mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
88 lines
3.3 KiB
C++
88 lines
3.3 KiB
C++
// 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;
|
|
|
|
// A set of tests to verify that different TensorExecutor strategies yields the
|
|
// same results for all the ops, supporting tiled execution.
|
|
|
|
template <typename Device, bool Vectorizable, bool Tileable, int Layout>
|
|
static void test_execute_binary_expr(Device d) {
|
|
// Pick a large enough tensor size to bypass small tensor block evaluation
|
|
// optimization.
|
|
int d0 = internal::random<int>(100, 200);
|
|
int d1 = internal::random<int>(100, 200);
|
|
int d2 = internal::random<int>(100, 200);
|
|
|
|
static constexpr int Options = 0;
|
|
using IndexType = int;
|
|
|
|
Tensor<float, 3, Options, IndexType> lhs(d0, d1, d2);
|
|
Tensor<float, 3, Options, IndexType> rhs(d0, d1, d2);
|
|
Tensor<float, 3, Options, IndexType> dst(d0, d1, d2);
|
|
|
|
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, Tileable>;
|
|
|
|
Executor::run(Assign(dst, expr), d);
|
|
|
|
for (int i = 0; i < d0; ++i) {
|
|
for (int j = 0; j < d1; ++j) {
|
|
for (int k = 0; k < d2; ++k) {
|
|
float sum = lhs(i, j, k) + rhs(i, j, k);
|
|
VERIFY_IS_EQUAL(sum, dst(i, j, k));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
#define CALL_SUBTEST_COMBINATIONS(NAME) \
|
|
CALL_SUBTEST((NAME<DefaultDevice, false, false, ColMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, false, true, ColMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, true, false, ColMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, true, true, ColMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, false, false, RowMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, false, true, RowMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, true, false, RowMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<DefaultDevice, true, true, RowMajor>(default_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, false, false, ColMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, false, true, ColMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, true, false, ColMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, true, true, ColMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, false, false, RowMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, false, true, RowMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, true, false, RowMajor>(tp_device))); \
|
|
CALL_SUBTEST((NAME<ThreadPoolDevice, true, true, RowMajor>(tp_device)))
|
|
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_executor) {
|
|
Eigen::DefaultDevice default_device;
|
|
|
|
const auto num_threads = internal::random<int>(1, 24);
|
|
Eigen::ThreadPool tp(num_threads);
|
|
Eigen::ThreadPoolDevice tp_device(&tp, num_threads);
|
|
|
|
CALL_SUBTEST_COMBINATIONS(test_execute_binary_expr);
|
|
}
|
|
|
|
#undef CALL_SUBTEST_COMBINATIONS
|