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94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import ray
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from single_controller.ray.base import RayWorkerGroup, RayResourcePool, RayClassWithInitArgs
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@ray.remote
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class RefBasicRayActor:
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...
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class DPEngineRayWorkerGroup(RayWorkerGroup):
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class DummyModule:
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def __init__(self, core, methods_names) -> None:
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self.core = core
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def func_generator(method_name):
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def func(*args, **kwargs):
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return self.core.execute_all_async("execute_engine", method_name, *args, **kwargs)
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return func
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for method_name in methods_names:
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setattr(self, method_name, func_generator(method_name))
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def __init__(self, name_prefix, process_dispatch_scheme, use_gpu, engine_type, *args, **kwargs) -> None:
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from torch import nn
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# print(f"in DataParallelEngineWrapper, name_prefix = {name_prefix}")
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if isinstance(process_dispatch_scheme, RayResourcePool):
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rpdc = process_dispatch_scheme
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else:
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rpdc = RayResourcePool(process_on_nodes=process_dispatch_scheme,
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use_gpu=use_gpu,
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name_prefix=name_prefix,
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max_colocate_count=1)
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rcia = RayClassWithInitArgs(cls=engine_type, *args, **kwargs)
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self._engine_type = engine_type
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super().__init__(rpdc, rcia)
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nn_module_methods = [
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method_name for method_name in dir(nn.Module)
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if callable(getattr(nn.Module, method_name)) and not method_name.startswith("__")
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]
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nn_module_methods += ["__call__"]
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def func_generator(method_name):
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def func(*args, **kwargs):
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return self.execute_all_async(method_name, *args, **kwargs)
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return func
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print(f"{engine_type} has methods: {dir(engine_type)}")
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for method_name in dir(engine_type):
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try:
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is_callable = callable(getattr(engine_type, method_name))
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except Exception as _:
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pass
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else:
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if is_callable and method_name not in dir(RefBasicRayActor):
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print(f"register method: {method_name}")
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setattr(self, method_name, func_generator(method_name))
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self.module = DPEngineRayWorkerGroup.DummyModule(self, nn_module_methods)
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@property
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def engine(self):
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return self.module
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def get_model_size_on_rank_zero(self):
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results = ray.get([worker.get_model_size_on_rank_zero.remote() for worker in self._workers])
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for result in results:
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if result is not None:
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return result
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