//***************************************************************************** // Copyright 2017-2019 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. //***************************************************************************** #include <sstream> #include <string> #include <vector> #include "gtest/gtest.h" #include "ngraph/codegen/compiler.hpp" #include "ngraph/codegen/execution_engine.hpp" #include "ngraph/file_util.hpp" #include "ngraph/log.hpp" #include "ngraph/op/concat.hpp" #include "ngraph/runtime/backend.hpp" #include "ngraph/serializer.hpp" #include "ngraph/util.hpp" #include "util/random.hpp" #include "util/test_tools.hpp" using namespace std; using namespace ngraph; // // Benchmarks a graph that concatenates six 32x1x200 arrays along the middle axis. // TEST(benchmark, concat_32x1x200_axis1_6) { const size_t n_arrays = 6; Shape shape_of_each_array = Shape{32, 1, 200}; size_t concatenation_axis = 1; Shape result_shape; result_shape = shape_of_each_array; result_shape[concatenation_axis] *= n_arrays; size_t elements_per_array = 1; for (size_t d : shape_of_each_array) { elements_per_array *= d; } vector<vector<float>> data_arrays(n_arrays); for (size_t i = 0; i < n_arrays; i++) { data_arrays[i] = vector<float>(elements_per_array); for (size_t j = 0; j < elements_per_array; j++) { data_arrays[i][j] = float(j + 1); } } bool using_ref_kernels = (std::getenv("NGRAPH_CPU_USE_REF_KERNELS") != nullptr); vector<std::string> backend_names{"INTERPRETER", "CPU"}; vector<int> n_runs{200, 200, using_ref_kernels ? 200 : 200000}; // one for each backend vector<std::function<void()>> test_callbacks; // one for each backend vector<std::shared_ptr<runtime::Tensor>> result_tvs; // one for each backend for (std::string backend_name : backend_names) { vector<std::shared_ptr<op::Parameter>> params(n_arrays); vector<std::shared_ptr<Node>> params_as_nodes(n_arrays); for (size_t i = 0; i < n_arrays; i++) { auto param = make_shared<op::Parameter>(element::f32, shape_of_each_array); params[i] = param; params_as_nodes[i] = param; } auto concat = make_shared<op::Concat>(params_as_nodes, concatenation_axis); auto f = make_shared<Function>(concat, params); auto backend = runtime::Backend::create(backend_name); vector<shared_ptr<runtime::Tensor>> input_vals; for (size_t i = 0; i < n_arrays; i++) { auto tv = backend->create_tensor(element::f32, shape_of_each_array); copy_data(tv, data_arrays[i]); input_vals.push_back(tv); } auto result_tv = backend->create_tensor(element::f32, result_shape); result_tvs.push_back(result_tv); std::function<void()> cb = [&]() { auto handle = backend->compile(f); handle->call_with_validate({result_tv}, input_vals); }; test_callbacks.push_back(cb); } for (size_t i = 0; i < backend_names.size(); i++) { std::cout << backend_names[i] << ": " << n_runs[i] << " tests in " << std::flush; stopwatch sw; std::function<void()> cb = test_callbacks[i]; sw.start(); for (int j = 0; j < n_runs[i]; j++) { cb(); } sw.stop(); std::cout << sw.get_milliseconds() << "ms (" << (sw.get_microseconds() / n_runs[i]) << " us/test)" << std::endl; } for (size_t i = 1; i < backend_names.size(); i++) { std::cout << "Verifying " << backend_names[i] << " result against " << backend_names[0] << "..." << std::flush; if (read_vector<float>(result_tvs[i]) == read_vector<float>(result_tvs[0])) { std::cout << " OK" << std::endl; } else { std::cout << " FAILED" << std::endl; ADD_FAILURE(); } } }