wehub-resource-sync
294 lines
11 KiB
Python
294 lines
11 KiB
Python
import logging
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from typing import Dict, List, Tuple
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import gymnasium as gym
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import numpy as np
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import tree # pip install dm_tree
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from gymnasium.spaces import Discrete, MultiDiscrete
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from ray._common.deprecation import deprecation_warning
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from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.policy.rnn_sequencing import add_time_dimension
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.policy.view_requirement import ViewRequirement
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from ray.rllib.utils.annotations import OldAPIStack, override
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
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from ray.rllib.utils.tf_utils import flatten_inputs_to_1d_tensor, one_hot
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from ray.rllib.utils.typing import ModelConfigDict, TensorType
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from ray.util.debug import log_once
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tf1, tf, tfv = try_import_tf()
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logger = logging.getLogger(__name__)
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@OldAPIStack
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class RecurrentNetwork(TFModelV2):
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"""Helper class to simplify implementing RNN models with TFModelV2.
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Instead of implementing forward(), you can implement forward_rnn() which
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takes batches with the time dimension added already.
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Here is an example implementation for a subclass
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``MyRNNClass(RecurrentNetwork)``::
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def __init__(self, *args, **kwargs):
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super(MyModelClass, self).__init__(*args, **kwargs)
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cell_size = 256
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# Define input layers
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input_layer = tf.keras.layers.Input(
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shape=(None, obs_space.shape[0]))
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state_in_h = tf.keras.layers.Input(shape=(256, ))
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state_in_c = tf.keras.layers.Input(shape=(256, ))
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seq_in = tf.keras.layers.Input(shape=(), dtype=tf.int32)
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# Send to LSTM cell
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lstm_out, state_h, state_c = tf.keras.layers.LSTM(
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cell_size, return_sequences=True, return_state=True,
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name="lstm")(
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inputs=input_layer,
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mask=tf.sequence_mask(seq_in),
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initial_state=[state_in_h, state_in_c])
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output_layer = tf.keras.layers.Dense(...)(lstm_out)
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# Create the RNN model
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self.rnn_model = tf.keras.Model(
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inputs=[input_layer, seq_in, state_in_h, state_in_c],
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outputs=[output_layer, state_h, state_c])
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self.rnn_model.summary()
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"""
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@override(ModelV2)
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def forward(
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self,
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input_dict: Dict[str, TensorType],
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state: List[TensorType],
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seq_lens: TensorType,
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) -> Tuple[TensorType, List[TensorType]]:
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"""Adds time dimension to batch before sending inputs to forward_rnn().
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You should implement forward_rnn() in your subclass."""
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# Creating a __init__ function that acts as a passthrough and adding the warning
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# there led to errors probably due to the multiple inheritance. We encountered
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# the same error if we add the Deprecated decorator. We therefore add the
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# deprecation warning here.
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if log_once("recurrent_network_tf"):
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deprecation_warning(
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old="ray.rllib.models.tf.recurrent_net.RecurrentNetwork"
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)
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assert seq_lens is not None
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flat_inputs = input_dict["obs_flat"]
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inputs = add_time_dimension(
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padded_inputs=flat_inputs, seq_lens=seq_lens, framework="tf"
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)
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output, new_state = self.forward_rnn(
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inputs,
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state,
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seq_lens,
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)
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return tf.reshape(output, [-1, self.num_outputs]), new_state
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def forward_rnn(
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self, inputs: TensorType, state: List[TensorType], seq_lens: TensorType
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) -> Tuple[TensorType, List[TensorType]]:
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"""Call the model with the given input tensors and state.
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Args:
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inputs: observation tensor with shape [B, T, obs_size].
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state: list of state tensors, each with shape [B, T, size].
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seq_lens: 1d tensor holding input sequence lengths.
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Returns:
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(outputs, new_state): The model output tensor of shape
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[B, T, num_outputs] and the list of new state tensors each with
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shape [B, size].
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Sample implementation for the ``MyRNNClass`` example::
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def forward_rnn(self, inputs, state, seq_lens):
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model_out, h, c = self.rnn_model([inputs, seq_lens] + state)
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return model_out, [h, c]
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"""
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raise NotImplementedError("You must implement this for a RNN model")
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def get_initial_state(self) -> List[TensorType]:
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"""Get the initial recurrent state values for the model.
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Returns:
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list of np.array objects, if any
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Sample implementation for the ``MyRNNClass`` example::
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def get_initial_state(self):
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return [
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np.zeros(self.cell_size, np.float32),
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np.zeros(self.cell_size, np.float32),
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]
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"""
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raise NotImplementedError("You must implement this for a RNN model")
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@OldAPIStack
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class LSTMWrapper(RecurrentNetwork):
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"""An LSTM wrapper serving as an interface for ModelV2s that set use_lstm."""
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def __init__(
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self,
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obs_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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num_outputs: int,
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model_config: ModelConfigDict,
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name: str,
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):
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super(LSTMWrapper, self).__init__(
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obs_space, action_space, None, model_config, name
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)
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# At this point, self.num_outputs is the number of nodes coming
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# from the wrapped (underlying) model. In other words, self.num_outputs
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# is the input size for the LSTM layer.
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# If None, set it to the observation space.
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if self.num_outputs is None:
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self.num_outputs = int(np.prod(self.obs_space.shape))
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self.cell_size = model_config["lstm_cell_size"]
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self.use_prev_action = model_config["lstm_use_prev_action"]
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self.use_prev_reward = model_config["lstm_use_prev_reward"]
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self.action_space_struct = get_base_struct_from_space(self.action_space)
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self.action_dim = 0
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for space in tree.flatten(self.action_space_struct):
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if isinstance(space, Discrete):
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self.action_dim += space.n
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elif isinstance(space, MultiDiscrete):
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self.action_dim += np.sum(space.nvec)
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elif space.shape is not None:
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self.action_dim += int(np.prod(space.shape))
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else:
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self.action_dim += int(len(space))
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# Add prev-action/reward nodes to input to LSTM.
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if self.use_prev_action:
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self.num_outputs += self.action_dim
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if self.use_prev_reward:
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self.num_outputs += 1
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# Define input layers.
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input_layer = tf.keras.layers.Input(
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shape=(None, self.num_outputs), name="inputs"
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)
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# Set self.num_outputs to the number of output nodes desired by the
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# caller of this constructor.
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self.num_outputs = num_outputs
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state_in_h = tf.keras.layers.Input(shape=(self.cell_size,), name="h")
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state_in_c = tf.keras.layers.Input(shape=(self.cell_size,), name="c")
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seq_in = tf.keras.layers.Input(shape=(), name="seq_in", dtype=tf.int32)
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# Preprocess observation with a hidden layer and send to LSTM cell
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lstm_out, state_h, state_c = tf.keras.layers.LSTM(
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self.cell_size, return_sequences=True, return_state=True, name="lstm"
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)(
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inputs=input_layer,
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mask=tf.sequence_mask(seq_in),
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initial_state=[state_in_h, state_in_c],
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)
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# Postprocess LSTM output with another hidden layer and compute values
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logits = tf.keras.layers.Dense(
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self.num_outputs, activation=tf.keras.activations.linear, name="logits"
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)(lstm_out)
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values = tf.keras.layers.Dense(1, activation=None, name="values")(lstm_out)
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# Create the RNN model
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self._rnn_model = tf.keras.Model(
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inputs=[input_layer, seq_in, state_in_h, state_in_c],
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outputs=[logits, values, state_h, state_c],
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)
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# Print out model summary in INFO logging mode.
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if logger.isEnabledFor(logging.INFO):
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self._rnn_model.summary()
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# Add prev-a/r to this model's view, if required.
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if model_config["lstm_use_prev_action"]:
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self.view_requirements[SampleBatch.PREV_ACTIONS] = ViewRequirement(
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SampleBatch.ACTIONS, space=self.action_space, shift=-1
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)
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if model_config["lstm_use_prev_reward"]:
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self.view_requirements[SampleBatch.PREV_REWARDS] = ViewRequirement(
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SampleBatch.REWARDS, shift=-1
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)
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@override(RecurrentNetwork)
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def forward(
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self,
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input_dict: Dict[str, TensorType],
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state: List[TensorType],
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seq_lens: TensorType,
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) -> Tuple[TensorType, List[TensorType]]:
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assert seq_lens is not None
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# Push obs through "unwrapped" net's `forward()` first.
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wrapped_out, _ = self._wrapped_forward(input_dict, [], None)
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# Concat. prev-action/reward if required.
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prev_a_r = []
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# Prev actions.
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if self.model_config["lstm_use_prev_action"]:
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prev_a = input_dict[SampleBatch.PREV_ACTIONS]
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# If actions are not processed yet (in their original form as
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# have been sent to environment):
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# Flatten/one-hot into 1D array.
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if self.model_config["_disable_action_flattening"]:
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prev_a_r.append(
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flatten_inputs_to_1d_tensor(
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prev_a,
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spaces_struct=self.action_space_struct,
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time_axis=False,
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)
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)
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# If actions are already flattened (but not one-hot'd yet!),
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# one-hot discrete/multi-discrete actions here.
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else:
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if isinstance(self.action_space, (Discrete, MultiDiscrete)):
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prev_a = one_hot(prev_a, self.action_space)
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prev_a_r.append(
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tf.reshape(tf.cast(prev_a, tf.float32), [-1, self.action_dim])
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)
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# Prev rewards.
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if self.model_config["lstm_use_prev_reward"]:
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prev_a_r.append(
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tf.reshape(
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tf.cast(input_dict[SampleBatch.PREV_REWARDS], tf.float32), [-1, 1]
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)
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)
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# Concat prev. actions + rewards to the "main" input.
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if prev_a_r:
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wrapped_out = tf.concat([wrapped_out] + prev_a_r, axis=1)
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# Push everything through our LSTM.
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input_dict["obs_flat"] = wrapped_out
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return super().forward(input_dict, state, seq_lens)
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@override(RecurrentNetwork)
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def forward_rnn(
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self, inputs: TensorType, state: List[TensorType], seq_lens: TensorType
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) -> Tuple[TensorType, List[TensorType]]:
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model_out, self._value_out, h, c = self._rnn_model([inputs, seq_lens] + state)
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return model_out, [h, c]
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@override(ModelV2)
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def get_initial_state(self) -> List[np.ndarray]:
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return [
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np.zeros(self.cell_size, np.float32),
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np.zeros(self.cell_size, np.float32),
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]
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@override(ModelV2)
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def value_function(self) -> TensorType:
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return tf.reshape(self._value_out, [-1])
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