScratch scratch(d); + + // TensorEvaluator is needed to produce tensor blocks of the expression. + auto eval = TensorEvaluator<const decltype(expr), Device>(expr, d); + eval.evalSubExprsIfNeeded(nullptr); + + // Choose a random offsets, sizes and TensorBlockDescriptor. + TensorBlockParams<NumDims> block_params = gen_block(); + + // Evaluate TensorBlock expression into a tensor. + Tensor<T, NumDims, Layout> block(block_params.desc.dimensions()); + + // Dimensions for the potential destination buffer. + DSizes<Index, NumDims> dst_dims; + if (internal::random<bool>()) { + dst_dims = block_params.desc.dimensions(); + } else { + for (int i = 0; i < NumDims; ++i) { + Index extent = internal::random<Index>(0, 5); + dst_dims[i] = block_params.desc.dimension(i) + extent; + } + } + + // Maybe use this tensor as a block desc destination. + Tensor<T, NumDims, Layout> dst(dst_dims); + dst.setZero(); + if (internal::random<bool>()) { + block_params.desc.template AddDestinationBuffer<Layout>( + dst.data(), internal::strides<Layout>(dst.dimensions())); + } + + const bool root_of_expr = internal::random<bool>(); + auto tensor_block = eval.block(block_params.desc, scratch, root_of_expr); + + if (tensor_block.kind() == internal::TensorBlockKind::kMaterializedInOutput) { + // Copy data from destination buffer. + if (dimensions_match(dst.dimensions(), block.dimensions())) { + block = dst; + } else { + DSizes<Index, NumDims> offsets; + for (int i = 0; i < NumDims; ++i) offsets[i] = 0; + block = dst.slice(offsets, block.dimensions()); + } + + } else { + // Assign to block from expression. + auto b_expr = tensor_block.expr(); + + // We explicitly disable vectorization and tiling, to run a simple coefficient + // wise assignment loop, because it's very simple and should be correct. + using BlockAssign = TensorAssignOp<decltype(block), const decltype(b_expr)>; + using BlockExecutor = TensorExecutor<const BlockAssign, Device, false, + internal::TiledEvaluation::Off>; + BlockExecutor::run(BlockAssign(block, b_expr), d); + } + + // Cleanup temporary buffers owned by a tensor block. + tensor_block.cleanup(); + + // Compute a Tensor slice corresponding to a Tensor block. + Tensor<T, NumDims, Layout> slice(block_params.desc.dimensions()); + auto s_expr = expr.slice(block_params.offsets, block_params.sizes); + + // Explicitly use coefficient assignment to evaluate slice expression. + using SliceAssign = TensorAssignOp<decltype(slice), const decltype(s_expr)>; + using SliceExecutor = TensorExecutor<const SliceAssign, Device, false, + internal::TiledEvaluation::Off>; + SliceExecutor::run(SliceAssign(slice, s_expr), d); + + // Tensor block and tensor slice must be the same. + for (Index i = 0; i < block.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(block.coeff(i), slice.coeff(i)); + } +} + +// -------------------------------------------------------------------------- // + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_block() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + // Identity tensor expression transformation. + VerifyBlockEvaluator<T, NumDims, Layout>( + input, [&dims]() { return RandomBlock<Layout>(dims, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_unary_expr_block() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.abs(), [&dims]() { return RandomBlock<Layout>(dims, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_binary_expr_block() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> lhs(dims), rhs(dims); + lhs.setRandom(); + rhs.setRandom(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + lhs * rhs, [&dims]() { return RandomBlock<Layout>(dims, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_binary_with_unary_expr_block() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> lhs(dims), rhs(dims); + lhs.setRandom(); + rhs.setRandom(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + (lhs.square() + rhs.square()).sqrt(), + [&dims]() { return RandomBlock<Layout>(dims, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_broadcast() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 10); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + DSizes<Index, NumDims> bcast = RandomDims<NumDims>(1, 5); + + DSizes<Index, NumDims> bcasted_dims; + for (int i = 0; i < NumDims; ++i) bcasted_dims[i] = dims[i] * bcast[i]; + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.broadcast(bcast), + [&bcasted_dims]() { return SkewedInnerBlock<Layout>(bcasted_dims); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.broadcast(bcast), + [&bcasted_dims]() { return RandomBlock<Layout>(bcasted_dims, 5, 10); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.broadcast(bcast), + [&bcasted_dims]() { return FixedSizeBlock(bcasted_dims); }); + + // Check that desc.destination() memory is not shared between two broadcast + // materializations. + VerifyBlockEvaluator<T, NumDims, Layout>( + input.broadcast(bcast) * input.abs().broadcast(bcast), + [&bcasted_dims]() { return SkewedInnerBlock<Layout>(bcasted_dims); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_reshape() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(1, 10); + + DSizes<Index, NumDims> shuffled = dims; + std::shuffle(&shuffled[0], &shuffled[NumDims - 1], std::mt19937(g_seed)); + + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.reshape(shuffled), + [&shuffled]() { return RandomBlock<Layout>(shuffled, 1, 10); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.reshape(shuffled), + [&shuffled]() { return SkewedInnerBlock<Layout>(shuffled); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_cast() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.template cast<int>().template cast<T>(), + [&dims]() { return RandomBlock<Layout>(dims, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_select() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> lhs(dims); + Tensor<T, NumDims, Layout> rhs(dims); + Tensor<bool, NumDims, Layout> cond(dims); + lhs.setRandom(); + rhs.setRandom(); + cond.setRandom(); + + VerifyBlockEvaluator<T, NumDims, Layout>(cond.select(lhs, rhs), [&dims]() { + return RandomBlock<Layout>(dims, 1, 20); + }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_padding() { + const int inner_dim = Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1; + + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + DSizes<Index, NumDims> pad_before = RandomDims<NumDims>(0, 4); + DSizes<Index, NumDims> pad_after = RandomDims<NumDims>(0, 4); + array<std::pair<Index, Index>, NumDims> paddings; + for (int i = 0; i < NumDims; ++i) { + paddings[i] = std::make_pair(pad_before[i], pad_after[i]); + } + + // Test squeezing reads from inner dim. + if (internal::random<bool>()) { + pad_before[inner_dim] = 0; + pad_after[inner_dim] = 0; + paddings[inner_dim] = std::make_pair(0, 0); + } + + DSizes<Index, NumDims> padded_dims; + for (int i = 0; i < NumDims; ++i) { + padded_dims[i] = dims[i] + pad_before[i] + pad_after[i]; + } + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.pad(paddings), + [&padded_dims]() { return FixedSizeBlock(padded_dims); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.pad(paddings), + [&padded_dims]() { return RandomBlock<Layout>(padded_dims, 1, 10); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.pad(paddings), + [&padded_dims]() { return SkewedInnerBlock<Layout>(padded_dims); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_chipping() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + Index chip_dim = internal::random<int>(0, NumDims - 1); + Index chip_offset = internal::random<Index>(0, dims[chip_dim] - 2); + + DSizes<Index, NumDims - 1> chipped_dims; + for (Index i = 0; i < chip_dim; ++i) { + chipped_dims[i] = dims[i]; + } + for (Index i = chip_dim + 1; i < NumDims; ++i) { + chipped_dims[i - 1] = dims[i]; + } + + // Block buffer forwarding. + VerifyBlockEvaluator<T, NumDims - 1, Layout>( + input.chip(chip_offset, chip_dim), + [&chipped_dims]() { return FixedSizeBlock(chipped_dims); }); + + VerifyBlockEvaluator<T, NumDims - 1, Layout>( + input.chip(chip_offset, chip_dim), + [&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); }); + + // Block expression assignment. + VerifyBlockEvaluator<T, NumDims - 1, Layout>( + input.abs().chip(chip_offset, chip_dim), + [&chipped_dims]() { return FixedSizeBlock(chipped_dims); }); + + VerifyBlockEvaluator<T, NumDims - 1, Layout>( + input.abs().chip(chip_offset, chip_dim), + [&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); }); +} + + +template<typename T, int NumDims> +struct SimpleTensorGenerator { + T operator()(const array<Index, NumDims>& coords) const { + T result = static_cast<T>(0); + for (int i = 0; i < NumDims; ++i) { + result += static_cast<T>((i + 1) * coords[i]); + } + return result; + } +}; + +// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC. +template<int NumDims> +struct SimpleTensorGenerator<bool, NumDims> { + bool operator()(const array<Index, NumDims>& coords) const { + bool result = false; + for (int i = 0; i < NumDims; ++i) { + result ^= coords[i]; + } + return result; + } +}; + + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_generator() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + auto generator = SimpleTensorGenerator<T, NumDims>(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.generate(generator), [&dims]() { return FixedSizeBlock(dims); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.generate(generator), + [&dims]() { return RandomBlock<Layout>(dims, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_reverse() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + // Randomly reverse dimensions. + Eigen::DSizes<bool, NumDims> reverse; + for (int i = 0; i < NumDims; ++i) reverse[i] = internal::random<bool>(); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.reverse(reverse), [&dims]() { return FixedSizeBlock(dims); }); + + VerifyBlockEvaluator<T, NumDims, Layout>(input.reverse(reverse), [&dims]() { + return RandomBlock<Layout>(dims, 1, 10); + }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_slice() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + // Pick a random slice of an input tensor. + DSizes<Index, NumDims> slice_start = RandomDims<NumDims>(5, 10); + DSizes<Index, NumDims> slice_size = RandomDims<NumDims>(5, 10); + + // Make sure that slice start + size do not overflow tensor dims. + for (int i = 0; i < NumDims; ++i) { + slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]); + slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]); + } + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.slice(slice_start, slice_size), + [&slice_size]() { return FixedSizeBlock(slice_size); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.slice(slice_start, slice_size), + [&slice_size]() { return RandomBlock<Layout>(slice_size, 1, 10); }); +} + +template <typename T, int NumDims, int Layout> +static void test_eval_tensor_shuffle() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15); + Tensor<T, NumDims, Layout> input(dims); + input.setRandom(); + + DSizes<Index, NumDims> shuffle; + for (int i = 0; i < NumDims; ++i) shuffle[i] = i; + + do { + DSizes<Index, NumDims> shuffled_dims; + for (int i = 0; i < NumDims; ++i) shuffled_dims[i] = dims[shuffle[i]]; + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.shuffle(shuffle), + [&shuffled_dims]() { return FixedSizeBlock(shuffled_dims); }); + + VerifyBlockEvaluator<T, NumDims, Layout>( + input.shuffle(shuffle), [&shuffled_dims]() { + return RandomBlock<Layout>(shuffled_dims, 1, 5); + }); + + break; + + } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims)); +} + +template <typename T, int Layout> +static void test_eval_tensor_reshape_with_bcast() { + Index dim = internal::random<Index>(1, 100); + + Tensor<T, 2, Layout> lhs(1, dim); + Tensor<T, 2, Layout> rhs(dim, 1); + lhs.setRandom(); + rhs.setRandom(); + + auto reshapeLhs = NByOne(dim); + auto reshapeRhs = OneByM(dim); + + auto bcastLhs = OneByM(dim); + auto bcastRhs = NByOne(dim); + + DSizes<Index, 2> dims(dim, dim); + + VerifyBlockEvaluator<T, 2, Layout>( + lhs.reshape(reshapeLhs).broadcast(bcastLhs) * + rhs.reshape(reshapeRhs).broadcast(bcastRhs), + [dims]() { return SkewedInnerBlock<Layout, 2>(dims); }); +} + +template <typename T, int Layout> +static void test_eval_tensor_forced_eval() { + Index dim = internal::random<Index>(1, 100); + + Tensor<T, 2, Layout> lhs(dim, 1); + Tensor<T, 2, Layout> rhs(1, dim); + lhs.setRandom(); + rhs.setRandom(); + + auto bcastLhs = OneByM(dim); + auto bcastRhs = NByOne(dim); + + DSizes<Index, 2> dims(dim, dim); + + VerifyBlockEvaluator<T, 2, Layout>( + (lhs.broadcast(bcastLhs) * rhs.broadcast(bcastRhs)).eval().reshape(dims), + [dims]() { return SkewedInnerBlock<Layout, 2>(dims); }); + + VerifyBlockEvaluator<T, 2, Layout>( + (lhs.broadcast(bcastLhs) * rhs.broadcast(bcastRhs)).eval().reshape(dims), + [dims]() { return RandomBlock<Layout, 2>(dims, 1, 50); }); +} + +template <typename T, int Layout> +static void test_eval_tensor_chipping_of_bcast() { + if (Layout != static_cast<int>(RowMajor)) return; + + Index dim0 = internal::random<Index>(1, 10); + Index dim1 = internal::random<Index>(1, 10); + Index dim2 = internal::random<Index>(1, 10); + + Tensor<T, 3, Layout> input(1, dim1, dim2); + input.setRandom(); + + Eigen::array<Index, 3> bcast = {{dim0, 1, 1}}; + DSizes<Index, 2> chipped_dims(dim0, dim2); + + VerifyBlockEvaluator<T, 2, Layout>( + input.broadcast(bcast).chip(0, 1), + [chipped_dims]() { return FixedSizeBlock(chipped_dims); }); + + VerifyBlockEvaluator<T, 2, Layout>( + input.broadcast(bcast).chip(0, 1), + [chipped_dims]() { return SkewedInnerBlock<Layout, 2>(chipped_dims); }); + + VerifyBlockEvaluator<T, 2, Layout>( + input.broadcast(bcast).chip(0, 1), + [chipped_dims]() { return RandomBlock<Layout, 2>(chipped_dims, 1, 5); }); +} + +// -------------------------------------------------------------------------- // +// Verify that assigning block to a Tensor expression produces the same result +// as an assignment to TensorSliceOp (writing a block is is identical to +// assigning one tensor to a slice of another tensor). + +template <typename T, int NumDims, int Layout, int NumExprDims = NumDims, + typename Expression, typename GenBlockParams> +static void VerifyBlockAssignment(Tensor<T, NumDims, Layout>& tensor, + Expression expr, GenBlockParams gen_block) { + using Device = DefaultDevice; + auto d = Device(); + + // We use tensor evaluator as a target for block and slice assignments. + auto eval = TensorEvaluator<decltype(expr), Device>(expr, d); + + // Generate a random block, or choose a block that fits in full expression. + TensorBlockParams<NumExprDims> block_params = gen_block(); + + // Generate random data of the selected block size. + Tensor<T, NumExprDims, Layout> block(block_params.desc.dimensions()); + block.setRandom(); + + // ************************************************************************ // + // (1) Assignment from a block. + + // Construct a materialize block from a random generated block tensor. + internal::TensorMaterializedBlock<T, NumExprDims, Layout> blk( + internal::TensorBlockKind::kView, block.data(), block.dimensions()); + + // Reset all underlying tensor values to zero. + tensor.setZero(); + + // Use evaluator to write block into a tensor. + eval.writeBlock(block_params.desc, blk); + + // Make a copy of the result after assignment. + Tensor<T, NumDims, Layout> block_assigned = tensor; + + // ************************************************************************ // + // (2) Assignment to a slice + + // Reset all underlying tensor values to zero. + tensor.setZero(); + + // Assign block to a slice of original expression + auto s_expr = expr.slice(block_params.offsets, block_params.sizes); + + // Explicitly use coefficient assignment to evaluate slice expression. + using SliceAssign = TensorAssignOp<decltype(s_expr), const decltype(block)>; + using SliceExecutor = TensorExecutor<const SliceAssign, Device, false, + internal::TiledEvaluation::Off>; + SliceExecutor::run(SliceAssign(s_expr, block), d); + + // Make a copy of the result after assignment. + Tensor<T, NumDims, Layout> slice_assigned = tensor; + + for (Index i = 0; i < tensor.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(block_assigned.coeff(i), slice_assigned.coeff(i)); + } +} + +// -------------------------------------------------------------------------- // + +template <typename T, int NumDims, int Layout> +static void test_assign_to_tensor() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> tensor(dims); + + TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map, [&dims]() { return RandomBlock<Layout>(dims, 10, 20); }); + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map, [&dims]() { return FixedSizeBlock(dims); }); +} + +template <typename T, int NumDims, int Layout> +static void test_assign_to_tensor_reshape() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> tensor(dims); + + TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims); + + DSizes<Index, NumDims> shuffled = dims; + std::shuffle(&shuffled[0], &shuffled[NumDims - 1], std::mt19937(g_seed)); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.reshape(shuffled), + [&shuffled]() { return RandomBlock<Layout>(shuffled, 1, 10); }); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.reshape(shuffled), + [&shuffled]() { return SkewedInnerBlock<Layout>(shuffled); }); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.reshape(shuffled), + [&shuffled]() { return FixedSizeBlock(shuffled); }); +} + +template <typename T, int NumDims, int Layout> +static void test_assign_to_tensor_chipping() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> tensor(dims); + + Index chip_dim = internal::random<int>(0, NumDims - 1); + Index chip_offset = internal::random<Index>(0, dims[chip_dim] - 2); + + DSizes<Index, NumDims - 1> chipped_dims; + for (Index i = 0; i < chip_dim; ++i) { + chipped_dims[i] = dims[i]; + } + for (Index i = chip_dim + 1; i < NumDims; ++i) { + chipped_dims[i - 1] = dims[i]; + } + + TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims); + + VerifyBlockAssignment<T, NumDims, Layout, NumDims - 1>( + tensor, map.chip(chip_offset, chip_dim), + [&chipped_dims]() { return RandomBlock<Layout>(chipped_dims, 1, 10); }); + + VerifyBlockAssignment<T, NumDims, Layout, NumDims - 1>( + tensor, map.chip(chip_offset, chip_dim), + [&chipped_dims]() { return SkewedInnerBlock<Layout>(chipped_dims); }); + + VerifyBlockAssignment<T, NumDims, Layout, NumDims - 1>( + tensor, map.chip(chip_offset, chip_dim), + [&chipped_dims]() { return FixedSizeBlock(chipped_dims); }); +} + +template <typename T, int NumDims, int Layout> +static void test_assign_to_tensor_slice() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20); + Tensor<T, NumDims, Layout> tensor(dims); + + // Pick a random slice of tensor. + DSizes<Index, NumDims> slice_start = RandomDims<NumDims>(5, 10); + DSizes<Index, NumDims> slice_size = RandomDims<NumDims>(5, 10); + + // Make sure that slice start + size do not overflow tensor dims. + for (int i = 0; i < NumDims; ++i) { + slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]); + slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]); + } + + TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.slice(slice_start, slice_size), + [&slice_size]() { return RandomBlock<Layout>(slice_size, 1, 10); }); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.slice(slice_start, slice_size), + [&slice_size]() { return SkewedInnerBlock<Layout>(slice_size); }); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.slice(slice_start, slice_size), + [&slice_size]() { return FixedSizeBlock(slice_size); }); +} + +template <typename T, int NumDims, int Layout> +static void test_assign_to_tensor_shuffle() { + DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15); + Tensor<T, NumDims, Layout> tensor(dims); + + DSizes<Index, NumDims> shuffle; + for (int i = 0; i < NumDims; ++i) shuffle[i] = i; + + TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims); + + do { + DSizes<Index, NumDims> shuffled_dims; + for (int i = 0; i < NumDims; ++i) shuffled_dims[i] = dims[shuffle[i]]; + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.shuffle(shuffle), + [&shuffled_dims]() { return FixedSizeBlock(shuffled_dims); }); + + VerifyBlockAssignment<T, NumDims, Layout>( + tensor, map.shuffle(shuffle), [&shuffled_dims]() { + return RandomBlock<Layout>(shuffled_dims, 1, 5); + }); + + } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims)); +} + +// -------------------------------------------------------------------------- // + +#define CALL_SUBTEST_PART(PART) \ + CALL_SUBTEST_##PART + +#define CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(PART, NAME) \ + CALL_SUBTEST_PART(PART)((NAME<float, 1, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 2, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 3, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 4, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 5, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 1, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 2, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 5, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 1, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 2, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 3, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 4, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 5, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 1, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 2, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<int, 5, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 1, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 2, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 3, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 4, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 5, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 1, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 2, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, 5, ColMajor>())) + +#define CALL_SUBTESTS_DIMS_LAYOUTS(PART, NAME) \ + CALL_SUBTEST_PART(PART)((NAME<float, 1, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 2, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 3, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 4, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 5, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 1, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 2, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 4, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, 5, ColMajor>())) + +#define CALL_SUBTESTS_LAYOUTS_TYPES(PART, NAME) \ + CALL_SUBTEST_PART(PART)((NAME<float, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<float, ColMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, RowMajor>())); \ + CALL_SUBTEST_PART(PART)((NAME<bool, ColMajor>())) + +EIGEN_DECLARE_TEST(cxx11_tensor_block_eval) { + // clang-format off + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(1, test_eval_tensor_block); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(1, test_eval_tensor_binary_expr_block); + CALL_SUBTESTS_DIMS_LAYOUTS(1, test_eval_tensor_unary_expr_block); + CALL_SUBTESTS_DIMS_LAYOUTS(2, test_eval_tensor_binary_with_unary_expr_block); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(2, test_eval_tensor_broadcast); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(2, test_eval_tensor_reshape); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_cast); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_select); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_padding); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(4, test_eval_tensor_chipping); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(4, test_eval_tensor_generator); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(4, test_eval_tensor_reverse); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(5, test_eval_tensor_slice); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(5, test_eval_tensor_shuffle); + + CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_reshape_with_bcast); + CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_forced_eval); + CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_chipping_of_bcast); + + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(7, test_assign_to_tensor); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(7, test_assign_to_tensor_reshape); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(7, test_assign_to_tensor_chipping); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_to_tensor_slice); + CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_to_tensor_shuffle); + + // Force CMake to split this test. + // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8 + + // clang-format on +} |