import copy import functools import logging import math import os import threading import time from typing import ( Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union, ) import gymnasium as gym import numpy as np import tree # pip install dm_tree 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, PolicyState 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 NullContextManager, force_list from ray.rllib.utils.annotations import OldAPIStack, override from ray.rllib.utils.error import ERR_MSG_TORCH_POLICY_CANNOT_SAVE_MODEL from ray.rllib.utils.framework import try_import_torch from ray.rllib.utils.metrics import ( DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_AGENT_STEPS_TRAINED, NUM_GRAD_UPDATES_LIFETIME, ) from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY from ray.rllib.utils.numpy import convert_to_numpy 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 ( AlgorithmConfigDict, GradInfoDict, ModelGradients, ModelWeights, TensorStructType, TensorType, ) torch, nn = try_import_torch() logger = logging.getLogger(__name__) @OldAPIStack class TorchPolicy(Policy): """PyTorch specific Policy class to use with RLlib.""" def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, config: AlgorithmConfigDict, *, 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]], Union[ Tuple[TensorType, TensorType, List[TensorType]], Tuple[TensorType, TensorType, TensorType, List[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 either a sampled action, its log-likelihood and updated state or a sampled action, its log-likelihood, updated state and action distribution inputs 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" self._loss_initialized = False 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. num_gpus = self._get_num_gpus_for_policy() gpu_ids = list(range(torch.cuda.device_count())) logger.info(f"Found {len(gpu_ids)} visible cuda devices.") # 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: 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: # We are a remote worker (WORKER_MODE=1): # GPUs should be assigned to us by ray. if ray._private.worker._mode() == ray._private.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) ) @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 = ( torch.tensor( [1] * len(state_batches[0]), dtype=torch.long, device=state_batches[0].device, ) if state_batches else None ) return self._compute_action_helper( input_dict, state_batches, seq_lens, explore, timestep ) @override(Policy) 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=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) 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, **kwargs, ) -> 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) 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) self.num_grad_updates += 1 if self.model: fetches["model"] = self.model.metrics() fetches.update( { "custom_metrics": learn_stats, NUM_AGENT_STEPS_TRAINED: postprocessed_batch.count, NUM_GRAD_UPDATES_LIFETIME: self.num_grad_updates, # -1, b/c we have to measure this diff before we do the update above. DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY: ( self.num_grad_updates - 1 - (postprocessed_batch.num_grad_updates or 0) ), } ) return fetches @override(Policy) 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]) @override(Policy) 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 sum(len(b) for b in self._loaded_batches[buffer_index]) @override(Policy) 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("minibatch_size") if device_batch_size is None: device_batch_size = self.config.get( "sgd_minibatch_size", self.config["train_batch_size"], ) device_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] ] # Callback handling. batch_fetches = {} for i, batch in enumerate(device_batches): custom_metrics = {} self.callbacks.on_learn_on_batch( policy=self, train_batch=batch, result=custom_metrics ) batch_fetches[f"tower_{i}"] = {"custom_metrics": custom_metrics} # 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) self.num_grad_updates += 1 for i, (model, batch) in enumerate(zip(self.model_gpu_towers, device_batches)): batch_fetches[f"tower_{i}"].update( { LEARNER_STATS_KEY: self.extra_grad_info(batch), "model": model.metrics(), NUM_GRAD_UPDATES_LIFETIME: self.num_grad_updates, # -1, b/c we have to measure this diff before we do the update # above. DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY: ( self.num_grad_updates - 1 - (batch.num_grad_updates or 0) ), } ) batch_fetches.update(self.extra_compute_grad_fetches()) return batch_fetches @with_lock @override(Policy) 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}) @override(Policy) 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() 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 @override(Policy) def get_weights(self) -> ModelWeights: return {k: v.cpu().detach().numpy() for k, v in self.model.state_dict().items()} @override(Policy) def set_weights(self, weights: ModelWeights) -> None: weights = convert_to_torch_tensor(weights, device=self.device) self.model.load_state_dict(weights) @override(Policy) def is_recurrent(self) -> bool: return self._is_recurrent @override(Policy) def num_state_tensors(self) -> int: return len(self.model.get_initial_state()) @override(Policy) def get_initial_state(self) -> List[TensorType]: return [s.detach().cpu().numpy() for s in self.model.get_initial_state()] @override(Policy) def get_state(self) -> PolicyState: 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. if self.exploration: # This is not compatible with RLModules, which have a method # `forward_exploration` to specify custom exploration behavior. state["_exploration_state"] = self.exploration.get_state() return state @override(Policy) def set_state(self, state: PolicyState) -> 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): # Torch optimizer param_groups include things like beta, etc. These # parameters should be left as scalar and not converted to tensors. # otherwise, torch.optim.step() will start to complain. optim_state_dict = {"param_groups": s["param_groups"]} optim_state_dict["state"] = convert_to_torch_tensor( s["state"], 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"]) # Restore global timestep. self.global_timestep = state["global_timestep"] # Then the Policy's (NN) weights and connectors. super().set_state(state) 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 {} 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. 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 {} 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 {} 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 self.exploration: optimizers = self.exploration.get_exploration_optimizer(optimizers) return optimizers @override(Policy) 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. """ os.makedirs(export_dir, exist_ok=True) if onnx: 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]) seq_lens = self._dummy_batch[SampleBatch.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" } 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] }, ) # Save the torch.Model (architecture and weights, so it can be retrieved # w/o access to the original (custom) Model or Policy code). else: filename = os.path.join(export_dir, "model.pt") try: torch.save(self.model, f=filename) except Exception: if os.path.exists(filename): os.remove(filename) logger.warning(ERR_MSG_TORCH_POLICY_CANNOT_SAVE_MODEL) @override(Policy) 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 action_sampler_outputs = self.action_sampler_fn( self, self.model, input_dict, state_batches, explore=explore, timestep=timestep, ) if len(action_sampler_outputs) == 4: actions, logp, dist_inputs, state_out = action_sampler_outputs else: actions, logp, state_out = action_sampler_outputs 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( # noqa: E501 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( f"Error In tower {shard_idx} on device " f"{device} during multi GPU parallel gradient " f"calculation:" f": {e}\n" f"Traceback: \n" f"{traceback.format_exc()}\n" ), 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 @OldAPIStack 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) is type(other) def __repr__(self): return "DirectStepOptimizer" _directStepOptimizerSingleton = DirectStepOptimizer()