Source code for ray.rllib.models.torch.torch_modelv2

import gymnasium as gym
from typing import Dict, List, Union

from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import OldAPIStack, override
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import ModelConfigDict, TensorType

_, nn = try_import_torch()

[docs]@OldAPIStack class TorchModelV2(ModelV2): """Torch version of ModelV2. Note that this class by itself is not a valid model unless you inherit from nn.Module and implement forward() in a subclass."""
[docs] def __init__( self, obs_space: gym.spaces.Space, action_space: gym.spaces.Space, num_outputs: int, model_config: ModelConfigDict, name: str, ): """Initialize a TorchModelV2. Here is an example implementation for a subclass ``MyModelClass(TorchModelV2, nn.Module)``:: def __init__(self, *args, **kwargs): TorchModelV2.__init__(self, *args, **kwargs) nn.Module.__init__(self) self._hidden_layers = nn.Sequential(...) self._logits = ... self._value_branch = ... """ if not isinstance(self, nn.Module): raise ValueError( "Subclasses of TorchModelV2 must also inherit from " "nn.Module, e.g., MyModel(TorchModelV2, nn.Module)" ) ModelV2.__init__( self, obs_space, action_space, num_outputs, model_config, name, framework="torch", ) # Dict to store per multi-gpu tower stats into. # In PyTorch multi-GPU, we use a single TorchPolicy and copy # it's Model(s) n times (1 copy for each GPU). When computing the loss # on each tower, we cannot store the stats (e.g. `entropy`) inside the # policy object as this would lead to race conditions between the # different towers all accessing the same property at the same time. self.tower_stats = {}
[docs] @override(ModelV2) def variables( self, as_dict: bool = False ) -> Union[List[TensorType], Dict[str, TensorType]]: p = list(self.parameters()) if as_dict: return {k: p[i] for i, k in enumerate(self.state_dict().keys())} return p
[docs] @override(ModelV2) def trainable_variables( self, as_dict: bool = False ) -> Union[List[TensorType], Dict[str, TensorType]]: if as_dict: return { k: v for k, v in self.variables(as_dict=True).items() if v.requires_grad } return [v for v in self.variables() if v.requires_grad]