/******************************************************************************* * 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 "ngraph/graph_util.hpp" #include "ngraph/log.hpp" #include "ngraph/types/element_type.hpp" #include "ngraph/util.hpp" #include "util/all_close.hpp" #include "util/test_tools.hpp" namespace ngraph { class Node; class Function; namespace runtime { class Backend; class Manager; } namespace autodiff { template <typename T> std::vector<std::shared_ptr<runtime::TensorView>> get_autodiff(const std::shared_ptr<runtime::Manager>& manager, const std::shared_ptr<runtime::Backend>& backend, std::shared_ptr<Function>& df, const std::vector<std::shared_ptr<runtime::TensorView>>& 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::TensorView>> 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::TensorView>> 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->make_primary_tensor_view<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->make_primary_tensor_view<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()); } // compile f' auto external = manager->compile(df); auto cf = backend->make_call_frame(external); // 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) cf->tensor_call(df_input_args, df_output_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::TensorView>> backprop_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>>& 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->make_primary_tensor_view<T>(y_shape); // df/dX* std::vector<std::shared_ptr<Node>> df_output_params; // for each x "of interest" for (auto x : indep_params) { // add df/dx to df/dX* auto x_shape = x->get_shape(); df_output_params.push_back(f->get_output_op(0)->backprop_node(x, c_param)); } // (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) auto df = std::make_shared<Function>(df_output_params, df_input_params); // (c, X) arguments std::vector<std::shared_ptr<runtime::TensorView>> 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>(manager, 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, {c_param}); // (y, cached) arguments std::vector<std::shared_ptr<runtime::TensorView>> mod_f_output_args; mod_f_output_args.push_back(backend->make_primary_tensor_view<T>(y_shape)); // (c, cached) arguments std::vector<std::shared_ptr<runtime::TensorView>> mod_df_input_args; mod_df_input_args.push_back(c_arg); // add cached nodes to both modified f output and modified f' input arguments for (auto node : fprop_cache.fprop_output_nodes) { auto tv = backend->make_primary_tensor_view<T>(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) NodeMap nm1; auto clone_fwd = clone_function(fprop_cache.fprop, nm1); auto cache_fwd = manager->compile(clone_fwd); auto cache_fwd_cf = backend->make_call_frame(cache_fwd); cache_fwd_cf->tensor_call(f_input_args, mod_f_output_args); // call modfied f'(c, cached) to get df/dX* NodeMap nm2; auto clone_bwd = clone_function(fprop_cache.bprop, nm2); auto cache_dfdx = get_autodiff<T>(manager, backend, clone_bwd, mod_df_input_args, indep_params); const auto numpy_atol = 1e-5f; const auto numpy_rtol = 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; } } }