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# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
the class of WorkerGroup
"""
import logging
import signal
import threading
import time
from typing import Any, Callable, Dict, List
from .decorator import MAGIC_ATTR, Dispatch, get_predefined_dispatch_fn, get_predefined_execute_fn
class ResourcePool:
"""The resource pool with meta info such as world_size."""
def __init__(self, process_on_nodes=None, max_colocate_count: int = 10, n_gpus_per_node=8) -> None:
if process_on_nodes is None:
process_on_nodes = []
self._store = process_on_nodes
self.max_colocate_count = max_colocate_count
self.n_gpus_per_node = n_gpus_per_node # this is left for future huawei GPU that contains 16 GPUs per node
def add_node(self, process_count):
self._store.append(process_count)
@property
def world_size(self):
return sum(self._store)
def __call__(self) -> Any:
return self._store
@property
def store(self):
return self._store
def local_world_size_list(self) -> List[int]:
nested_local_world_size_list = [[local_world_size for _ in range(local_world_size)] for local_world_size in self._store]
return [item for row in nested_local_world_size_list for item in row]
def local_rank_list(self) -> List[int]:
nested_local_rank_list = [[i for i in range(local_world_size)] for local_world_size in self._store]
return [item for row in nested_local_rank_list for item in row]
class ClassWithInitArgs:
"""
This class stores a class constructor and the args/kwargs to construct the class.
It is used to instantiate the remote class.
"""
def __init__(self, cls, *args, **kwargs) -> None:
self.cls = cls
self.args = args
self.kwargs = kwargs
self.fused_worker_used = False
# def add_arg(self, arg):
# self.args += (arg,)
# def add_kwarg(self, key, value):
# self.kwargs[key] = value
def __call__(self) -> Any:
return self.cls(*self.args, **self.kwargs)
def check_workers_alive(workers: List, is_alive: Callable, gap_time: float = 1) -> None:
import time
while True:
for worker in workers:
if not is_alive(worker):
logging.warning(f"worker {worker} is not alive sending signal to main thread")
signal.raise_signal(signal.SIGABRT)
time.sleep(gap_time)
class WorkerGroup:
"""A group of workers"""
fused_worker_execute_fn_name = "_fuw_execute"
def __init__(self, resource_pool: ResourcePool, **kwargs) -> None:
self._is_init_with_detached_workers = resource_pool is None
self.fused_worker_used = False
if resource_pool is not None:
# handle the case when WorkGroup is attached to an existing one
self._procecss_dispatch_config = resource_pool()
else:
self._procecss_dispatch_config = None
self._workers = []
self._worker_names = []
self._master_addr = None
self._master_port = None
self._checker_thread: threading.Thread = None
def _is_worker_alive(self, worker):
raise NotImplementedError("WorkerGroup._is_worker_alive called, should be implemented in derived class.")
def _block_until_all_workers_alive(self) -> None:
while True:
all_state = [self._is_worker_alive(worker) for worker in self._workers]
if False in all_state:
time.sleep(1)
else:
break
def start_worker_aliveness_check(self, every_n_seconds=1) -> None:
# before starting checking worker aliveness, make sure all workers are already alive
self._block_until_all_workers_alive()
self._checker_thread = threading.Thread(target=check_workers_alive, args=(self._workers, self._is_worker_alive, every_n_seconds))
self._checker_thread.start()
@property
def world_size(self):
return len(self._workers)
# execute_all_async and execute_rank_zero_async should be implemented by RayWorkerGroup, TorchRPCWorkerGroup,
# MegatronWorkerGroup, XperfWorkerGroup should skip
def _bind_worker_method(self, user_defined_cls, func_generator):
"""
Bind the worker method to the WorkerGroup
"""
method_names = []
for method_name in dir(user_defined_cls):
try:
method = getattr(user_defined_cls, method_name)
assert callable(method), f"{method_name} in {user_defined_cls} is not callable"
except Exception:
# if it is a property, it will fail because Class doesn't have instance property
continue
if hasattr(method, MAGIC_ATTR):
# this method is decorated by register
attribute = getattr(method, MAGIC_ATTR)
assert isinstance(attribute, Dict), f"attribute must be a dictionary. Got {type(attribute)}"
assert "dispatch_mode" in attribute, "attribute must contain dispatch_mode in its key"
dispatch_mode = attribute["dispatch_mode"]
execute_mode = attribute["execute_mode"]
blocking = attribute["blocking"]
# get dispatch fn
if isinstance(dispatch_mode, Dispatch):
# get default dispatch fn
fn = get_predefined_dispatch_fn(dispatch_mode=dispatch_mode)
dispatch_fn = fn["dispatch_fn"]
collect_fn = fn["collect_fn"]
else:
assert isinstance(dispatch_mode, dict)
assert "dispatch_fn" in dispatch_mode
assert "collect_fn" in dispatch_mode
dispatch_fn = dispatch_mode["dispatch_fn"]
collect_fn = dispatch_mode["collect_fn"]
# get execute_fn_name
execute_mode = get_predefined_execute_fn(execute_mode=execute_mode)
wg_execute_fn_name = execute_mode["execute_fn_name"]
# get execute_fn from string
try:
execute_fn = getattr(self, wg_execute_fn_name)
assert callable(execute_fn), "execute_fn must be callable"
except Exception:
print(f"execute_fn {wg_execute_fn_name} is invalid")
raise
# bind a new method to the RayWorkerGroup
func = func_generator(
self,
method_name,
dispatch_fn=dispatch_fn,
collect_fn=collect_fn,
execute_fn=execute_fn,
blocking=blocking,
)
try:
setattr(self, method_name, func)
method_names.append(method_name)
except Exception as e:
raise ValueError(f"Fail to set method_name {method_name}") from e
return method_names
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