import logging import tensorflow as tf from tensorflow.python.client import device_lib from mlagents.envs.timers import timed from mlagents.trainers.models import EncoderType, LearningRateSchedule from mlagents.trainers.ppo.policy import PPOPolicy from mlagents.trainers.ppo.models import PPOModel from mlagents.trainers.components.reward_signals.reward_signal_factory import ( create_reward_signal, ) # Variable scope in which created variables will be placed under TOWER_SCOPE_NAME = "tower" logger = logging.getLogger("mlagents.trainers") class MultiGpuPPOPolicy(PPOPolicy): def __init__(self, seed, brain, trainer_params, is_training, load): """ Policy for Proximal Policy Optimization Networks with multi-GPU training :param seed: Random seed. :param brain: Assigned Brain object. :param trainer_params: Defined training parameters. :param is_training: Whether the model should be trained. :param load: Whether a pre-trained model will be loaded or a new one created. """ super().__init__(seed, brain, trainer_params, is_training, load) def create_model( self, brain, trainer_params, reward_signal_configs, is_training, load, seed ): """ Create PPO models, one on each device :param brain: Assigned Brain object. :param trainer_params: Defined training parameters. :param reward_signal_configs: Reward signal config :param seed: Random seed. """ self.devices = get_devices() self.towers = [] with self.graph.as_default(): with tf.variable_scope("", reuse=tf.AUTO_REUSE): for device in self.devices: with tf.device(device): self.towers.append( PPOModel( brain=brain, lr=float(trainer_params["learning_rate"]), lr_schedule=LearningRateSchedule( trainer_params.get( "learning_rate_schedule", "linear" ) ), h_size=int(trainer_params["hidden_units"]), epsilon=float(trainer_params["epsilon"]), beta=float(trainer_params["beta"]), max_step=float(trainer_params["max_steps"]), normalize=trainer_params["normalize"], use_recurrent=trainer_params["use_recurrent"], num_layers=int(trainer_params["num_layers"]), m_size=self.m_size, seed=seed, stream_names=list(reward_signal_configs.keys()), vis_encode_type=EncoderType( trainer_params.get("vis_encode_type", "simple") ), ) ) self.towers[-1].create_ppo_optimizer() self.model = self.towers[0] avg_grads = self.average_gradients([t.grads for t in self.towers]) update_batch = self.model.optimizer.apply_gradients(avg_grads) avg_value_loss = tf.reduce_mean( tf.stack([model.value_loss for model in self.towers]), 0 ) avg_policy_loss = tf.reduce_mean( tf.stack([model.policy_loss for model in self.towers]), 0 ) self.inference_dict.update( { "action": self.model.output, "log_probs": self.model.all_log_probs, "value_heads": self.model.value_heads, "value": self.model.value, "entropy": self.model.entropy, "learning_rate": self.model.learning_rate, } ) if self.use_continuous_act: self.inference_dict["pre_action"] = self.model.output_pre if self.use_recurrent: self.inference_dict["memory_out"] = self.model.memory_out if ( is_training and self.use_vec_obs and trainer_params["normalize"] and not load ): self.inference_dict["update_mean"] = self.model.update_normalization self.total_policy_loss = self.model.abs_policy_loss self.update_dict.update( { "value_loss": avg_value_loss, "policy_loss": avg_policy_loss, "update_batch": update_batch, } ) def create_reward_signals(self, reward_signal_configs): """ Create reward signals :param reward_signal_configs: Reward signal config. """ self.reward_signal_towers = [] with self.graph.as_default(): with tf.variable_scope(TOWER_SCOPE_NAME, reuse=tf.AUTO_REUSE): for device_id, device in enumerate(self.devices): with tf.device(device): reward_tower = {} for reward_signal, config in reward_signal_configs.items(): reward_tower[reward_signal] = create_reward_signal( self, self.towers[device_id], reward_signal, config ) for k, v in reward_tower[reward_signal].update_dict.items(): self.update_dict[k + "_" + str(device_id)] = v self.reward_signal_towers.append(reward_tower) for _, reward_tower in self.reward_signal_towers[0].items(): for _, update_key in reward_tower.stats_name_to_update_name.items(): all_reward_signal_stats = tf.stack( [ self.update_dict[update_key + "_" + str(i)] for i in range(len(self.towers)) ] ) mean_reward_signal_stats = tf.reduce_mean( all_reward_signal_stats, 0 ) self.update_dict.update({update_key: mean_reward_signal_stats}) self.reward_signals = self.reward_signal_towers[0] @timed def update(self, mini_batch, num_sequences): """ Updates model using buffer. :param n_sequences: Number of trajectories in batch. :param mini_batch: Experience batch. :return: Output from update process. """ feed_dict = {} stats_needed = self.stats_name_to_update_name device_batch_size = num_sequences // len(self.devices) device_batches = [] for i in range(len(self.devices)): device_batches.append( { k: v[ i * device_batch_size : i * device_batch_size + device_batch_size ] for (k, v) in mini_batch.items() } ) for batch, tower, reward_tower in zip( device_batches, self.towers, self.reward_signal_towers ): feed_dict.update(self.construct_feed_dict(tower, batch, num_sequences)) stats_needed.update(self.stats_name_to_update_name) for _, reward_signal in reward_tower.items(): feed_dict.update( reward_signal.prepare_update(tower, batch, num_sequences) ) stats_needed.update(reward_signal.stats_name_to_update_name) update_vals = self._execute_model(feed_dict, self.update_dict) update_stats = {} for stat_name, update_name in stats_needed.items(): update_stats[stat_name] = update_vals[update_name] return update_stats def average_gradients(self, tower_grads): """ Average gradients from all towers :param tower_grads: Gradients from all towers """ average_grads = [] for grad_and_vars in zip(*tower_grads): grads = [g for g, _ in grad_and_vars if g is not None] if not grads: continue avg_grad = tf.reduce_mean(tf.stack(grads), 0) var = grad_and_vars[0][1] average_grads.append((avg_grad, var)) return average_grads def get_devices(): """ Get all available GPU devices """ local_device_protos = device_lib.list_local_devices() devices = [x.name for x in local_device_protos if x.device_type == "GPU"] return devices