96 lines
3.2 KiB
Python
96 lines
3.2 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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import torch
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.core.step_result import Result
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from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
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try:
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from hydra.utils import to_absolute_path, get_original_cwd
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from hydra.core.hydra_config import HydraConfig
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except ImportError:
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HYDRA_AVAILABLE = False
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else:
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HYDRA_AVAILABLE = True
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class DDP2Backend(DDPBase):
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def __init__(self, trainer):
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super().__init__(trainer)
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self.task_idx = None
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def setup(self, model):
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self._resolve_task_idx()
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self.trainer.model = model
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def _resolve_task_idx(self):
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if self.trainer.is_slurm_managing_tasks:
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self.task_idx = int(os.environ['SLURM_LOCALID'])
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else:
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# torchelastic or general non_slurm ddp2
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try:
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self.task_idx = int(os.environ['LOCAL_RANK'])
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except Exception as e:
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m = 'ddp2 only works in SLURM or via torchelastic with the WORLD_SIZE, LOCAL_RANK, GROUP_RANK flags'
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raise MisconfigurationException(m)
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def train(self):
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model = self.trainer.model
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self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model)
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def training_step_end(self, output):
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if isinstance(output, Result):
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output.dp_reduce()
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return output
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def validation_step_end(self, output):
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if isinstance(output, Result):
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output.dp_reduce()
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return output
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def test_step_end(self, output):
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if isinstance(output, Result):
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output.dp_reduce()
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return output
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def set_world_ranks(self, process_idx):
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self.trainer.local_rank = self.trainer.node_rank
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self.trainer.global_rank = self.trainer.node_rank
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self.trainer.world_size = self.trainer.num_nodes
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def model_to_device(self, model, process_idx, is_master):
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gpu_idx = process_idx
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# when using ddp, the master process (proc 0) continues running as the main one
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# this means that the local rank will always be 0
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# (even if cuda visible devices has other visible gpus)
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# this means that the master process needs to pull the 0th visible index as the device number
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if is_master:
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available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
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gpu_idx = int(available_gpus[self.trainer.local_rank])
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self.trainer.root_gpu = gpu_idx
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torch.cuda.set_device(self.trainer.root_gpu)
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model.cuda(self.trainer.root_gpu)
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def get_device_ids(self):
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device_ids = self.trainer.data_parallel_device_ids
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return device_ids
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