83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
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# 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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import torch
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import torch.multiprocessing as mp
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from pytorch_lightning.utilities.distributed import find_free_network_port
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from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
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try:
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from apex import amp
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except ImportError:
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amp = None
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class DDPCPUSpawnBackend(DDPBase):
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def __init__(self, trainer, nprocs):
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super().__init__(trainer)
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self.mp_queue = None
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self.nprocs = nprocs
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def setup(self, model):
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os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
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# pass in a state q
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smp = mp.get_context('spawn')
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self.mp_queue = smp.SimpleQueue()
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self.trainer.model = model
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def train(self):
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model = self.trainer.model
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# train in children process
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mp.spawn(self.ddp_train_tmp, nprocs=self.nprocs, args=(self.mp_queue, model,))
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# restore main state with best weights
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best_path = self.mp_queue.get()
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results = self.mp_queue.get()
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last_path = self.mp_queue.get()
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# recover the weights of the processes trained in the children
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self.__recover_child_process_weights(model, best_path, last_path)
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return results
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def __recover_child_process_weights(self, model, best_path, last_path):
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# transfer back the best path to the trainer
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if self.trainer.checkpoint_callback:
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self.trainer.checkpoint_callback.best_model_path = best_path
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# todo, pass also best score
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# load last weights
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if last_path is not None and not self.trainer.testing:
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ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
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model.load_state_dict(ckpt)
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self.trainer.model = model
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def set_world_ranks(self, process_idx):
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self.trainer.local_rank = process_idx
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self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
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self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
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def model_to_device(self, model, process_idx):
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pass
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def get_device_ids(self):
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device_ids = None
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return device_ids
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