minigpt2/lib/python3.10/site-packages/ray/rllib/utils/policy.py

import gymnasium as gym
import logging
import numpy as np
from typing import (
    Callable,
    Dict,
    List,
    Optional,
    Tuple,
    Type,
    Union,
    TYPE_CHECKING,
)
import tree  # pip install dm_tree


import ray.cloudpickle as pickle
from ray.rllib.core.rl_module import validate_module_id
from ray.rllib.models.preprocessors import ATARI_OBS_SHAPE
from ray.rllib.policy.policy import PolicySpec
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.deprecation import Deprecated
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.typing import (
    ActionConnectorDataType,
    AgentConnectorDataType,
    AgentConnectorsOutput,
    PartialAlgorithmConfigDict,
    PolicyState,
    TensorStructType,
    TensorType,
)
from ray.util import log_once
from ray.util.annotations import PublicAPI

if TYPE_CHECKING:
    from ray.rllib.policy.policy import Policy

logger = logging.getLogger(__name__)

tf1, tf, tfv = try_import_tf()


@PublicAPI
def create_policy_for_framework(
    policy_id: str,
    policy_class: Type["Policy"],
    merged_config: PartialAlgorithmConfigDict,
    observation_space: gym.Space,
    action_space: gym.Space,
    worker_index: int = 0,
    session_creator: Optional[Callable[[], "tf1.Session"]] = None,
    seed: Optional[int] = None,
):
    """Framework-specific policy creation logics.

    Args:
        policy_id: Policy ID.
        policy_class: Policy class type.
        merged_config: Complete policy config.
        observation_space: Observation space of env.
        action_space: Action space of env.
        worker_index: Index of worker holding this policy. Default is 0.
        session_creator: An optional tf1.Session creation callable.
        seed: Optional random seed.
    """
    from ray.rllib.algorithms.algorithm_config import AlgorithmConfig

    if isinstance(merged_config, AlgorithmConfig):
        merged_config = merged_config.to_dict()

    # add policy_id to merged_config
    merged_config["__policy_id"] = policy_id

    framework = merged_config.get("framework", "tf")
    # Tf.
    if framework in ["tf2", "tf"]:
        var_scope = policy_id + (f"_wk{worker_index}" if worker_index else "")
        # For tf static graph, build every policy in its own graph
        # and create a new session for it.
        if framework == "tf":
            with tf1.Graph().as_default():
                # Session creator function provided manually -> Use this one to
                # create the tf1 session.
                if session_creator:
                    sess = session_creator()
                # Use a default session creator, based only on our `tf_session_args` in
                # the config.
                else:
                    sess = tf1.Session(
                        config=tf1.ConfigProto(**merged_config["tf_session_args"])
                    )

                with sess.as_default():
                    # Set graph-level seed.
                    if seed is not None:
                        tf1.set_random_seed(seed)
                    with tf1.variable_scope(var_scope):
                        return policy_class(
                            observation_space, action_space, merged_config
                        )
        # For tf-eager: no graph, no session.
        else:
            with tf1.variable_scope(var_scope):
                return policy_class(observation_space, action_space, merged_config)
    # Non-tf: No graph, no session.
    else:
        return policy_class(observation_space, action_space, merged_config)


@PublicAPI(stability="alpha")
def parse_policy_specs_from_checkpoint(
    path: str,
) -> Tuple[PartialAlgorithmConfigDict, Dict[str, PolicySpec], Dict[str, PolicyState]]:
    """Read and parse policy specifications from a checkpoint file.

    Args:
        path: Path to a policy checkpoint.

    Returns:
        A tuple of: base policy config, dictionary of policy specs, and
        dictionary of policy states.
    """
    with open(path, "rb") as f:
        checkpoint_dict = pickle.load(f)
    # Policy data is contained as a serialized binary blob under their
    # ID keys.
    w = pickle.loads(checkpoint_dict["worker"])

    policy_config = w["policy_config"]
    policy_states = w.get("policy_states", w["state"])
    serialized_policy_specs = w["policy_specs"]
    policy_specs = {
        id: PolicySpec.deserialize(spec) for id, spec in serialized_policy_specs.items()
    }

    return policy_config, policy_specs, policy_states


@PublicAPI(stability="alpha")
def local_policy_inference(
    policy: "Policy",
    env_id: str,
    agent_id: str,
    obs: TensorStructType,
    reward: Optional[float] = None,
    terminated: Optional[bool] = None,
    truncated: Optional[bool] = None,
    info: Optional[Dict] = None,
    explore: bool = None,
    timestep: Optional[int] = None,
) -> TensorStructType:
    """Run a connector enabled policy using environment observation.

    policy_inference manages policy and agent/action connectors,
    so the user does not have to care about RNN state buffering or
    extra fetch dictionaries.
    Note that connectors are intentionally run separately from
    compute_actions_from_input_dict(), so we can have the option
    of running per-user connectors on the client side in a
    server-client deployment.

    Args:
        policy: Policy object used in inference.
        env_id: Environment ID. RLlib builds environments' trajectories internally with
            connectors based on this, i.e. one trajectory per (env_id, agent_id) tuple.
        agent_id: Agent ID. RLlib builds agents' trajectories internally with connectors
            based on this, i.e. one trajectory per (env_id, agent_id) tuple.
        obs: Environment observation to base the action on.
        reward: Reward that is potentially used during inference. If not required,
            may be left empty. Some policies have ViewRequirements that require this.
            This can be set to zero at the first inference step - for example after
            calling gmy.Env.reset.
        terminated: `Terminated` flag that is potentially used during inference. If not
            required, may be left None. Some policies have ViewRequirements that
            require this extra information.
        truncated: `Truncated` flag that is potentially used during inference. If not
            required, may be left None. Some policies have ViewRequirements that
            require this extra information.
        info: Info that is potentially used durin inference. If not required,
            may be left empty. Some policies have ViewRequirements that require this.
        explore: Whether to pick an exploitation or exploration action
            (default: None -> use self.config["explore"]).
        timestep: The current (sampling) time step.

    Returns:
        List of outputs from policy forward pass.
    """
    assert (
        policy.agent_connectors
    ), "policy_inference only works with connector enabled policies."

    __check_atari_obs_space(obs)

    # Put policy in inference mode, so we don't spend time on training
    # only transformations.
    policy.agent_connectors.in_eval()
    policy.action_connectors.in_eval()

    # TODO(jungong) : support multiple env, multiple agent inference.
    input_dict = {SampleBatch.NEXT_OBS: obs}
    if reward is not None:
        input_dict[SampleBatch.REWARDS] = reward
    if terminated is not None:
        input_dict[SampleBatch.TERMINATEDS] = terminated
    if truncated is not None:
        input_dict[SampleBatch.TRUNCATEDS] = truncated
    if info is not None:
        input_dict[SampleBatch.INFOS] = info

    acd_list: List[AgentConnectorDataType] = [
        AgentConnectorDataType(env_id, agent_id, input_dict)
    ]
    ac_outputs: List[AgentConnectorsOutput] = policy.agent_connectors(acd_list)
    outputs = []
    for ac in ac_outputs:
        policy_output = policy.compute_actions_from_input_dict(
            ac.data.sample_batch,
            explore=explore,
            timestep=timestep,
        )

        # Note (Kourosh): policy output is batched, the AgentConnectorDataType should
        # not be batched during inference. This is the assumption made in AgentCollector
        policy_output = tree.map_structure(lambda x: x[0], policy_output)

        action_connector_data = ActionConnectorDataType(
            env_id, agent_id, ac.data.raw_dict, policy_output
        )

        if policy.action_connectors:
            acd = policy.action_connectors(action_connector_data)
            actions = acd.output
        else:
            actions = policy_output[0]

        outputs.append(actions)

        # Notify agent connectors with this new policy output.
        # Necessary for state buffering agent connectors, for example.
        policy.agent_connectors.on_policy_output(action_connector_data)
    return outputs


@PublicAPI
def compute_log_likelihoods_from_input_dict(
    policy: "Policy", batch: Union[SampleBatch, Dict[str, TensorStructType]]
):
    """Returns log likelihood for actions in given batch for policy.

    Computes likelihoods by passing the observations through the current
    policy's `compute_log_likelihoods()` method

    Args:
        batch: The SampleBatch or MultiAgentBatch to calculate action
            log likelihoods from. This batch/batches must contain OBS
            and ACTIONS keys.

    Returns:
        The probabilities of the actions in the batch, given the
        observations and the policy.
    """
    num_state_inputs = 0
    for k in batch.keys():
        if k.startswith("state_in_"):
            num_state_inputs += 1
    state_keys = ["state_in_{}".format(i) for i in range(num_state_inputs)]
    log_likelihoods: TensorType = policy.compute_log_likelihoods(
        actions=batch[SampleBatch.ACTIONS],
        obs_batch=batch[SampleBatch.OBS],
        state_batches=[batch[k] for k in state_keys],
        prev_action_batch=batch.get(SampleBatch.PREV_ACTIONS),
        prev_reward_batch=batch.get(SampleBatch.PREV_REWARDS),
        actions_normalized=policy.config.get("actions_in_input_normalized", False),
    )
    return log_likelihoods


@Deprecated(new="Policy.from_checkpoint([checkpoint path], [policy IDs]?)", error=True)
def load_policies_from_checkpoint(path, policy_ids=None):
    pass


def __check_atari_obs_space(obs):
    # TODO(Artur): Remove this after we have migrated deepmind style preprocessing into
    #  connectors (and don't auto-wrap in RW anymore)
    if any(
        o.shape == ATARI_OBS_SHAPE if isinstance(o, np.ndarray) else False
        for o in tree.flatten(obs)
    ):
        if log_once("warn_about_possibly_non_wrapped_atari_env"):
            logger.warning(
                "The observation you fed into local_policy_inference() has "
                "dimensions (210, 160, 3), which is the standard for atari "
                "environments. If RLlib raises an error including a related "
                "dimensionality mismatch, you may need to use "
                "ray.rllib.env.wrappers.atari_wrappers.wrap_deepmind to wrap "
                "you environment."
            )


# @OldAPIStack
validate_policy_id = validate_module_id