mirror of
https://github.com/gryf/coach.git
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67eb9e4c28
* Adding checkpointing framework as well as mxnet checkpointing implementation. - MXNet checkpoint for each network is saved in a separate file. * Adding checkpoint restore for mxnet to graph-manager * Add unit-test for get_checkpoint_state() * Added match.group() to fix unit-test failing on CI * Added ONNX export support for MXNet
277 lines
13 KiB
Python
277 lines
13 KiB
Python
#
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# Copyright (c) 2017 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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from typing import List, Tuple
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from rl_coach.base_parameters import Frameworks, AgentParameters
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from rl_coach.logger import failed_imports
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from rl_coach.saver import SaverCollection
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from rl_coach.spaces import SpacesDefinition
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try:
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import tensorflow as tf
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from rl_coach.architectures.tensorflow_components.general_network import GeneralTensorFlowNetwork
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except ImportError:
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failed_imports.append("tensorflow")
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try:
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import mxnet as mx
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from rl_coach.architectures.mxnet_components.general_network import GeneralMxnetNetwork
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except ImportError:
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failed_imports.append("mxnet")
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class NetworkWrapper(object):
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"""
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The network wrapper contains multiple copies of the same network, each one with a different set of weights which is
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updating in a different time scale. The network wrapper will always contain an online network.
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It will contain an additional slow updating target network if it was requested by the user,
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and it will contain a global network shared between different workers, if Coach is run in a single-node
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multi-process distributed mode. The network wrapper contains functionality for managing these networks and syncing
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between them.
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"""
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def __init__(self, agent_parameters: AgentParameters, has_target: bool, has_global: bool, name: str,
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spaces: SpacesDefinition, replicated_device=None, worker_device=None):
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self.ap = agent_parameters
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self.network_parameters = self.ap.network_wrappers[name]
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self.has_target = has_target
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self.has_global = has_global
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self.name = name
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self.sess = None
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if self.network_parameters.framework == Frameworks.tensorflow:
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if "tensorflow" not in failed_imports:
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general_network = GeneralTensorFlowNetwork
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else:
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raise Exception('Install tensorflow before using it as framework')
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elif self.network_parameters.framework == Frameworks.mxnet:
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if "mxnet" not in failed_imports:
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general_network = GeneralMxnetNetwork
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else:
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raise Exception('Install mxnet before using it as framework')
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else:
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raise Exception("{} Framework is not supported"
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.format(Frameworks().to_string(self.network_parameters.framework)))
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with tf.variable_scope("{}/{}".format(self.ap.full_name_id, name)):
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# Global network - the main network shared between threads
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self.global_network = None
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if self.has_global:
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# we assign the parameters of this network on the parameters server
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with tf.device(replicated_device):
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self.global_network = general_network(agent_parameters=agent_parameters,
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name='{}/global'.format(name),
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global_network=None,
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network_is_local=False,
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spaces=spaces,
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network_is_trainable=True)
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# Online network - local copy of the main network used for playing
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self.online_network = None
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with tf.device(worker_device):
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self.online_network = general_network(agent_parameters=agent_parameters,
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name='{}/online'.format(name),
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global_network=self.global_network,
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network_is_local=True,
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spaces=spaces,
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network_is_trainable=True)
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# Target network - a local, slow updating network used for stabilizing the learning
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self.target_network = None
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if self.has_target:
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with tf.device(worker_device):
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self.target_network = general_network(agent_parameters=agent_parameters,
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name='{}/target'.format(name),
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global_network=self.global_network,
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network_is_local=True,
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spaces=spaces,
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network_is_trainable=False)
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def sync(self):
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"""
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Initializes the weights of the networks to match each other
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:return:
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"""
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self.update_online_network()
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self.update_target_network()
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def update_target_network(self, rate=1.0):
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"""
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Copy weights: online network >>> target network
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:param rate: the rate of copying the weights - 1 for copying exactly
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"""
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if self.target_network:
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self.target_network.set_weights(self.online_network.get_weights(), rate)
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def update_online_network(self, rate=1.0):
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"""
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Copy weights: global network >>> online network
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:param rate: the rate of copying the weights - 1 for copying exactly
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"""
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if self.global_network:
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self.online_network.set_weights(self.global_network.get_weights(), rate)
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def apply_gradients_to_global_network(self, gradients=None):
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"""
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Apply gradients from the online network on the global network
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:param gradients: optional gradients that will be used instead of teh accumulated gradients
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:return:
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"""
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if gradients is None:
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gradients = self.online_network.accumulated_gradients
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if self.network_parameters.shared_optimizer:
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self.global_network.apply_gradients(gradients)
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else:
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self.online_network.apply_gradients(gradients)
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def apply_gradients_to_online_network(self, gradients=None):
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"""
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Apply gradients from the online network on itself
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:return:
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"""
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if gradients is None:
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gradients = self.online_network.accumulated_gradients
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self.online_network.apply_gradients(gradients)
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def train_and_sync_networks(self, inputs, targets, additional_fetches=[], importance_weights=None):
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"""
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A generic training function that enables multi-threading training using a global network if necessary.
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:param inputs: The inputs for the network.
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:param targets: The targets corresponding to the given inputs
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:param additional_fetches: Any additional tensor the user wants to fetch
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:param importance_weights: A coefficient for each sample in the batch, which will be used to rescale the loss
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error of this sample. If it is not given, the samples losses won't be scaled
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:return: The loss of the training iteration
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"""
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result = self.online_network.accumulate_gradients(inputs, targets, additional_fetches=additional_fetches,
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importance_weights=importance_weights, no_accumulation=True)
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self.apply_gradients_and_sync_networks(reset_gradients=False)
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return result
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def apply_gradients_and_sync_networks(self, reset_gradients=True):
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"""
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Applies the gradients accumulated in the online network to the global network or to itself and syncs the
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networks if necessary
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:param reset_gradients: If set to True, the accumulated gradients wont be reset to 0 after applying them to
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the network. this is useful when the accumulated gradients are overwritten instead
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if accumulated by the accumulate_gradients function. this allows reducing time
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complexity for this function by around 10%
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"""
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if self.global_network:
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self.apply_gradients_to_global_network()
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if reset_gradients:
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self.online_network.reset_accumulated_gradients()
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self.update_online_network()
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else:
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if reset_gradients:
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self.online_network.apply_and_reset_gradients(self.online_network.accumulated_gradients)
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else:
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self.online_network.apply_gradients(self.online_network.accumulated_gradients)
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def parallel_prediction(self, network_input_tuples: List[Tuple]):
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"""
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Run several network prediction in parallel. Currently this only supports running each of the network once.
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:param network_input_tuples: a list of tuples where the first element is the network (online_network,
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target_network or global_network) and the second element is the inputs
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:return: the outputs of all the networks in the same order as the inputs were given
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"""
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return type(self.online_network).parallel_predict(self.sess, network_input_tuples)
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def get_local_variables(self):
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"""
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Get all the variables that are local to the thread
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:return: a list of all the variables that are local to the thread
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"""
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local_variables = [v for v in tf.local_variables() if self.online_network.name in v.name]
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if self.has_target:
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local_variables += [v for v in tf.local_variables() if self.target_network.name in v.name]
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return local_variables
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def get_global_variables(self):
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"""
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Get all the variables that are shared between threads
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:return: a list of all the variables that are shared between threads
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"""
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global_variables = [v for v in tf.global_variables() if self.global_network.name in v.name]
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return global_variables
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def set_is_training(self, state: bool):
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"""
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Set the phase of the network between training and testing
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:param state: The current state (True = Training, False = Testing)
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:return: None
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"""
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self.online_network.set_is_training(state)
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if self.has_target:
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self.target_network.set_is_training(state)
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def set_session(self, sess):
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self.sess = sess
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self.online_network.set_session(sess)
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if self.global_network:
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self.global_network.set_session(sess)
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if self.target_network:
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self.target_network.set_session(sess)
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def __str__(self):
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sub_networks = []
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if self.global_network:
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sub_networks.append("global network")
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if self.online_network:
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sub_networks.append("online network")
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if self.target_network:
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sub_networks.append("target network")
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result = []
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result.append("Network: {}, Copies: {} ({})".format(self.name, len(sub_networks), ' | '.join(sub_networks)))
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result.append("-"*len(result[-1]))
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result.append(str(self.online_network))
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result.append("")
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return '\n'.join(result)
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def collect_savers(self, parent_path_suffix: str) -> SaverCollection:
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"""
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Collect all of network's savers for global or online network
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Note: global, online, and target network are all copies fo the same network which parameters that are
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updated at different rates. So we only need to save one of the networks; the one that holds the most
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recent parameters. target network is created for some agents and used for stabilizing training by
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updating parameters from online network at a slower rate. As a result, target network never contains
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the most recent set of parameters. In single-worker training, no global network is created and online
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network contains the most recent parameters. In vertical distributed training with more than one worker,
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global network is updated by all workers and contains the most recent parameters.
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Therefore preference is given to global network if it exists, otherwise online network is used
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for saving.
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:param parent_path_suffix: path suffix of the parent of the network wrapper
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(e.g. could be name of level manager plus name of agent)
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:return: collection of all checkpoint objects
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"""
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if self.global_network:
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savers = self.global_network.collect_savers(parent_path_suffix)
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else:
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savers = self.online_network.collect_savers(parent_path_suffix)
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return savers
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