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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.
from enum import Enum, auto
from functools import wraps
from types import FunctionType
from typing import TYPE_CHECKING, Dict, List, Literal, Union
import ray
from ...protocol import DataProto, DataProtoFuture
if TYPE_CHECKING:
from .worker_group import WorkerGroup
# here we add a magic number of avoid user-defined function already have this attribute
MAGIC_ATTR = "attrs_3141562937"
class Dispatch(Enum):
RANK_ZERO = auto()
ONE_TO_ALL = auto()
ALL_TO_ALL = auto()
DP_COMPUTE = auto()
DP_COMPUTE_PROTO = auto()
DP_COMPUTE_PROTO_WITH_FUNC = auto()
DP_COMPUTE_METRIC = auto()
class Execute(Enum):
ALL = 0
RANK_ZERO = 1
def _split_args_kwargs_data_proto(chunks: int, *args, **kwargs):
splitted_args = []
for arg in args:
assert isinstance(arg, (DataProto, DataProtoFuture))
splitted_args.append(arg.chunk(chunks=chunks))
splitted_kwargs = {}
for key, value in kwargs.items():
assert isinstance(value, (DataProto, DataProtoFuture))
splitted_kwargs[key] = value.chunk(chunks=chunks)
return splitted_args, splitted_kwargs
def dispatch_one_to_all(worker_group: "WorkerGroup", *args, **kwargs):
args = tuple([arg] * worker_group.world_size for arg in args)
kwargs = {k: [v] * worker_group.world_size for k, v in kwargs.items()}
return args, kwargs
def dispatch_all_to_all(worker_group: "WorkerGroup", *args, **kwargs):
return args, kwargs
def collect_all_to_all(worker_group: "WorkerGroup", output):
return output
def _concat_data_proto_or_future(outputs: List[DataProto]) -> DataProto:
# make sure all the elements in output has the same type
for output in outputs:
assert type(output) is type(outputs[0])
output = outputs[0]
if isinstance(output, DataProto):
return DataProto.concat(outputs)
elif isinstance(output, ray.ObjectRef):
return DataProtoFuture.concat(outputs)
else:
raise NotImplementedError
def dispatch_dp_compute(worker_group: "WorkerGroup", *args, **kwargs):
for arg in args:
assert isinstance(arg, (tuple, list)) and len(arg) == worker_group.world_size
for value in kwargs.values():
assert isinstance(value, (tuple, list)) and len(value) == worker_group.world_size
return args, kwargs
def collect_dp_compute(worker_group: "WorkerGroup", outputs: List[DataProto]) -> List[DataProto]:
assert len(outputs) == worker_group.world_size
return outputs
def dispatch_dp_compute_data_proto(worker_group: "WorkerGroup", *args, **kwargs):
splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(worker_group.world_size, *args, **kwargs)
return splitted_args, splitted_kwargs
def dispatch_dp_compute_data_proto_with_func(worker_group: "WorkerGroup", *args, **kwargs):
assert type(args[0]) is FunctionType # NOTE: The first one args is a function!
splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(worker_group.world_size, *args[1:], **kwargs)
splitted_args_with_func = [[args[0]] * worker_group.world_size] + splitted_args
return splitted_args_with_func, splitted_kwargs
def collect_dp_compute_data_proto(worker_group: "WorkerGroup", outputs: List[DataProto]) -> DataProto:
for output in outputs:
assert isinstance(output, (DataProto, ray.ObjectRef)), f"Expect a DataProto, but got {type(output)}"
outputs = collect_dp_compute(worker_group, outputs)
return _concat_data_proto_or_future(outputs)
def get_predefined_dispatch_fn(dispatch_mode: Dispatch):
predefined_dispatch_mode_fn = {
Dispatch.ONE_TO_ALL: {
"dispatch_fn": dispatch_one_to_all,
"collect_fn": collect_all_to_all,
},
Dispatch.ALL_TO_ALL: {
"dispatch_fn": dispatch_all_to_all,
"collect_fn": collect_all_to_all,
},
Dispatch.DP_COMPUTE: {
"dispatch_fn": dispatch_dp_compute,
"collect_fn": collect_dp_compute,
},
Dispatch.DP_COMPUTE_PROTO: {
"dispatch_fn": dispatch_dp_compute_data_proto,
"collect_fn": collect_dp_compute_data_proto,
},
Dispatch.DP_COMPUTE_PROTO_WITH_FUNC: {
"dispatch_fn": dispatch_dp_compute_data_proto_with_func,
"collect_fn": collect_dp_compute_data_proto,
},
Dispatch.DP_COMPUTE_METRIC: {
"dispatch_fn": dispatch_dp_compute_data_proto,
"collect_fn": collect_dp_compute,
},
}
return predefined_dispatch_mode_fn[dispatch_mode]
def get_predefined_execute_fn(execute_mode: Execute):
"""
Note that here we only asks execute_all and execute_rank_zero to be implemented
Leave the choice of how these two functions handle argument 'blocking' to users
"""
predefined_execute_mode_fn = {
Execute.ALL: {"execute_fn_name": "execute_all"},
Execute.RANK_ZERO: {"execute_fn_name": "execute_rank_zero"},
}
return predefined_execute_mode_fn[execute_mode]
def _check_dispatch_mode(dispatch_mode: Union[Dispatch, Dict[Literal["dispatch_fn", "collect_fn"], FunctionType]]):
assert isinstance(dispatch_mode, (Dispatch, dict)), (
f"dispatch_mode must be a Dispatch or a Dict. Got {dispatch_mode}"
)
if isinstance(dispatch_mode, dict):
necessary_keys = ["dispatch_fn", "collect_fn"]
for key in necessary_keys:
assert key in dispatch_mode, f"key {key} should be in dispatch_mode if it is a dictionary"
def _check_execute_mode(execute_mode: Execute):
assert isinstance(execute_mode, Execute), f"execute_mode must be a Execute. Got {execute_mode}"
def _materialize_futures(*args, **kwargs):
new_args = []
for arg in args:
if isinstance(arg, DataProtoFuture):
arg = arg.get()
# add more type to materialize
new_args.append(arg)
for key, value in kwargs.items():
if isinstance(value, DataProtoFuture):
kwargs[key] = value.get()
new_args = tuple(new_args)
return new_args, kwargs
def register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.ALL, blocking=True, materialize_futures=True):
_check_dispatch_mode(dispatch_mode=dispatch_mode)
_check_execute_mode(execute_mode=execute_mode)
def decorator(func):
@wraps(func)
def inner(*args, **kwargs):
if materialize_futures:
args, kwargs = _materialize_futures(*args, **kwargs)
return func(*args, **kwargs)
attrs = {"dispatch_mode": dispatch_mode, "execute_mode": execute_mode, "blocking": blocking}
setattr(inner, MAGIC_ATTR, attrs)
return inner
return decorator
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