0acd153a6c
* GroupConvolutionBackpropData: Added backend unit tests * GroupConvolutionBackpropData: Refactor SLT and added tests for 1D * GroupConvolutionBackpropData: Added Serialization tests * GroupConvolutionBackpropData: Added GroupConvolutionBackpropData reference implementation * GroupConvolutionBackpropData specification refactoring. * GroupConvolutionBackpropData: Added validation node checks for the op * GroupConvolutionBackpropData: Copyright year fixed * GroupConvolutionBackpropData: Enhanced output shape inference with dynamic shapes * GroupConvolutionBackpropData: Remove redefinition of helper variables * Spec refactoring: add ticks to types and layouts. * Minor refactoring. * GroupConvolutionBackpropData: Moved backend tests from GroupConvolution to corresponding file * GroupConvolutionBackpropData: Improved output shape inference for fully dynamic inputs * GroupConvolutionBackpropData: Clean up type_prop tests * Fix banner in GroupConvolution shared test class. Co-authored-by: ggalieroc <gabriele.galiero.casay@intel.com>
323 lines
11 KiB
C++
323 lines
11 KiB
C++
//*****************************************************************************
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// Copyright 2017-2021 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//*****************************************************************************
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#include "int_executable.hpp"
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#include <cstring>
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#include "backend_manager.hpp"
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#include "evaluates_map.hpp"
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#include "ngraph/except.hpp"
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#include "ngraph/ops.hpp"
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#include "ngraph/type/bfloat16.hpp"
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#include "ngraph/type/float16.hpp"
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#include "ngraph/util.hpp"
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using namespace std;
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using namespace ngraph;
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NGRAPH_SUPPRESS_DEPRECATED_START
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runtime::interpreter::INTExecutable::INTExecutable(const shared_ptr<Function>& function,
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bool enable_performance_collection)
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: m_is_compiled{true}
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, m_performance_counters_enabled{enable_performance_collection}
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{
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m_function = clone_function(*function);
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for (auto node : m_function->get_ordered_ops())
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{
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m_nodes.push_back(node);
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}
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set_parameters_and_results(*m_function);
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}
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bool runtime::interpreter::INTExecutable::call(const vector<shared_ptr<runtime::Tensor>>& outputs,
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const vector<shared_ptr<runtime::Tensor>>& inputs)
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{
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// convert inputs to HostTensor
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vector<shared_ptr<HostTensor>> func_inputs;
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for (const auto& tensor : inputs)
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{
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auto host_tensor = static_pointer_cast<runtime::HostTensor>(tensor);
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func_inputs.push_back(host_tensor);
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}
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if (m_nan_check_enabled)
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{
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perform_nan_check(func_inputs);
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}
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// convert outputs to HostTensor
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vector<shared_ptr<HostTensor>> func_outputs;
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for (const auto& tensor : outputs)
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{
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auto host_tensor = static_pointer_cast<runtime::HostTensor>(tensor);
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func_outputs.push_back(host_tensor);
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}
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// map function params -> HostTensor
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unordered_map<descriptor::Tensor*, shared_ptr<HostTensor>> tensor_map;
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size_t input_count = 0;
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for (const auto& param : get_parameters())
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{
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for (size_t i = 0; i < param->get_output_size(); ++i)
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{
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descriptor::Tensor* tensor = ¶m->output(i).get_tensor();
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tensor_map.insert({tensor, func_inputs[input_count++]});
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}
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}
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// map function outputs -> HostTensor
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for (size_t output_count = 0; output_count < get_results().size(); ++output_count)
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{
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auto output = get_results()[output_count];
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if (!is_type<op::Result>(output))
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{
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throw ngraph_error("One of function's outputs isn't op::Result");
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}
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descriptor::Tensor* tensor = &output->get_output_tensor(0);
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tensor_map.insert({tensor, func_outputs[output_count]});
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}
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// for each ordered op in the graph
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for (const auto& op : m_nodes)
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{
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if (dynamic_pointer_cast<op::Parameter>(op) != nullptr)
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{
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continue;
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}
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// get op inputs from map
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vector<shared_ptr<HostTensor>> op_inputs;
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for (auto input : op->inputs())
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{
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descriptor::Tensor* tensor = &input.get_tensor();
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op_inputs.push_back(tensor_map.at(tensor));
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}
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// get op outputs from map or create
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vector<shared_ptr<HostTensor>> op_outputs;
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for (size_t i = 0; i < op->get_output_size(); ++i)
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{
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descriptor::Tensor* tensor = &op->output(i).get_tensor();
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shared_ptr<HostTensor> host_tensor;
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auto it = tensor_map.find(tensor);
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if (it == tensor_map.end())
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{
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host_tensor = make_shared<HostTensor>(op->output(i));
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tensor_map.insert({tensor, host_tensor});
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}
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else
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{
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host_tensor = it->second;
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}
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op_outputs.push_back(host_tensor);
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}
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// get op type
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element::Type type;
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if (is_type<op::Convert>(op) || is_type<op::PriorBox>(op))
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{
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type = op->get_input_element_type(0);
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}
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else if (is_type<op::v1::Equal>(op) || is_type<op::v1::Greater>(op) ||
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is_type<op::v1::GreaterEqual>(op) || is_type<op::v1::Less>(op) ||
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is_type<op::v1::LessEqual>(op) || is_type<op::v1::NotEqual>(op))
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{
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// Get the type of the second input, not the first
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// All BinaryElementwiseComparision ops have the same type for inputs
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// Select has bool for first input and the type we are interested in for the second
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type = op->get_input_element_type(1);
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}
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else
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{
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type = op->get_output_element_type(0);
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}
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if (m_performance_counters_enabled)
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{
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m_timer_map[op].start();
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}
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if (!op->evaluate(op_outputs, op_inputs))
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{
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evaluate_node(op, op_outputs, op_inputs);
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}
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if (m_performance_counters_enabled)
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{
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m_timer_map[op].stop();
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}
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if (m_nan_check_enabled)
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{
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perform_nan_check(op_outputs, op.get());
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}
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}
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return true;
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}
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vector<runtime::PerformanceCounter>
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runtime::interpreter::INTExecutable::get_performance_data() const
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{
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vector<runtime::PerformanceCounter> rc;
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for (const pair<shared_ptr<const Node>, stopwatch> p : m_timer_map)
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{
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rc.emplace_back(p.first, p.second.get_total_microseconds(), p.second.get_call_count());
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}
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return rc;
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}
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void runtime::interpreter::INTExecutable::perform_nan_check(
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const vector<shared_ptr<HostTensor>>& tensors, const Node* op)
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{
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size_t arg_number = 1;
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for (const shared_ptr<HostTensor>& tensor : tensors)
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{
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const element::Type& type = tensor->get_element_type();
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if (type == element::f32)
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{
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const float* data = tensor->get_data_ptr<float>();
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for (size_t i = 0; i < tensor->get_element_count(); i++)
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{
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if (std::isnan(data[i]))
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{
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if (op)
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{
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throw runtime_error("nan found in op '" + op->get_name() + "' output");
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}
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else
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{
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throw runtime_error("nan found in function's input tensor number " +
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to_string(arg_number));
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}
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}
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}
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}
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else if (type == element::f64)
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{
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const double* data = tensor->get_data_ptr<double>();
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for (size_t i = 0; i < tensor->get_element_count(); i++)
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{
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if (std::isnan(data[i]))
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{
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if (op)
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{
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throw runtime_error("nan found in op '" + op->get_name() + "' output");
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}
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else
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{
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throw runtime_error("nan found in function's input tensor number " +
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to_string(arg_number));
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}
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}
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}
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}
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arg_number++;
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}
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}
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shared_ptr<ngraph::op::Parameter>
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runtime::interpreter::INTExecutable::get_parameter(size_t index) const
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{
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const ParameterVector& parameters = get_parameters();
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NGRAPH_CHECK(index < parameters.size(), "create_tensor for input out of bounds");
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return parameters[index];
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}
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shared_ptr<ngraph::op::Result> runtime::interpreter::INTExecutable::get_result(size_t index) const
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{
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const ResultVector& results = get_results();
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NGRAPH_CHECK(index < results.size(), "create_tensor for input out of bounds");
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return results[index];
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}
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shared_ptr<runtime::Tensor>
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runtime::interpreter::INTExecutable::create_input_tensor(size_t input_index)
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{
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shared_ptr<op::Parameter> parameter = get_parameter(input_index);
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return make_shared<runtime::HostTensor>(parameter->get_element_type(), parameter->get_shape());
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}
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shared_ptr<runtime::Tensor>
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runtime::interpreter::INTExecutable::create_output_tensor(size_t output_index)
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{
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shared_ptr<op::Result> result = get_result(output_index);
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return make_shared<runtime::HostTensor>(result->get_element_type(), result->get_shape());
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}
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vector<shared_ptr<runtime::Tensor>>
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runtime::interpreter::INTExecutable::create_input_tensor(size_t input_index,
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size_t pipeline_depth)
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{
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vector<shared_ptr<runtime::HostTensor>> tensors;
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shared_ptr<op::Parameter> parameter = get_parameter(input_index);
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for (size_t i = 0; i < pipeline_depth; i++)
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{
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shared_ptr<runtime::HostTensor> tensor;
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auto t =
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make_shared<runtime::HostTensor>(parameter->get_element_type(), parameter->get_shape());
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tensor = static_pointer_cast<runtime::HostTensor>(t);
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tensors.push_back(tensor);
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}
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vector<shared_ptr<runtime::Tensor>> result_tensors;
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for (const shared_ptr<runtime::HostTensor>& tensor : tensors)
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{
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result_tensors.push_back(tensor);
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}
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return result_tensors;
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}
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vector<shared_ptr<runtime::Tensor>>
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runtime::interpreter::INTExecutable::create_output_tensor(size_t output_index,
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size_t pipeline_depth)
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{
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vector<shared_ptr<runtime::HostTensor>> tensors;
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shared_ptr<op::Result> result = get_result(output_index);
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for (size_t i = 0; i < pipeline_depth; i++)
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{
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shared_ptr<runtime::HostTensor> tensor;
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auto t = make_shared<runtime::HostTensor>(result->get_element_type(), result->get_shape());
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tensor = static_pointer_cast<runtime::HostTensor>(t);
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tensors.push_back(tensor);
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}
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vector<shared_ptr<runtime::Tensor>> result_tensors;
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for (const shared_ptr<runtime::HostTensor>& tensor : tensors)
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{
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result_tensors.push_back(tensor);
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}
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return result_tensors;
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}
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bool runtime::interpreter::INTExecutable::evaluate_node(const std::shared_ptr<Node>& node,
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const HostTensorVector& outputs,
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const HostTensorVector& inputs) const
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{
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auto& map = runtime::interpreter::get_evaluators_map();
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auto it = map.find(node->get_type_info());
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bool res = false;
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if (it != map.end())
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{
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res = it->second(node, outputs, inputs);
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if (!res)
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{
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throw ngraph_error(std::string("Running evaluate method for OP ") +
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node->get_type_info().name + std::string(" failed!"));
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}
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}
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else
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{
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throw unsupported_op(
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std::string("Interpreter backend doesn't implement evaluate method for OP ") +
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node->get_type_info().name);
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}
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return res;
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}
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