//***************************************************************************** // Copyright 2017-2020 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/file_util.hpp" #include "ngraph/graph_util.hpp" #include "ngraph/log.hpp" #include "ngraph/ngraph.hpp" #include "ngraph/op/sum.hpp" #include "ngraph/pass/graph_rewrite.hpp" #include "ngraph/pass/manager.hpp" #include "ngraph/pass/reshape_elimination.hpp" #include "ngraph/pass/visualize_tree.hpp" #include "ngraph/pattern/matcher.hpp" #include "ngraph/pattern/op/label.hpp" #include "ngraph/pattern/op/skip.hpp" #include "ngraph/serializer.hpp" #include "ngraph/util.hpp" #include "ngraph/util.hpp" #include "util/all_close.hpp" #include "util/matcher.hpp" #include "util/random.hpp" #include "util/test_tools.hpp" using namespace ngraph; using namespace std; #ifndef NGRAPH_JSON_DISABLE TEST(reshape_elimination, remove_reshape) { pass::Manager pass_manager; pass_manager.register_pass<pass::ReshapeElimination>(); const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/bn_fprop.json"); const string json_string = file_util::read_file_to_string(json_path); stringstream ss(json_string); shared_ptr<Function> func = ngraph::deserialize(ss); size_t count_before = count_ops_of_type<op::Reshape>(func); pass_manager.run_passes(func); size_t count_after = count_ops_of_type<op::Reshape>(func); ASSERT_TRUE(count_after < count_before); } TEST(reshape_elimination, remove_tranpose) { pass::Manager pass_manager; pass_manager.register_pass<pass::ReshapeElimination>(); const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/tranpose.json"); const string json_string = file_util::read_file_to_string(json_path); stringstream ss(json_string); shared_ptr<Function> func = ngraph::deserialize(ss); size_t count_before = count_ops_of_type<op::Reshape>(func); pass_manager.run_passes(func); size_t count_after = count_ops_of_type<op::Reshape>(func); ASSERT_TRUE(count_after < count_before); } TEST(reshape_elimination, bn_bprop_rewrite) { pass::Manager pass_manager; pass_manager.register_pass<pass::ReshapeElimination>(); const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/bn_bprop.json"); const string json_string = file_util::read_file_to_string(json_path); stringstream ss(json_string); shared_ptr<Function> func = ngraph::deserialize(ss); size_t count_before = count_ops_of_type<op::Reshape>(func); pass_manager.run_passes(func); size_t count_after = count_ops_of_type<op::Reshape>(func); ASSERT_TRUE(count_after < count_before); } #endif TEST(reshape_elimination, transpose_reshape_pattern_fuse) { auto generate_func = []() { auto input = make_shared<op::Parameter>(element::f32, Shape{8, 2, 4, 6}); auto transpose = make_shared<op::Reshape>(input, AxisVector{0, 2, 1, 3}, Shape{8, 2, 4, 6}); auto reshape = make_shared<op::Reshape>(transpose, AxisVector{0, 1, 2, 3}, Shape{8, 4, 2, 6}); return make_shared<Function>(reshape, ParameterVector{input}); }; auto fuse_func = generate_func(); auto nofuse_func = generate_func(); pass::Manager pass_manager; pass_manager.register_pass<pass::ReshapeElimination>(); pass_manager.run_passes(fuse_func); ASSERT_TRUE(count_ops_of_type<op::Reshape>(fuse_func) == 1); ASSERT_TRUE(count_ops_of_type<op::Reshape>(nofuse_func) == 2); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(Shape{8, 2, 4, 6})); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(fuse_func, args, "INTERPRETER"); auto optimized_results = execute(nofuse_func, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); } TEST(reshape_elimination, transpose_reshape_pattern_nofuse) { auto input = make_shared<op::Parameter>(element::f32, Shape{8, 2, 4, 6}); auto transpose = make_shared<op::Reshape>(input, AxisVector{0, 2, 1, 3}, Shape{8, 2, 4, 6}); auto reshape = make_shared<op::Reshape>(transpose, AxisVector{2, 1, 0, 3}, Shape{8, 4, 2, 6}); auto f = make_shared<Function>(reshape, ParameterVector{input}); pass::Manager pass_manager; pass_manager.register_pass<pass::ReshapeElimination>(); pass_manager.run_passes(f); ASSERT_TRUE(count_ops_of_type<op::Reshape>(f) == 2); } TEST(reshape_elimination, dot_transpose_to_dot_w_transpose_args) { Shape shape_w{2, 4}; Shape shape_x{4, 1}; auto W = make_shared<op::Parameter>(element::f32, shape_w); auto x = make_shared<op::Parameter>(element::f32, shape_x); auto dot = make_shared<op::Dot>(W, x); auto reshape_dot = std::make_shared<op::Reshape>(dot, AxisVector{1, 0}, Shape{1, 2}); auto graph = make_shared<op::Abs>(reshape_dot); pass::Manager pass_manager; pass_manager.register_pass<pass::ReshapeElimination>(); auto func = make_shared<Function>(graph, ParameterVector{W, x}); pass_manager.run_passes(func); auto gdot = graph->get_argument(0); ASSERT_TRUE(as_type_ptr<op::Dot>(gdot)); ASSERT_TRUE(as_type_ptr<op::Reshape>(gdot->get_argument(0))); ASSERT_TRUE(as_type_ptr<op::Reshape>(gdot->get_argument(1))); ASSERT_EQ(gdot->get_argument(0)->get_argument(0), x); ASSERT_EQ(gdot->get_argument(1)->get_argument(0), W); ASSERT_EQ(gdot->get_shape(), (Shape{1, 2})); } TEST(reshape_elimination, recurrent_reshapes) { Shape shape_a{2, 2, 3, 3, 2, 4}; auto generate_func = [shape_a]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_r_1{3, 2, 2, 4, 6}; Shape shape_r_2{6, 8, 3, 2}; Shape shape_r_3{6, 8, 6}; Shape shape_r_4{6, 2, 2, 2, 6}; Shape shape_r_5{2, 3, 2, 2, 2, 3, 2}; Shape shape_r_6{48, 6}; auto r_1 = make_shared<op::Reshape>(A, AxisVector{2, 4, 0, 5, 3, 1}, shape_r_1); auto r_2 = make_shared<op::Reshape>(r_1, AxisVector{0, 1, 2, 3, 4}, shape_r_2); auto r_3 = make_shared<op::Reshape>(r_2, AxisVector{0, 1, 2, 3}, shape_r_3); auto r_4 = make_shared<op::Reshape>(r_3, AxisVector{0, 1, 2}, shape_r_4); auto r_5 = make_shared<op::Reshape>(r_4, AxisVector{0, 1, 2, 3, 4}, shape_r_5); auto r_6 = make_shared<op::Reshape>(r_5, AxisVector{0, 1, 2, 3, 4, 5, 6}, shape_r_6); auto f = make_shared<Function>(r_6, ParameterVector{A}); return f; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>("before_recurrent_reshapes.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>("after_recurrent_reshapes.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 1); } TEST(reshape_elimination, recurrent_reshapes_elimination) { Shape shape_a{2, 2, 3, 3, 2, 4}; auto generate_func = [shape_a]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_r_1{3, 2, 2, 4, 6}; Shape shape_r_2{6, 8, 3, 2}; Shape shape_r_3{6, 8, 6}; Shape shape_r_4{6, 2, 2, 2, 6}; Shape shape_r_5{2, 3, 2, 2, 2, 3, 2}; Shape shape_r_6{48, 6}; Shape shape_r_7{2, 2, 3, 3, 2, 4}; auto r_1 = make_shared<op::Reshape>(A, AxisVector{0, 1, 2, 3, 4, 5}, shape_r_1); auto r_2 = make_shared<op::Reshape>(r_1, AxisVector{0, 1, 2, 3, 4}, shape_r_2); auto r_3 = make_shared<op::Reshape>(r_2, AxisVector{0, 1, 2, 3}, shape_r_3); auto r_4 = make_shared<op::Reshape>(r_3, AxisVector{0, 1, 2}, shape_r_4); auto r_5 = make_shared<op::Reshape>(r_4, AxisVector{0, 1, 2, 3, 4}, shape_r_5); auto r_6 = make_shared<op::Reshape>(r_5, AxisVector{0, 1, 2, 3, 4, 5, 6}, shape_r_6); auto r_7 = make_shared<op::Reshape>(r_6, AxisVector{0, 1}, shape_r_7); auto f = make_shared<Function>(r_7, ParameterVector{A}); return f; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>("before_recurrent_reshapes_elimination.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>("after_1_recurrent_reshapes_elimination.png"); pass_manager.register_pass<pass::ReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>("after_2_recurrent_reshapes_elimination.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 0); } TEST(reshape_elimination, recurrent_reshapes_fan_out) { Shape shape_a{4, 6, 10, 2}; auto generate_func = [shape_a]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_r_1{6, 4, 5, 4}; Shape shape_r_2{24, 20}; auto reshape_1 = make_shared<op::Reshape>(A, AxisVector{0, 3, 2, 1}, shape_r_1); auto reshape_2 = make_shared<op::Reshape>(reshape_1, AxisVector{0, 1, 2, 3}, shape_r_2); auto reshape_3 = make_shared<op::Reshape>(reshape_2, AxisVector{0, 1}, shape_a); auto f_ = make_shared<Function>(NodeVector{reshape_2, reshape_3}, ParameterVector{A}); return f_; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>("before_recurrent_reshapes_fan_out.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>("after_recurrent_reshapes_fan_out.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 2); } TEST(reshape_elimination, recurrent_reshapes_fan_out_at_end) { Shape shape_a{12, 8, 1, 1}; auto generate_func = [shape_a]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); auto reshape_1 = make_shared<op::Reshape>(A, AxisVector{0, 3, 2, 1}, Shape{4, 3, 8, 1}); auto reshape_2 = make_shared<op::Reshape>(reshape_1, AxisVector{0, 1, 2, 3}, shape_a); auto reshape_3 = make_shared<op::Reshape>(reshape_2, AxisVector{0, 1, 2, 3}, Shape{4, 3, 8, 1}); auto abs_1 = make_shared<op::Abs>(reshape_3); auto f_ = make_shared<Function>(NodeVector{abs_1, reshape_3}, ParameterVector{A}); return f_; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>("before_recurrent_reshapes_fan_out_at_end.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>("after_recurrent_reshapes_fan_out_at_end.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 1); } TEST(reshape_elimination, recurrent_reshapes_multiple_fusions) { Shape shape_a{2, 2, 3, 3, 2, 4}; auto generate_func = [shape_a]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_r_1{3, 2, 2, 4, 6}; Shape shape_r_2{6, 8, 3, 2}; Shape shape_r_3{6, 8, 6}; Shape shape_r_4{6, 2, 2, 2, 6}; Shape shape_r_5{2, 3, 2, 2, 2, 3, 2}; Shape shape_r_6{48, 6}; auto r_1 = make_shared<op::Reshape>(A, AxisVector{2, 4, 0, 5, 3, 1}, shape_r_1); auto r_2 = make_shared<op::Reshape>(r_1, AxisVector{0, 1, 2, 3, 4}, shape_r_2); auto r_3 = make_shared<op::Reshape>(r_2, AxisVector{0, 1, 2, 3}, shape_r_3); auto r_4 = make_shared<op::Reshape>(r_3, AxisVector{1, 0, 2}, shape_r_4); auto r_5 = make_shared<op::Reshape>(r_4, AxisVector{0, 1, 2, 3, 4}, shape_r_5); auto r_6 = make_shared<op::Reshape>(r_5, AxisVector{0, 1, 2, 3, 4, 5, 6}, shape_r_6); auto f = make_shared<Function>(r_6, ParameterVector{A}); return f; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>( // "before_recurrent_reshapes_multiple_fusions.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>( // "after_recurrent_reshapes_multiple_fusions.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 2); } TEST(reshape_elimination, nonrecurrent_reshapes) { Shape shape_a{8, 6, 1, 1}; Shape shape_r{2, 24}; auto generate_func = [shape_a, shape_r]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); auto reshape_1 = make_shared<op::Reshape>(A, AxisVector{3, 0, 2, 1}, shape_r); auto abs_1 = make_shared<op::Abs>(reshape_1); auto reshape_2 = make_shared<op::Reshape>(abs_1, AxisVector{0, 1}, shape_a); auto abs_2 = make_shared<op::Abs>(reshape_2); auto reshape_3 = make_shared<op::Reshape>(abs_2, AxisVector{0, 1, 2, 3}, shape_a); auto f_ = make_shared<Function>(NodeVector{reshape_3}, ParameterVector{A}); return f_; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>("before_nonrecurrent_reshapes.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>("after_nonrecurrent_reshapes.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 3); } TEST(reshape_elimination, recurrent_reshapes_multiple_branches) { Shape shape_a{2, 2, 3, 3, 2, 4}; auto generate_func = [shape_a]() { auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_r_1{3, 2, 2, 4, 6}; Shape shape_r_2{6, 8, 3, 2}; Shape shape_r_3{6, 8, 6}; Shape shape_r_4{6, 2, 2, 2, 6}; Shape shape_r_5{2, 3, 2, 2, 2, 3, 2}; Shape shape_r_6{48, 6}; auto r_1 = make_shared<op::Reshape>(A, AxisVector{2, 4, 0, 5, 3, 1}, shape_r_1); auto r_2 = make_shared<op::Reshape>(r_1, AxisVector{0, 1, 2, 3, 4}, shape_r_2); auto r_3 = make_shared<op::Reshape>(r_2, AxisVector{0, 1, 2, 3}, shape_r_3); auto r_4 = make_shared<op::Reshape>(r_3, AxisVector{0, 1, 2}, shape_r_4); auto r_5 = make_shared<op::Reshape>(r_4, AxisVector{0, 1, 2, 3, 4}, shape_r_5); auto r_6 = make_shared<op::Reshape>(r_5, AxisVector{0, 1, 2, 3, 4, 5, 6}, shape_r_6); auto r_7 = make_shared<op::Reshape>(A, AxisVector{2, 4, 0, 5, 3, 1}, shape_r_2); auto r_8 = make_shared<op::Reshape>(r_7, AxisVector{0, 1, 2, 3}, shape_r_3); auto f = make_shared<Function>(NodeVector{r_6, r_8}, ParameterVector{A}); return f; }; auto baseline_f = generate_func(); auto optimized_f = generate_func(); auto baseline_input_shape = baseline_f->get_parameters().at(0)->get_shape(); pass::Manager pass_manager; // pass_manager.register_pass<pass::VisualizeTree>( // "before_recurrent_reshapes_multiple_branches.png"); pass_manager.register_pass<pass::RecurrentReshapeElimination>(); // pass_manager.register_pass<pass::VisualizeTree>( // "after_recurrent_reshapes_multiple_branches.png"); pass_manager.run_passes(optimized_f); test::Uniform<float> rng(0.0f, 100.0f); vector<vector<float>> args; vector<float> tensor_val(shape_size(baseline_input_shape)); rng.initialize(tensor_val); args.push_back(tensor_val); auto baseline_results = execute(baseline_f, args, "INTERPRETER"); auto optimized_results = execute(optimized_f, args, "INTERPRETER"); EXPECT_TRUE(test::all_close(baseline_results.at(0), optimized_results.at(0))); size_t num_reshapes_optimized = count_ops_of_type<op::Reshape>(optimized_f); ASSERT_EQ(num_reshapes_optimized, 2); } TEST(reshape_elimination, pass_property) { { auto pass = std::make_shared<ngraph::pass::ReshapeElimination>(); ASSERT_FALSE(pass->get_property(pass::PassProperty::REQUIRE_STATIC_SHAPE)); ASSERT_FALSE(pass->get_property(pass::PassProperty::CHANGE_DYNAMIC_STATE)); } { auto pass = std::make_shared<ngraph::pass::RecurrentReshapeElimination>(); ASSERT_FALSE(pass->get_property(pass::PassProperty::REQUIRE_STATIC_SHAPE)); ASSERT_FALSE(pass->get_property(pass::PassProperty::CHANGE_DYNAMIC_STATE)); } }