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eigen/mirror/68db0e246aeea98fe8d323cd63375d99cd20102e/./contrib/test/tensor_block_eval.cpp
blob: 8663fc9d639128c9a79bdddaeadbf0e218d50ece [file]
// 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/.
// SPDX-FileCopyrightText: The Eigen Authors
// SPDX-License-Identifier: MPL-2.0
// clang-format off
#include "main.h"
#include <Eigen/Tensor>
#include "../Eigen/SpecialFunctions"
// clang-format on
using Eigen::internal::TensorBlockDescriptor;
using Eigen::internal::TensorExecutor;
template <typename T, int NumDims, int Layout>
static void setRandomForBinaryProduct(Tensor<T, NumDims, Layout>& tensor) {
EIGEN_IF_CONSTEXPR ((std::is_integral<T>::value && !std::is_same<T, bool>::value)) {
setRandomDataInRange(tensor, T(-1000), T(1000));
} else {
tensor.setRandom();
}
}
// -------------------------------------------------------------------------- //
// 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 std::enable_if_t<NumDims == 0, void> SetSkewedInnerBlockInnerOffset(DSizes<Index, NumDims>& /*offsets*/,
Index /*index*/) {}
template <int Layout, int NumDims>
static std::enable_if_t<(NumDims > 0), void> SetSkewedInnerBlockInnerOffset(DSizes<Index, NumDims>& offsets,
Index index) {
EIGEN_IF_CONSTEXPR (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
offsets[0] = index;
} else {
offsets[NumDims - 1] = index;
}
}
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();
EIGEN_IF_CONSTEXPR (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;
}
} else {
for (int i = 0; i < NumDims - 1; ++i) {
const Index idx = index / strides[i];
index -= idx * strides[i];
offsets[i] = idx;
}
}
SetSkewedInnerBlockInnerOffset<Layout, NumDims>(offsets, 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));
}
// Release evaluator-owned temporaries (e.g. the materialized buffer that
// TensorScan / TensorFFT allocate during evalSubExprsIfNeeded).
eval.cleanup();
}
// -------------------------------------------------------------------------- //
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);
setRandomForBinaryProduct(lhs);
setRandomForBinaryProduct(rhs);
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.abs() + rhs.abs()).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.
Tensor<T, NumDims, Layout> product_input(dims);
setRandomForBinaryProduct(product_input);
VerifyBlockEvaluator<T, NumDims, Layout>(product_input.broadcast(bcast) * product_input.abs().broadcast(bcast),
[&bcasted_dims]() { return SkewedInnerBlock<Layout>(bcasted_dims); });
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_scan() {
// Scan eagerly materializes m_output during evalSubExprsIfNeeded; this
// exercises the block() wrapper around that buffer.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(4, 12);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
constexpr Index axis = NumDims == 1 ? 0 : NumDims / 2;
VerifyBlockEvaluator<T, NumDims, Layout>(input.cumsum(axis), [&dims]() { return RandomBlock<Layout>(dims, 1, 5); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.cumsum(axis), [&dims]() { return FixedSizeBlock(dims); });
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_fft() {
// FFT eagerly materializes m_data during evalSubExprsIfNeeded; this
// exercises the block() wrapper around that buffer. Use RealPart so
// the output type matches the input type and the harness's slice
// comparison stays straightforward.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(4, 12);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
Eigen::array<int, 1> fft_dims = {0};
VerifyBlockEvaluator<T, NumDims, Layout>(input.template fft<RealPart, FFT_FORWARD>(fft_dims),
[&dims]() { return RandomBlock<Layout>(dims, 1, 5); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.template fft<RealPart, FFT_FORWARD>(fft_dims),
[&dims]() { return FixedSizeBlock(dims); });
}
// A destination buffer whose strides do not match the block's dense strides
// cannot back a tensor expression, but it can still absorb the block when the
// block is the root of the expression tree, because nothing reads the result
// back through `expr()`. Verify that such a destination is used rather than
// routed through scratch, and that the values landing in it are correct.
template <typename T, int NumDims, int Layout, typename Expression>
static void VerifyStridedDestinationAtRoot(Expression expr, const DSizes<Index, NumDims>& expr_dims) {
using Device = DefaultDevice;
using Desc = TensorBlockDescriptor<NumDims, Index>;
auto d = Device();
internal::TensorBlockScratchAllocator<Device> scratch(d);
auto eval = TensorEvaluator<const Expression, Device>(expr, d);
eval.evalSubExprsIfNeeded(nullptr);
// A block strictly inside the expression, and a destination strictly larger
// than the block, so that the destination strides are dense in no dimension
// but the innermost.
DSizes<Index, NumDims> offsets, sizes, dst_dims;
for (int i = 0; i < NumDims; ++i) {
offsets[i] = 1;
sizes[i] = expr_dims[i] - 2;
dst_dims[i] = sizes[i] + 2;
}
Index offset = 0;
const DSizes<Index, NumDims> expr_strides = internal::strides<Layout>(expr_dims);
for (int i = 0; i < NumDims; ++i) offset += expr_strides[i] * offsets[i];
Desc desc(offset, sizes);
Tensor<T, NumDims, Layout> dst(dst_dims);
dst.setZero();
desc.template AddDestinationBuffer<Layout>(dst.data(), internal::strides<Layout>(dst.dimensions()));
VERIFY(desc.destination().kind() == Desc::DestinationBuffer::kStrided);
auto tensor_block = eval.block(desc, scratch, /*root_of_expr_ast=*/true);
VERIFY(tensor_block.kind() == internal::TensorBlockKind::kMaterializedInOutput);
tensor_block.cleanup();
DSizes<Index, NumDims> zeros;
for (int i = 0; i < NumDims; ++i) zeros[i] = 0;
Tensor<T, NumDims, Layout> block = dst.slice(zeros, sizes);
// Reference: the same slice evaluated coefficient-wise.
Tensor<T, NumDims, Layout> slice(sizes);
auto s_expr = expr.slice(offsets, sizes);
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);
for (Index i = 0; i < sizes.TotalSize(); ++i) {
VERIFY_IS_EQUAL(block.coeff(i), slice.coeff(i));
}
eval.cleanup();
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_layout_swap() {
// The swap_layout expression has the opposite layout of its operand. Build
// the input with the opposite layout and assert the block evaluator on the
// resulting expression matches the slice-based reference.
constexpr int InputLayout = (Layout == ColMajor) ? RowMajor : ColMajor;
DSizes<Index, NumDims> input_dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, InputLayout> input(input_dims);
setRandomForBinaryProduct(input);
DSizes<Index, NumDims> swapped_dims;
for (int i = 0; i < NumDims; ++i) {
swapped_dims[i] = input_dims[NumDims - 1 - i];
}
VerifyBlockEvaluator<T, NumDims, Layout>(input.swap_layout(),
[&swapped_dims]() { return RandomBlock<Layout>(swapped_dims, 1, 10); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.swap_layout(),
[&swapped_dims]() { return FixedSizeBlock(swapped_dims); });
// An operand without a raw buffer: the block request is forwarded to the
// operand with reversed dimensions. A shuffle serves materialized blocks
// (re-wrapped without a copy), while a cwise expression on top of it serves
// lazy blocks (materialized by the layout swap itself).
DSizes<Index, NumDims> reversing_shuffle;
for (int i = 0; i < NumDims; ++i) reversing_shuffle[i] = NumDims - 1 - i;
VerifyBlockEvaluator<T, NumDims, Layout>(input.shuffle(reversing_shuffle).swap_layout(),
[&input_dims]() { return RandomBlock<Layout>(input_dims, 1, 10); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.shuffle(reversing_shuffle).swap_layout(),
[&input_dims]() { return FixedSizeBlock(input_dims); });
VerifyBlockEvaluator<T, NumDims, Layout>(
(input.shuffle(reversing_shuffle) * input.shuffle(reversing_shuffle)).swap_layout(),
[&input_dims]() { return RandomBlock<Layout>(input_dims, 1, 10); });
// Both forwarding branches must take a strided destination at the root of
// the expression tree instead of paying for a scratch round trip. A
// one-dimensional destination is never strided, so it has nothing to pin.
EIGEN_IF_CONSTEXPR (NumDims > 1) {
VerifyStridedDestinationAtRoot<T, NumDims, Layout>(input.shuffle(reversing_shuffle).swap_layout(), input_dims);
VerifyStridedDestinationAtRoot<T, NumDims, Layout>(
(input.shuffle(reversing_shuffle) * input.shuffle(reversing_shuffle)).swap_layout(), input_dims);
}
}
// Regression for the original failure mode this MR fixes: TensorPaddingOp's
// BlockAccess gates on m_impl.RawAccess (true for Scan/FFT/LayoutSwap/Contraction)
// and its getResourceRequirements() unconditionally calls the operand's. Before
// the operand-side block plumbing was added, instantiating the executor's
// Tiling=On path through these compositions failed to compile. Driving block()
// over the composed expressions exercises both sides of that compile path.
template <typename T, int NumDims, int Layout>
static void test_eval_composed_block_ops() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(4, 8);
array<std::pair<Index, Index>, NumDims> paddings;
DSizes<Index, NumDims> padded_dims;
for (int i = 0; i < NumDims; ++i) {
paddings[i] = std::make_pair(1, 2);
padded_dims[i] = dims[i] + 3;
}
constexpr Index axis = NumDims == 1 ? 0 : NumDims / 2;
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
// cumsum(...).pad(...) — TensorScan + TensorPadding.
VerifyBlockEvaluator<T, NumDims, Layout>(input.cumsum(axis).pad(paddings),
[&padded_dims]() { return RandomBlock<Layout>(padded_dims, 1, 5); });
// swap_layout().pad(...) — operand built with the opposite layout so the
// composed expression evaluates in the test's Layout.
constexpr int InputLayout = (Layout == ColMajor) ? RowMajor : ColMajor;
Tensor<T, NumDims, InputLayout> swap_input(dims);
swap_input.setRandom();
DSizes<Index, NumDims> swap_padded_dims;
for (int i = 0; i < NumDims; ++i) {
swap_padded_dims[i] = dims[NumDims - 1 - i] + 3;
}
VerifyBlockEvaluator<T, NumDims, Layout>(swap_input.swap_layout().pad(paddings), [&swap_padded_dims]() {
return RandomBlock<Layout>(swap_padded_dims, 1, 5);
});
}
// 2D-specific regression: contract(...).pad(...) hit the same composition bug
// because TensorContraction has RawAccess=true but lacked getResourceRequirements().
template <typename T, int Layout>
static void test_eval_contract_pad_composition() {
Tensor<T, 2, Layout> A(8, 6);
Tensor<T, 2, Layout> B(6, 4);
A.setRandom();
B.setRandom();
Eigen::array<IndexPair<Index>, 1> contract_dims = {IndexPair<Index>(1, 0)};
array<std::pair<Index, Index>, 2> paddings = {std::pair<Index, Index>{1, 1}, std::pair<Index, Index>{2, 2}};
DSizes<Index, 2> padded_dims(8 + 2, 4 + 4);
VerifyBlockEvaluator<T, 2, Layout>(A.contract(B, contract_dims).pad(paddings),
[&padded_dims]() { return RandomBlock<Layout>(padded_dims, 1, 5); });
}
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_ternary() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> a(dims);
Tensor<T, NumDims, Layout> b(dims);
Tensor<T, NumDims, Layout> x(dims);
// betainc requires a > 0, b > 0 and x in [0, 1].
a.setRandom();
b.setRandom();
x.setRandom();
a = a.abs() + a.constant(T(0.5));
b = b.abs() + b.constant(T(0.5));
x = (x + x.constant(T(1))) * x.constant(T(0.5));
VerifyBlockEvaluator<T, NumDims, Layout>(Eigen::betainc(a, b, x),
[&dims]() { return RandomBlock<Layout>(dims, 1, 20); });
VerifyBlockEvaluator<T, NumDims, Layout>(Eigen::betainc(a, b, x), [&dims]() { return FixedSizeBlock(dims); });
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_nullary() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
VerifyBlockEvaluator<T, NumDims, Layout>(input.constant(T(7)),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.constant(T(7)), [&dims]() { return FixedSizeBlock(dims); });
// Nullary leaf composed under a block-capable binary expression.
VerifyBlockEvaluator<T, NumDims, Layout>(input * input.constant(T(1)),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_random_nullary() {
// Random generators advance their state on every call, so block and linear
// evaluation produce different (equally distributed) values; only
// distribution properties can be checked.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> input(dims);
using Device = DefaultDevice;
auto d = Device();
auto expr = input.random();
auto eval = TensorEvaluator<const decltype(expr), Device>(expr, d);
eval.evalSubExprsIfNeeded(nullptr);
auto materialize = [&](TensorBlockParams<NumDims> block_params) {
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
TensorBlockScratch scratch(d);
auto tensor_block = eval.block(block_params.desc, scratch);
Tensor<T, NumDims, Layout> block(block_params.desc.dimensions());
auto b_expr = tensor_block.expr();
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);
tensor_block.cleanup();
return block;
};
// A block at a random offset only permits a range check: for 1-D dims the
// clamped block can be as small as a single element.
Tensor<T, NumDims, Layout> offset_block = materialize(RandomBlock<Layout>(dims, 5, 10));
for (Index i = 0; i < offset_block.size(); ++i) {
VERIFY(offset_block.coeff(i) >= T(0) && offset_block.coeff(i) < T(1));
}
// The full tensor always has >= 10 elements; all of them collapsing to one
// value means the generator was not actually invoked per element.
Tensor<T, NumDims, Layout> block = materialize(FixedSizeBlock(dims));
bool all_equal = true;
for (Index i = 0; i < block.size(); ++i) {
VERIFY(block.coeff(i) >= T(0) && block.coeff(i) < T(1));
all_equal = all_equal && (block.coeff(i) == block.coeff(0));
}
VERIFY(!all_equal);
}
// A zero-argument operator() alone does not imply index independence: when an
// indexed overload also exists, nullary_wrapper dispatches to it, so blocks
// must be materialized with true tensor-linear indices rather than served
// lazily with indices restarting at each block origin.
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_indexed_nullary() {
struct BothOverloads {
T operator()() const { return T(-1); }
T operator()(Index i) const { return static_cast<T>(i); }
};
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> input(dims);
VerifyBlockEvaluator<T, NumDims, Layout>(input.nullaryExpr(BothOverloads()),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.nullaryExpr(BothOverloads()),
[&dims]() { return FixedSizeBlock(dims); });
}
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() {
constexpr 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, int Layout>
static void test_eval_tensor_concatenation() {
DSizes<Index, NumDims> lhs_dims = RandomDims<NumDims>(5, 12);
DSizes<Index, NumDims> rhs_dims = lhs_dims;
const Index axis = internal::random<int>(0, NumDims - 1);
rhs_dims[axis] = internal::random<Index>(1, 10);
Tensor<T, NumDims, Layout> lhs(lhs_dims);
Tensor<T, NumDims, Layout> rhs(rhs_dims);
lhs.setRandom();
rhs.setRandom();
DSizes<Index, NumDims> out_dims = lhs_dims;
out_dims[axis] = lhs_dims[axis] + rhs_dims[axis];
VerifyBlockEvaluator<T, NumDims, Layout>(lhs.concatenate(rhs, axis),
[&out_dims]() { return FixedSizeBlock(out_dims); });
VerifyBlockEvaluator<T, NumDims, Layout>(lhs.concatenate(rhs, axis),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 5); });
VerifyBlockEvaluator<T, NumDims, Layout>(lhs.concatenate(rhs, axis),
[&out_dims]() { return SkewedInnerBlock<Layout>(out_dims); });
}
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 = result != (coords[i] != 0);
}
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); });
}
// Exercise the block evaluator for bool slices as a sub-expression of a
// block-aware parent op at sizes spanning Packet16b (16-lane bool packet)
// boundaries. Before the BlockAccess fix for bool slicing, the parent op's
// BlockAccess would be forced to false and this composition would never
// dispatch through the block path.
template <int NumDims, int Layout>
static void test_eval_tensor_slice_bool_composite() {
const Index boundary_sizes[] = {15, 16, 17, 31, 32, 33, 47, 48, 49};
for (Index sz : boundary_sizes) {
DSizes<Index, NumDims> dims;
for (int i = 0; i < NumDims; ++i) dims[i] = sz;
Tensor<bool, NumDims, Layout> lhs(dims);
Tensor<bool, NumDims, Layout> rhs(dims);
lhs.setRandom();
rhs.setRandom();
// Slice skewed off the boundary so the block layout straddles packet
// boundaries of the underlying tensor.
DSizes<Index, NumDims> slice_start;
DSizes<Index, NumDims> slice_size;
for (int i = 0; i < NumDims; ++i) {
slice_start[i] = sz >= 2 ? 1 : 0;
slice_size[i] = sz - slice_start[i];
}
auto expr = lhs.slice(slice_start, slice_size) && rhs.slice(slice_start, slice_size);
VerifyBlockEvaluator<bool, NumDims, Layout>(expr, [&slice_size]() { return FixedSizeBlock(slice_size); });
VerifyBlockEvaluator<bool, NumDims, Layout>(expr,
[&slice_size, sz]() { return RandomBlock<Layout>(slice_size, 1, sz); });
}
}
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 NumDims, int Layout>
static void test_eval_tensor_inflation() {
// Keep the input small: inflation multiplies every dimension by its stride.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(2, 5);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
DSizes<Index, NumDims> strides;
DSizes<Index, NumDims> inflated_dims;
for (int i = 0; i < NumDims; ++i) {
strides[i] = internal::random<Index>(1, 3);
inflated_dims[i] = (dims[i] - 1) * strides[i] + 1;
}
VerifyBlockEvaluator<T, NumDims, Layout>(input.inflate(strides),
[&inflated_dims]() { return RandomBlock<Layout>(inflated_dims, 1, 10); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.inflate(strides),
[&inflated_dims]() { return SkewedInnerBlock<Layout>(inflated_dims); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.inflate(strides),
[&inflated_dims]() { return FixedSizeBlock(inflated_dims); });
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_strided_slice() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> input(dims);
input.setRandom();
// Sweep unit, dilated, and negative strides; start/stop are chosen in-range
// so that clamping does not kick in.
const Index stride_choices[] = {1, 2, 3, -1, -2};
for (const Index s : stride_choices) {
DSizes<Index, NumDims> start, stop, strides, out_dims;
for (int i = 0; i < NumDims; ++i) {
strides[i] = s;
if (s > 0) {
start[i] = 1;
stop[i] = dims[i] - 1;
} else {
start[i] = dims[i] - 2;
stop[i] = 0;
}
const Index interval = stop[i] - start[i];
out_dims[i] = interval / strides[i] + (interval % strides[i] != 0 ? 1 : 0);
}
VerifyBlockEvaluator<T, NumDims, Layout>(input.stridedSlice(start, stop, strides),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 5); });
VerifyBlockEvaluator<T, NumDims, Layout>(input.stridedSlice(start, stop, strides),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
// The identity strided slice serves blocks straight from the input buffer.
DSizes<Index, NumDims> zeros, ones;
for (int i = 0; i < NumDims; ++i) {
zeros[i] = 0;
ones[i] = 1;
}
VerifyBlockEvaluator<T, NumDims, Layout>(input.stridedSlice(zeros, dims, ones),
[&dims]() { return RandomBlock<Layout>(dims, 1, 5); });
}
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); });
}
template <typename T, int Layout>
static void test_eval_tensor_patch() {
DSizes<Index, 3> dims = RandomDims<3>(4, 10);
Tensor<T, 3, Layout> input(dims);
input.setRandom();
DSizes<Index, 3> patch_dims;
Index num_patches = 1;
for (int i = 0; i < 3; ++i) {
patch_dims[i] = internal::random<Index>(1, dims[i]);
num_patches *= (dims[i] - patch_dims[i] + 1);
}
DSizes<Index, 4> out_dims;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
out_dims = DSizes<Index, 4>(patch_dims[0], patch_dims[1], patch_dims[2], num_patches);
} else {
out_dims = DSizes<Index, 4>(num_patches, patch_dims[0], patch_dims[1], patch_dims[2]);
}
VerifyBlockEvaluator<T, 4, Layout>(input.extract_patches(patch_dims),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 4, Layout>(input.extract_patches(patch_dims),
[&out_dims]() { return SkewedInnerBlock<Layout>(out_dims); });
VerifyBlockEvaluator<T, 4, Layout>(input.extract_patches(patch_dims),
[&out_dims]() { return FixedSizeBlock(out_dims); });
// Dispatch check: stride() serves neither blocks nor raw buffers, so this
// pins down that the patch block path needs no capability bit from its
// argument, only coeff().
{
const array<Index, 3> strides = {{2, 2, 2}};
auto strided = input.stride(strides);
typedef TensorEvaluator<const decltype(strided), DefaultDevice> StridedEval;
static_assert(!StridedEval::BlockAccess && !StridedEval::RawAccess,
"stride() must stay a coeff()-only argument for this check to be meaningful");
DSizes<Index, 3> strided_dims;
DSizes<Index, 3> strided_patch_dims;
Index strided_num_patches = 1;
for (int i = 0; i < 3; ++i) {
strided_dims[i] = (dims[i] - 1) / 2 + 1;
strided_patch_dims[i] = internal::random<Index>(1, strided_dims[i]);
strided_num_patches *= (strided_dims[i] - strided_patch_dims[i] + 1);
}
DSizes<Index, 4> strided_out_dims;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
strided_out_dims =
DSizes<Index, 4>(strided_patch_dims[0], strided_patch_dims[1], strided_patch_dims[2], strided_num_patches);
} else {
strided_out_dims =
DSizes<Index, 4>(strided_num_patches, strided_patch_dims[0], strided_patch_dims[1], strided_patch_dims[2]);
}
auto strided_patch = strided.extract_patches(strided_patch_dims);
typedef TensorEvaluator<const decltype(strided_patch), DefaultDevice> PatchEval;
static_assert(PatchEval::BlockAccess && PatchEval::PreferBlockAccess,
"the patch block path must dispatch for a coeff()-only argument");
VerifyBlockEvaluator<T, 4, Layout>(strided_patch,
[&strided_out_dims]() { return RandomBlock<Layout>(strided_out_dims, 1, 10); });
}
}
template <typename T, int Layout>
static void test_eval_tensor_image_patch() {
static constexpr bool kColMajor = static_cast<int>(Layout) == static_cast<int>(ColMajor);
const Index depth = internal::random<Index>(1, 5);
const Index rows = internal::random<Index>(6, 12);
const Index cols = internal::random<Index>(6, 12);
const Index batch = internal::random<Index>(1, 3);
DSizes<Index, 4> input_dims =
kColMajor ? DSizes<Index, 4>(depth, rows, cols, batch) : DSizes<Index, 4>(batch, cols, rows, depth);
Tensor<T, 4, Layout> input(input_dims);
input.setRandom();
const Index pr = internal::random<Index>(1, 3);
const Index pc = internal::random<Index>(1, 3);
auto make_out_dims = [&](Index out_r, Index out_c) {
return kColMajor ? DSizes<Index, 5>(depth, pr, pc, out_r * out_c, batch)
: DSizes<Index, 5>(batch, out_r * out_c, pc, pr, depth);
};
// No padding, unit strides.
{
DSizes<Index, 5> out_dims = make_out_dims(rows - pr + 1, cols - pc + 1);
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 1, 1, 1, 1, PADDING_VALID),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 1, 1, 1, 1, PADDING_VALID),
[&out_dims]() { return SkewedInnerBlock<Layout>(out_dims); });
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 1, 1, 1, 1, PADDING_VALID),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Same padding with strided patch extraction (blocks contain padding runs).
{
DSizes<Index, 5> out_dims = make_out_dims(numext::div_ceil(rows, Index(2)), numext::div_ceil(cols, Index(2)));
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 2, 2, 1, 1, PADDING_SAME),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 2, 2, 1, 1, PADDING_SAME),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Dilated patches (non-unit in-row/in-col strides).
{
const Index pr_eff = pr + (pr - 1);
const Index pc_eff = pc + (pc - 1);
DSizes<Index, 5> out_dims = make_out_dims(rows - pr_eff + 1, cols - pc_eff + 1);
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 1, 1, 2, 2, PADDING_VALID),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 5, Layout>(input.extract_image_patches(pr, pc, 1, 1, 2, 2, PADDING_VALID),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Inflated (zero-interleaved) input. Only the long overload can set the
// inflate strides, and they are what makes an in-range coordinate land
// between samples, which is a branch none of the cases above reach.
{
const Index rows_eff = (rows - 1) * 2 + 1;
const Index cols_eff = (cols - 1) * 2 + 1;
DSizes<Index, 5> out_dims = make_out_dims(rows_eff - pr + 1, cols_eff - pc + 1);
auto inflated = input.extract_image_patches(pr, pc, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, T(0));
VerifyBlockEvaluator<T, 5, Layout>(inflated, [&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 5, Layout>(inflated, [&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Dispatch check: stride() serves neither blocks nor raw buffers, so this
// pins down that the block path needs no capability bit from its argument.
{
const array<Index, 4> strides = {{1, 2, 2, 1}};
auto strided_patch = input.stride(strides).extract_image_patches(pr, pc, 1, 1, 1, 1, PADDING_VALID);
typedef TensorEvaluator<const decltype(strided_patch), DefaultDevice> PatchEval;
static_assert(PatchEval::BlockAccess && PatchEval::PreferBlockAccess,
"the image-patch block path must dispatch for a coeff()-only argument");
const Index rows_s = (rows - 1) / 2 + 1;
const Index cols_s = (cols - 1) / 2 + 1;
DSizes<Index, 5> out_dims = make_out_dims(rows_s - pr + 1, cols_s - pc + 1);
VerifyBlockEvaluator<T, 5, Layout>(strided_patch, [&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
}
}
template <typename T, int Layout>
static void test_eval_tensor_volume_patch() {
static constexpr bool kColMajor = static_cast<int>(Layout) == static_cast<int>(ColMajor);
const Index depth = internal::random<Index>(1, 4);
const Index planes = internal::random<Index>(5, 8);
const Index rows = internal::random<Index>(5, 8);
const Index cols = internal::random<Index>(5, 8);
const Index batch = internal::random<Index>(1, 2);
DSizes<Index, 5> input_dims = kColMajor ? DSizes<Index, 5>(depth, planes, rows, cols, batch)
: DSizes<Index, 5>(batch, cols, rows, planes, depth);
Tensor<T, 5, Layout> input(input_dims);
input.setRandom();
const Index pp = internal::random<Index>(1, 3);
const Index pr = internal::random<Index>(1, 3);
const Index pc = internal::random<Index>(1, 3);
auto make_out_dims = [&](Index out_p, Index out_r, Index out_c) {
return kColMajor ? DSizes<Index, 6>(depth, pp, pr, pc, out_p * out_r * out_c, batch)
: DSizes<Index, 6>(batch, out_p * out_r * out_c, pc, pr, pp, depth);
};
// No padding, unit strides.
{
DSizes<Index, 6> out_dims = make_out_dims(planes - pp + 1, rows - pr + 1, cols - pc + 1);
VerifyBlockEvaluator<T, 6, Layout>(input.extract_volume_patches(pp, pr, pc, 1, 1, 1, PADDING_VALID),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 6, Layout>(input.extract_volume_patches(pp, pr, pc, 1, 1, 1, PADDING_VALID),
[&out_dims]() { return SkewedInnerBlock<Layout>(out_dims); });
VerifyBlockEvaluator<T, 6, Layout>(input.extract_volume_patches(pp, pr, pc, 1, 1, 1, PADDING_VALID),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Same padding with strided patch extraction (blocks contain padding runs).
{
DSizes<Index, 6> out_dims = make_out_dims(numext::div_ceil(planes, Index(2)), numext::div_ceil(rows, Index(2)),
numext::div_ceil(cols, Index(2)));
VerifyBlockEvaluator<T, 6, Layout>(input.extract_volume_patches(pp, pr, pc, 2, 2, 2, PADDING_SAME),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 6, Layout>(input.extract_volume_patches(pp, pr, pc, 2, 2, 2, PADDING_SAME),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Inflated (zero-interleaved) input, reaching the branch that rejects an
// in-range coordinate for landing between samples.
{
const Index planes_eff = (planes - 1) * 2 + 1;
const Index rows_eff = (rows - 1) * 2 + 1;
const Index cols_eff = (cols - 1) * 2 + 1;
DSizes<Index, 6> out_dims = make_out_dims(planes_eff - pp + 1, rows_eff - pr + 1, cols_eff - pc + 1);
auto inflated = input.extract_volume_patches(pp, pr, pc, 1, 1, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, T(0));
VerifyBlockEvaluator<T, 6, Layout>(inflated, [&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
VerifyBlockEvaluator<T, 6, Layout>(inflated, [&out_dims]() { return FixedSizeBlock(out_dims); });
}
// Dispatch check: stride() serves neither blocks nor raw buffers, so this
// pins down that the block path needs no capability bit from its argument.
{
const array<Index, 5> strides = {{1, 2, 2, 2, 1}};
auto strided_patch = input.stride(strides).extract_volume_patches(pp, pr, pc, 1, 1, 1, PADDING_VALID);
typedef TensorEvaluator<const decltype(strided_patch), DefaultDevice> PatchEval;
static_assert(PatchEval::BlockAccess && PatchEval::PreferBlockAccess,
"the volume-patch block path must dispatch for a coeff()-only argument");
const Index planes_s = (planes - 1) / 2 + 1;
const Index rows_s = (rows - 1) / 2 + 1;
const Index cols_s = (cols - 1) / 2 + 1;
DSizes<Index, 6> out_dims = make_out_dims(planes_s - pp + 1, rows_s - pr + 1, cols_s - pc + 1);
VerifyBlockEvaluator<T, 6, Layout>(strided_patch, [&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 10); });
}
}
// -------------------------------------------------------------------------- //
// 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).
// `Layout` is the layout of the expression and the blocks written through it;
// `TensorLayout` is the layout of the underlying destination tensor. They only
// differ when the expression contains a layout swap.
template <typename T, int NumDims, int Layout, int NumExprDims = NumDims, int TensorLayout = Layout,
typename Expression, typename GenBlockParams>
static void VerifyBlockAssignment(Tensor<T, NumDims, TensorLayout>& 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, TensorLayout> 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, TensorLayout> 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));
}
template <typename T, int NumDims, int Layout>
static void test_assign_to_tensor_layout_swap() {
// The swap_layout lvalue has the opposite layout of the underlying tensor.
// static so the lambdas below can name it without capturing: MSVC 14.29
// rejects an uncaptured non-static constexpr local there (error C3493).
static constexpr int SwappedLayout = (Layout == ColMajor) ? RowMajor : ColMajor;
DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15);
Tensor<T, NumDims, Layout> tensor(dims);
DSizes<Index, NumDims> swapped_dims;
for (int i = 0; i < NumDims; ++i) swapped_dims[i] = dims[NumDims - 1 - i];
TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
VerifyBlockAssignment<T, NumDims, SwappedLayout>(
tensor, map.swap_layout(), [&swapped_dims]() { return RandomBlock<SwappedLayout>(swapped_dims, 1, 10); });
VerifyBlockAssignment<T, NumDims, SwappedLayout>(tensor, map.swap_layout(),
[&swapped_dims]() { return FixedSizeBlock(swapped_dims); });
}
template <typename T, int NumDims, int Layout>
static void test_assign_to_tensor_strided_slice() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> tensor(dims);
TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
// Sweep unit, dilated, and negative strides; start/stop are chosen in-range
// so that clamping does not kick in.
const Index stride_choices[] = {1, 2, -1, -2};
for (const Index s : stride_choices) {
DSizes<Index, NumDims> start, stop, strides, out_dims;
for (int i = 0; i < NumDims; ++i) {
strides[i] = s;
if (s > 0) {
start[i] = 1;
stop[i] = dims[i] - 1;
} else {
start[i] = dims[i] - 2;
stop[i] = 0;
}
const Index interval = stop[i] - start[i];
out_dims[i] = interval / strides[i] + (interval % strides[i] != 0 ? 1 : 0);
}
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.stridedSlice(start, stop, strides),
[&out_dims]() { return RandomBlock<Layout>(out_dims, 1, 5); });
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.stridedSlice(start, stop, strides),
[&out_dims]() { return FixedSizeBlock(out_dims); });
}
}
// A lazy rhs expression has no raw buffer, so the tiled executor drives
// writeBlock with a block expression: assigned directly into the destination
// for a unit inner stride, and through a materialized temporary otherwise.
// VerifyBlockAssignment cannot reach these paths because it always feeds
// writeBlock a materialized block.
template <typename T, int NumDims, int Layout>
static void test_assign_expr_to_tensor_strided_slice() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
Tensor<T, NumDims, Layout> tensor(dims);
TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
const Index stride_choices[] = {1, 2, -1, -2};
for (const Index s : stride_choices) {
DSizes<Index, NumDims> start, stop, strides, out_dims;
for (int i = 0; i < NumDims; ++i) {
strides[i] = s;
if (s > 0) {
start[i] = 1;
stop[i] = dims[i] - 1;
} else {
start[i] = dims[i] - 2;
stop[i] = 0;
}
const Index interval = stop[i] - start[i];
out_dims[i] = interval / strides[i] + (interval % strides[i] != 0 ? 1 : 0);
}
Tensor<T, NumDims, Layout> rhs(out_dims);
rhs.setRandom();
auto lhs_expr = map.stridedSlice(start, stop, strides);
auto rhs_expr = rhs + rhs.constant(T(1));
using Assign = TensorAssignOp<decltype(lhs_expr), const decltype(rhs_expr)>;
tensor.setZero();
TensorExecutor<const Assign, DefaultDevice, /*Vectorizable=*/true, internal::TiledEvaluation::On>::run(
Assign(lhs_expr, rhs_expr), DefaultDevice());
Tensor<T, NumDims, Layout> tiled = tensor;
tensor.setZero();
TensorExecutor<const Assign, DefaultDevice, /*Vectorizable=*/false, internal::TiledEvaluation::Off>::run(
Assign(lhs_expr, rhs_expr), DefaultDevice());
for (Index i = 0; i < tensor.size(); ++i) {
VERIFY_IS_EQUAL(tiled.coeff(i), tensor.coeff(i));
}
}
}
template <typename T, int NumDims, int Layout>
static void test_assign_to_tensor_reverse() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15);
Tensor<T, NumDims, Layout> tensor(dims);
TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
array<bool, NumDims> reverse;
for (int i = 0; i < NumDims; ++i) reverse[i] = false;
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reverse(reverse),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
for (int i = 0; i < NumDims; ++i) reverse[i] = true;
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reverse(reverse),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reverse(reverse), [&dims]() { return FixedSizeBlock(dims); });
for (int i = 0; i < NumDims; ++i) reverse[i] = internal::random<bool>();
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.reverse(reverse),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
}
template <typename T, int NumDims, int Layout>
static void test_assign_to_tensor_roll() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15);
Tensor<T, NumDims, Layout> tensor(dims);
TensorMap<Tensor<T, NumDims, Layout>> map(tensor.data(), dims);
array<Index, NumDims> rolls;
for (int i = 0; i < NumDims; ++i) rolls[i] = 0;
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.roll(rolls),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
// Shifts beyond the dimension size and negative shifts must wrap.
for (int i = 0; i < NumDims; ++i) rolls[i] = internal::random<Index>(-2 * dims[i], 2 * dims[i]);
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.roll(rolls),
[&dims]() { return RandomBlock<Layout>(dims, 1, 10); });
VerifyBlockAssignment<T, NumDims, Layout>(tensor, map.roll(rolls), [&dims]() { return FixedSizeBlock(dims); });
}
template <typename T, int NumDims, int Layout>
static void test_assign_to_tensor_concatenation() {
// A concatenation writes into two operands, which VerifyBlockAssignment's
// single-tensor contract cannot express; compare writeBlock against a
// scalar reference instead. This is also the only place the RowMajor lvalue
// evaluator is instantiated: its coeffRef/writePacket are ColMajor-only, so
// a slice-assignment reference would not compile.
DSizes<Index, NumDims> dims = RandomDims<NumDims>(5, 15);
const int axis = internal::random<int>(0, NumDims - 1);
const Index left_axis_size = internal::random<Index>(1, dims[axis] - 1);
DSizes<Index, NumDims> left_dims = dims;
left_dims[axis] = left_axis_size;
DSizes<Index, NumDims> right_dims = dims;
right_dims[axis] = dims[axis] - left_axis_size;
Tensor<T, NumDims, Layout> left(left_dims);
Tensor<T, NumDims, Layout> right(right_dims);
TensorMap<Tensor<T, NumDims, Layout>> left_map(left.data(), left_dims);
TensorMap<Tensor<T, NumDims, Layout>> right_map(right.data(), right_dims);
auto expr = left_map.concatenate(right_map, axis);
auto eval = TensorEvaluator<decltype(expr), DefaultDevice>(expr, DefaultDevice());
const DSizes<Index, NumDims> strides = internal::strides<Layout>(dims);
// Whether a block straddles the concat axis decides which of the two copies
// in writeBlock run, so pin all three cases instead of leaving it to chance:
// a random block, one that spans the seam, and one wholly in the right
// operand.
auto verify = [&](TensorBlockParams<NumDims> params) {
Tensor<T, NumDims, Layout> block(params.desc.dimensions());
block.setRandom();
left.setZero();
right.setZero();
internal::TensorMaterializedBlock<T, NumDims, Layout> blk(internal::TensorBlockKind::kView, block.data(),
block.dimensions());
eval.writeBlock(params.desc, blk);
// Scalar reference: land every block coefficient in the expected operand.
Tensor<T, NumDims, Layout> expected_left(left_dims);
Tensor<T, NumDims, Layout> expected_right(right_dims);
expected_left.setZero();
expected_right.setZero();
DSizes<Index, NumDims> it;
for (int i = 0; i < NumDims; ++i) it[i] = 0;
for (Index count = 0; count < block.size(); ++count) {
array<Index, NumDims> dst;
for (int i = 0; i < NumDims; ++i) dst[i] = params.offsets[i] + it[i];
if (dst[axis] < left_axis_size) {
expected_left(dst) = block(it);
} else {
dst[axis] -= left_axis_size;
expected_right(dst) = block(it);
}
for (int i = 0; i < NumDims; ++i) {
if (++it[i] < params.desc.dimension(i)) break;
it[i] = 0;
}
}
for (Index i = 0; i < left.size(); ++i) VERIFY_IS_EQUAL(left.coeff(i), expected_left.coeff(i));
for (Index i = 0; i < right.size(); ++i) VERIFY_IS_EQUAL(right.coeff(i), expected_right.coeff(i));
};
// Builds the descriptor for an explicit offset/extent box.
auto make_params = [&](DSizes<Index, NumDims> offsets, DSizes<Index, NumDims> sizes) {
Index offset = 0;
for (int i = 0; i < NumDims; ++i) offset += strides[i] * offsets[i];
return TensorBlockParams<NumDims>{offsets, sizes, TensorBlockDescriptor<NumDims, Index>(offset, sizes)};
};
verify(RandomBlock<Layout>(dims, 1, 10));
// Straddling the seam: both the left and the right copy run.
{
DSizes<Index, NumDims> offsets;
DSizes<Index, NumDims> sizes;
for (int i = 0; i < NumDims; ++i) {
offsets[i] = 0;
sizes[i] = numext::mini(dims[i], Index(3));
}
offsets[axis] = left_axis_size - 1;
sizes[axis] = numext::mini(Index(2), dims[axis] - offsets[axis]);
verify(make_params(offsets, sizes));
}
// Wholly inside the right operand: only the right copy runs.
{
DSizes<Index, NumDims> offsets;
DSizes<Index, NumDims> sizes;
for (int i = 0; i < NumDims; ++i) {
offsets[i] = 0;
sizes[i] = numext::mini(dims[i], Index(3));
}
offsets[axis] = left_axis_size;
sizes[axis] = numext::mini(Index(2), dims[axis] - offsets[axis]);
verify(make_params(offsets, sizes));
}
}
// -------------------------------------------------------------------------- //
#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, 3, 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, 3, 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, 3, 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, 3, 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(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(2, test_eval_tensor_layout_swap);
CALL_SUBTEST_PART(2)((test_eval_tensor_scan<float, 2, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_scan<float, 3, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_scan<float, 4, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_scan<float, 2, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_scan<float, 3, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_scan<float, 4, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_fft<float, 2, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_fft<float, 3, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_fft<float, 4, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_fft<float, 2, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_fft<float, 3, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_tensor_fft<float, 4, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_composed_block_ops<float, 2, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_composed_block_ops<float, 3, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_composed_block_ops<float, 4, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_composed_block_ops<float, 2, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_composed_block_ops<float, 3, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_composed_block_ops<float, 4, ColMajor>()));
CALL_SUBTEST_PART(2)((test_eval_contract_pad_composition<float, RowMajor>()));
CALL_SUBTEST_PART(2)((test_eval_contract_pad_composition<float, ColMajor>()));
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_cast);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_nullary);
CALL_SUBTESTS_DIMS_LAYOUTS(3, test_eval_tensor_random_nullary);
CALL_SUBTESTS_DIMS_LAYOUTS(3, test_eval_tensor_indexed_nullary);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(3, test_eval_tensor_select);
CALL_SUBTESTS_DIMS_LAYOUTS(3, test_eval_tensor_ternary);
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_concatenation);
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_SUBTEST_PART(5)((test_eval_tensor_slice_bool_composite<1, RowMajor>()));
CALL_SUBTEST_PART(5)((test_eval_tensor_slice_bool_composite<2, RowMajor>()));
CALL_SUBTEST_PART(5)((test_eval_tensor_slice_bool_composite<3, RowMajor>()));
CALL_SUBTEST_PART(5)((test_eval_tensor_slice_bool_composite<1, ColMajor>()));
CALL_SUBTEST_PART(5)((test_eval_tensor_slice_bool_composite<2, ColMajor>()));
CALL_SUBTEST_PART(5)((test_eval_tensor_slice_bool_composite<3, ColMajor>()));
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(5, test_eval_tensor_shuffle);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(5, test_eval_tensor_strided_slice);
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(6, test_eval_tensor_inflation);
CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_patch);
CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_image_patch);
CALL_SUBTESTS_LAYOUTS_TYPES(6, test_eval_tensor_volume_patch);
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);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_to_tensor_layout_swap);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_to_tensor_strided_slice);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(8, test_assign_expr_to_tensor_strided_slice);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(9, test_assign_to_tensor_reverse);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(9, test_assign_to_tensor_roll);
CALL_SUBTESTS_DIMS_LAYOUTS_TYPES(9, test_assign_to_tensor_concatenation);
// Force CMake to split this test.
// EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9
// clang-format on
}