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import numpy as np
from time import sleep, time
from dataclasses import fields
from typing import TYPE_CHECKING, List, Dict, Any, Optional, Union
from ditk import logging
from ding.framework import task
from ding.data import StorageLoader, Storage, ModelLoader
if TYPE_CHECKING:
from ding.framework.context import Context
from torch.nn import Module
class ContextExchanger:
def __init__(self, skip_n_iter: int = 1, storage_loader: Optional[StorageLoader] = None) -> None:
"""
Overview:
Exchange context between processes,
support properties: trajectories, episodes, env_step, env_episode, train_iter
Arguments:
- skip_n_iter (:obj:`int`): For collectors, it may be necessary to skip waiting \
for the first n iterations to collect data for the learner to learn. This parameter \
will not work on learner.
- storage_loader (:obj:`Optional[StorageLoader]`): Turn data into storage class to reduce \
the network overhead.
"""
if not task.router.is_active:
raise RuntimeError("ContextHandler should be used in parallel mode!")
self._state = {}
self._local_state = {} # just save local state, not send to remote node
if task.has_role(task.role.COLLECTOR):
self._local_state['env_step'] = 0
self._local_state['env_episode'] = 0
self._event_name = "context_exchanger_{role}"
self._skip_n_iter = skip_n_iter
self._storage_loader = storage_loader
for role in task.role: # Only subscribe to other roles
if not task.has_role(role):
task.on(self._event_name.format(role=role), self.put)
if storage_loader:
task.once("finish", lambda _: storage_loader.shutdown())
def __new__(cls, *args, **kwargs):
if not task.router.is_active:
return task.void()
if len(task.roles) == 0:
logging.warning("The task does not have any roles defined, the ContextExchanger will not work.")
return task.void()
if len(task.roles) > 1:
logging.warning(
"Use multiple roles in one exchanger may lead to unexpected result, please check your code."
)
return super(ContextExchanger, cls).__new__(cls)
def __call__(self, ctx: "Context"):
self.merge(ctx)
yield
payload = self.fetch(ctx)
if payload:
if self._storage_loader and task.has_role(task.role.COLLECTOR):
payload = self._storage_loader.save(payload)
for role in task.roles:
task.emit(self._event_name.format(role=role), payload, only_remote=True)
def __del__(self):
if self._storage_loader:
self._storage_loader.shutdown()
def put(self, payload: Union[Dict, Storage]):
"""
Overview:
Get attributes from ctx on the callback of event.
Each attribute should have a standalone put handler, which named `_put_{key}`
"""
def callback(payload: Dict):
for key, item in payload.items():
fn_name = "_put_{}".format(key)
if hasattr(self, fn_name):
getattr(self, fn_name)(item)
else:
logging.warning("Receive unexpected key ({}) in context exchanger".format(key))
if isinstance(payload, Storage):
assert self._storage_loader is not None, "Storage loader is not defined when data is a storage object."
self._storage_loader.load(payload, callback)
else:
callback(payload)
def fetch(self, ctx: "Context") -> Dict[str, Any]:
"""
Overview:
Fetch attributes from ctx before emit them to the event bus.
Each attribute should have a standalone fetch handler, which named `_fetch_{key}`
"""
payload = {}
for field in fields(ctx):
key, item = field.name, getattr(ctx, field.name)
fn_name = "_fetch_{}".format(key)
if hasattr(self, fn_name):
value = getattr(self, fn_name)(item)
if value is not None:
payload[key] = value
return payload
def merge(self, ctx: "Context"):
if task.has_role(task.role.LEARNER):
# Learner should always wait for trajs.
# TODO: Automaticlly wait based on properties, not roles.
while len(self._state) == 0:
sleep(0.01)
elif ctx.total_step >= self._skip_n_iter:
start = time()
while len(self._state) == 0:
if time() - start > 60:
logging.warning("Timeout when waiting for new context! Node id: {}".format(task.router.node_id))
break
sleep(0.01)
for k, v in self._state.items():
if not task.has_role(task.role.COLLECTOR) and k.startswith('increment_'):
pure_k = k.split('increment_')[-1]
setattr(ctx, pure_k, getattr(ctx, pure_k) + v)
else:
setattr(ctx, k, v)
self._state = {}
# Handle each attibute of context
def _put_trajectories(self, traj: List[Any]):
if not task.has_role(task.role.LEARNER):
return
if "trajectories" not in self._state:
self._state["trajectories"] = []
self._state["trajectories"].extend(traj)
def _fetch_trajectories(self, traj: List[Any]):
if task.has_role(task.role.COLLECTOR):
return traj
def _put_episodes(self, episodes: List[Any]):
if not task.has_role(task.role.LEARNER):
return
if "episodes" not in self._state:
self._state["episodes"] = []
self._state["episodes"].extend(episodes)
def _fetch_episodes(self, episodes: List[Any]):
if task.has_role(task.role.COLLECTOR):
return episodes
def _put_trajectory_end_idx(self, trajectory_end_idx: List[str]):
if not task.has_role(task.role.LEARNER):
return
if "trajectory_end_idx" not in self._state:
self._state["trajectory_end_idx"] = []
self._state["trajectory_end_idx"].extend(trajectory_end_idx)
def _fetch_trajectory_end_idx(self, trajectory_end_idx: List[str]):
if task.has_role(task.role.COLLECTOR):
return trajectory_end_idx
def _put_env_step(self, increment_env_step: int):
if not task.has_role(task.role.COLLECTOR):
if 'increment_env_step' not in self._state:
self._state['increment_env_step'] = 0
self._state["increment_env_step"] += increment_env_step
def _fetch_env_step(self, env_step: int):
if task.has_role(task.role.COLLECTOR):
increment_env_step = env_step - self._local_state['env_step']
self._local_state['env_step'] = env_step
return increment_env_step
def _put_env_episode(self, increment_env_episode: int):
if not task.has_role(task.role.COLLECTOR):
if 'increment_env_episode' not in self._state:
self._state['increment_env_episode'] = 0
self._state["increment_env_episode"] += increment_env_episode
def _fetch_env_episode(self, env_episode: int):
if task.has_role(task.role.COLLECTOR):
increment_env_episode = env_episode - self._local_state['env_episode']
self._local_state['env_episode'] = env_episode
return increment_env_episode
def _put_train_iter(self, train_iter: int):
if not task.has_role(task.role.LEARNER):
self._state["train_iter"] = train_iter
def _fetch_train_iter(self, train_iter: int):
if task.has_role(task.role.LEARNER):
return train_iter
class ModelExchanger:
def __init__(self, model: "Module", model_loader: Optional[ModelLoader] = None) -> None:
"""
Overview:
Exchange model between processes, only the learner will send the model,
otherwise the model will only be received.
If you are using a shared model on a single host, there is no need to use this middleware.
Arguments:
- model (:obj:`torch.nn.Module`): Pytorch module.
- model_loader (:obj:`ModelLoader`): Encode model in subprocess.
"""
self._model = model
self._model_loader = model_loader
self._event_name = "model_exchanger"
self._state_dict_cache: Optional[Union[object, Storage]] = None
self._is_learner = task.has_role(task.role.LEARNER)
if not self._is_learner:
task.on(self._event_name, self._cache_state_dict)
if model_loader:
task.once("finish", lambda _: model_loader.shutdown())
def _cache_state_dict(self, state_dict: Union[object, Storage]):
self._state_dict_cache = state_dict
def __new__(cls, *args, **kwargs):
if not task.router.is_active:
return task.void()
if len(task.roles) == 0:
logging.warning("The task does not have any roles defined, the ModelExchanger will not work.")
return task.void()
if len(task.roles) > 1:
logging.warning(
"Use multiple roles in one exchanger may lead to unexpected result, please check your code."
)
return super(ModelExchanger, cls).__new__(cls)
def __call__(self, ctx: "Context") -> Any:
if self._model_loader:
self._model_loader.start()
if not self._is_learner:
if ctx.total_step != 0: # Skip first iteration
self._update_model()
else:
yield
self._send_model()
def _update_model(self):
start = time()
while True:
if task.finish:
return
if time() - start > 60:
logging.warning("Timeout when waiting for new model! Node id: {}".format(task.router.node_id))
break
if self._state_dict_cache is None:
sleep(0.01)
else:
if isinstance(self._state_dict_cache, Storage) and self._model_loader is not None:
try:
self._model.load_state_dict(self._model_loader.load(self._state_dict_cache))
self._state_dict_cache = None
break
except FileNotFoundError as e:
logging.warning(
"Model file has been deleted on node {}, maybe you can increase the ttl.".format(
task.router.node_id
)
)
self._state_dict_cache = None
continue
else:
self._model.load_state_dict(self._state_dict_cache)
self._state_dict_cache = None
break
def _send_model(self):
if self._model_loader:
self._model_loader.save(self._send_callback)
else:
task.emit(self._event_name, self._model.state_dict(), only_remote=True)
def _send_callback(self, storage: Storage):
if task.running:
task.emit(self._event_name, storage, only_remote=True)
def __del__(self):
if self._model_loader:
self._model_loader.shutdown()
class PeriodicalModelExchanger:
def __init__(
self,
model: "Module",
mode: str,
period: int = 1,
delay_toleration: float = np.inf,
stale_toleration: int = 1,
event_name: str = "model_exchanger",
model_loader: Optional[ModelLoader] = None
) -> None:
"""
Overview:
Exchange model between processes, set the mode to "send" or "receive" to specify the role of the process.
If you are using a shared model on a single host, there is no need to use this middleware.
Arguments:
- model (:obj:`torch.nn.Module`): Pytorch module.
- mode (:obj:`str`): "send" or "receive".
- period (:obj:`int`): The period of model exchange.
- delay_toleration (:obj:`float`): The permitted time interval for receiving model after being sent.
- stale_toleration (:obj:`int`): The permitted number of iterations for receiving model after being sent.
- event_name (:obj:`str`): The event name for model exchange.
- model_loader (:obj:`ModelLoader`): ModelLoader for this PeriodicalModelExchanger to use.
"""
self._model = model
self._model_loader = model_loader
self._event_name = event_name
self._period = period
self._mode = mode
if self._mode == "receive":
self._id_counter = -1
self._model_id = -1
else:
self._id_counter = 0
self._stale_toleration = stale_toleration
self._model_stale = stale_toleration
self._delay_toleration = delay_toleration
self._state_dict_cache: Optional[Union[object, Storage]] = None
if self._mode == "receive":
task.on(self._event_name, self._cache_state_dict)
if model_loader:
task.once("finish", lambda _: model_loader.shutdown())
def _cache_state_dict(self, msg: Dict[str, Any]):
if msg['id'] % self._period == 0:
self._state_dict_cache = msg['model']
self._id_counter = msg['id']
self._time = msg['time']
def __new__(cls, *args, **kwargs):
return super(PeriodicalModelExchanger, cls).__new__(cls)
def __call__(self, ctx: "Context") -> Any:
if self._model_loader:
self._model_loader.start()
if self._mode == "receive":
if ctx.total_step != 0: # Skip first iteration
self._update_model()
elif self._mode == "send":
yield
if self._id_counter % self._period == 0:
self._send_model(id=self._id_counter)
self._id_counter += 1
else:
raise NotImplementedError
def _update_model(self):
start = time()
while True:
if task.finish:
return
if time() - start > 60:
logging.warning("Timeout when waiting for new model! Node id: {}".format(task.router.node_id))
self._model_stale += 1
break
if self._state_dict_cache is None:
if self._model_stale < self._stale_toleration and time() - self._time < self._delay_toleration:
self._model_stale += 1
break
else:
sleep(0.01)
else:
if self._id_counter > self._model_id and time() - self._time < self._delay_toleration:
if isinstance(self._state_dict_cache, Storage) and self._model_loader is not None:
try:
self._model.load_state_dict(self._model_loader.load(self._state_dict_cache))
self._state_dict_cache = None
self._model_id = self._id_counter
self._model_stale = 1
break
except FileNotFoundError as e:
logging.warning(
"Model file has been deleted on node {}, maybe you can increase the ttl.".format(
task.router.node_id
)
)
self._state_dict_cache = None
continue
else:
self._model.load_state_dict(self._state_dict_cache)
self._state_dict_cache = None
self._model_id = self._id_counter
self._model_stale = 1
break
else:
self._model_stale += 1
def _send_model(self, id: int):
if self._model_loader:
self._model_loader.save(self._send_callback)
else:
task.emit(self._event_name, {'id': id, 'model': self._model.state_dict(), 'time': time()}, only_remote=True)
def _send_callback(self, storage: Storage):
if task.running:
task.emit(self._event_name, storage, only_remote=True)
def __del__(self):
if self._model_loader:
self._model_loader.shutdown()
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