230 lines
8.3 KiB
Python
230 lines
8.3 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 subprocess
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import sys
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from time import sleep
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import numpy as np
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from os.path import abspath
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from pytorch_lightning.utilities import NATIVE_AMP_AVALAIBLE
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from pytorch_lightning import _logger as log
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from typing import Optional
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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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try:
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from apex import amp
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except ImportError:
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APEX_AVAILABLE = False
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else:
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APEX_AVAILABLE = True
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class DDPBackend(object):
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def __init__(self, trainer):
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self.trainer = trainer
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self.task_idx = None
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def slurm_setup(self):
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self.task_idx = int(os.environ['SLURM_LOCALID'])
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def torchelastic_setup(self):
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self.task_idx = int(os.environ['LOCAL_RANK'])
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def train(self, model):
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self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
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def spawn_ddp_children(self, model):
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port = os.environ['MASTER_PORT']
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master_address = '127.0.0.1' if 'MASTER_ADDR' not in os.environ else os.environ['MASTER_ADDR']
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os.environ['MASTER_PORT'] = f'{port}'
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os.environ['MASTER_ADDR'] = f'{master_address}'
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# allow the user to pass the node rank
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node_rank = '0'
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if 'NODE_RANK' in os.environ:
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node_rank = os.environ['NODE_RANK']
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if 'GROUP_RANK' in os.environ:
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node_rank = os.environ['GROUP_RANK']
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os.environ['NODE_RANK'] = node_rank
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os.environ['LOCAL_RANK'] = '0'
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# when user is using hydra find the absolute path
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path_lib = abspath if not HYDRA_AVAILABLE else to_absolute_path
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# pull out the commands used to run the script and resolve the abs file path
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command = sys.argv
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try:
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full_path = path_lib(command[0])
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except Exception as e:
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full_path = abspath(command[0])
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command[0] = full_path
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# use the same python interpreter and actually running
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command = [sys.executable] + command
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# since this script sets the visible devices we replace the gpus flag with a number
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num_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',').__len__()
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if '--gpus' in command:
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gpu_flag_idx = command.index('--gpus')
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command[gpu_flag_idx + 1] = f'{num_gpus}'
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os.environ['WORLD_SIZE'] = f'{num_gpus * self.trainer.num_nodes}'
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self.trainer.interactive_ddp_procs = []
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for local_rank in range(1, self.trainer.num_processes):
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env_copy = os.environ.copy()
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env_copy['LOCAL_RANK'] = f'{local_rank}'
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# start process
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# if hydra is available and initialized, make sure to set the cwd correctly
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cwd: Optional[str] = None
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if HYDRA_AVAILABLE:
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if HydraConfig.initialized():
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cwd = get_original_cwd()
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proc = subprocess.Popen(command, env=env_copy, cwd=cwd)
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self.trainer.interactive_ddp_procs.append(proc)
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# starting all processes at once can cause issues
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# with dataloaders delay between 1-10 seconds
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delay = np.random.uniform(1, 5, 1)[0]
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sleep(delay)
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local_rank = 0
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results = self.ddp_train(local_rank, mp_queue=None, model=model, is_master=True)
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del os.environ['WORLD_SIZE']
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return results
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def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
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"""
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Entry point for ddp
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Args:
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process_idx:
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mp_queue: multiprocessing queue
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model:
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is_master:
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proc_offset:
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Returns:
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"""
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# offset the process id if requested
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process_idx = process_idx + proc_offset
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# show progressbar only on progress_rank 0
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if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
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self.trainer.progress_bar_callback.disable()
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# determine which process we are and world size
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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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# set warning rank
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rank_zero_only.rank = self.trainer.global_rank
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# set up server using proc 0's ip address
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# try to init for 20 times at max in case ports are taken
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# where to store ip_table
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model.trainer = self.trainer
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model.init_ddp_connection(
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self.trainer.global_rank,
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self.trainer.world_size,
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self.trainer.is_slurm_managing_tasks
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)
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# call setup after the ddp process has connected
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self.trainer.call_setup_hook(model)
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# on world_size=0 let everyone know training is starting
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if self.trainer.is_global_zero:
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log.info('-' * 100)
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log.info(f'distributed_backend={self.trainer.distributed_backend}')
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log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
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log.info('-' * 100)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
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self.trainer.optimizers = optimizers
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self.trainer.lr_schedulers = lr_schedulers
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self.trainer.optimizer_frequencies = optimizer_frequencies
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# MODEL
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# copy model to each gpu
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if self.trainer.on_gpu:
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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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# set model properties before going into wrapper
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self.trainer.copy_trainer_model_properties(model)
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# AMP
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# run through amp wrapper before going to distributed DP
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# TODO: remove with dropping NVIDIA AMP support
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if self.trainer.use_amp and not NATIVE_AMP_AVALAIBLE:
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model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
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self.trainer.optimizers = optimizers
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self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
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# DDP2 uses all GPUs on the machine
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if self.trainer.distributed_backend == 'ddp' or self.trainer.distributed_backend == 'ddp_spawn':
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device_ids = [self.trainer.root_gpu]
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else: # includes ddp_cpu
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device_ids = None
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# allow user to configure ddp
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model = model.configure_ddp(model, device_ids)
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# continue training routine
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results = self.trainer.run_pretrain_routine(model)
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# get original model
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model = self.trainer.get_model()
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# persist info in ddp_spawn
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self.trainer.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
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# clean up memory
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torch.cuda.empty_cache()
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if self.trainer.global_rank == 0 and self.trainer.distributed_backend not in ['ddp_spawn', 'ddp_cpu']:
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return results
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