//***************************************************************************** // 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/test_control.hpp" #include "util/test_tools.hpp" using namespace std; using namespace ngraph; static string s_manifest = "${MANIFEST}"; NGRAPH_TEST(dynamic_${BACKEND_NAME}, create) { auto backend = runtime::Backend::create("${BACKEND_NAME}", true); ASSERT_NE(backend, nullptr); ASSERT_TRUE(backend->supports_dynamic_tensors()); } NGRAPH_TEST(dynamic_${BACKEND_NAME}, create_no_dynamic) { auto backend = runtime::Backend::create("${BACKEND_NAME}"); ASSERT_NE(backend, nullptr); ASSERT_FALSE(backend->supports_dynamic_tensors()); } NGRAPH_TEST(dynamic_${BACKEND_NAME}, create_dynamic_tensor) { auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto t = backend->create_dynamic_tensor(element::f32, PartialShape{2, Dimension::dynamic(), 3}); ASSERT_TRUE(t->get_partial_shape().same_scheme(PartialShape{2, Dimension::dynamic(), 3})); } NGRAPH_TEST(dynamic_${BACKEND_NAME}, abc) { // // Create a graph for f(a,b,c) = (a+b)*c, where a, b, c all have shape {2,?,3}. // auto a = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3}); auto b = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3}); auto c = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3}); auto a_plus_b_times_c = (a + b) * c; auto f = make_shared<Function>(NodeVector{a_plus_b_times_c}, ParameterVector{a, b, c}); // // Get a backend with dynamic support, and compile f. // auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); // // Create a dynamic output tensor with shape {2,?,3}. // auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape{2, Dimension::dynamic(), 3}); // // For each of n=[0,...,5), run the compiled executable against a test vector of shape // {2,n,3}, and check the results. // for (size_t middle_dim = 0; middle_dim < 5; middle_dim++) { // Fill in some test input values, which we'll use for a, b, and c. vector<float> inputs(2 * middle_dim * 3); for (size_t i = 0; i < 2 * middle_dim * 3; i++) { inputs[i] = i; } // Create static tensors for the inputs and copy data. auto t_a = backend->create_tensor(element::f32, Shape{2, middle_dim, 3}); auto t_b = backend->create_tensor(element::f32, Shape{2, middle_dim, 3}); auto t_c = backend->create_tensor(element::f32, Shape{2, middle_dim, 3}); copy_data(t_a, inputs); copy_data(t_b, inputs); copy_data(t_c, inputs); // Call ex, writing result into t_r (note we're using the same t_r from outside the loop.) ex->call_with_validate({t_r}, {t_a, t_b, t_c}); // After call, t_r should have a shape of {2,n,3}. ASSERT_EQ(t_r->get_shape(), (Shape{2, middle_dim, 3})); // Read out the results, and compare them against expected values. auto results = read_vector<float>(t_r); vector<float> expected_values(2 * middle_dim * 3); for (size_t i = 0; i < 2 * middle_dim * 3; i++) { expected_values[i] = (i + i) * i; } EXPECT_TRUE(test::all_close_f(results, expected_values)); } } NGRAPH_TEST(dynamic_${BACKEND_NAME}, transpose) { // // Create a graph for f(x,perm) = Transpose(x,Convert<i64>(perm)). We'll do the permutation in // i32 and cast it to i64, just for fun (and to mirror the TensorFlow test I am porting here). // auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic()); auto perm = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto perm_i64 = make_shared<op::Convert>(perm, element::i64); auto x_transpose = make_shared<op::Transpose>(x, perm_i64); auto f = make_shared<Function>(NodeVector{x_transpose}, ParameterVector{x, perm}); auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic()); std::vector<Shape> x_shapes{Shape{2, 3}, Shape{2, 3}, Shape{2, 2, 3}}; std::vector<std::vector<int32_t>> perms{{0, 1}, {1, 0}, {2, 1, 0}}; std::vector<std::vector<float>> inputs{ {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}}; std::vector<Shape> expected_result_shapes{Shape{2, 3}, Shape{3, 2}, {3, 2, 2}}; // Generated with numpy, so don't worry. :) std::vector<std::vector<float>> expected_results{ {1, 2, 3, 4, 5, 6}, {1, 4, 2, 5, 3, 6}, {1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12}}; for (size_t i = 0; i < x_shapes.size(); i++) { auto t_x = backend->create_tensor(element::f32, x_shapes[i]); auto t_perm = backend->create_tensor(element::i32, Shape{perms[i].size()}); copy_data(t_x, inputs[i]); copy_data(t_perm, perms[i]); ex->call_with_validate({t_r}, {t_x, t_perm}); ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]); auto results = read_vector<float>(t_r); ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS)); } } NGRAPH_TEST(dynamic_${BACKEND_NAME}, broadcast) { // Create a graph for // f(x,shape:i32,axes:32) = Broadcast(x,Convert<i64>(shape),Convert<i64>(axes)). auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic()); auto shape = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto shape_i64 = make_shared<op::Convert>(shape, element::i64); auto axes_i64 = make_shared<op::Convert>(axes, element::i64); auto bc = make_shared<op::DynBroadcast>(x, shape_i64, axes_i64); auto f = make_shared<Function>(NodeVector{bc}, ParameterVector{x, shape, axes}); auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic()); std::vector<Shape> x_shapes{Shape{}, Shape{}, Shape{2}, Shape{2}}; std::vector<std::vector<int32_t>> shapes{{2, 2}, {2, 2, 2}, {3, 2}, {2, 3}}; std::vector<std::vector<int32_t>> axeses{{0, 1}, {0, 1, 2}, {0}, {1}}; std::vector<std::vector<float>> inputs{{6}, {7}, {10, 11}, {10, 11}}; std::vector<Shape> expected_result_shapes{ Shape{2, 2}, Shape{2, 2, 2}, Shape{3, 2}, Shape{2, 3}}; std::vector<std::vector<float>> expected_results{ {6, 6, 6, 6}, {7, 7, 7, 7, 7, 7, 7, 7}, {10, 11, 10, 11, 10, 11}, {10, 10, 10, 11, 11, 11}}; for (size_t i = 0; i < x_shapes.size(); i++) { auto t_x = backend->create_tensor(element::f32, x_shapes[i]); auto t_shape = backend->create_tensor(element::i32, Shape{shapes[i].size()}); auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()}); copy_data(t_x, inputs[i]); copy_data(t_shape, shapes[i]); copy_data(t_axes, axeses[i]); ex->call_with_validate({t_r}, {t_x, t_shape, t_axes}); ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]); auto results = read_vector<float>(t_r); ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS)); } } NGRAPH_TEST(dynamic_${BACKEND_NAME}, sum) { // Create a graph for f(x,axes:int32) = Sum(x,Convert<int64>(axes)). auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic()); auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto axes_i64 = make_shared<op::Convert>(axes, element::i64); auto sum = make_shared<op::Sum>(x, axes_i64); ASSERT_TRUE(sum->get_output_partial_shape(0).rank().is_dynamic()); auto f = make_shared<Function>(NodeVector{sum}, ParameterVector{x, axes}); auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic()); std::vector<Shape> x_shapes{ Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{5}, Shape{5}}; std::vector<std::vector<int32_t>> axeses{{}, {0}, {1}, {0, 1}, {}, {0}}; std::vector<std::vector<float>> inputs{{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5}, {1, 2, 3, 4, 5}}; std::vector<Shape> expected_result_shapes{ Shape{2, 3}, Shape{3}, Shape{2}, Shape{}, Shape{5}, Shape{}}; std::vector<std::vector<float>> expected_results{ {1, 2, 3, 4, 5, 6}, {5, 7, 9}, {6, 15}, {21}, {1, 2, 3, 4, 5}, {15}}; for (size_t i = 0; i < x_shapes.size(); i++) { auto t_x = backend->create_tensor(element::f32, x_shapes[i]); auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()}); copy_data(t_x, inputs[i]); copy_data(t_axes, axeses[i]); ex->call_with_validate({t_r}, {t_x, t_axes}); ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]); auto results = read_vector<float>(t_r); ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS)); } } NGRAPH_TEST(dynamic_${BACKEND_NAME}, all) { // Create a graph for f(x,axes:int32) = All(x,Convert<int64>(axes)). auto x = make_shared<op::Parameter>(element::boolean, PartialShape::dynamic()); auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto axes_i64 = make_shared<op::Convert>(axes, element::i64); auto all = make_shared<op::All>(x, axes_i64); ASSERT_TRUE(all->get_output_partial_shape(0).rank().is_dynamic()); auto f = make_shared<Function>(NodeVector{all}, ParameterVector{x, axes}); auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); auto t_r = backend->create_dynamic_tensor(element::boolean, PartialShape::dynamic()); std::vector<Shape> x_shapes{ Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{2, 3}, Shape{5}, Shape{5}}; std::vector<std::vector<int32_t>> axeses{{}, {0}, {1}, {0, 1}, {}, {0}}; std::vector<std::vector<char>> inputs{{1, 0, 1, 0, 1, 0}, {1, 0, 1, 0, 0, 1}, {1, 0, 1, 1, 1, 1}, {1, 0, 1, 0, 1, 0}, {1, 0, 1, 0, 1}, {1, 0, 1, 0, 1}}; std::vector<Shape> expected_result_shapes{ Shape{2, 3}, Shape{3}, Shape{2}, Shape{}, Shape{5}, Shape{}}; std::vector<std::vector<char>> expected_results{ {1, 0, 1, 0, 1, 0}, {0, 0, 1}, {0, 1}, {0}, {1, 0, 1, 0, 1}, {0}}; for (size_t i = 0; i < x_shapes.size(); i++) { auto t_x = backend->create_tensor(element::boolean, x_shapes[i]); auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()}); copy_data(t_x, inputs[i]); copy_data(t_axes, axeses[i]); ex->call_with_validate({t_r}, {t_x, t_axes}); ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]); auto results = read_vector<char>(t_r); ASSERT_EQ(results, expected_results[i]); } } template <typename T> struct RangeTest { T start; T stop; T step; Shape expected_result_shape; std::vector<T> expected_result; }; // TODO(amprocte): We should test this with more than just int32, but there is a bug in the // handling of element type-changing that is currently blocking doing that easily. NGRAPH_TEST(dynamic_${BACKEND_NAME}, range) { // Create a graph for f(start,stop,step) = Range(start,stop,step). auto start = make_shared<op::Parameter>(element::i32, Shape{}); auto stop = make_shared<op::Parameter>(element::i32, Shape{}); auto step = make_shared<op::Parameter>(element::i32, Shape{}); auto range = make_shared<op::Range>(start, stop, step); ASSERT_TRUE(range->get_output_partial_shape(0).same_scheme(PartialShape::dynamic(1))); auto f = make_shared<Function>(NodeVector{range}, ParameterVector{start, stop, step}); auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); auto t_r = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic()); std::vector<RangeTest<int32_t>> int32_tests = { RangeTest<int32_t>{0, 10, 1, Shape{10}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}}, RangeTest<int32_t>{-5, 6, 3, Shape{4}, {-5, -2, 1, 4}}, RangeTest<int32_t>{10, 0, 1, Shape{0}, {}}, RangeTest<int32_t>{10, 5, -3, Shape{2}, {10, 7}}}; for (auto& test : int32_tests) { auto t_start = backend->create_tensor(element::i32, Shape{}); auto t_stop = backend->create_tensor(element::i32, Shape{}); auto t_step = backend->create_tensor(element::i32, Shape{}); copy_data(t_start, std::vector<int32_t>{test.start}); copy_data(t_stop, std::vector<int32_t>{test.stop}); copy_data(t_step, std::vector<int32_t>{test.step}); ex->call_with_validate({t_r}, {t_start, t_stop, t_step}); ASSERT_EQ(t_r->get_element_type(), element::i32); ASSERT_EQ(t_r->get_shape(), test.expected_result_shape); auto results = read_vector<int32_t>(t_r); ASSERT_EQ(results, test.expected_result); } } NGRAPH_TEST(dynamic_${BACKEND_NAME}, reshape) { auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto build_graph = [&backend](bool zero_flag) { // Create a graph for f(x,shape) = DynReshape(x,shape,zero_flag=zero_flag). auto x = make_shared<op::Parameter>(element::i32, PartialShape::dynamic()); auto shape = make_shared<op::Parameter>(element::i64, PartialShape::dynamic(1)); auto dyn_reshape = make_shared<op::DynReshape>(x, shape, zero_flag); EXPECT_TRUE(dyn_reshape->get_output_partial_shape(0).same_scheme(PartialShape::dynamic())); auto f = make_shared<Function>(NodeVector{dyn_reshape}, ParameterVector{x, shape}); auto ex = backend->compile(f); return ex; }; auto t_r = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic()); auto ex_flag_off = build_graph(false); auto ex_flag_on = build_graph(true); std::vector<std::tuple<bool, Shape, std::vector<int32_t>, std::vector<int64_t>, Shape>> tests; tests.emplace_back(make_tuple( false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{6}, Shape{6})); tests.emplace_back(make_tuple( true, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{6}, Shape{6})); tests.emplace_back(make_tuple( false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{-1}, Shape{6})); tests.emplace_back(make_tuple(false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{2, -1}, Shape{2, 3})); tests.emplace_back(make_tuple(false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{3, -1}, Shape{3, 2})); tests.emplace_back(make_tuple(false, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{3, 2, -1}, Shape{3, 2, 1})); tests.emplace_back(make_tuple(true, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{3, 2, -1}, Shape{3, 2, 1})); tests.emplace_back(make_tuple(true, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{0, 0, -1}, Shape{2, 3, 1})); tests.emplace_back(make_tuple(true, Shape{2, 3}, vector<int32_t>{1, 2, 3, 4, 5, 6}, vector<int64_t>{2, 0, -1}, Shape{2, 3, 1})); tests.emplace_back(make_tuple( true, Shape{0, 3, 4}, vector<int32_t>{}, vector<int64_t>{3, -1, 2}, Shape{3, 0, 2})); for (auto& test : tests) { bool zero_flag = get<0>(test); const Shape& in_shape = get<1>(test); const std::vector<int32_t>& data = get<2>(test); const std::vector<int64_t>& dims = get<3>(test); const Shape& out_shape = get<4>(test); auto t_x = backend->create_tensor(element::i32, in_shape); auto t_shape = backend->create_tensor(element::i64, Shape{dims.size()}); copy_data(t_x, data); copy_data(t_shape, dims); auto ex = zero_flag ? ex_flag_on : ex_flag_off; ex->call_with_validate({t_r}, {t_x, t_shape}); ASSERT_EQ(t_r->get_element_type(), element::i32); ASSERT_EQ(t_r->get_shape(), out_shape); auto results = read_vector<int32_t>(t_r); ASSERT_EQ(results, data); } }