mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
207 lines
8.9 KiB
Python
207 lines
8.9 KiB
Python
import logging
|
|
from typing import Callable, Iterable, List, Optional, Type, Union
|
|
|
|
from ray.rllib.agents.trainer import Trainer, COMMON_CONFIG
|
|
from ray.rllib.env.env_context import EnvContext
|
|
from ray.rllib.evaluation.worker_set import WorkerSet
|
|
from ray.rllib.policy import Policy
|
|
from ray.rllib.utils import add_mixins
|
|
from ray.rllib.utils.annotations import override
|
|
from ray.rllib.utils.deprecation import Deprecated
|
|
from ray.rllib.utils.typing import EnvConfigDict, EnvType, \
|
|
PartialTrainerConfigDict, ResultDict, TrainerConfigDict
|
|
from ray.tune.logger import Logger
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@Deprecated(
|
|
new="Sub-class from Trainer (or another Trainer sub-class) directly! "
|
|
"See e.g. ray.rllib.agents.dqn.dqn.py for an example.",
|
|
error=False)
|
|
def build_trainer(
|
|
name: str,
|
|
*,
|
|
default_config: Optional[TrainerConfigDict] = None,
|
|
validate_config: Optional[Callable[[TrainerConfigDict], None]] = None,
|
|
default_policy: Optional[Type[Policy]] = None,
|
|
get_policy_class: Optional[Callable[[TrainerConfigDict], Optional[Type[
|
|
Policy]]]] = None,
|
|
validate_env: Optional[Callable[[EnvType, EnvContext], None]] = None,
|
|
before_init: Optional[Callable[[Trainer], None]] = None,
|
|
after_init: Optional[Callable[[Trainer], None]] = None,
|
|
before_evaluate_fn: Optional[Callable[[Trainer], None]] = None,
|
|
mixins: Optional[List[type]] = None,
|
|
execution_plan: Optional[Union[Callable[
|
|
[WorkerSet, TrainerConfigDict], Iterable[ResultDict]], Callable[[
|
|
Trainer, WorkerSet, TrainerConfigDict
|
|
], Iterable[ResultDict]]]] = None,
|
|
allow_unknown_configs: bool = False,
|
|
allow_unknown_subkeys: Optional[List[str]] = None,
|
|
override_all_subkeys_if_type_changes: Optional[List[str]] = None,
|
|
) -> Type[Trainer]:
|
|
"""Helper function for defining a custom Trainer class.
|
|
|
|
Functions will be run in this order to initialize the trainer:
|
|
1. Config setup: validate_config, get_policy.
|
|
2. Worker setup: before_init, execution_plan.
|
|
3. Post setup: after_init.
|
|
|
|
Args:
|
|
name: name of the trainer (e.g., "PPO")
|
|
default_config: The default config dict of the algorithm,
|
|
otherwise uses the Trainer default config.
|
|
validate_config: Optional callable that takes the config to check
|
|
for correctness. It may mutate the config as needed.
|
|
default_policy: The default Policy class to use if `get_policy_class`
|
|
returns None.
|
|
get_policy_class: Optional callable that takes a config and returns
|
|
the policy class or None. If None is returned, will use
|
|
`default_policy` (which must be provided then).
|
|
validate_env: Optional callable to validate the generated environment
|
|
(only on worker=0).
|
|
before_init: Optional callable to run before anything is constructed
|
|
inside Trainer (Workers with Policies, execution plan, etc..).
|
|
Takes the Trainer instance as argument.
|
|
after_init: Optional callable to run at the end of trainer init
|
|
(after all Workers and the exec. plan have been constructed).
|
|
Takes the Trainer instance as argument.
|
|
before_evaluate_fn: Callback to run before evaluation. This takes
|
|
the trainer instance as argument.
|
|
mixins: List of any class mixins for the returned trainer class.
|
|
These mixins will be applied in order and will have higher
|
|
precedence than the Trainer class.
|
|
execution_plan: Optional callable that sets up the
|
|
distributed execution workflow.
|
|
allow_unknown_configs: Whether to allow unknown top-level config keys.
|
|
allow_unknown_subkeys: List of top-level keys
|
|
with value=dict, for which new sub-keys are allowed to be added to
|
|
the value dict. Appends to Trainer class defaults.
|
|
override_all_subkeys_if_type_changes: List of top level keys with
|
|
value=dict, for which we always override the entire value (dict),
|
|
iff the "type" key in that value dict changes. Appends to Trainer
|
|
class defaults.
|
|
|
|
Returns:
|
|
A Trainer sub-class configured by the specified args.
|
|
"""
|
|
|
|
original_kwargs = locals().copy()
|
|
base = add_mixins(Trainer, mixins)
|
|
|
|
class trainer_cls(base):
|
|
_name = name
|
|
_default_config = default_config or COMMON_CONFIG
|
|
_policy_class = default_policy
|
|
|
|
def __init__(self,
|
|
config: TrainerConfigDict = None,
|
|
env: Union[str, EnvType, None] = None,
|
|
logger_creator: Callable[[], Logger] = None,
|
|
remote_checkpoint_dir: Optional[str] = None,
|
|
sync_function_tpl: Optional[str] = None):
|
|
Trainer.__init__(self, config, env, logger_creator,
|
|
remote_checkpoint_dir, sync_function_tpl)
|
|
|
|
@override(base)
|
|
def setup(self, config: PartialTrainerConfigDict):
|
|
if allow_unknown_subkeys is not None:
|
|
self._allow_unknown_subkeys += allow_unknown_subkeys
|
|
self._allow_unknown_configs = allow_unknown_configs
|
|
if override_all_subkeys_if_type_changes is not None:
|
|
self._override_all_subkeys_if_type_changes += \
|
|
override_all_subkeys_if_type_changes
|
|
Trainer.setup(self, config)
|
|
|
|
def _init(self, config: TrainerConfigDict,
|
|
env_creator: Callable[[EnvConfigDict], EnvType]):
|
|
|
|
# No `get_policy_class` function.
|
|
if get_policy_class is None:
|
|
# Default_policy must be provided (unless in multi-agent mode,
|
|
# where each policy can have its own default policy class).
|
|
if not config["multiagent"]["policies"]:
|
|
assert default_policy is not None
|
|
# Query the function for a class to use.
|
|
else:
|
|
self._policy_class = get_policy_class(config)
|
|
# If None returned, use default policy (must be provided).
|
|
if self._policy_class is None:
|
|
assert default_policy is not None
|
|
self._policy_class = default_policy
|
|
|
|
if before_init:
|
|
before_init(self)
|
|
|
|
# Creating all workers (excluding evaluation workers).
|
|
self.workers = self._make_workers(
|
|
env_creator=env_creator,
|
|
validate_env=validate_env,
|
|
policy_class=self._policy_class,
|
|
config=config,
|
|
num_workers=self.config["num_workers"])
|
|
|
|
self.train_exec_impl = self.execution_plan(
|
|
self.workers, config, **self._kwargs_for_execution_plan())
|
|
|
|
if after_init:
|
|
after_init(self)
|
|
|
|
@override(Trainer)
|
|
def validate_config(self, config: PartialTrainerConfigDict):
|
|
# Call super's validation method.
|
|
Trainer.validate_config(self, config)
|
|
# Then call user defined one, if any.
|
|
if validate_config is not None:
|
|
validate_config(config)
|
|
|
|
@staticmethod
|
|
@override(Trainer)
|
|
def execution_plan(workers, config, **kwargs):
|
|
# `execution_plan` is provided, use it inside
|
|
# `self.execution_plan()`.
|
|
if execution_plan is not None:
|
|
return execution_plan(workers, config, **kwargs)
|
|
# If `execution_plan` is not provided (None), the Trainer will use
|
|
# it's already existing default `execution_plan()` static method
|
|
# instead.
|
|
else:
|
|
return Trainer.execution_plan(workers, config, **kwargs)
|
|
|
|
@override(Trainer)
|
|
def _before_evaluate(self):
|
|
if before_evaluate_fn:
|
|
before_evaluate_fn(self)
|
|
|
|
@staticmethod
|
|
@override(Trainer)
|
|
def with_updates(**overrides) -> Type[Trainer]:
|
|
"""Build a copy of this trainer class with the specified overrides.
|
|
|
|
Keyword Args:
|
|
overrides (dict): use this to override any of the arguments
|
|
originally passed to build_trainer() for this policy.
|
|
|
|
Returns:
|
|
Type[Trainer]: A the Trainer sub-class using `original_kwargs`
|
|
and `overrides`.
|
|
|
|
Examples:
|
|
>>> from ray.rllib.agents.ppo import PPOTrainer
|
|
>>> MyPPOClass = PPOTrainer.with_updates({"name": "MyPPO"})
|
|
>>> issubclass(MyPPOClass, PPOTrainer)
|
|
False
|
|
>>> issubclass(MyPPOClass, Trainer)
|
|
True
|
|
>>> trainer = MyPPOClass()
|
|
>>> print(trainer)
|
|
MyPPO
|
|
"""
|
|
return build_trainer(**dict(original_kwargs, **overrides))
|
|
|
|
def __repr__(self):
|
|
return self._name
|
|
|
|
trainer_cls.__name__ = name
|
|
trainer_cls.__qualname__ = name
|
|
return trainer_cls
|