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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 <unordered_map>
#include "ngraph/autodiff/adjoints.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/type/element_type.hpp"
#include "ngraph/util.hpp"
#include "util/all_close.hpp"
#include "util/test_tools.hpp"
namespace ngraph
{
class Node;
class Function;
static std::unordered_map<std::shared_ptr<Function>, std::shared_ptr<Function>> s_df_map;
static std::unordered_map<std::shared_ptr<Function>, std::shared_ptr<Function>> s_clone_fwd_map;
static std::unordered_map<std::shared_ptr<Function>, std::shared_ptr<Function>> s_clone_bwd_map;
namespace runtime
{
class Backend;
class Manager;
}
namespace autodiff
{
template <typename T>
std::vector<std::shared_ptr<runtime::Tensor>>
get_autodiff(runtime::Backend* backend,
std::shared_ptr<Function>& df,
const std::vector<std::shared_ptr<runtime::Tensor>>& df_input_args,
const std::vector<std::shared_ptr<op::Parameter>>& indep_params)
{
// df/dX* = f'(c, ...)
// using X* to denote all x "of interest" (represented by indep_params)
// return value for this function
std::vector<std::shared_ptr<runtime::Tensor>> results;
// adjoint
auto c_arg = df_input_args[0];
auto y_shape = c_arg->get_shape();
// df/dX* arguments
std::vector<std::shared_ptr<runtime::Tensor>> df_output_args;
// for each x "of interest"
for (auto x : indep_params)
{
// add df/dx to df/dX* arguments
auto x_shape = x->get_shape();
df_output_args.push_back(backend->create_tensor<T>(x_shape));
// each element of y has a derivative with respect to each element of x
// hence, create a y by x sized tensor for this result
auto y_by_x_shape = y_shape;
y_by_x_shape.insert(y_by_x_shape.end(), x_shape.begin(), x_shape.end());
results.push_back(backend->create_tensor<T>(y_by_x_shape));
}
// create storage for results
std::vector<std::vector<T>> result_vect;
std::vector<typename std::vector<T>::iterator> result_pos;
for (auto result : results)
{
result_vect.push_back(read_vector<T>(result));
result_pos.push_back(result_vect.back().begin());
}
// get adjoint and force to all elements to zero
auto c_vec = read_vector<T>(c_arg);
fill(c_vec.begin(), c_vec.end(), 0);
// for each element of the adjoint
// same as saying for each element of y
for (size_t i = 0; i < c_vec.size(); i++)
{
// set a single adjoint element
c_vec[i] = 1;
write_vector(c_arg, c_vec);
// call modified df/dX* = f'(c, cached)
backend->call_with_validate(df, df_output_args, df_input_args);
// reset the adjoint element
c_vec[i] = 0;
write_vector(c_arg, c_vec);
// for each result
// same as saying for each x "of interest"
for (size_t j = 0; j < results.size(); j++)
{
// copy df/dx to storage for this element of y
auto dfdx = read_vector<T>(df_output_args[j]);
result_pos[j] = std::copy(dfdx.begin(), dfdx.end(), result_pos[j]);
}
}
// copy storage to results and return
for (size_t j = 0; j < results.size(); j++)
{
write_vector(results[j], result_vect[j]);
}
return results;
}
template <typename T>
std::vector<std::shared_ptr<runtime::Tensor>>
backprop_derivative(runtime::Backend* backend,
const std::shared_ptr<Function>& f,
const std::vector<std::shared_ptr<runtime::Tensor>>& f_input_args,
const std::vector<std::shared_ptr<op::Parameter>>& indep_params)
{
// y = f(X)
// using X (upper case) to denote all paramenters of f (represented by f_input_args)
// using x (lower case) to denote an individual paramemter of f
// using X* to denote all x "of interest" (represented by indep_params)
Shape y_shape = f->get_output_shape(0);
// adjoint
auto c_param = std::make_shared<op::Parameter>(element::from<T>(), y_shape);
auto c_arg = backend->create_tensor<T>(y_shape);
// df/dX*
std::vector<std::shared_ptr<Node>> df_output_params;
Adjoints adjoints(NodeVector{f->get_output_op(0)}, NodeVector{c_param});
// for each x "of interest"
for (auto x : indep_params)
{
// add df/dx to df/dX*
df_output_params.push_back(adjoints.backprop_node(x));
}
// (c, X)
std::vector<std::shared_ptr<op::Parameter>> df_input_params = f->get_parameters();
df_input_params.insert(df_input_params.begin(), c_param);
// df/dX* = f'(c, X)
if (!s_df_map[f])
{
s_df_map[f] = std::make_shared<Function>(df_output_params, df_input_params);
}
auto df = s_df_map[f];
// (c, X) arguments
std::vector<std::shared_ptr<runtime::Tensor>> df_input_args = f_input_args;
df_input_args.insert(df_input_args.begin(), c_arg);
// call f'(c,X) to get df/dX*
auto dfdx = get_autodiff<T>(backend, df, df_input_args, indep_params);
// create fprop cache
// creates modified forward function -> (y, cached) = f(x)
// creates modified backward function -> df/dX* = f'(c, cached)
auto fprop_cache = cache_fprop(f, df);
// (y, cached) arguments
std::vector<std::shared_ptr<runtime::Tensor>> mod_f_output_args;
mod_f_output_args.push_back(backend->create_tensor<T>(y_shape));
// (c, cached) arguments
std::vector<std::shared_ptr<runtime::Tensor>> mod_df_input_args = df_input_args;
// add cached nodes to both modified f output and modified f' input arguments
for (auto node : fprop_cache.fprop_output_nodes)
{
auto tv = backend->create_tensor(node->get_element_type(), node->get_shape());
mod_f_output_args.push_back(tv);
mod_df_input_args.push_back(tv);
}
// compile and run modified (y, cached) = f(x)
if (!s_clone_fwd_map[f])
{
s_clone_fwd_map[f] = clone_function(*fprop_cache.fprop);
}
auto clone_fwd = s_clone_fwd_map[f];
backend->call_with_validate(clone_fwd, mod_f_output_args, f_input_args);
// call modfied f'(c, cached) to get df/dX*
if (!s_clone_bwd_map[f])
{
s_clone_bwd_map[f] = clone_function(*fprop_cache.bprop);
}
auto clone_bwd = s_clone_bwd_map[f];
auto cache_dfdx = get_autodiff<T>(backend, clone_bwd, mod_df_input_args, indep_params);
const T numpy_atol = static_cast<const T>(1e-5f);
const T numpy_rtol = static_cast<const T>(1e-8f);
auto close = ngraph::test::all_close<T>(dfdx, cache_dfdx, numpy_atol, numpy_rtol);
if (!close)
{
throw ngraph_error(
"Derivatives mismatch between cache and non-cache bprop functions");
}
return dfdx;
}
}
}