image-match/python/py/Lib/site-packages/ray/rllib/policy/tf_policy_template.py

from typing import Callable, Dict, List, Optional, Tuple, Type, Union

import gymnasium as gym

from ray._common.deprecation import (
    DEPRECATED_VALUE,
    deprecation_warning,
)
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
from ray.rllib.policy import eager_tf_policy
from ray.rllib.policy.dynamic_tf_policy import DynamicTFPolicy
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.utils import add_mixins, force_list
from ray.rllib.utils.annotations import OldAPIStack, override
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
from ray.rllib.utils.typing import (
    AlgorithmConfigDict,
    ModelGradients,
    TensorType,
)

tf1, tf, tfv = try_import_tf()


@OldAPIStack
def build_tf_policy(
    name: str,
    *,
    loss_fn: Callable[
        [Policy, ModelV2, Type[TFActionDistribution], SampleBatch],
        Union[TensorType, List[TensorType]],
    ],
    get_default_config: Optional[Callable[[None], AlgorithmConfigDict]] = None,
    postprocess_fn=None,
    stats_fn: Optional[Callable[[Policy, SampleBatch], Dict[str, TensorType]]] = None,
    optimizer_fn: Optional[
        Callable[[Policy, AlgorithmConfigDict], "tf.keras.optimizers.Optimizer"]
    ] = None,
    compute_gradients_fn: Optional[
        Callable[[Policy, "tf.keras.optimizers.Optimizer", TensorType], ModelGradients]
    ] = None,
    apply_gradients_fn: Optional[
        Callable[
            [Policy, "tf.keras.optimizers.Optimizer", ModelGradients], "tf.Operation"
        ]
    ] = None,
    grad_stats_fn: Optional[
        Callable[[Policy, SampleBatch, ModelGradients], Dict[str, TensorType]]
    ] = None,
    extra_action_out_fn: Optional[Callable[[Policy], Dict[str, TensorType]]] = None,
    extra_learn_fetches_fn: Optional[Callable[[Policy], Dict[str, TensorType]]] = None,
    validate_spaces: Optional[
        Callable[[Policy, gym.Space, gym.Space, AlgorithmConfigDict], None]
    ] = None,
    before_init: Optional[
        Callable[[Policy, gym.Space, gym.Space, AlgorithmConfigDict], None]
    ] = None,
    before_loss_init: Optional[
        Callable[
            [Policy, gym.spaces.Space, gym.spaces.Space, AlgorithmConfigDict], None
        ]
    ] = None,
    after_init: Optional[
        Callable[[Policy, gym.Space, gym.Space, AlgorithmConfigDict], None]
    ] = None,
    make_model: Optional[
        Callable[
            [Policy, gym.spaces.Space, gym.spaces.Space, AlgorithmConfigDict], ModelV2
        ]
    ] = None,
    action_sampler_fn: Optional[
        Callable[[TensorType, List[TensorType]], Tuple[TensorType, TensorType]]
    ] = None,
    action_distribution_fn: Optional[
        Callable[
            [Policy, ModelV2, TensorType, TensorType, TensorType],
            Tuple[TensorType, type, List[TensorType]],
        ]
    ] = None,
    mixins: Optional[List[type]] = None,
    get_batch_divisibility_req: Optional[Callable[[Policy], int]] = None,
    # Deprecated args.
    obs_include_prev_action_reward=DEPRECATED_VALUE,
    extra_action_fetches_fn=None,  # Use `extra_action_out_fn`.
    gradients_fn=None,  # Use `compute_gradients_fn`.
) -> Type[DynamicTFPolicy]:
    """Helper function for creating a dynamic tf policy at runtime.

    Functions will be run in this order to initialize the policy:
        1. Placeholder setup: postprocess_fn
        2. Loss init: loss_fn, stats_fn
        3. Optimizer init: optimizer_fn, gradients_fn, apply_gradients_fn,
                           grad_stats_fn

    This means that you can e.g., depend on any policy attributes created in
    the running of `loss_fn` in later functions such as `stats_fn`.

    In eager mode, the following functions will be run repeatedly on each
    eager execution: loss_fn, stats_fn, gradients_fn, apply_gradients_fn,
    and grad_stats_fn.

    This means that these functions should not define any variables internally,
    otherwise they will fail in eager mode execution. Variable should only
    be created in make_model (if defined).

    Args:
        name: Name of the policy (e.g., "PPOTFPolicy").
            loss_fn (Callable[[
                Policy, ModelV2, Type[TFActionDistribution], SampleBatch],
                Union[TensorType, List[TensorType]]]): Callable for calculating a
                loss tensor.
            get_default_config (Optional[Callable[[None], AlgorithmConfigDict]]):
                Optional callable that returns the default config to merge with any
                overrides. If None, uses only(!) the user-provided
                PartialAlgorithmConfigDict as dict for this Policy.
            postprocess_fn (Optional[Callable[[Policy, SampleBatch,
                Optional[Dict[AgentID, SampleBatch]], Episode], None]]):
                Optional callable for post-processing experience batches (called
                after the parent class' `postprocess_trajectory` method).
            stats_fn (Optional[Callable[[Policy, SampleBatch],
                Dict[str, TensorType]]]): Optional callable that returns a dict of
                TF tensors to fetch given the policy and batch input tensors. If
                None, will not compute any stats.
            optimizer_fn (Optional[Callable[[Policy, AlgorithmConfigDict],
                "tf.keras.optimizers.Optimizer"]]): Optional callable that returns
                a tf.Optimizer given the policy and config. If None, will call
                the base class' `optimizer()` method instead (which returns a
                tf1.train.AdamOptimizer).
            compute_gradients_fn (Optional[Callable[[Policy,
                "tf.keras.optimizers.Optimizer", TensorType], ModelGradients]]):
                Optional callable that returns a list of gradients. If None,
                this defaults to optimizer.compute_gradients([loss]).
            apply_gradients_fn (Optional[Callable[[Policy,
                "tf.keras.optimizers.Optimizer", ModelGradients],
                "tf.Operation"]]): Optional callable that returns an apply
                gradients op given policy, tf-optimizer, and grads_and_vars. If
                None, will call the base class' `build_apply_op()` method instead.
            grad_stats_fn (Optional[Callable[[Policy, SampleBatch, ModelGradients],
                Dict[str, TensorType]]]): Optional callable that returns a dict of
                TF fetches given the policy, batch input, and gradient tensors. If
                None, will not collect any gradient stats.
            extra_action_out_fn (Optional[Callable[[Policy],
                Dict[str, TensorType]]]): Optional callable that returns
                a dict of TF fetches given the policy object. If None, will not
                perform any extra fetches.
            extra_learn_fetches_fn (Optional[Callable[[Policy],
                Dict[str, TensorType]]]): Optional callable that returns a dict of
                extra values to fetch and return when learning on a batch. If None,
                will call the base class' `extra_compute_grad_fetches()` method
                instead.
            validate_spaces (Optional[Callable[[Policy, gym.Space, gym.Space,
                AlgorithmConfigDict], None]]): Optional callable that takes the
                Policy, observation_space, action_space, and config to check
                the spaces for correctness. If None, no spaces checking will be
                done.
            before_init (Optional[Callable[[Policy, gym.Space, gym.Space,
                AlgorithmConfigDict], None]]): Optional callable to run at the
                beginning of policy init that takes the same arguments as the
                policy constructor. If None, this step will be skipped.
            before_loss_init (Optional[Callable[[Policy, gym.spaces.Space,
                gym.spaces.Space, AlgorithmConfigDict], None]]): Optional callable to
                run prior to loss init. If None, this step will be skipped.
            after_init (Optional[Callable[[Policy, gym.Space, gym.Space,
                AlgorithmConfigDict], None]]): Optional callable to run at the end of
                policy init. If None, this step will be skipped.
            make_model (Optional[Callable[[Policy, gym.spaces.Space,
                gym.spaces.Space, AlgorithmConfigDict], ModelV2]]): Optional callable
                that returns a ModelV2 object.
                All policy variables should be created in this function. If None,
                a default ModelV2 object will be created.
            action_sampler_fn (Optional[Callable[[TensorType, List[TensorType]],
                Tuple[TensorType, TensorType]]]): A callable returning a sampled
                action and its log-likelihood given observation and state inputs.
                If None, will either use `action_distribution_fn` or
                compute actions by calling self.model, then sampling from the
                so parameterized action distribution.
            action_distribution_fn (Optional[Callable[[Policy, ModelV2, TensorType,
                TensorType, TensorType],
                Tuple[TensorType, type, List[TensorType]]]]): Optional 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). If None, will either
                use `action_sampler_fn` or compute actions by calling self.model,
                then sampling from the so parameterized action distribution.
            mixins (Optional[List[type]]): Optional list of any class mixins for
                the returned policy class. These mixins will be applied in order
                and will have higher precedence than the DynamicTFPolicy class.
            get_batch_divisibility_req (Optional[Callable[[Policy], int]]):
                Optional callable that returns the divisibility requirement for
                sample batches. If None, will assume a value of 1.

    Returns:
        Type[DynamicTFPolicy]: A child class of DynamicTFPolicy based on the
            specified args.
    """
    original_kwargs = locals().copy()
    base = add_mixins(DynamicTFPolicy, mixins)

    if obs_include_prev_action_reward != DEPRECATED_VALUE:
        deprecation_warning(old="obs_include_prev_action_reward", error=True)

    if extra_action_fetches_fn is not None:
        deprecation_warning(
            old="extra_action_fetches_fn", new="extra_action_out_fn", error=True
        )

    if gradients_fn is not None:
        deprecation_warning(old="gradients_fn", new="compute_gradients_fn", error=True)

    class policy_cls(base):
        def __init__(
            self,
            obs_space,
            action_space,
            config,
            existing_model=None,
            existing_inputs=None,
        ):
            if validate_spaces:
                validate_spaces(self, obs_space, action_space, config)

            if before_init:
                before_init(self, obs_space, action_space, config)

            def before_loss_init_wrapper(policy, obs_space, action_space, config):
                if before_loss_init:
                    before_loss_init(policy, obs_space, action_space, config)

                if extra_action_out_fn is None or policy._is_tower:
                    extra_action_fetches = {}
                else:
                    extra_action_fetches = extra_action_out_fn(policy)

                if hasattr(policy, "_extra_action_fetches"):
                    policy._extra_action_fetches.update(extra_action_fetches)
                else:
                    policy._extra_action_fetches = extra_action_fetches

            DynamicTFPolicy.__init__(
                self,
                obs_space=obs_space,
                action_space=action_space,
                config=config,
                loss_fn=loss_fn,
                stats_fn=stats_fn,
                grad_stats_fn=grad_stats_fn,
                before_loss_init=before_loss_init_wrapper,
                make_model=make_model,
                action_sampler_fn=action_sampler_fn,
                action_distribution_fn=action_distribution_fn,
                existing_inputs=existing_inputs,
                existing_model=existing_model,
                get_batch_divisibility_req=get_batch_divisibility_req,
            )

            if after_init:
                after_init(self, obs_space, action_space, config)

            # Got to reset global_timestep again after this fake run-through.
            self.global_timestep = 0

        @override(Policy)
        def postprocess_trajectory(
            self, sample_batch, other_agent_batches=None, episode=None
        ):
            # Call super's postprocess_trajectory first.
            sample_batch = Policy.postprocess_trajectory(self, sample_batch)
            if postprocess_fn:
                return postprocess_fn(self, sample_batch, other_agent_batches, episode)
            return sample_batch

        @override(TFPolicy)
        def optimizer(self):
            if optimizer_fn:
                optimizers = optimizer_fn(self, self.config)
            else:
                optimizers = base.optimizer(self)
            optimizers = force_list(optimizers)
            if self.exploration:
                optimizers = self.exploration.get_exploration_optimizer(optimizers)

            # No optimizers produced -> Return None.
            if not optimizers:
                return None
            # New API: Allow more than one optimizer to be returned.
            # -> Return list.
            elif self.config["_tf_policy_handles_more_than_one_loss"]:
                return optimizers
            # Old API: Return a single LocalOptimizer.
            else:
                return optimizers[0]

        @override(TFPolicy)
        def gradients(self, optimizer, loss):
            optimizers = force_list(optimizer)
            losses = force_list(loss)

            if compute_gradients_fn:
                # New API: Allow more than one optimizer -> Return a list of
                # lists of gradients.
                if self.config["_tf_policy_handles_more_than_one_loss"]:
                    return compute_gradients_fn(self, optimizers, losses)
                # Old API: Return a single List of gradients.
                else:
                    return compute_gradients_fn(self, optimizers[0], losses[0])
            else:
                return base.gradients(self, optimizers, losses)

        @override(TFPolicy)
        def build_apply_op(self, optimizer, grads_and_vars):
            if apply_gradients_fn:
                return apply_gradients_fn(self, optimizer, grads_and_vars)
            else:
                return base.build_apply_op(self, optimizer, grads_and_vars)

        @override(TFPolicy)
        def extra_compute_action_fetches(self):
            return dict(
                base.extra_compute_action_fetches(self), **self._extra_action_fetches
            )

        @override(TFPolicy)
        def extra_compute_grad_fetches(self):
            if extra_learn_fetches_fn:
                # TODO: (sven) in torch, extra_learn_fetches do not exist.
                #  Hence, things like td_error are returned by the stats_fn
                #  and end up under the LEARNER_STATS_KEY. We should
                #  change tf to do this as well. However, this will confilct
                #  the handling of LEARNER_STATS_KEY inside the multi-GPU
                #  train op.
                # Auto-add empty learner stats dict if needed.
                return dict({LEARNER_STATS_KEY: {}}, **extra_learn_fetches_fn(self))
            else:
                return base.extra_compute_grad_fetches(self)

    def with_updates(**overrides):
        """Allows creating a TFPolicy cls based on settings of another one.

        Keyword Args:
            **overrides: The settings (passed into `build_tf_policy`) that
                should be different from the class that this method is called
                on.

        Returns:
            type: A new TFPolicy sub-class.

        Examples:
        >> MySpecialDQNPolicyClass = DQNTFPolicy.with_updates(
        ..    name="MySpecialDQNPolicyClass",
        ..    loss_function=[some_new_loss_function],
        .. )
        """
        return build_tf_policy(**dict(original_kwargs, **overrides))

    def as_eager():
        return eager_tf_policy._build_eager_tf_policy(**original_kwargs)

    policy_cls.with_updates = staticmethod(with_updates)
    policy_cls.as_eager = staticmethod(as_eager)
    policy_cls.__name__ = name
    policy_cls.__qualname__ = name
    return policy_cls