//***************************************************************************** // 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 "gtest/gtest.h" #include "ngraph/ngraph.hpp" #include "util/all_close_f.hpp" #include "util/ndarray.hpp" #include "util/random.hpp" #include "util/test_control.hpp" #include "util/test_tools.hpp" using namespace std; using namespace ngraph; static string s_manifest = "${MANIFEST}"; // This tests a backend's implementation of the two parameter version of create_tensor NGRAPH_TEST(${BACKEND_NAME}, create_tensor_1) { Shape shape{2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto B = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Add>(A, B), ParameterVector{A, B}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output vector<float> av = {1, 2, 3, 4}; vector<float> bv = {5, 6, 7, 8}; shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shape); shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shape); copy_data(a, av); copy_data(b, bv); shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shape); auto handle = backend->compile(f); handle->call_with_validate({result}, {a, b}); vector<float> expected = {6, 8, 10, 12}; EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS)); } // This tests a backend's implementation of the three parameter version of create_tensor // Testing using this tensor as a Function input NGRAPH_TEST(${BACKEND_NAME}, create_tensor_2_input) { Shape shape{2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto B = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Add>(A, B), ParameterVector{A, B}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output vector<float> av = {1, 2, 3, 4}; vector<float> bv = {5, 6, 7, 8}; shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shape, av.data()); shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shape, bv.data()); shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shape); auto handle = backend->compile(f); handle->call_with_validate({result}, {a, b}); vector<float> expected = {6, 8, 10, 12}; EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS)); } // This tests a backend's implementation of the three parameter version of create_tensor // Testing using this tensor as a Function output NGRAPH_TEST(${BACKEND_NAME}, create_tensor_2_output) { Shape shape{2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto B = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Add>(A, B), ParameterVector{A, B}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output vector<float> av = {1, 2, 3, 4}; vector<float> bv = {5, 6, 7, 8}; shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shape); shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shape); copy_data(a, av); copy_data(b, bv); vector<float> actual(4); shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shape, actual.data()); auto handle = backend->compile(f); handle->call_with_validate({result}, {a, b}); vector<float> expected = {6, 8, 10, 12}; EXPECT_TRUE(test::all_close_f(actual, expected, MIN_FLOAT_TOLERANCE_BITS)); } // This tests a backend's implementation of the copy_from for tensor NGRAPH_TEST(${BACKEND_NAME}, tensor_copy_from) { Shape shape{2, 2}; auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output vector<float> av = {1, 2, 3, 4}; vector<float> bv = {5, 6, 7, 8}; shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shape); shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shape); copy_data(a, av); copy_data(b, bv); a->copy_from(*b); EXPECT_TRUE(test::all_close_f(bv, read_vector<float>(a), MIN_FLOAT_TOLERANCE_BITS)); } NGRAPH_TEST(${BACKEND_NAME}, get_parameters_and_results) { Shape shape{2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto B = make_shared<op::Parameter>(element::f32, shape); auto C = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>((A + B) * C, ParameterVector{A, B, C}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shape); shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shape); shared_ptr<runtime::Tensor> c = backend->create_tensor(element::f32, shape); shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shape); copy_data(a, test::NDArray<float, 2>({{1, 2}, {3, 4}}).get_vector()); copy_data(b, test::NDArray<float, 2>({{5, 6}, {7, 8}}).get_vector()); copy_data(c, test::NDArray<float, 2>({{9, 10}, {11, 12}}).get_vector()); auto handle = backend->compile(f); auto parameters = handle->get_parameters(); auto results = handle->get_results(); ASSERT_EQ(parameters.size(), 3); ASSERT_EQ(results.size(), 1); // This part can't be enabled until we force backends to make a copy of the source graph // auto func_parameters = f->get_parameters(); // auto func_results = f->get_results(); // for (size_t i = 0; i < 3; ++i) // { // EXPECT_NE(parameters[i], func_parameters[i]); // } // for (size_t i = 0; i < 1; ++i) // { // EXPECT_NE(results[i], func_results[i]); // } }