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//*****************************************************************************
// 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 "ngraph/log.hpp"
#include "ngraph/type/element_type.hpp"
#include "util/all_close.hpp"
#include "util/autodiff/backprop_derivative.hpp"
#include "util/autodiff/numeric_derivative.hpp"
#include "util/test_tools.hpp"
// TODO: Consider removing template since only <float> is being used in tests and numerical
// derivative does not work with int types
// TODO: Always compute the numerical derivatives in double
template <typename T>
::testing::AssertionResult
autodiff_numeric_compare(ngraph::runtime::Backend* backend,
std::shared_ptr<ngraph::Function> f,
std::shared_ptr<ngraph::Function> g,
const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& args,
T rtol,
T atol)
{
T delta = static_cast<T>(0.0009765625f); // Binary-representable number near 0.001
// Use INTERPRETER to compute numerical derivatives
auto interpreter_backend = ngraph::runtime::Backend::create("INTERPRETER");
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> interpreter_args;
for (auto arg : args)
{
auto interpreter_arg =
interpreter_backend->create_tensor(arg->get_element_type(), arg->get_shape());
// TODO: copy_data should not require T. Quick fix here for bool used in `Select`
if (arg->get_element_type() == ngraph::element::boolean)
{
copy_data(interpreter_arg, read_vector<char>(arg));
}
else
{
copy_data(interpreter_arg, read_vector<T>(arg));
}
interpreter_args.push_back(interpreter_arg);
}
auto results_num = ngraph::autodiff::numeric_derivative<T>(
interpreter_backend.get(), f, interpreter_args, delta, f->get_parameters());
// Use the backend being tested to compute symbolic derivatives
auto results_sym =
ngraph::autodiff::backprop_derivative<T>(backend, g, args, g->get_parameters());
// Cast to HostTensor for comparision
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> interpreter_results_sym;
for (auto result : results_sym)
{
auto interpreter_result =
interpreter_backend->create_tensor(ngraph::element::from<T>(), result->get_shape());
copy_data(interpreter_result, read_vector<T>(result));
interpreter_results_sym.push_back(interpreter_result);
}
return ngraph::test::all_close(results_num, interpreter_results_sym, rtol, atol);
}
template <typename T>
::testing::AssertionResult
autodiff_numeric_compare(ngraph::runtime::Backend* backend,
std::function<std::shared_ptr<ngraph::Function>()> make_graph,
const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& args,
T rtol,
T atol)
{
return autodiff_numeric_compare(backend, make_graph(), make_graph(), args, rtol, atol);
}
template <typename T>
::testing::AssertionResult autodiff_numeric_compare_selective(
ngraph::runtime::Backend* backend,
std::shared_ptr<ngraph::Function> f,
std::shared_ptr<ngraph::Function> g,
const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& args,
T rtol,
T atol,
const std::vector<bool>& indep_param_mask)
{
// Use INTERPRETER to compute numerical derivatives
std::vector<std::shared_ptr<ngraph::op::Parameter>> f_indep_params;
size_t i = 0;
for (auto b : indep_param_mask)
{
if (b)
{
f_indep_params.push_back(f->get_parameters().at(i));
}
i++;
}
auto interpreter_backend = ngraph::runtime::Backend::create("INTERPRETER");
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> interpreter_args;
for (auto arg : args)
{
auto interpreter_arg =
interpreter_backend->create_tensor(arg->get_element_type(), arg->get_shape());
// TODO: copy_data should not require T. Quick fix here for bool used in `Select`
if (arg->get_element_type() == ngraph::element::boolean)
{
copy_data(interpreter_arg, read_vector<char>(arg));
}
else
{
copy_data(interpreter_arg, read_vector<T>(arg));
}
interpreter_args.push_back(interpreter_arg);
}
auto results_num = ngraph::autodiff::numeric_derivative<T>(
interpreter_backend.get(), f, interpreter_args, .001f, f_indep_params);
// Use the backend being tested to compute symbolic derivatives
std::vector<std::shared_ptr<ngraph::op::Parameter>> g_indep_params;
i = 0;
for (auto b : indep_param_mask)
{
if (b)
{
g_indep_params.push_back(g->get_parameters().at(i));
}
i++;
}
auto results_sym = ngraph::autodiff::backprop_derivative<T>(backend, g, args, g_indep_params);
// Cast to HostTensor for comparision
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> interpreter_results_sym;
for (auto result : results_sym)
{
auto interpreter_result =
interpreter_backend->create_tensor(ngraph::element::from<T>(), result->get_shape());
copy_data(interpreter_result, read_vector<T>(result));
interpreter_results_sym.push_back(interpreter_result);
}
return ngraph::test::all_close(results_num, interpreter_results_sym, rtol, atol);
}
template <typename T>
::testing::AssertionResult autodiff_numeric_compare_selective(
ngraph::runtime::Backend* backend,
std::function<std::shared_ptr<ngraph::Function>()> make_graph,
const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& args,
T rtol,
T atol,
const std::vector<bool>& indep_param_mask)
{
return autodiff_numeric_compare_selective(
backend, make_graph(), make_graph(), args, rtol, atol, indep_param_mask);
}