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