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/*******************************************************************************
* Copyright 2017-2018 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.
*******************************************************************************/
#pragma once
#include <memory>
#include <vector>
#include "ngraph/runtime/backend.hpp"
#include "ngraph/runtime/manager.hpp"
#include "ngraph/types/element_type.hpp"
namespace ngraph
{
namespace autodiff
{
/// @brief numeric approximation of the derivative
/// @param f A function
/// @param args Values for the arguments (the independent variables)
/// @param delta increment for the variables
/// @param indep_params parameters with respect to which to compute derivatives
/// @returns vector of dy/dvar, where each dy/dvar's shape is concat(y.shape(), var.shape())
template <typename T>
std::vector<std::shared_ptr<runtime::TensorView>>
numeric_derivative(const std::shared_ptr<runtime::Manager>& manager,
const std::shared_ptr<runtime::Backend>& backend,
const std::shared_ptr<Function>& f,
const std::vector<std::shared_ptr<runtime::TensorView>>& args,
T delta,
const std::vector<std::shared_ptr<op::Parameter>>& indep_params)
{
Shape y_shape = f->get_output_shape(0);
auto params = f->get_parameters();
// Results for each derivative, shape Y|X_i
std::vector<std::shared_ptr<runtime::TensorView>> results;
for (auto param : indep_params)
{
Shape s = y_shape;
auto param_shape = param->get_shape();
s.insert(s.end(), param_shape.begin(), param_shape.end());
results.push_back(backend->make_primary_tensor_view<T>(s));
}
auto external = manager->compile(f);
auto cf = backend->make_call_frame(external);
// ref_y is the function evaluated at the args
auto ref_y = backend->make_primary_tensor_view<T>(y_shape);
cf->tensor_call(args, std::vector<std::shared_ptr<ngraph::runtime::TensorView>>{ref_y});
auto ref_vec = read_vector<T>(ref_y);
// inc_y will hold f(x+dx) values
auto inc_y = backend->make_primary_tensor_view<T>(y_shape);
// Assuming vars, y, and results are row-major
T inv_delta = 1 / delta;
size_t pos = 0;
for (size_t i = 0; i < args.size(); ++i)
{
if (std::find(indep_params.begin(), indep_params.end(), params[i]) !=
indep_params.end())
{
auto arg = args[i];
auto res = read_vector<T>(results[pos]);
auto vec = read_vector<T>(arg);
for (size_t j = 0; j < vec.size(); j++)
{
auto old_val = vec[j];
vec[j] += delta;
write_vector(arg, vec);
cf->tensor_call(args, {inc_y});
auto inc_vec = read_vector<T>(inc_y);
vec[j] = old_val;
write_vector(arg, vec);
size_t res_k = j;
for (size_t k = 0; k < inc_vec.size(); k++)
{
auto y1 = inc_vec[k];
auto y0 = ref_vec[k];
res[res_k] = inv_delta * (y1 - y0);
res_k += vec.size();
}
}
write_vector(results[pos], res);
pos++;
}
}
return results;
}
}
}