252 lines
7.6 KiB
C++
252 lines
7.6 KiB
C++
// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <algorithm>
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#include "ngraph/ngraph.hpp"
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#include "ngraph/util.hpp"
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#include "test_tools.hpp"
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NGRAPH_SUPPRESS_DEPRECATED_START
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using namespace std;
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using namespace ngraph;
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// This function traverses the vector of ops and verifies that each op's dependencies (its inputs)
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// is located earlier in the vector. That is enough to be valid
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bool validate_list(const vector<shared_ptr<Node>>& nodes)
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{
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bool rc = true;
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for (auto it = nodes.rbegin(); it != nodes.rend(); it++)
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{
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auto node_tmp = *it;
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NodeVector dependencies_tmp;
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for (auto& val : node_tmp->input_values())
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dependencies_tmp.emplace_back(val.get_node_shared_ptr());
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vector<Node*> dependencies;
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for (shared_ptr<Node> n : dependencies_tmp)
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{
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dependencies.push_back(n.get());
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}
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auto tmp = it;
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for (tmp++; tmp != nodes.rend(); tmp++)
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{
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auto dep_tmp = *tmp;
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auto found = find(dependencies.begin(), dependencies.end(), dep_tmp.get());
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if (found != dependencies.end())
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{
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dependencies.erase(found);
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}
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}
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if (dependencies.size() > 0)
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{
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rc = false;
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}
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}
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return rc;
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}
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shared_ptr<Function> make_test_graph()
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{
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auto arg_0 = make_shared<op::Parameter>(element::f32, Shape{2, 2});
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auto arg_1 = make_shared<op::Parameter>(element::f32, Shape{2, 2});
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auto arg_2 = make_shared<op::Parameter>(element::f32, Shape{2, 2});
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auto arg_3 = make_shared<op::Parameter>(element::f32, Shape{2, 2});
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auto arg_4 = make_shared<op::Parameter>(element::f32, Shape{2, 2});
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auto arg_5 = make_shared<op::Parameter>(element::f32, Shape{2, 2});
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auto t0 = make_shared<op::v1::Add>(arg_0, arg_1);
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auto t1 = make_shared<op::MatMul>(t0, arg_2);
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auto t2 = make_shared<op::v1::Multiply>(t0, arg_3);
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auto t3 = make_shared<op::v1::Add>(t1, arg_4);
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auto t4 = make_shared<op::v1::Add>(t2, arg_5);
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auto r0 = make_shared<op::v1::Add>(t3, t4);
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auto f0 = make_shared<Function>(r0, ParameterVector{arg_0, arg_1, arg_2, arg_3, arg_4, arg_5});
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return f0;
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}
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template <>
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void copy_data<bool>(std::shared_ptr<ngraph::runtime::Tensor> tv, const std::vector<bool>& data)
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{
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std::vector<char> data_char(data.begin(), data.end());
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copy_data(tv, data_char);
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}
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template <>
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void init_int_tv<char>(ngraph::runtime::Tensor* tv,
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std::default_random_engine& engine,
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char min,
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char max)
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{
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size_t size = tv->get_element_count();
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std::uniform_int_distribution<int16_t> dist(static_cast<short>(min), static_cast<short>(max));
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std::vector<char> vec(size);
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for (char& element : vec)
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{
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element = static_cast<char>(dist(engine));
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}
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tv->write(vec.data(), vec.size() * sizeof(char));
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}
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template <>
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void init_int_tv<int8_t>(ngraph::runtime::Tensor* tv,
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std::default_random_engine& engine,
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int8_t min,
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int8_t max)
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{
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size_t size = tv->get_element_count();
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std::uniform_int_distribution<int16_t> dist(static_cast<short>(min), static_cast<short>(max));
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std::vector<int8_t> vec(size);
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for (int8_t& element : vec)
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{
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element = static_cast<int8_t>(dist(engine));
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}
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tv->write(vec.data(), vec.size() * sizeof(int8_t));
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}
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template <>
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void init_int_tv<uint8_t>(ngraph::runtime::Tensor* tv,
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std::default_random_engine& engine,
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uint8_t min,
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uint8_t max)
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{
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size_t size = tv->get_element_count();
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std::uniform_int_distribution<int16_t> dist(static_cast<short>(min), static_cast<short>(max));
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std::vector<uint8_t> vec(size);
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for (uint8_t& element : vec)
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{
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element = static_cast<uint8_t>(dist(engine));
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}
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tv->write(vec.data(), vec.size() * sizeof(uint8_t));
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}
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void random_init(ngraph::runtime::Tensor* tv, std::default_random_engine& engine)
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{
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element::Type et = tv->get_element_type();
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if (et == element::boolean)
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{
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init_int_tv<char>(tv, engine, 0, 1);
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}
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else if (et == element::f32)
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{
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init_real_tv<float>(tv, engine, numeric_limits<float>::min(), 1.0f);
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}
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else if (et == element::f64)
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{
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init_real_tv<double>(tv, engine, numeric_limits<double>::min(), 1.0);
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}
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else if (et == element::i8)
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{
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init_int_tv<int8_t>(tv, engine, -1, 1);
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}
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else if (et == element::i16)
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{
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init_int_tv<int16_t>(tv, engine, -1, 1);
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}
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else if (et == element::i32)
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{
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init_int_tv<int32_t>(tv, engine, 0, 1);
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}
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else if (et == element::i64)
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{
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init_int_tv<int64_t>(tv, engine, 0, 1);
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}
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else if (et == element::u8)
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{
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init_int_tv<uint8_t>(tv, engine, 0, 1);
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}
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else if (et == element::u16)
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{
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init_int_tv<uint16_t>(tv, engine, 0, 1);
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}
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else if (et == element::u32)
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{
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init_int_tv<uint32_t>(tv, engine, 0, 1);
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}
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else if (et == element::u64)
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{
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init_int_tv<uint64_t>(tv, engine, 0, 1);
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}
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else
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{
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throw runtime_error("unsupported type");
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}
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}
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template <>
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string get_results_str(const std::vector<char>& ref_data,
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const std::vector<char>& actual_data,
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size_t max_results)
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{
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stringstream ss;
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size_t num_results = std::min(static_cast<size_t>(max_results), ref_data.size());
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ss << "First " << num_results << " results";
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for (size_t i = 0; i < num_results; ++i)
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{
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ss << std::endl
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<< std::setw(4) << i << " ref: " << std::setw(16) << std::left
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<< static_cast<int>(ref_data[i]) << " actual: " << std::setw(16) << std::left
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<< static_cast<int>(actual_data[i]);
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}
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ss << std::endl;
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return ss.str();
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}
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::testing::AssertionResult test_ordered_ops(shared_ptr<Function> f, const NodeVector& required_ops)
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{
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unordered_set<Node*> seen;
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for (auto& node_ptr : f->get_ordered_ops())
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{
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Node* node = node_ptr.get();
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if (seen.count(node) > 0)
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{
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return ::testing::AssertionFailure() << "Duplication in ordered ops";
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}
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size_t arg_count = node->get_input_size();
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for (size_t i = 0; i < arg_count; ++i)
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{
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Node* dep = node->get_input_node_ptr(i);
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if (seen.count(dep) == 0)
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{
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return ::testing::AssertionFailure()
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<< "Argument " << *dep << " does not occur before op" << *node;
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}
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}
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for (auto& dep_ptr : node->get_control_dependencies())
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{
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if (seen.count(dep_ptr.get()) == 0)
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{
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return ::testing::AssertionFailure()
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<< "Control dependency " << *dep_ptr << " does not occur before op" << *node;
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}
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}
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seen.insert(node);
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}
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for (auto& node_ptr : required_ops)
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{
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if (seen.count(node_ptr.get()) == 0)
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{
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return ::testing::AssertionFailure()
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<< "Required op " << *node_ptr << "does not occur in ordered ops";
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}
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}
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return ::testing::AssertionSuccess();
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}
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constexpr NodeTypeInfo ngraph::TestOpMultiOut::type_info;
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bool ngraph::TestOpMultiOut::evaluate(const HostTensorVector& outputs,
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const HostTensorVector& inputs) const
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{
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inputs[0]->read(outputs[0]->get_data_ptr(), inputs[0]->get_size_in_bytes());
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inputs[1]->read(outputs[1]->get_data_ptr(), inputs[1]->get_size_in_bytes());
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return true;
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}
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