Source code for ray.rllib.models.modelv2

from collections import OrderedDict
import contextlib
import gym
import numpy as np
from typing import Dict, List, Any, Union

from ray.rllib.models.preprocessors import get_preprocessor, \
from ray.rllib.models.repeated_values import RepeatedValues
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.view_requirement import ViewRequirement
from ray.rllib.utils import NullContextManager
from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI
from ray.rllib.utils.framework import try_import_tf, try_import_torch, \
from ray.rllib.utils.spaces.repeated import Repeated
from ray.rllib.utils.typing import ModelConfigDict, ModelInputDict, \

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()

[docs]@PublicAPI class ModelV2: """Defines an abstract neural network model for use with RLlib. Custom models should extend either TFModelV2 or TorchModelV2 instead of this class directly. Data flow: obs -> forward() -> model_out value_function() -> V(s) """ def __init__(self, obs_space: gym.spaces.Space, action_space: gym.spaces.Space, num_outputs: int, model_config: ModelConfigDict, name: str, framework: str): """Initializes a ModelV2 object. This method should create any variables used by the model. Args: obs_space (gym.spaces.Space): Observation space of the target gym env. This may have an `original_space` attribute that specifies how to unflatten the tensor into a ragged tensor. action_space (gym.spaces.Space): Action space of the target gym env. num_outputs (int): Number of output units of the model. model_config (ModelConfigDict): Config for the model, documented in ModelCatalog. name (str): Name (scope) for the model. framework (str): Either "tf" or "torch". """ self.obs_space: gym.spaces.Space = obs_space self.action_space: gym.spaces.Space = action_space self.num_outputs: int = num_outputs self.model_config: ModelConfigDict = model_config str = name or "default_model" self.framework: str = framework self._last_output = None self.time_major = self.model_config.get("_time_major") # Basic view requirement for all models: Use the observation as input. self.view_requirements = { SampleBatch.OBS: ViewRequirement(shift=0, space=self.obs_space), } # TODO: (sven): Get rid of `get_initial_state` once Trajectory # View API is supported across all of RLlib.
[docs] @PublicAPI def get_initial_state(self) -> List[np.ndarray]: """Get the initial recurrent state values for the model. Returns: List[np.ndarray]: List of np.array objects containing the initial hidden state of an RNN, if applicable. Examples: >>> def get_initial_state(self): >>> return [ >>> np.zeros(self.cell_size, np.float32), >>> np.zeros(self.cell_size, np.float32), >>> ] """ return []
[docs] @PublicAPI def forward(self, input_dict: Dict[str, TensorType], state: List[TensorType], seq_lens: TensorType) -> (TensorType, List[TensorType]): """Call the model with the given input tensors and state. Any complex observations (dicts, tuples, etc.) will be unpacked by __call__ before being passed to forward(). To access the flattened observation tensor, refer to input_dict["obs_flat"]. This method can be called any number of times. In eager execution, each call to forward() will eagerly evaluate the model. In symbolic execution, each call to forward creates a computation graph that operates over the variables of this model (i.e., shares weights). Custom models should override this instead of __call__. Args: input_dict (dict): dictionary of input tensors, including "obs", "obs_flat", "prev_action", "prev_reward", "is_training", "eps_id", "agent_id", "infos", and "t". state (list): list of state tensors with sizes matching those returned by get_initial_state + the batch dimension seq_lens (Tensor): 1d tensor holding input sequence lengths Returns: (outputs, state): The model output tensor of size [BATCH, num_outputs], and the new RNN state. Examples: >>> def forward(self, input_dict, state, seq_lens): >>> model_out, self._value_out = self.base_model( ... input_dict["obs"]) >>> return model_out, state """ raise NotImplementedError
[docs] @PublicAPI def value_function(self) -> TensorType: """Returns the value function output for the most recent forward pass. Note that a `forward` call has to be performed first, before this methods can return anything and thus that calling this method does not cause an extra forward pass through the network. Returns: value estimate tensor of shape [BATCH]. """ raise NotImplementedError
[docs] @PublicAPI def custom_loss(self, policy_loss: TensorType, loss_inputs: Dict[str, TensorType]) -> TensorType: """Override to customize the loss function used to optimize this model. This can be used to incorporate self-supervised losses (by defining a loss over existing input and output tensors of this model), and supervised losses (by defining losses over a variable-sharing copy of this model's layers). You can find an runnable example in examples/ Args: policy_loss (Union[List[Tensor],Tensor]): List of or single policy loss(es) from the policy. loss_inputs (dict): map of input placeholders for rollout data. Returns: Union[List[Tensor],Tensor]: List of or scalar tensor for the customized loss(es) for this model. """ return policy_loss
[docs] @PublicAPI def metrics(self) -> Dict[str, TensorType]: """Override to return custom metrics from your model. The stats will be reported as part of the learner stats, i.e., info.learner.[policy_id, e.g. "default_policy"].model.key1=metric1 Returns: Dict[str, TensorType]: The custom metrics for this model. """ return {}
def __call__( self, input_dict: Union[SampleBatch, ModelInputDict], state: List[Any] = None, seq_lens: TensorType = None) -> (TensorType, List[TensorType]): """Call the model with the given input tensors and state. This is the method used by RLlib to execute the forward pass. It calls forward() internally after unpacking nested observation tensors. Custom models should override forward() instead of __call__. Args: input_dict (Union[SampleBatch, ModelInputDict]): Dictionary of input tensors. state (list): list of state tensors with sizes matching those returned by get_initial_state + the batch dimension seq_lens (Tensor): 1D tensor holding input sequence lengths. Returns: (outputs, state): The model output tensor of size [BATCH, output_spec.size] or a list of tensors corresponding to output_spec.shape_list, and a list of state tensors of [BATCH, state_size_i]. """ # Original observations will be stored in "obs". # Flattened (preprocessed) obs will be stored in "obs_flat". # SampleBatch case: Models can now be called directly with a # SampleBatch (which also includes tracking-dict case (deprecated now), # where tensors get automatically converted). if isinstance(input_dict, SampleBatch): restored = input_dict.copy(shallow=True) # Backward compatibility. if seq_lens is None: seq_lens = input_dict.get(SampleBatch.SEQ_LENS) if not state: state = [] i = 0 while "state_in_{}".format(i) in input_dict: state.append(input_dict["state_in_{}".format(i)]) i += 1 input_dict["is_training"] = input_dict.is_training else: restored = input_dict.copy() # No Preprocessor used: `config.preprocessor_pref`=None. # TODO: This is unnecessary for when no preprocessor is used. # Obs are not flat then anymore. However, we'll keep this # here for backward-compatibility until Preprocessors have # been fully deprecated. if self.model_config.get("_no_preprocessing"): restored["obs_flat"] = input_dict["obs"] # Input to this Model went through a Preprocessor. # Generate extra keys: "obs_flat" (vs "obs", which will hold the # original obs). else: restored["obs"] = restore_original_dimensions( input_dict["obs"], self.obs_space, self.framework) try: if len(input_dict["obs"].shape) > 2: restored["obs_flat"] = flatten(input_dict["obs"], self.framework) else: restored["obs_flat"] = input_dict["obs"] except AttributeError: restored["obs_flat"] = input_dict["obs"] with self.context(): res = self.forward(restored, state or [], seq_lens) if ((not isinstance(res, list) and not isinstance(res, tuple)) or len(res) != 2): raise ValueError( "forward() must return a tuple of (output, state) tensors, " "got {}".format(res)) outputs, state_out = res if not isinstance(state_out, list): raise ValueError( "State output is not a list: {}".format(state_out)) self._last_output = outputs return outputs, state_out if len(state_out) > 0 else (state or []) # TODO: (sven) obsolete this method at some point (replace by # simply calling model directly with a sample_batch as only input).
[docs] @PublicAPI def from_batch(self, train_batch: SampleBatch, is_training: bool = True) -> (TensorType, List[TensorType]): """Convenience function that calls this model with a tensor batch. All this does is unpack the tensor batch to call this model with the right input dict, state, and seq len arguments. """ input_dict = train_batch.copy() input_dict["is_training"] = is_training states = [] i = 0 while "state_in_{}".format(i) in input_dict: states.append(input_dict["state_in_{}".format(i)]) i += 1 ret = self.__call__(input_dict, states, input_dict.get(SampleBatch.SEQ_LENS)) return ret
[docs] def import_from_h5(self, h5_file: str) -> None: """Imports weights from an h5 file. Args: h5_file (str): The h5 file name to import weights from. Example: >>> trainer = MyTrainer() >>> trainer.import_policy_model_from_h5("/tmp/weights.h5") >>> for _ in range(10): >>> trainer.train() """ raise NotImplementedError
[docs] @PublicAPI def last_output(self) -> TensorType: """Returns the last output returned from calling the model.""" return self._last_output
[docs] @PublicAPI def context(self) -> contextlib.AbstractContextManager: """Returns a contextmanager for the current forward pass.""" return NullContextManager()
[docs] @PublicAPI def variables(self, as_dict: bool = False ) -> Union[List[TensorType], Dict[str, TensorType]]: """Returns the list (or a dict) of variables for this model. Args: as_dict(bool): Whether variables should be returned as dict-values (using descriptive str keys). Returns: Union[List[any],Dict[str,any]]: The list (or dict if `as_dict` is True) of all variables of this ModelV2. """ raise NotImplementedError
[docs] @PublicAPI def trainable_variables( self, as_dict: bool = False ) -> Union[List[TensorType], Dict[str, TensorType]]: """Returns the list of trainable variables for this model. Args: as_dict(bool): Whether variables should be returned as dict-values (using descriptive keys). Returns: Union[List[any],Dict[str,any]]: The list (or dict if `as_dict` is True) of all trainable (tf)/requires_grad (torch) variables of this ModelV2. """ raise NotImplementedError
[docs] @PublicAPI def is_time_major(self) -> bool: """If True, data for calling this ModelV2 must be in time-major format. Returns bool: Whether this ModelV2 requires a time-major (TxBx...) data format. """ return self.time_major is True
@DeveloperAPI def flatten(obs: TensorType, framework: str) -> TensorType: """Flatten the given tensor.""" if framework in ["tf2", "tf", "tfe"]: return tf1.keras.layers.Flatten()(obs) elif framework == "torch": assert torch is not None return torch.flatten(obs, start_dim=1) else: raise NotImplementedError("flatten", framework) @DeveloperAPI def restore_original_dimensions(obs: TensorType, obs_space: gym.spaces.Space, tensorlib: Any = tf) -> TensorStructType: """Unpacks Dict and Tuple space observations into their original form. This is needed since we flatten Dict and Tuple observations in transit within a SampleBatch. Before sending them to the model though, we should unflatten them into Dicts or Tuples of tensors. Args: obs (TensorType): The flattened observation tensor. obs_space (gym.spaces.Space): The flattened obs space. If this has the `original_space` attribute, we will unflatten the tensor to that shape. tensorlib: The library used to unflatten (reshape) the array/tensor. Returns: single tensor or dict / tuple of tensors matching the original observation space. """ if tensorlib in ["tf", "tfe", "tf2"]: assert tf is not None tensorlib = tf elif tensorlib == "torch": assert torch is not None tensorlib = torch original_space = getattr(obs_space, "original_space", obs_space) return _unpack_obs(obs, original_space, tensorlib=tensorlib) # Cache of preprocessors, for if the user is calling unpack obs often. _cache = {} def _unpack_obs(obs: TensorType, space: gym.Space, tensorlib: Any = tf) -> TensorStructType: """Unpack a flattened Dict or Tuple observation array/tensor. Args: obs: The flattened observation tensor, with last dimension equal to the flat size and any number of batch dimensions. For example, for Box(4,), the obs may have shape [B, 4], or [B, N, M, 4] in case the Box was nested under two Repeated spaces. space: The original space prior to flattening tensorlib: The library used to unflatten (reshape) the array/tensor """ if isinstance(space, (gym.spaces.Dict, gym.spaces.Tuple, Repeated)): if id(space) in _cache: prep = _cache[id(space)] else: prep = get_preprocessor(space)(space) # Make an attempt to cache the result, if enough space left. if len(_cache) < 999: _cache[id(space)] = prep # Already unpacked? if (isinstance(space, gym.spaces.Tuple) and isinstance(obs, (list, tuple))) or \ (isinstance(space, gym.spaces.Dict) and isinstance(obs, dict)): return obs elif len(obs.shape) < 2 or obs.shape[-1] != prep.shape[0]: raise ValueError( "Expected flattened obs shape of [..., {}], got {}".format( prep.shape[0], obs.shape)) offset = 0 if tensorlib == tf: batch_dims = [ v if isinstance(v, int) else v.value for v in obs.shape[:-1] ] batch_dims = [-1 if v is None else v for v in batch_dims] else: batch_dims = list(obs.shape[:-1]) if isinstance(space, gym.spaces.Tuple): assert len(prep.preprocessors) == len(space.spaces), \ (len(prep.preprocessors) == len(space.spaces)) u = [] for p, v in zip(prep.preprocessors, space.spaces): obs_slice = obs[..., offset:offset + p.size] offset += p.size u.append( _unpack_obs( tensorlib.reshape(obs_slice, batch_dims + list(p.shape)), v, tensorlib=tensorlib)) elif isinstance(space, gym.spaces.Dict): assert len(prep.preprocessors) == len(space.spaces), \ (len(prep.preprocessors) == len(space.spaces)) u = OrderedDict() for p, (k, v) in zip(prep.preprocessors, space.spaces.items()): obs_slice = obs[..., offset:offset + p.size] offset += p.size u[k] = _unpack_obs( tensorlib.reshape(obs_slice, batch_dims + list(p.shape)), v, tensorlib=tensorlib) # Repeated space. else: assert isinstance(prep, RepeatedValuesPreprocessor), prep child_size = prep.child_preprocessor.size # The list lengths are stored in the first slot of the flat obs. lengths = obs[..., 0] # [B, ..., 1 + max_len * child_sz] -> [B, ..., max_len, child_sz] with_repeat_dim = tensorlib.reshape( obs[..., 1:], batch_dims + [space.max_len, child_size]) # Retry the unpack, dropping the List container space. u = _unpack_obs( with_repeat_dim, space.child_space, tensorlib=tensorlib) return RepeatedValues( u, lengths=lengths, max_len=prep._obs_space.max_len) return u else: return obs