//***************************************************************************** // Copyright 2017-2018 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 <algorithm> #include <cstdio> #include <iostream> #include <list> #include <memory> #include "gtest/gtest.h" #include "ngraph/autodiff/adjoints.hpp" #include "ngraph/file_util.hpp" #include "ngraph/graph_util.hpp" #include "ngraph/log.hpp" #include "ngraph/ngraph.hpp" #include "ngraph/op/batch_norm.hpp" #include "ngraph/op/get_output_element.hpp" #include "ngraph/op/parameter.hpp" #include "ngraph/pass/manager.hpp" #include "ngraph/pass/visualize_tree.hpp" #include "ngraph/runtime/cpu/pass/cpu_fusion.hpp" #include "ngraph/serializer.hpp" #include "ngraph/util.hpp" #include "nlohmann/json.hpp" #include "util/all_close.hpp" #include "util/autodiff/backprop_function.hpp" #include "util/autodiff/numeric_compare.hpp" #include "util/ndarray.hpp" #include "util/random.hpp" #include "util/test_tools.hpp" using namespace ngraph; using namespace std; class UnhandledOp : public ngraph::op::Abs { public: UnhandledOp(const std::shared_ptr<Node>& arg) : Abs(arg) { } }; TEST(cpu_test, unhandled_op) { auto A = make_shared<op::Parameter>(element::f32, Shape{}); auto unhandled = make_shared<UnhandledOp>(A); auto f = make_shared<Function>(unhandled, op::ParameterVector{A}); auto backend = runtime::Backend::create("CPU"); ASSERT_THROW(backend->compile(f), unsupported_op); } TEST(cpu_test, trivial_in_place_relu) { auto A = make_shared<op::Parameter>(element::f32, Shape{16, 1}); auto B = make_shared<op::Parameter>(element::f32, Shape{16, 1}); auto add = A + B; auto relu = make_shared<op::Relu>(add); auto f = make_shared<Function>(relu, op::ParameterVector{A, B}); auto backend = runtime::Backend::create("CPU"); (backend->compile(f)); ASSERT_EQ(relu->get_outputs().at(0).get_tensor().get_pool_offset(), add->get_outputs().at(0).get_tensor().get_pool_offset()); } TEST(cpu_test, trivial_in_place_relu_fail) { auto A = make_shared<op::Parameter>(element::f32, Shape{16, 1}); auto B = make_shared<op::Parameter>(element::f32, Shape{16, 1}); auto add = A + B; auto relu = make_shared<op::Relu>(add); auto add2 = relu + add; auto f = make_shared<Function>(add2, op::ParameterVector{A, B}); auto backend = runtime::Backend::create("CPU"); (backend->compile(f)); ASSERT_NE(relu->get_outputs().at(0).get_tensor().get_pool_offset(), add->get_outputs().at(0).get_tensor().get_pool_offset()); } #ifdef NGRAPH_TBB_ENABLE TEST(cpu_test, abc_tbb) { // Force TBB flow graph generation in the CPU backend // This has no effect on other backends bool use_tbb = (getenv("NGRAPH_CPU_USE_TBB") != nullptr); if (!use_tbb) { setenv("NGRAPH_CPU_USE_TBB", "1", 1); } 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, op::ParameterVector{A, B, C}); auto backend = runtime::Backend::create("CPU"); // Create some tensors for input/output shared_ptr<runtime::TensorView> a = backend->create_tensor(element::f32, shape); shared_ptr<runtime::TensorView> b = backend->create_tensor(element::f32, shape); shared_ptr<runtime::TensorView> c = backend->create_tensor(element::f32, shape); shared_ptr<runtime::TensorView> 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()); backend->call_with_validate(f, {result}, {a, b, c}); EXPECT_EQ(read_vector<float>(result), (test::NDArray<float, 2>({{54, 80}, {110, 144}})).get_vector()); backend->call_with_validate(f, {result}, {b, a, c}); EXPECT_EQ(read_vector<float>(result), (test::NDArray<float, 2>({{54, 80}, {110, 144}})).get_vector()); backend->call_with_validate(f, {result}, {a, c, b}); EXPECT_EQ(read_vector<float>(result), (test::NDArray<float, 2>({{50, 72}, {98, 128}})).get_vector()); if (!use_tbb) { unsetenv("NGRAPH_CPU_USE_TBB"); } } #endif // NGRAPH_TBB_ENABLE TEST(cpu_test, mkldnn_layouts) { Shape shape_a{1, 16, 2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_b{32, 16, 1, 1}; auto B = make_shared<op::Parameter>(element::f32, shape_b); Shape shape_r{1, 32, 2, 2}; auto conv1 = make_shared<op::Convolution>(A, B, Strides{1, 1}, Strides{1, 1}, CoordinateDiff{0, 0}, CoordinateDiff{0, 0}, Strides{1, 1}); Shape pool_shape{1, 1}; auto pool1 = make_shared<op::AvgPool>(conv1, pool_shape); auto pool1_result = make_shared<op::Result>(pool1); // Request result in default layout pool1_result->set_needs_default_layout(true); auto f = make_shared<Function>(ResultVector{pool1_result}, op::ParameterVector{A, B}); auto backend = runtime::Backend::create("CPU"); vector<float> input(64, 1.0f); vector<float> weights; vector<float> rv(128); for (int i = 0; i < 128; i++) { weights.push_back(0.0f); } for (int i = 0; i < 384; i++) { weights.push_back(1.0f); } auto a = backend->create_tensor(element::f32, shape_a, input.data()); auto b = backend->create_tensor(element::f32, shape_b, weights.data()); auto result = backend->create_tensor(element::f32, shape_r, rv.data()); vector<float> expected_result; for (int i = 0; i < 32; i++) { expected_result.push_back(0.0f); } for (int i = 0; i < 96; i++) { expected_result.push_back(16.0f); } backend->call_with_validate(f, {result}, {a, b}); EXPECT_EQ(vector<float>{expected_result}, rv); }