Source code for ray.rllib.policy.torch_policy

import copy
import functools
import gym
import logging
import numpy as np
import os
import time
import threading
import tree  # pip install dm_tree
from typing import Callable, Dict, List, Optional, Set, Tuple, Type, Union, \

import ray
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
from ray.rllib.utils import force_list, NullContextManager
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.schedules import ConstantSchedule, PiecewiseSchedule
from ray.rllib.utils.threading import with_lock
from ray.rllib.utils.torch_ops import convert_to_non_torch_type, \
from ray.rllib.utils.typing import ModelGradients, ModelWeights, TensorType, \

    from ray.rllib.evaluation import MultiAgentEpisode

torch, nn = try_import_torch()

logger = logging.getLogger(__name__)

[docs]@DeveloperAPI class TorchPolicy(Policy): """Template for a PyTorch policy and loss to use with RLlib. Attributes: observation_space (gym.Space): observation space of the policy. action_space (gym.Space): action space of the policy. config (dict): config of the policy. model (TorchModel): Torch model instance. dist_class (type): Torch action distribution class. """ @DeveloperAPI def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, config: TrainerConfigDict, *, model: ModelV2, loss: Callable[[ Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch ], Union[TensorType, List[TensorType]]], action_distribution_class: Type[TorchDistributionWrapper], action_sampler_fn: Optional[Callable[[ TensorType, List[TensorType] ], Tuple[TensorType, TensorType]]] = None, action_distribution_fn: Optional[Callable[[ Policy, ModelV2, TensorType, TensorType, TensorType ], Tuple[TensorType, Type[TorchDistributionWrapper], List[ TensorType]]]] = None, max_seq_len: int = 20, get_batch_divisibility_req: Optional[Callable[[Policy], int]] = None, ): """Build a policy from policy and loss torch modules. Note that model will be placed on GPU device if CUDA_VISIBLE_DEVICES is set. Only single GPU is supported for now. Args: observation_space (gym.spaces.Space): observation space of the policy. action_space (gym.spaces.Space): action space of the policy. config (TrainerConfigDict): The Policy config dict. model (ModelV2): PyTorch policy module. Given observations as input, this module must return a list of outputs where the first item is action logits, and the rest can be any value. loss (Callable[[Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch], Union[TensorType, List[TensorType]]]): Callable that returns a single scalar loss or a list of loss terms. action_distribution_class (Type[TorchDistributionWrapper]): Class for a torch action distribution. action_sampler_fn (Callable[[TensorType, List[TensorType]], Tuple[TensorType, TensorType]]): A callable returning a sampled action and its log-likelihood given Policy, ModelV2, input_dict, explore, timestep, and is_training. action_distribution_fn (Optional[Callable[[Policy, ModelV2, ModelInputDict, TensorType, TensorType], Tuple[TensorType, type, List[TensorType]]]]): A callable returning distribution inputs (parameters), a dist-class to generate an action distribution object from, and internal-state outputs (or an empty list if not applicable). Note: No Exploration hooks have to be called from within `action_distribution_fn`. It's should only perform a simple forward pass through some model. If None, pass inputs through `self.model()` to get distribution inputs. The callable takes as inputs: Policy, ModelV2, ModelInputDict, explore, timestep, is_training. max_seq_len (int): Max sequence length for LSTM training. get_batch_divisibility_req (Optional[Callable[[Policy], int]]]): Optional callable that returns the divisibility requirement for sample batches given the Policy. """ self.framework = "torch" super().__init__(observation_space, action_space, config) # Log device and worker index. from ray.rllib.evaluation.rollout_worker import get_global_worker worker = get_global_worker() worker_idx = worker.worker_index if worker else 0 # Create multi-GPU model towers, if necessary. # - The central main model will be stored under self.model, residing on # self.device. # - Each GPU will have a copy of that model under # self.model_gpu_towers, matching the devices in self.devices. # - Parallelization is done by splitting the train batch and passing # it through the model copies in parallel, then averaging over the # resulting gradients, applying these averages on the main model and # updating all towers' weights from the main model. # - In case of just one device (1 (fake) GPU or 1 CPU), no # parallelization will be done. if config["_fake_gpus"] or config["num_gpus"] == 0 or \ not torch.cuda.is_available():"TorchPolicy (worker={}) running on {}.".format( worker_idx if worker_idx > 0 else "local", "{} fake-GPUs".format(config["num_gpus"]) if config["_fake_gpus"] else "CPU")) self.device = torch.device("cpu") self.devices = [ self.device for _ in range(config["num_gpus"] or 1) ] self.model_gpu_towers = [ model if i == 0 else copy.deepcopy(model) for i in range(config["num_gpus"] or 1) ] self.model = model else:"TorchPolicy (worker={}) running on {} GPU(s).".format( worker_idx if worker_idx > 0 else "local", config["num_gpus"])) gpu_ids = ray.get_gpu_ids() self.devices = [ torch.device("cuda:{}".format(i)) for i, id_ in enumerate(gpu_ids) if i < config["num_gpus"] ] self.device = self.devices[0] ids = [ id_ for i, id_ in enumerate(gpu_ids) if i < config["num_gpus"] ] self.model_gpu_towers = [] for i, _ in enumerate(ids): model_copy = copy.deepcopy(model) self.model_gpu_towers.append([i])) self.model = self.model_gpu_towers[0] # Lock used for locking some methods on the object-level. # This prevents possible race conditions when calling the model # first, then its value function (e.g. in a loss function), in # between of which another model call is made (e.g. to compute an # action). self._lock = threading.RLock() self._state_inputs = self.model.get_initial_state() self._is_recurrent = len(self._state_inputs) > 0 # Auto-update model's inference view requirements, if recurrent. self._update_model_view_requirements_from_init_state() # Combine view_requirements for Model and Policy. self.view_requirements.update(self.model.view_requirements) self.exploration = self._create_exploration() self.unwrapped_model = model # used to support DistributedDataParallel self._loss = loss self._optimizers = force_list(self.optimizer()) # Store, which params (by index within the model's list of # parameters) should be updated per optimizer. # Maps optimizer idx to set or param indices. self.multi_gpu_param_groups: List[Set[int]] = [] main_params = {p: i for i, p in enumerate(self.model.parameters())} for o in self._optimizers: param_indices = [] for pg_idx, pg in enumerate(o.param_groups): for p in pg["params"]: param_indices.append(main_params[p]) self.multi_gpu_param_groups.append(set(param_indices)) self.dist_class = action_distribution_class self.action_sampler_fn = action_sampler_fn self.action_distribution_fn = action_distribution_fn # If set, means we are using distributed allreduce during learning. self.distributed_world_size = None self.max_seq_len = max_seq_len self.batch_divisibility_req = get_batch_divisibility_req(self) if \ callable(get_batch_divisibility_req) else \ (get_batch_divisibility_req or 1)
[docs] @override(Policy) @DeveloperAPI def compute_actions( self, obs_batch: Union[List[TensorType], TensorType], state_batches: Optional[List[TensorType]] = None, prev_action_batch: Union[List[TensorType], TensorType] = None, prev_reward_batch: Union[List[TensorType], TensorType] = None, info_batch: Optional[Dict[str, list]] = None, episodes: Optional[List["MultiAgentEpisode"]] = None, explore: Optional[bool] = None, timestep: Optional[int] = None, **kwargs) -> \ Tuple[TensorType, List[TensorType], Dict[str, TensorType]]: with torch.no_grad(): seq_lens = torch.ones(len(obs_batch), dtype=torch.int32) input_dict = self._lazy_tensor_dict( SampleBatch({ SampleBatch.CUR_OBS: np.asarray(obs_batch), })) if prev_action_batch is not None: input_dict[SampleBatch.PREV_ACTIONS] = \ np.asarray(prev_action_batch) if prev_reward_batch is not None: input_dict[SampleBatch.PREV_REWARDS] = \ np.asarray(prev_reward_batch) state_batches = [ convert_to_torch_tensor(s, self.device) for s in (state_batches or []) ] return self._compute_action_helper(input_dict, state_batches, seq_lens, explore, timestep)
[docs] @override(Policy) def compute_actions_from_input_dict( self, input_dict: Dict[str, TensorType], explore: bool = None, timestep: Optional[int] = None, **kwargs) -> \ Tuple[TensorType, List[TensorType], Dict[str, TensorType]]: with torch.no_grad(): # Pass lazy (torch) tensor dict to Model as `input_dict`. input_dict = self._lazy_tensor_dict(input_dict) # Pack internal state inputs into (separate) list. state_batches = [ input_dict[k] for k in input_dict.keys() if "state_in" in k[:8] ] # Calculate RNN sequence lengths. seq_lens = np.array([1] * len(input_dict["obs"])) \ if state_batches else None return self._compute_action_helper(input_dict, state_batches, seq_lens, explore, timestep)
@with_lock def _compute_action_helper(self, input_dict, state_batches, seq_lens, explore, timestep): """Shared forward pass logic (w/ and w/o trajectory view API). Returns: Tuple: - actions, state_out, extra_fetches, logp. """ explore = explore if explore is not None else self.config["explore"] timestep = timestep if timestep is not None else self.global_timestep self._is_recurrent = state_batches is not None and state_batches != [] # Switch to eval mode. if self.model: self.model.eval() if self.action_sampler_fn: action_dist = dist_inputs = None actions, logp, state_out = self.action_sampler_fn( self, self.model, input_dict, state_batches, explore=explore, timestep=timestep) else: # Call the exploration before_compute_actions hook. self.exploration.before_compute_actions( explore=explore, timestep=timestep) if self.action_distribution_fn: # Try new action_distribution_fn signature, supporting # state_batches and seq_lens. try: dist_inputs, dist_class, state_out = \ self.action_distribution_fn( self, self.model, input_dict=input_dict, state_batches=state_batches, seq_lens=seq_lens, explore=explore, timestep=timestep, is_training=False) # Trying the old way (to stay backward compatible). # TODO: Remove in future. except TypeError as e: if "positional argument" in e.args[0] or \ "unexpected keyword argument" in e.args[0]: dist_inputs, dist_class, state_out = \ self.action_distribution_fn( self, self.model, input_dict[SampleBatch.CUR_OBS], explore=explore, timestep=timestep, is_training=False) else: raise e else: dist_class = self.dist_class dist_inputs, state_out = self.model(input_dict, state_batches, seq_lens) if not (isinstance(dist_class, functools.partial) or issubclass(dist_class, TorchDistributionWrapper)): raise ValueError( "`dist_class` ({}) not a TorchDistributionWrapper " "subclass! Make sure your `action_distribution_fn` or " "`make_model_and_action_dist` return a correct " "distribution class.".format(dist_class.__name__)) action_dist = dist_class(dist_inputs, self.model) # Get the exploration action from the forward results. actions, logp = \ self.exploration.get_exploration_action( action_distribution=action_dist, timestep=timestep, explore=explore) input_dict[SampleBatch.ACTIONS] = actions # Add default and custom fetches. extra_fetches = self.extra_action_out(input_dict, state_batches, self.model, action_dist) # Action-dist inputs. if dist_inputs is not None: extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = dist_inputs # Action-logp and action-prob. if logp is not None: extra_fetches[SampleBatch.ACTION_PROB] = \ torch.exp(logp.float()) extra_fetches[SampleBatch.ACTION_LOGP] = logp # Update our global timestep by the batch size. self.global_timestep += len(input_dict[SampleBatch.CUR_OBS]) return convert_to_non_torch_type((actions, state_out, extra_fetches)) @with_lock @override(Policy) @DeveloperAPI def compute_log_likelihoods( self, actions: Union[List[TensorType], TensorType], obs_batch: Union[List[TensorType], TensorType], state_batches: Optional[List[TensorType]] = None, prev_action_batch: Optional[Union[List[TensorType], TensorType]] = None, prev_reward_batch: Optional[Union[List[ TensorType], TensorType]] = None) -> TensorType: if self.action_sampler_fn and self.action_distribution_fn is None: raise ValueError("Cannot compute log-prob/likelihood w/o an " "`action_distribution_fn` and a provided " "`action_sampler_fn`!") with torch.no_grad(): input_dict = self._lazy_tensor_dict({ SampleBatch.CUR_OBS: obs_batch, SampleBatch.ACTIONS: actions }) if prev_action_batch is not None: input_dict[SampleBatch.PREV_ACTIONS] = prev_action_batch if prev_reward_batch is not None: input_dict[SampleBatch.PREV_REWARDS] = prev_reward_batch seq_lens = torch.ones(len(obs_batch), dtype=torch.int32) state_batches = [ convert_to_torch_tensor(s, self.device) for s in (state_batches or []) ] # Exploration hook before each forward pass. self.exploration.before_compute_actions(explore=False) # Action dist class and inputs are generated via custom function. if self.action_distribution_fn: # Try new action_distribution_fn signature, supporting # state_batches and seq_lens. try: dist_inputs, dist_class, state_out = \ self.action_distribution_fn( self, self.model, input_dict=input_dict, state_batches=state_batches, seq_lens=seq_lens, explore=False, is_training=False) # Trying the old way (to stay backward compatible). # TODO: Remove in future. except TypeError as e: if "positional argument" in e.args[0] or \ "unexpected keyword argument" in e.args[0]: dist_inputs, dist_class, _ = \ self.action_distribution_fn( policy=self, model=self.model, obs_batch=input_dict[SampleBatch.CUR_OBS], explore=False, is_training=False) else: raise e # Default action-dist inputs calculation. else: dist_class = self.dist_class dist_inputs, _ = self.model(input_dict, state_batches, seq_lens) action_dist = dist_class(dist_inputs, self.model) log_likelihoods = action_dist.logp(input_dict[SampleBatch.ACTIONS]) return log_likelihoods @with_lock @override(Policy) @DeveloperAPI def learn_on_batch( self, postprocessed_batch: SampleBatch) -> Dict[str, TensorType]: # Set Model to train mode. if self.model: self.model.train() # Callback handling. learn_stats = {} self.callbacks.on_learn_on_batch( policy=self, train_batch=postprocessed_batch, result=learn_stats) # Compute gradients (will calculate all losses and `backward()` # them to get the grads). grads, fetches = self.compute_gradients(postprocessed_batch) # Step the optimizers. self.apply_gradients(_directStepOptimizerSingleton) if self.model: fetches["model"] = self.model.metrics() fetches.update({"custom_metrics": learn_stats}) return fetches @with_lock @override(Policy) @DeveloperAPI def compute_gradients(self, postprocessed_batch: SampleBatch) -> ModelGradients: if not isinstance(postprocessed_batch, SampleBatch) or \ not postprocessed_batch.zero_padded: pad_batch_to_sequences_of_same_size( postprocessed_batch, max_seq_len=self.max_seq_len, shuffle=False, batch_divisibility_req=self.batch_divisibility_req, view_requirements=self.view_requirements, ) # Mark the batch as "is_training" so the Model can use this # information. postprocessed_batch.is_training = True # Single device case: Use batch as-is (no slicing). if len(self.devices) == 1: batches = [self._lazy_tensor_dict(postprocessed_batch)] # Multi-GPU case: Slice inputs into n (roughly) equal batches. else: len_ = len(postprocessed_batch) batches = [] start = 0 for i, device in enumerate(self.devices): shard_len = len_ // (len(self.devices) - i) batch = self._lazy_tensor_dict( postprocessed_batch.slice(start, start + shard_len), device=device) batches.append(batch) len_ -= shard_len start += shard_len # Copy weights of main model to all towers. state_dict = self.model.state_dict() for tower in self.model_gpu_towers: tower.load_state_dict(state_dict) # Do the (maybe parallelized) gradient calculation step. tower_outputs = self._multi_gpu_parallel_grad_calc(batches) # Multi device (GPU) case. if len(self.devices) > 1: # Mean-reduce over GPU-towers. all_grads = [] for i in range(len(tower_outputs[0][0])): if tower_outputs[0][0][i] is not None: all_grads.append( torch.mean( torch.stack([ t[0][i].to(self.device) for t in tower_outputs ]), dim=0)) else: all_grads.append(None) # Set main model's grads to mean-reduced values. for i, p in enumerate(self.model.parameters()): p.grad = all_grads[i] # Reduce stats over towers as well. from ray.rllib.execution.train_ops import all_tower_reduce grad_info = tree.map_structure_with_path( lambda p, *t: all_tower_reduce(p, *t), *[t[1] for t in tower_outputs]) # Single device case. else: all_grads, grad_info = tower_outputs[0] grad_info["allreduce_latency"] /= len(self._optimizers) grad_info.update(self.extra_grad_info(postprocessed_batch)) fetches = self.extra_compute_grad_fetches() return all_grads, dict(fetches, **{LEARNER_STATS_KEY: grad_info})
[docs] @override(Policy) @DeveloperAPI def apply_gradients(self, gradients: ModelGradients) -> None: if gradients == _directStepOptimizerSingleton: for i, opt in enumerate(self._optimizers): opt.step() else: # TODO(sven): Not supported for multiple optimizers yet. assert len(self._optimizers) == 1 for g, p in zip(gradients, self.model.parameters()): if g is not None: if torch.is_tensor(g): p.grad = else: p.grad = torch.from_numpy(g).to(self.device) self._optimizers[0].step()
[docs] @override(Policy) @DeveloperAPI def get_weights(self) -> ModelWeights: return { k: v.cpu().detach().numpy() for k, v in self.model.state_dict().items() }
[docs] @override(Policy) @DeveloperAPI def set_weights(self, weights: ModelWeights) -> None: weights = convert_to_torch_tensor(weights, device=self.device) self.model.load_state_dict(weights)
[docs] @override(Policy) @DeveloperAPI def is_recurrent(self) -> bool: return self._is_recurrent
[docs] @override(Policy) @DeveloperAPI def num_state_tensors(self) -> int: return len(self.model.get_initial_state())
[docs] @override(Policy) @DeveloperAPI def get_initial_state(self) -> List[TensorType]: return [ s.detach().cpu().numpy() for s in self.model.get_initial_state() ]
[docs] @override(Policy) @DeveloperAPI def get_state(self) -> Union[Dict[str, TensorType], List[TensorType]]: state = super().get_state() state["_optimizer_variables"] = [] for i, o in enumerate(self._optimizers): optim_state_dict = convert_to_non_torch_type(o.state_dict()) state["_optimizer_variables"].append(optim_state_dict) return state
[docs] @override(Policy) @DeveloperAPI def set_state(self, state: object) -> None: state = state.copy() # shallow copy # Set optimizer vars first. optimizer_vars = state.pop("_optimizer_variables", None) if optimizer_vars: assert len(optimizer_vars) == len(self._optimizers) for o, s in zip(self._optimizers, optimizer_vars): optim_state_dict = convert_to_torch_tensor( s, device=self.device) o.load_state_dict(optim_state_dict) # Then the Policy's (NN) weights. super().set_state(state)
[docs] @DeveloperAPI def extra_grad_process(self, optimizer: "torch.optim.Optimizer", loss: TensorType): """Called after each optimizer.zero_grad() + loss.backward() call. Called for each self._optimizers/loss-value pair. Allows for gradient processing before optimizer.step() is called. E.g. for gradient clipping. Args: optimizer (torch.optim.Optimizer): A torch optimizer object. loss (TensorType): The loss tensor associated with the optimizer. Returns: Dict[str, TensorType]: An dict with information on the gradient processing step. """ return {}
[docs] @DeveloperAPI def extra_compute_grad_fetches(self) -> Dict[str, any]: """Extra values to fetch and return from compute_gradients(). Returns: Dict[str, any]: Extra fetch dict to be added to the fetch dict of the compute_gradients call. """ return {LEARNER_STATS_KEY: {}} # e.g, stats, td error, etc.
[docs] @DeveloperAPI def extra_action_out( self, input_dict: Dict[str, TensorType], state_batches: List[TensorType], model: TorchModelV2, action_dist: TorchDistributionWrapper) -> Dict[str, TensorType]: """Returns dict of extra info to include in experience batch. Args: input_dict (Dict[str, TensorType]): Dict of model input tensors. state_batches (List[TensorType]): List of state tensors. model (TorchModelV2): Reference to the model object. action_dist (TorchDistributionWrapper): Torch action dist object to get log-probs (e.g. for already sampled actions). Returns: Dict[str, TensorType]: Extra outputs to return in a compute_actions() call (3rd return value). """ return {}
[docs] @DeveloperAPI def extra_grad_info(self, train_batch: SampleBatch) -> Dict[str, TensorType]: """Return dict of extra grad info. Args: train_batch (SampleBatch): The training batch for which to produce extra grad info for. Returns: Dict[str, TensorType]: The info dict carrying grad info per str key. """ return {}
[docs] @DeveloperAPI def optimizer( self ) -> Union[List["torch.optim.Optimizer"], "torch.optim.Optimizer"]: """Custom the local PyTorch optimizer(s) to use. Returns: Union[List[torch.optim.Optimizer], torch.optim.Optimizer]: The local PyTorch optimizer(s) to use for this Policy. """ if hasattr(self, "config"): return torch.optim.Adam( self.model.parameters(), lr=self.config["lr"]) else: return torch.optim.Adam(self.model.parameters())
[docs] @override(Policy) @DeveloperAPI def export_model(self, export_dir: str) -> None: """Exports the Policy's Model to local directory for serving. Creates a TorchScript model and saves it. Args: export_dir (str): Local writable directory or filename. """ self._lazy_tensor_dict(self._dummy_batch) # Provide dummy state inputs if not an RNN (torch cannot jit with # returned empty internal states list). if "state_in_0" not in self._dummy_batch: self._dummy_batch["state_in_0"] = \ self._dummy_batch["seq_lens"] = np.array([1.0]) seq_lens = self._dummy_batch["seq_lens"] state_ins = [] i = 0 while "state_in_{}".format(i) in self._dummy_batch: state_ins.append(self._dummy_batch["state_in_{}".format(i)]) i += 1 dummy_inputs = { k: self._dummy_batch[k] for k in self._dummy_batch.keys() if k != "is_training" } traced = torch.jit.trace(self.model, (dummy_inputs, state_ins, seq_lens)) if not os.path.exists(export_dir): os.makedirs(export_dir) file_name = os.path.join(export_dir, "")
[docs] @override(Policy) @DeveloperAPI def export_checkpoint(self, export_dir: str) -> None: """TODO(sven): implement for torch. """ raise NotImplementedError
[docs] @override(Policy) @DeveloperAPI def import_model_from_h5(self, import_file: str) -> None: """Imports weights into torch model.""" return self.model.import_from_h5(import_file)
def _lazy_tensor_dict(self, postprocessed_batch: SampleBatch, device=None): # TODO: (sven): Keep for a while to ensure backward compatibility. if not isinstance(postprocessed_batch, SampleBatch): postprocessed_batch = SampleBatch(postprocessed_batch) postprocessed_batch.set_get_interceptor( functools.partial( convert_to_torch_tensor, device=device or self.device)) return postprocessed_batch def _multi_gpu_parallel_grad_calc(self, sample_batches): """Performs a parallelized loss and gradient calculation over the batch. Splits up the given train batch into n shards (n=number of this Policy's devices) and passes each data shard (in parallel) through the loss function using the individual devices' models (self.model_gpu_towers). Then returns each tower's outputs. Args: sample_batches (List[SampleBatch]): A list of SampleBatch shards to calculate loss and gradients for. Returns: List[Tuple[List[TensorType], StatsDict]]: A list (one item per device) of 2-tuples with 1) gradient list and 2) stats dict. """ assert len(self.model_gpu_towers) == len(sample_batches) lock = threading.Lock() results = {} grad_enabled = torch.is_grad_enabled() def _worker(shard_idx, model, sample_batch, device): torch.set_grad_enabled(grad_enabled) try: with NullContextManager( ) if device.type == "cpu" else torch.cuda.device(device): loss_out = force_list( self._loss(self, model, self.dist_class, sample_batch)) # Call Model's custom-loss with Policy loss outputs and # train_batch. loss_out = model.custom_loss(loss_out, sample_batch) assert len(loss_out) == len(self._optimizers) # Loop through all optimizers. grad_info = {"allreduce_latency": 0.0} parameters = list(model.parameters()) all_grads = [None for _ in range(len(parameters))] for opt_idx, opt in enumerate(self._optimizers): # Erase gradients in all vars of the tower that this # optimizer would affect. param_indices = self.multi_gpu_param_groups[opt_idx] for param_idx, param in enumerate(parameters): if param_idx in param_indices and \ param.grad is not None: # Recompute gradients of loss over all variables. loss_out[opt_idx].backward(retain_graph=True) grad_info.update( self.extra_grad_process(opt, loss_out[opt_idx])) grads = [] # Note that return values are just references; # Calling zero_grad would modify the values. for param_idx, param in enumerate(parameters): if param_idx in param_indices: if param.grad is not None: grads.append(param.grad) all_grads[param_idx] = param.grad if self.distributed_world_size: start = time.time() if torch.cuda.is_available(): # Sadly, allreduce_coalesced does not work with # CUDA yet. for g in grads: torch.distributed.all_reduce( g, op=torch.distributed.ReduceOp.SUM) else: torch.distributed.all_reduce_coalesced( grads, op=torch.distributed.ReduceOp.SUM) for param_group in opt.param_groups: for p in param_group["params"]: if p.grad is not None: p.grad /= self.distributed_world_size grad_info[ "allreduce_latency"] += time.time() - start with lock: results[shard_idx] = (all_grads, grad_info) except Exception as e: with lock: results[shard_idx] = ValueError( e.args[0] + "\n" + "In tower {} on device {}".format(shard_idx, device)) # Single device (GPU) case. if len(self.devices) == 1: _worker(0, self.model, sample_batches[0], self.device) if isinstance(results[0], ValueError): raise (results[0]) return [results[0]] # Multi device (GPU) case: Parallelize via threads. else: threads = [ threading.Thread( target=_worker, args=(shard_idx, model, sample_batch, device)) for shard_idx, (model, sample_batch, device) in enumerate( zip(self.model_gpu_towers, sample_batches, self.devices)) ] for thread in threads: thread.start() for thread in threads: thread.join() # Gather all threads' outputs and return. outputs = [] for shard_idx in range(len(sample_batches)): output = results[shard_idx] if isinstance(output, Exception): raise output outputs.append(results[shard_idx]) return outputs
# TODO: (sven) Unify hyperparam annealing procedures across RLlib (tf/torch) # and for all possible hyperparams, not just lr. @DeveloperAPI class LearningRateSchedule: """Mixin for TFPolicy that adds a learning rate schedule.""" @DeveloperAPI def __init__(self, lr, lr_schedule): self._lr_schedule = None if lr_schedule is None: self.cur_lr = lr else: self._lr_schedule = PiecewiseSchedule( lr_schedule, outside_value=lr_schedule[-1][-1], framework=None) self.cur_lr = self._lr_schedule.value(0) @override(Policy) def on_global_var_update(self, global_vars): super().on_global_var_update(global_vars) if self._lr_schedule: self.cur_lr = self._lr_schedule.value(global_vars["timestep"]) for opt in self._optimizers: for p in opt.param_groups: p["lr"] = self.cur_lr @DeveloperAPI class EntropyCoeffSchedule: """Mixin for TorchPolicy that adds entropy coeff decay.""" @DeveloperAPI def __init__(self, entropy_coeff, entropy_coeff_schedule): self.entropy_coeff = entropy_coeff if entropy_coeff_schedule is None: self.entropy_coeff_schedule = ConstantSchedule( entropy_coeff, framework=None) else: # Allows for custom schedule similar to lr_schedule format if isinstance(entropy_coeff_schedule, list): self.entropy_coeff_schedule = PiecewiseSchedule( entropy_coeff_schedule, outside_value=entropy_coeff_schedule[-1][-1], framework=None) else: # Implements previous version but enforces outside_value self.entropy_coeff_schedule = PiecewiseSchedule( [[0, entropy_coeff], [entropy_coeff_schedule, 0.0]], outside_value=0.0, framework=None) @override(Policy) def on_global_var_update(self, global_vars): super(EntropyCoeffSchedule, self).on_global_var_update(global_vars) self.entropy_coeff = self.entropy_coeff_schedule.value( global_vars["timestep"]) @DeveloperAPI class DirectStepOptimizer: """Typesafe method for indicating apply gradients can directly step the optimizers with in-place gradients. """ _instance = None def __new__(cls): if DirectStepOptimizer._instance is None: DirectStepOptimizer._instance = super().__new__(cls) return DirectStepOptimizer._instance def __eq__(self, other): return type(self) == type(other) def __repr__(self): return "DirectStepOptimizer" _directStepOptimizerSingleton = DirectStepOptimizer()