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Diffstat (limited to 'unsupported/test/cxx11_tensor_executor.cpp')
| -rw-r--r-- | unsupported/test/cxx11_tensor_executor.cpp | 731 |
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diff --git a/unsupported/test/cxx11_tensor_executor.cpp b/unsupported/test/cxx11_tensor_executor.cpp new file mode 100644 index 000000000..66b06e8ee --- /dev/null +++ b/unsupported/test/cxx11_tensor_executor.cpp @@ -0,0 +1,731 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com> +// +// 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/. + +#define EIGEN_USE_THREADS + +#include "main.h" + +#include <Eigen/CXX11/Tensor> + +using Eigen::Tensor; +using Eigen::RowMajor; +using Eigen::ColMajor; +using Eigen::internal::TiledEvaluation; + +// A set of tests to verify that different TensorExecutor strategies yields the +// same results for all the ops, supporting tiled evaluation. + +// Default assignment that does no use block evaluation or vectorization. +// We assume that default coefficient evaluation is well tested and correct. +template <typename Dst, typename Expr> +static void DefaultAssign(Dst& dst, Expr expr) { + using Assign = Eigen::TensorAssignOp<Dst, const Expr>; + using Executor = + Eigen::internal::TensorExecutor<const Assign, DefaultDevice, + /*Vectorizable=*/false, + /*Tiling=*/TiledEvaluation::Off>; + + Executor::run(Assign(dst, expr), DefaultDevice()); +} + +// Assignment with specified device and tiling strategy. +template <bool Vectorizable, TiledEvaluation Tiling, typename Device, + typename Dst, typename Expr> +static void DeviceAssign(Device& d, Dst& dst, Expr expr) { + using Assign = Eigen::TensorAssignOp<Dst, const Expr>; + using Executor = Eigen::internal::TensorExecutor<const Assign, Device, + Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); +} + +template <int NumDims> +static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) { + array<Index, NumDims> dims; + for (int i = 0; i < NumDims; ++i) { + dims[i] = internal::random<int>(min_dim, max_dim); + } + return dims; +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_unary_expr(Device d) +{ + static constexpr int Options = 0 | Layout; + + // Pick a large enough tensor size to bypass small tensor block evaluation + // optimization. + auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims); + + Tensor<T, NumDims, Options, Index> src(dims); + Tensor<T, NumDims, Options, Index> dst(dims); + + src.setRandom(); + const auto expr = src.square(); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + T square = src.coeff(i) * src.coeff(i); + VERIFY_IS_EQUAL(square, dst.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_binary_expr(Device d) +{ + static constexpr int Options = 0 | Layout; + + // Pick a large enough tensor size to bypass small tensor block evaluation + // optimization. + auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims); + + Tensor<T, NumDims, Options, Index> lhs(dims); + Tensor<T, NumDims, Options, Index> rhs(dims); + Tensor<T, NumDims, Options, Index> dst(dims); + + lhs.setRandom(); + rhs.setRandom(); + + const auto expr = lhs + rhs; + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + T sum = lhs.coeff(i) + rhs.coeff(i); + VERIFY_IS_EQUAL(sum, dst.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_broadcasting(Device d) +{ + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(1, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + const auto broadcasts = RandomDims<NumDims>(1, 7); + const auto expr = src.broadcast(broadcasts); + + // We assume that broadcasting on a default device is tested and correct, so + // we can rely on it to verify correctness of tensor executor and tiling. + Tensor<T, NumDims, Options, Index> golden; + golden = expr; + + // Now do the broadcasting using configured tensor executor. + Tensor<T, NumDims, Options, Index> dst(golden.dimensions()); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_chipping_rvalue(Device d) +{ + auto dims = RandomDims<NumDims>(1, 10); + Tensor<T, NumDims, Layout, Index> src(dims); + src.setRandom(); + +#define TEST_CHIPPING(CHIP_DIM) \ + if (NumDims > (CHIP_DIM)) { \ + const auto offset = internal::random<Index>(0, dims[(CHIP_DIM)] - 1); \ + const auto expr = src.template chip<(CHIP_DIM)>(offset); \ + \ + Tensor<T, NumDims - 1, Layout, Index> golden; \ + golden = expr; \ + \ + Tensor<T, NumDims - 1, Layout, Index> dst(golden.dimensions()); \ + \ + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; \ + using Executor = internal::TensorExecutor<const Assign, Device, \ + Vectorizable, Tiling>; \ + \ + Executor::run(Assign(dst, expr), d); \ + \ + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { \ + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); \ + } \ + } + + TEST_CHIPPING(0) + TEST_CHIPPING(1) + TEST_CHIPPING(2) + TEST_CHIPPING(3) + TEST_CHIPPING(4) + TEST_CHIPPING(5) + +#undef TEST_CHIPPING +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_chipping_lvalue(Device d) +{ + auto dims = RandomDims<NumDims>(1, 10); + +#define TEST_CHIPPING(CHIP_DIM) \ + if (NumDims > (CHIP_DIM)) { \ + /* Generate random data that we'll assign to the chipped tensor dim. */ \ + array<Index, NumDims - 1> src_dims; \ + for (int i = 0; i < NumDims - 1; ++i) { \ + int dim = i < (CHIP_DIM) ? i : i + 1; \ + src_dims[i] = dims[dim]; \ + } \ + \ + Tensor<T, NumDims - 1, Layout, Index> src(src_dims); \ + src.setRandom(); \ + \ + const auto offset = internal::random<Index>(0, dims[(CHIP_DIM)] - 1); \ + \ + Tensor<T, NumDims, Layout, Index> random(dims); \ + random.setZero(); \ + \ + Tensor<T, NumDims, Layout, Index> golden(dims); \ + golden = random; \ + golden.template chip<(CHIP_DIM)>(offset) = src; \ + \ + Tensor<T, NumDims, Layout, Index> dst(dims); \ + dst = random; \ + auto expr = dst.template chip<(CHIP_DIM)>(offset); \ + \ + using Assign = TensorAssignOp<decltype(expr), const decltype(src)>; \ + using Executor = internal::TensorExecutor<const Assign, Device, \ + Vectorizable, Tiling>; \ + \ + Executor::run(Assign(expr, src), d); \ + \ + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { \ + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); \ + } \ + } + + TEST_CHIPPING(0) + TEST_CHIPPING(1) + TEST_CHIPPING(2) + TEST_CHIPPING(3) + TEST_CHIPPING(4) + TEST_CHIPPING(5) + +#undef TEST_CHIPPING +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_shuffle_rvalue(Device d) +{ + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(1, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + DSizes<Index, NumDims> shuffle; + for (int i = 0; i < NumDims; ++i) shuffle[i] = i; + + // Test all possible shuffle permutations. + do { + DSizes<Index, NumDims> shuffled_dims; + for (int i = 0; i < NumDims; ++i) { + shuffled_dims[i] = dims[shuffle[i]]; + } + + const auto expr = src.shuffle(shuffle); + + // We assume that shuffling on a default device is tested and correct, so + // we can rely on it to verify correctness of tensor executor and tiling. + Tensor<T, NumDims, Options, Index> golden(shuffled_dims); + DefaultAssign(golden, expr); + + // Now do the shuffling using configured tensor executor. + Tensor<T, NumDims, Options, Index> dst(shuffled_dims); + DeviceAssign<Vectorizable, Tiling>(d, dst, expr); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } + + } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims)); +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_shuffle_lvalue(Device d) +{ + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(5, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + DSizes<Index, NumDims> shuffle; + for (int i = 0; i < NumDims; ++i) shuffle[i] = i; + + // Test all possible shuffle permutations. + do { + DSizes<Index, NumDims> shuffled_dims; + for (int i = 0; i < NumDims; ++i) shuffled_dims[shuffle[i]] = dims[i]; + + // We assume that shuffling on a default device is tested and correct, so + // we can rely on it to verify correctness of tensor executor and tiling. + Tensor<T, NumDims, Options, Index> golden(shuffled_dims); + auto golden_shuffle = golden.shuffle(shuffle); + DefaultAssign(golden_shuffle, src); + + // Now do the shuffling using configured tensor executor. + Tensor<T, NumDims, Options, Index> dst(shuffled_dims); + auto dst_shuffle = dst.shuffle(shuffle); + DeviceAssign<Vectorizable, Tiling>(d, dst_shuffle, src); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } + + } while (std::next_permutation(&shuffle[0], &shuffle[0] + NumDims)); +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_reshape(Device d) +{ + static_assert(NumDims >= 2, "NumDims must be greater or equal than 2"); + + static constexpr int ReshapedDims = NumDims - 1; + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(5, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + // Multiple 0th dimension and then shuffle. + std::vector<Index> shuffle; + for (int i = 0; i < ReshapedDims; ++i) shuffle.push_back(i); + std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937()); + + DSizes<Index, ReshapedDims> reshaped_dims; + reshaped_dims[shuffle[0]] = dims[0] * dims[1]; + for (int i = 1; i < ReshapedDims; ++i) reshaped_dims[shuffle[i]] = dims[i + 1]; + + Tensor<T, ReshapedDims, Options, Index> golden = src.reshape(reshaped_dims); + + // Now reshape using configured tensor executor. + Tensor<T, ReshapedDims, Options, Index> dst(golden.dimensions()); + + auto expr = src.reshape(reshaped_dims); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_slice_rvalue(Device d) +{ + static_assert(NumDims >= 2, "NumDims must be greater or equal than 2"); + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(5, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + // Pick a random slice of src tensor. + auto slice_start = DSizes<Index, NumDims>(RandomDims<NumDims>()); + auto slice_size = DSizes<Index, NumDims>(RandomDims<NumDims>()); + + // 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]); + } + + Tensor<T, NumDims, Options, Index> golden = + src.slice(slice_start, slice_size); + + // Now reshape using configured tensor executor. + Tensor<T, NumDims, Options, Index> dst(golden.dimensions()); + + auto expr = src.slice(slice_start, slice_size); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_slice_lvalue(Device d) +{ + static_assert(NumDims >= 2, "NumDims must be greater or equal than 2"); + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(5, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + // Pick a random slice of src tensor. + auto slice_start = DSizes<Index, NumDims>(RandomDims<NumDims>(1, 10)); + auto slice_size = DSizes<Index, NumDims>(RandomDims<NumDims>(1, 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]); + } + + Tensor<T, NumDims, Options, Index> slice(slice_size); + slice.setRandom(); + + // Assign a slice using default executor. + Tensor<T, NumDims, Options, Index> golden = src; + golden.slice(slice_start, slice_size) = slice; + + // And using configured execution strategy. + Tensor<T, NumDims, Options, Index> dst = src; + auto expr = dst.slice(slice_start, slice_size); + + using Assign = TensorAssignOp<decltype(expr), const decltype(slice)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(expr, slice), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_broadcasting_of_forced_eval(Device d) +{ + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(1, 10); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + const auto broadcasts = RandomDims<NumDims>(1, 7); + const auto expr = src.square().eval().broadcast(broadcasts); + + // We assume that broadcasting on a default device is tested and correct, so + // we can rely on it to verify correctness of tensor executor and tiling. + Tensor<T, NumDims, Options, Index> golden; + golden = expr; + + // Now do the broadcasting using configured tensor executor. + Tensor<T, NumDims, Options, Index> dst(golden.dimensions()); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template<typename T, int NumDims> +struct DummyGenerator { + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE + T operator()(const array <Index, NumDims>& dims) const { + T result = static_cast<T>(0); + for (int i = 0; i < NumDims; ++i) { + result += static_cast<T>((i + 1) * dims[i]); + } + return result; + } +}; + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_generator_op(Device d) +{ + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(20, 30); + Tensor<T, NumDims, Options, Index> src(dims); + src.setRandom(); + + const auto expr = src.generate(DummyGenerator<T, NumDims>()); + + // We assume that generator on a default device is tested and correct, so + // we can rely on it to verify correctness of tensor executor and tiling. + Tensor<T, NumDims, Options, Index> golden; + golden = expr; + + // Now do the broadcasting using configured tensor executor. + Tensor<T, NumDims, Options, Index> dst(golden.dimensions()); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_execute_reverse_rvalue(Device d) +{ + static constexpr int Options = 0 | Layout; + + auto dims = RandomDims<NumDims>(1, numext::pow(1000000.0, 1.0 / NumDims)); + Tensor <T, NumDims, Options, Index> src(dims); + src.setRandom(); + + // Reverse half of the dimensions. + Eigen::array<bool, NumDims> reverse; + for (int i = 0; i < NumDims; ++i) reverse[i] = internal::random<bool>(); + + const auto expr = src.reverse(reverse); + + // We assume that reversing on a default device is tested and correct, so + // we can rely on it to verify correctness of tensor executor and tiling. + Tensor <T, NumDims, Options, Index> golden; + golden = expr; + + // Now do the reversing using configured tensor executor. + Tensor <T, NumDims, Options, Index> dst(golden.dimensions()); + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using Executor = + internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; + + Executor::run(Assign(dst, expr), d); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_async_execute_unary_expr(Device d) +{ + static constexpr int Options = 0 | Layout; + + // Pick a large enough tensor size to bypass small tensor block evaluation + // optimization. + auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims); + + Tensor<T, NumDims, Options, Index> src(dims); + Tensor<T, NumDims, Options, Index> dst(dims); + + src.setRandom(); + const auto expr = src.square(); + + Eigen::Barrier done(1); + auto on_done = [&done]() { done.Notify(); }; + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using DoneCallback = decltype(on_done); + using Executor = internal::TensorAsyncExecutor<const Assign, Device, DoneCallback, + Vectorizable, Tiling>; + + Executor::runAsync(Assign(dst, expr), d, on_done); + done.Wait(); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + T square = src.coeff(i) * src.coeff(i); + VERIFY_IS_EQUAL(square, dst.coeff(i)); + } +} + +template <typename T, int NumDims, typename Device, bool Vectorizable, + TiledEvaluation Tiling, int Layout> +static void test_async_execute_binary_expr(Device d) +{ + static constexpr int Options = 0 | Layout; + + // Pick a large enough tensor size to bypass small tensor block evaluation + // optimization. + auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims); + + Tensor<T, NumDims, Options, Index> lhs(dims); + Tensor<T, NumDims, Options, Index> rhs(dims); + Tensor<T, NumDims, Options, Index> dst(dims); + + lhs.setRandom(); + rhs.setRandom(); + + const auto expr = lhs + rhs; + + Eigen::Barrier done(1); + auto on_done = [&done]() { done.Notify(); }; + + using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; + using DoneCallback = decltype(on_done); + using Executor = internal::TensorAsyncExecutor<const Assign, Device, DoneCallback, + Vectorizable, Tiling>; + + Executor::runAsync(Assign(dst, expr), d, on_done); + done.Wait(); + + for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { + T sum = lhs.coeff(i) + rhs.coeff(i); + VERIFY_IS_EQUAL(sum, dst.coeff(i)); + } +} + +#ifdef EIGEN_DONT_VECTORIZE +#define VECTORIZABLE(VAL) !EIGEN_DONT_VECTORIZE && VAL +#else +#define VECTORIZABLE(VAL) VAL +#endif + +#define CALL_SUBTEST_PART(PART) \ + CALL_SUBTEST_##PART + +#define CALL_SUBTEST_COMBINATIONS(PART, NAME, T, NUM_DIMS) \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::Off, ColMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::On, ColMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(true), TiledEvaluation::Off, ColMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(true), TiledEvaluation::On, ColMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::Off, RowMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, false, TiledEvaluation::On, RowMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(true), TiledEvaluation::Off, RowMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, DefaultDevice, VECTORIZABLE(true), TiledEvaluation::On, RowMajor>(default_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::Off, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::On, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, RowMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, RowMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::Off, RowMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::On, RowMajor>(tp_device))) + +// NOTE: Currently only ThreadPoolDevice supports async expression evaluation. +#define CALL_ASYNC_SUBTEST_COMBINATIONS(PART, NAME, T, NUM_DIMS) \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::Off, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::On, ColMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::Off, RowMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, false, TiledEvaluation::On, RowMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::Off, RowMajor>(tp_device))); \ + CALL_SUBTEST_PART(PART)((NAME<T, NUM_DIMS, ThreadPoolDevice, VECTORIZABLE(true), TiledEvaluation::On, RowMajor>(tp_device))) + +EIGEN_DECLARE_TEST(cxx11_tensor_executor) { + Eigen::DefaultDevice default_device; + // Default device is unused in ASYNC tests. + EIGEN_UNUSED_VARIABLE(default_device); + + const auto num_threads = internal::random<int>(20, 24); + Eigen::ThreadPool tp(num_threads); + Eigen::ThreadPoolDevice tp_device(&tp, num_threads); + + CALL_SUBTEST_COMBINATIONS(1, test_execute_unary_expr, float, 3); + CALL_SUBTEST_COMBINATIONS(1, test_execute_unary_expr, float, 4); + CALL_SUBTEST_COMBINATIONS(1, test_execute_unary_expr, float, 5); + + CALL_SUBTEST_COMBINATIONS(2, test_execute_binary_expr, float, 3); + CALL_SUBTEST_COMBINATIONS(2, test_execute_binary_expr, float, 4); + CALL_SUBTEST_COMBINATIONS(2, test_execute_binary_expr, float, 5); + + CALL_SUBTEST_COMBINATIONS(3, test_execute_broadcasting, float, 3); + CALL_SUBTEST_COMBINATIONS(3, test_execute_broadcasting, float, 4); + CALL_SUBTEST_COMBINATIONS(3, test_execute_broadcasting, float, 5); + + CALL_SUBTEST_COMBINATIONS(4, test_execute_chipping_rvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(4, test_execute_chipping_rvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(4, test_execute_chipping_rvalue, float, 5); + + CALL_SUBTEST_COMBINATIONS(5, test_execute_chipping_lvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(5, test_execute_chipping_lvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(5, test_execute_chipping_lvalue, float, 5); + + CALL_SUBTEST_COMBINATIONS(6, test_execute_shuffle_rvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(6, test_execute_shuffle_rvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(6, test_execute_shuffle_rvalue, float, 5); + + CALL_SUBTEST_COMBINATIONS(7, test_execute_shuffle_lvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(7, test_execute_shuffle_lvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(7, test_execute_shuffle_lvalue, float, 5); + + CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 2); + CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 3); + CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 4); + CALL_SUBTEST_COMBINATIONS(9, test_execute_reshape, float, 5); + + CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 2); + CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(10, test_execute_slice_rvalue, float, 5); + + CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 2); + CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(11, test_execute_slice_lvalue, float, 5); + + CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 2); + CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 3); + CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 4); + CALL_SUBTEST_COMBINATIONS(12, test_execute_broadcasting_of_forced_eval, float, 5); + + CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 2); + CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 3); + CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 4); + CALL_SUBTEST_COMBINATIONS(13, test_execute_generator_op, float, 5); + + CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 1); + CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 2); + CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 3); + CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 4); + CALL_SUBTEST_COMBINATIONS(14, test_execute_reverse_rvalue, float, 5); + + CALL_ASYNC_SUBTEST_COMBINATIONS(15, test_async_execute_unary_expr, float, 3); + CALL_ASYNC_SUBTEST_COMBINATIONS(15, test_async_execute_unary_expr, float, 4); + CALL_ASYNC_SUBTEST_COMBINATIONS(15, test_async_execute_unary_expr, float, 5); + + CALL_ASYNC_SUBTEST_COMBINATIONS(16, test_async_execute_binary_expr, float, 3); + CALL_ASYNC_SUBTEST_COMBINATIONS(16, test_async_execute_binary_expr, float, 4); + CALL_ASYNC_SUBTEST_COMBINATIONS(16, test_async_execute_binary_expr, float, 5); + + // Force CMake to split this test. + // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16 +} |