125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
# Copyright The PyTorch Lightning team.
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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 os
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from contextlib import suppress
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from typing import Optional
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import torch
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
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from pytorch_lightning.utilities import _RPC_AVAILABLE
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DEFAULT_RPC_TIMEOUT_SEC = 60.
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if _RPC_AVAILABLE:
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from torch.distributed import rpc
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with suppress(ModuleNotFoundError, ImportError):
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from torch.distributed.rpc.constants import DEFAULT_RPC_TIMEOUT_SEC
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class RPCPlugin(DDPPlugin):
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"""
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Backbone for RPC Plugins built on top of DDP.
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RPC introduces different communication behaviour than DDP. Unlike DDP, processes potentially are not
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required to run the same code as the main process.
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This leads to edge cases where logic needs to be re-defined. This class contains special cases
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that need to be addressed when using RPC communication when building custom RPC Plugins.
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"""
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def __init__(self, rpc_timeout_sec: float = DEFAULT_RPC_TIMEOUT_SEC, **kwargs):
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self.rpc_timeout_sec = rpc_timeout_sec
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self.rpc_initialized = False
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super().__init__(**kwargs)
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def init_rpc_connection(self,
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global_rank: int,
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world_size: int) -> None:
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os.environ['MASTER_PORT'] = os.getenv('RPC_MASTER_PORT', '15000')
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rpc.init_rpc(f"worker{global_rank}", rank=global_rank, world_size=world_size)
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rpc._set_rpc_timeout(self.rpc_timeout_sec)
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self.rpc_initialized = True
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def rpc_save_model(self,
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save_model_fn,
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last_filepath,
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trainer,
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pl_module) -> None:
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"""
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Override to save model to disk.
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This is required as the main process will be required to handle aggregating model states from RPC processes.
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Args:
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save_model_fn: The saving function to save final model.
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last_filepath: The filepath to save the model to.
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trainer: The trainer object.
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pl_module: The LightningModule.
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"""
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raise NotImplementedError
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def on_main_rpc_connection(self, trainer) -> None:
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"""
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Called when main rpc connection has been established.
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Args:
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trainer: The trainer object.
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"""
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raise NotImplementedError
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def on_accelerator_exit_rpc_process(self, trainer) -> None:
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"""
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Called to exit RPC process within the accelerator, that is being managed by main process.
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Args:
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trainer: The trainer object.
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"""
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self.exit_rpc_process()
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def exit_rpc_process(self):
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if self.rpc_initialized:
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torch.distributed.rpc.shutdown()
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self.rpc_initialized = False
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@property
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def return_after_exit_rpc_process(self) -> bool:
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"""
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Override to decide whether to skip train/test function after shutdown completed.
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Usually RPC shutdown is a join/exit function, afterwards we want to exit the process.
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Returns: Whether to return after rpc exit.
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"""
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raise NotImplementedError
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def worker_optimizer_step(self,
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model: LightningModule,
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opt_idx: int,
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*args,
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**kwargs) -> None:
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"""
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Called when optimizer step is run on the main process. Used to signal any RPC workers to run optimizer step.
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Args:
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model: The LightningModule.
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opt_idx: The idx of the optimizer to carry out step on.
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"""
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raise NotImplementedError
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@property
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def is_main_rpc_process(self) -> bool:
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"""
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Override to add logic to determine current process is main RPC process.
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"""
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raise NotImplementedError
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def barrier(self, name: Optional[str] = None) -> None:
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"""
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Override to define distributed sync communication. This needs to be handled differently due to
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the RPC connection managing certain processes at the same time.
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"""
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raise NotImplementedError
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