124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
# --------------------------------------------------------
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# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
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# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Modified by Xueyan Zou (xueyan@cs.wisc.edu)
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# --------------------------------------------------------
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import os
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import logging
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from mpi4py import MPI
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import torch
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from .utils.hook import add_hook
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from .utils.mpi_adapter import MPIAdapter
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from .utils.misc import save_opt_to_yaml
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logger = logging.getLogger(__name__)
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class DistributedTrainer:
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def __init__(self, opt):
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self.opt = opt
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# parse environment information for distributed training
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adapter = MPIAdapter(self.opt['PORT'])
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self.opt['world_size'] = adapter.world_size
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self.opt['local_size'] = adapter.local_size
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self.opt['rank'] = adapter.rank
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self.opt['local_rank'] = adapter.local_rank
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self.set_opt_hook()
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# set up device
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if not self.opt['CUDA']:
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self.opt['device'] = torch.device("cpu")
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logger.info("Using CPU")
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else:
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torch.cuda.set_device(self.opt['local_rank'])
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self.opt['device'] = torch.device("cuda", self.opt['local_rank'])
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logger.info("Using CUDA")
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# init distributed training
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adapter.log_info()
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if torch.distributed.is_available() and self.opt['world_size'] > 1:
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adapter.init_process_group(backend='nccl')
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# save config file
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self.save_folder = self.opt['SAVE_DIR']
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if self.opt['world_size'] > 1:
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torch.distributed.barrier()
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if self.opt['rank'] == 0:
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os.makedirs(self.save_folder, exist_ok=True)
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logger.info(f"Save config file to {os.path.join(self.save_folder, 'conf_copy.yaml')}")
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save_opt_to_yaml(self.opt, os.path.join(self.save_folder, 'conf_copy.yaml'))
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# ddp: log stats and update learning rate
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self.grad_acc_steps = self.opt['GRADIENT_ACCUMULATE_STEP']
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logger.info(f"Base learning rate: {self.opt['SOLVER']['BASE_LR']}")
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logger.info(f"Number of GPUs: {self.opt['world_size']}")
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logger.info(f"Gradient accumulation steps: {self.grad_acc_steps}")
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if self.opt['world_size'] > 1:
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add_hook()
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# prepare metadata for save folder
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conf_file = self.opt['conf_files'][0]
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if 'BASENAME' not in self.opt:
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self.opt['BASENAME'] = os.path.basename(conf_file)
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self.init_save_folder()
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def set_opt_hook(self):
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# Fill in the default values for required keywords
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self.opt['CUDA'] = self.opt.get('CUDA', True) and torch.cuda.is_available()
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self.opt['FP16'] = self.opt.get('FP16', False) and self.opt['CUDA']
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self.opt['GRADIENT_ACCUMULATE_STEP'] = int(self.opt.get('GRADIENT_ACCUMULATE_STEP', 1))
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self.opt['EVAL_PER_UPDATE_NUM'] = int(self.opt.get('EVAL_PER_UPDATE_NUM', 0))
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self.opt['LR_SCHEDULER_PARAMS'] = self.opt.get('LR_SCHEDULER_PARAMS', {})
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if 'SAVE_DIR' not in self.opt:
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assert False, "Please initialize SAVE_DIR in your config file."
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self.opt['SAVE_DIR'] = os.path.normpath(self.opt['SAVE_DIR'])
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logger.info(f"Setting SAVE_DIR as {self.opt['SAVE_DIR']}")
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def init_save_folder(self):
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"""
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Initialize the save folder for logs, model, checkpoint, and evaluation.
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"""
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runid = 1
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if self.opt['world_size'] > 1:
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torch.distributed.barrier()
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if self.opt['rank'] == 0:
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while True:
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save_folder = os.path.join(self.opt['SAVE_DIR'], f"{self.opt['BASENAME']}_conf~", f"run_{runid}")
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try:
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os.makedirs(save_folder, exist_ok=False)
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break
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except FileExistsError:
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runid = runid + 1
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if self.opt['world_size'] > 1:
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torch.distributed.barrier()
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if self.opt['world_size'] > 1:
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runid = 1
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while True:
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save_folder = os.path.join(self.opt['SAVE_DIR'], f"{self.opt['BASENAME']}_conf~", f"run_{runid}")
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if not os.path.exists(save_folder):
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break
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else:
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runid += 1
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runid -= 1
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save_folder = os.path.join(self.opt['SAVE_DIR'], f"{self.opt['BASENAME']}_conf~", f"run_{runid}")
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# this second os.makedirs() call on all ranks is to force sync the save_folder creation between blobFuse and local fs
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os.makedirs(save_folder, exist_ok=True)
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self.save_folder = save_folder |