277 lines
10 KiB
Python
277 lines
10 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 subprocess
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import sys
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from os.path import abspath
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from time import sleep
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from typing import Optional
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import numpy as np
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import torch
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from pytorch_lightning import _logger as log
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from pytorch_lightning.utilities import AMPType
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from pytorch_lightning.utilities.distributed import rank_zero_only, find_free_network_port
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from pytorch_lightning.accelerators.base_backend import Accelerator
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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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amp = None
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class DDPBackend(Accelerator):
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def __init__(self, trainer, mode: str = 'ddp'):
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super().__init__(trainer)
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self.task_idx = None
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self._has_spawned_children = False
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self.mode = mode
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def setup(self, model):
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if self.mode == 'ddp':
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self.__ddp_script_mode_setup()
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elif self.mode == 'slurm_ddp':
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self.__slurm_setup()
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elif self.mode == 'torchelastic_ddp':
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self.__torchelastic_setup()
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self.trainer.model = model
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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 __ddp_script_mode_setup(self):
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assert self.trainer.global_rank == 0
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self._check_can_spawn_children()
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self._has_spawned_children = True
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os.environ['MASTER_ADDR'] = os.environ.get('MASTER_ADDR', '127.0.0.1')
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os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
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# allow the user to pass the node rank
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node_rank = '0'
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node_rank = os.environ.get('NODE_RANK', node_rank)
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node_rank = os.environ.get('GROUP_RANK', node_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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# the visible devices tell us how many GPUs we want to use.
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# when the trainer script was called the device has already been scoped by the time
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# code reaches this point. so, to call the scripts, we need to leave cuda visible devices alone
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# but forward the GPUs selected via environment variables
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gpu_ids = os.environ.get('CUDA_VISIBLE_DEVICES', '')
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if len(gpu_ids) == 1:
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gpu_ids = f'{gpu_ids},'
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num_gpus = max(1, len(gpu_ids.split(',')))
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# set the flag for ddp scripts
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os.environ['PL_TRAINER_GPUS'] = gpu_ids
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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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self.task_idx = 0
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def train(self):
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model = self.trainer.model
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if self.mode == 'ddp':
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results = self.ddp_train(process_idx=self.task_idx, 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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else:
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self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
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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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# call sync_bn before .cuda(), configure_apex and configure_ddp
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if self.trainer.sync_batchnorm:
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model = model.configure_sync_batchnorm(model)
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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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# 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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# set model properties before going into wrapper
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self.trainer.model_connector.copy_trainer_model_properties(model)
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# AMP - run through amp wrapper before going to distributed DP
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if self.trainer.amp_backend == AMPType.APEX:
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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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# set up training routine
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self.trainer.setup_training(model)
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# train or test
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results = self.trainer.train_or_test()
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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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def training_step(self, args):
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if self.trainer.amp_backend == AMPType.NATIVE:
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with torch.cuda.amp.autocast():
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output = self.trainer.model(*args)
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else:
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output = self.trainer.model(*args)
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return output
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def validation_step(self, args):
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output = self.training_step(args)
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return output
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def test_step(self, args):
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output = self.training_step(args)
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return output
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def _check_can_spawn_children(self):
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if self._has_spawned_children:
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raise RuntimeError(
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"You tried to run `.fit` or `.test` multiple times in the same script."
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" This is not supported in DDP mode, switch to `distributed_backend='ddp_spawn'` instead."
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)
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