Source code for ray.rllib.policy.torch_policy

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

import ray
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils import force_list, NullContextManager
from ray.rllib.utils.annotations import DeveloperAPI, override
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.schedules import PiecewiseSchedule
from ray.rllib.utils.spaces.space_utils import normalize_action
from ray.rllib.utils.threading import with_lock
from ray.rllib.utils.torch_utils import convert_to_torch_tensor
from ray.rllib.utils.typing import GradInfoDict, ModelGradients, \
    ModelWeights, TensorType, TensorStructType, TrainerConfigDict

if TYPE_CHECKING:
    from ray.rllib.evaluation import Episode  # noqa

torch, nn = try_import_torch()

logger = logging.getLogger(__name__)


[docs]@DeveloperAPI class TorchPolicy(Policy): """PyTorch specific Policy class to use with RLlib."""
[docs] @DeveloperAPI def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, config: TrainerConfigDict, *, model: Optional[TorchModelV2] = None, loss: Optional[Callable[[ Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch ], Union[TensorType, List[TensorType]]]] = None, action_distribution_class: Optional[Type[ TorchDistributionWrapper]] = None, 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, ): """Initializes a TorchPolicy instance. Args: observation_space: Observation space of the policy. action_space: Action space of the policy. config: The Policy's config dict. model: 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 that returns one or more (a list of) scalar loss terms. action_distribution_class: Class for a torch action distribution. action_sampler_fn: A callable returning a sampled action and its log-likelihood given Policy, ModelV2, input_dict, state batches (optional), explore, and timestep. Provide `action_sampler_fn` if you would like to have full control over the action computation step, including the model forward pass, possible sampling from a distribution, and exploration logic. Note: If `action_sampler_fn` is given, `action_distribution_fn` must be None. If both `action_sampler_fn` and `action_distribution_fn` are None, RLlib will simply pass inputs through `self.model` to get distribution inputs, create the distribution object, sample from it, and apply some exploration logic to the results. The callable takes as inputs: Policy, ModelV2, input_dict (SampleBatch), state_batches (optional), explore, and timestep. action_distribution_fn: 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). Provide `action_distribution_fn` if you would like to only customize the model forward pass call. The resulting distribution parameters are then used by RLlib to create a distribution object, sample from it, and execute any exploration logic. Note: If `action_distribution_fn` is given, `action_sampler_fn` must be None. If both `action_sampler_fn` and `action_distribution_fn` are None, RLlib will simply pass inputs through `self.model` to get distribution inputs, create the distribution object, sample from it, and apply some exploration logic to the results. The callable takes as inputs: Policy, ModelV2, ModelInputDict, explore, timestep, is_training. max_seq_len: Max sequence length for LSTM training. get_batch_divisibility_req: Optional callable that returns the divisibility requirement for sample batches given the Policy. """ self.framework = config["framework"] = "torch" super().__init__(observation_space, action_space, config) # Create multi-GPU model towers, if necessary. # - The central main model will be stored under self.model, residing # on self.device (normally, a CPU). # - 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 or real) GPU or 1 CPU), no # parallelization will be done. # If no Model is provided, build a default one here. if model is None: dist_class, logit_dim = ModelCatalog.get_action_dist( action_space, self.config["model"], framework=self.framework) model = ModelCatalog.get_model_v2( obs_space=self.observation_space, action_space=self.action_space, num_outputs=logit_dim, model_config=self.config["model"], framework=self.framework) if action_distribution_class is None: action_distribution_class = dist_class # Get devices to build the graph on. worker_idx = self.config.get("worker_index", 0) if not config["_fake_gpus"] and \ ray.worker._mode() == ray.worker.LOCAL_MODE: num_gpus = 0 elif worker_idx == 0: num_gpus = config["num_gpus"] else: num_gpus = config["num_gpus_per_worker"] gpu_ids = list(range(torch.cuda.device_count())) # Place on one or more CPU(s) when either: # - Fake GPU mode. # - num_gpus=0 (either set by user or we are in local_mode=True). # - No GPUs available. if config["_fake_gpus"] or num_gpus == 0 or not gpu_ids: logger.info("TorchPolicy (worker={}) running on {}.".format( worker_idx if worker_idx > 0 else "local", "{} fake-GPUs".format(num_gpus) if config["_fake_gpus"] else "CPU")) self.device = torch.device("cpu") self.devices = [ self.device for _ in range(int(math.ceil(num_gpus)) or 1) ] self.model_gpu_towers = [ model if i == 0 else copy.deepcopy(model) for i in range(int(math.ceil(num_gpus)) or 1) ] if hasattr(self, "target_model"): self.target_models = { m: self.target_model for m in self.model_gpu_towers } self.model = model # Place on one or more actual GPU(s), when: # - num_gpus > 0 (set by user) AND # - local_mode=False AND # - actual GPUs available AND # - non-fake GPU mode. else: logger.info("TorchPolicy (worker={}) running on {} GPU(s).".format( worker_idx if worker_idx > 0 else "local", num_gpus)) # We are a remote worker (WORKER_MODE=1): # GPUs should be assigned to us by ray. if ray.worker._mode() == ray.worker.WORKER_MODE: gpu_ids = ray.get_gpu_ids() if len(gpu_ids) < num_gpus: raise ValueError( "TorchPolicy was not able to find enough GPU IDs! Found " f"{gpu_ids}, but num_gpus={num_gpus}.") self.devices = [ torch.device("cuda:{}".format(i)) for i, id_ in enumerate(gpu_ids) if i < num_gpus ] self.device = self.devices[0] ids = [id_ for i, id_ in enumerate(gpu_ids) if i < num_gpus] self.model_gpu_towers = [] for i, _ in enumerate(ids): model_copy = copy.deepcopy(model) self.model_gpu_towers.append(model_copy.to(self.devices[i])) if hasattr(self, "target_model"): self.target_models = { m: copy.deepcopy(self.target_model).to(self.devices[i]) for i, m in enumerate(self.model_gpu_towers) } 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 # To ensure backward compatibility: # Old way: If `loss` provided here, use as-is (as a function). if loss is not None: self._loss = loss # New way: Convert the overridden `self.loss` into a plain function, # so it can be called the same way as `loss` would be, ensuring # backward compatibility. elif self.loss.__func__.__qualname__ != "Policy.loss": self._loss = self.loss.__func__ # `loss` not provided nor overridden from Policy -> Set to None. else: self._loss = None 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)) # Create n sample-batch buffers (num_multi_gpu_tower_stacks), each # one with m towers (num_gpus). num_buffers = self.config.get("num_multi_gpu_tower_stacks", 1) self._loaded_batches = [[] for _ in range(num_buffers)] 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) 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) input_dict.set_training(True) # 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)
[docs] @override(Policy) @DeveloperAPI def compute_actions( self, obs_batch: Union[List[TensorStructType], TensorStructType], state_batches: Optional[List[TensorType]] = None, prev_action_batch: Union[List[TensorStructType], TensorStructType] = None, prev_reward_batch: Union[List[TensorStructType], TensorStructType] = None, info_batch: Optional[Dict[str, list]] = None, episodes: Optional[List["Episode"]] = None, explore: Optional[bool] = None, timestep: Optional[int] = None, **kwargs) -> \ Tuple[TensorStructType, 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.CUR_OBS: obs_batch, "is_training": False, }) 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)
@with_lock @override(Policy) @DeveloperAPI def compute_log_likelihoods( self, actions: Union[List[TensorStructType], TensorStructType], obs_batch: Union[List[TensorStructType], TensorStructType], state_batches: Optional[List[TensorType]] = None, prev_action_batch: Optional[Union[List[TensorStructType], TensorStructType]] = None, prev_reward_batch: Optional[Union[List[TensorStructType], TensorStructType]] = None, actions_normalized: bool = True, ) -> 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) # Normalize actions if necessary. actions = input_dict[SampleBatch.ACTIONS] if not actions_normalized and self.config["normalize_actions"]: actions = normalize_action(actions, self.action_space_struct) log_likelihoods = action_dist.logp(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
[docs] @override(Policy) @DeveloperAPI def load_batch_into_buffer( self, batch: SampleBatch, buffer_index: int = 0, ) -> int: # Set the is_training flag of the batch. batch.set_training(True) # Shortcut for 1 CPU only: Store batch in `self._loaded_batches`. if len(self.devices) == 1 and self.devices[0].type == "cpu": assert buffer_index == 0 pad_batch_to_sequences_of_same_size( batch=batch, max_seq_len=self.max_seq_len, shuffle=False, batch_divisibility_req=self.batch_divisibility_req, view_requirements=self.view_requirements, ) self._lazy_tensor_dict(batch) self._loaded_batches[0] = [batch] return len(batch) # Batch (len=28, seq-lens=[4, 7, 4, 10, 3]): # 0123 0123456 0123 0123456789ABC # 1) split into n per-GPU sub batches (n=2). # [0123 0123456] [012] [3 0123456789 ABC] # (len=14, 14 seq-lens=[4, 7, 3] [1, 10, 3]) slices = batch.timeslices(num_slices=len(self.devices)) # 2) zero-padding (max-seq-len=10). # - [0123000000 0123456000 0120000000] # - [3000000000 0123456789 ABC0000000] for slice in slices: pad_batch_to_sequences_of_same_size( batch=slice, max_seq_len=self.max_seq_len, shuffle=False, batch_divisibility_req=self.batch_divisibility_req, view_requirements=self.view_requirements, ) # 3) Load splits into the given buffer (consisting of n GPUs). slices = [ slice.to_device(self.devices[i]) for i, slice in enumerate(slices) ] self._loaded_batches[buffer_index] = slices # Return loaded samples per-device. return len(slices[0])
[docs] @override(Policy) @DeveloperAPI def get_num_samples_loaded_into_buffer(self, buffer_index: int = 0) -> int: if len(self.devices) == 1 and self.devices[0] == "/cpu:0": assert buffer_index == 0 return len(self._loaded_batches[buffer_index])
[docs] @override(Policy) @DeveloperAPI def learn_on_loaded_batch(self, offset: int = 0, buffer_index: int = 0): if not self._loaded_batches[buffer_index]: raise ValueError( "Must call Policy.load_batch_into_buffer() before " "Policy.learn_on_loaded_batch()!") # Get the correct slice of the already loaded batch to use, # based on offset and batch size. device_batch_size = \ self.config.get( "sgd_minibatch_size", self.config["train_batch_size"]) // \ len(self.devices) # Set Model to train mode. if self.model_gpu_towers: for t in self.model_gpu_towers: t.train() # Shortcut for 1 CPU only: Batch should already be stored in # `self._loaded_batches`. if len(self.devices) == 1 and self.devices[0].type == "cpu": assert buffer_index == 0 if device_batch_size >= len(self._loaded_batches[0][0]): batch = self._loaded_batches[0][0] else: batch = self._loaded_batches[0][0][offset:offset + device_batch_size] return self.learn_on_batch(batch) if len(self.devices) > 1: # Copy weights of main model (tower-0) to all other towers. state_dict = self.model.state_dict() # Just making sure tower-0 is really the same as self.model. assert self.model_gpu_towers[0] is self.model for tower in self.model_gpu_towers[1:]: tower.load_state_dict(state_dict) if device_batch_size >= sum( len(s) for s in self._loaded_batches[buffer_index]): device_batches = self._loaded_batches[buffer_index] else: device_batches = [ b[offset:offset + device_batch_size] for b in self._loaded_batches[buffer_index] ] # Do the (maybe parallelized) gradient calculation step. tower_outputs = self._multi_gpu_parallel_grad_calc(device_batches) # Mean-reduce gradients over GPU-towers (do this on CPU: self.device). 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] self.apply_gradients(_directStepOptimizerSingleton) batch_fetches = {} for i, batch in enumerate(device_batches): batch_fetches[f"tower_{i}"] = { LEARNER_STATS_KEY: self.extra_grad_info(batch) } batch_fetches.update(self.extra_compute_grad_fetches()) return batch_fetches
@with_lock @override(Policy) @DeveloperAPI def compute_gradients(self, postprocessed_batch: SampleBatch) -> ModelGradients: assert len(self.devices) == 1 # If not done yet, see whether we have to zero-pad this batch. if not postprocessed_batch.zero_padded: pad_batch_to_sequences_of_same_size( batch=postprocessed_batch, max_seq_len=self.max_seq_len, shuffle=False, batch_divisibility_req=self.batch_divisibility_req, view_requirements=self.view_requirements, ) postprocessed_batch.set_training(True) self._lazy_tensor_dict(postprocessed_batch, device=self.devices[0]) # Do the (maybe parallelized) gradient calculation step. tower_outputs = self._multi_gpu_parallel_grad_calc( [postprocessed_batch]) 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 = g.to(self.device) else: p.grad = torch.from_numpy(g).to(self.device) self._optimizers[0].step()
[docs] @DeveloperAPI def get_tower_stats(self, stats_name: str) -> List[TensorStructType]: """Returns list of per-tower stats, copied to this Policy's device. Args: stats_name: The name of the stats to average over (this str must exist as a key inside each tower's `tower_stats` dict). Returns: The list of stats tensor (structs) of all towers, copied to this Policy's device. Raises: AssertionError: If the `stats_name` cannot be found in any one of the tower's `tower_stats` dicts. """ data = [] for tower in self.model_gpu_towers: if stats_name in tower.tower_stats: data.append( tree.map_structure(lambda s: s.to(self.device), tower.tower_stats[stats_name])) assert len(data) > 0, \ f"Stats `{stats_name}` not found in any of the towers (you have " \ f"{len(self.model_gpu_towers)} towers in total)! Make " \ "sure you call the loss function on at least one of the towers." return data
[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_numpy(o.state_dict()) state["_optimizer_variables"].append(optim_state_dict) # Add exploration state. state["_exploration_state"] = \ self.exploration.get_state() return state
[docs] @override(Policy) @DeveloperAPI def set_state(self, state: dict) -> None: # Set optimizer vars first. optimizer_vars = state.get("_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) # Set exploration's state. if hasattr(self, "exploration") and "_exploration_state" in state: self.exploration.set_state(state=state["_exploration_state"]) # Then the Policy's (NN) weights. super().set_state(state)
[docs] @DeveloperAPI def extra_grad_process(self, optimizer: "torch.optim.Optimizer", loss: TensorType) -> Dict[str, 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: A torch optimizer object. loss: The loss tensor associated with the optimizer. Returns: 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: 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 of model input tensors. state_batches: List of state tensors. model: Reference to the model object. action_dist: Torch action dist object to get log-probs (e.g. for already sampled actions). Returns: Extra outputs to return in a `compute_actions_from_input_dict()` 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: The training batch for which to produce extra grad info for. Returns: 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: The local PyTorch optimizer(s) to use for this Policy. """ if hasattr(self, "config"): optimizers = [ torch.optim.Adam( self.model.parameters(), lr=self.config["lr"]) ] else: optimizers = [torch.optim.Adam(self.model.parameters())] if getattr(self, "exploration", None): optimizers = self.exploration.get_exploration_optimizer(optimizers) return optimizers
[docs] @override(Policy) @DeveloperAPI def export_model(self, export_dir: str, onnx: Optional[int] = None) -> None: """Exports the Policy's Model to local directory for serving. Creates a TorchScript model and saves it. Args: export_dir: Local writable directory or filename. onnx: If given, will export model in ONNX format. The value of this parameter set the ONNX OpSet version to use. """ 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[SampleBatch.SEQ_LENS] = np.array([1.0]) 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" } if not os.path.exists(export_dir): os.makedirs(export_dir) seq_lens = self._dummy_batch[SampleBatch.SEQ_LENS] if onnx: file_name = os.path.join(export_dir, "model.onnx") torch.onnx.export( self.model, (dummy_inputs, state_ins, seq_lens), file_name, export_params=True, opset_version=onnx, do_constant_folding=True, input_names=list(dummy_inputs.keys()) + ["state_ins", SampleBatch.SEQ_LENS], output_names=["output", "state_outs"], dynamic_axes={ k: { 0: "batch_size" } for k in list(dummy_inputs.keys()) + ["state_ins", SampleBatch.SEQ_LENS] }) else: traced = torch.jit.trace(self.model, (dummy_inputs, state_ins, seq_lens)) file_name = os.path.join(export_dir, "model.pt") traced.save(file_name)
[docs] @override(Policy) def export_checkpoint(self, export_dir: str) -> None: 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)
@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: A tuple consisting of a) actions, b) state_out, c) extra_fetches. """ 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_numpy((actions, state_out, extra_fetches)) 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: List[SampleBatch] ) -> List[Tuple[List[TensorType], GradInfoDict]]: """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: A list of SampleBatch shards to calculate loss and gradients for. Returns: A list (one item per device) of 2-tuples, each with 1) gradient list and 2) grad info 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: param.grad.data.zero_() # 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: import traceback with lock: results[shard_idx] = (ValueError( e.args[0] + "\n traceback" + traceback.format_exc() + "\n" + "In tower {} on device {}".format(shard_idx, device)), e) # Single device (GPU) or fake-GPU case (serialize for better # debugging). if len(self.devices) == 1 or self.config["_fake_gpus"]: for shard_idx, (model, sample_batch, device) in enumerate( zip(self.model_gpu_towers, sample_batches, self.devices)): _worker(shard_idx, model, sample_batch, device) # Raise errors right away for better debugging. last_result = results[len(results) - 1] if isinstance(last_result[0], ValueError): raise last_result[0] from last_result[1] # 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[0], Exception): raise output[0] from output[1] 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 TorchPolicy 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_schedule = None if entropy_coeff_schedule is None: self.entropy_coeff = entropy_coeff 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) self.entropy_coeff = self._entropy_coeff_schedule.value(0) @override(Policy) def on_global_var_update(self, global_vars): super(EntropyCoeffSchedule, self).on_global_var_update(global_vars) if self._entropy_coeff_schedule is not None: 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()