515 lines
23 KiB
Python
515 lines
23 KiB
Python
#
|
|
# Copyright (c) 2018, Salesforce, Inc.
|
|
# The Board of Trustees of the Leland Stanford Junior University
|
|
# All rights reserved.
|
|
#
|
|
# Redistribution and use in source and binary forms, with or without
|
|
# modification, are permitted provided that the following conditions are met:
|
|
#
|
|
# * Redistributions of source code must retain the above copyright notice, this
|
|
# list of conditions and the following disclaimer.
|
|
#
|
|
# * Redistributions in binary form must reproduce the above copyright notice,
|
|
# this list of conditions and the following disclaimer in the documentation
|
|
# and/or other materials provided with the distribution.
|
|
#
|
|
# * Neither the name of the copyright holder nor the names of its
|
|
# contributors may be used to endorse or promote products derived from
|
|
# this software without specific prior written permission.
|
|
#
|
|
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
|
|
|
|
import os
|
|
import math
|
|
import time
|
|
import sys
|
|
from copy import deepcopy
|
|
|
|
import logging
|
|
from pprint import pformat
|
|
from logging import handlers
|
|
from collections import defaultdict
|
|
import numpy as np
|
|
import itertools
|
|
|
|
import torch
|
|
|
|
from .text import torchtext
|
|
|
|
from tensorboardX import SummaryWriter
|
|
|
|
from . import arguments
|
|
from . import models
|
|
from .validate import validate
|
|
from .multiprocess import Multiprocess, DistributedDataParallel
|
|
from .util import elapsed_time, get_splits, batch_fn, set_seed, preprocess_examples, get_trainable_params, count_params
|
|
from .utils.saver import Saver
|
|
|
|
|
|
def initialize_logger(args, rank='main'):
|
|
# set up file logger
|
|
logger = logging.getLogger(f'process_{rank}')
|
|
logger.setLevel(logging.DEBUG)
|
|
handler = handlers.RotatingFileHandler(os.path.join(args.log_dir, f'process_{rank}.log'), maxBytes=1024*1024*10, backupCount=1)
|
|
handler.setLevel(logging.DEBUG)
|
|
formatter = logging.Formatter('%(name)s - %(message)s')
|
|
handler.setFormatter(formatter)
|
|
logger.addHandler(handler)
|
|
handler = logging.StreamHandler()
|
|
handler.setFormatter(formatter)
|
|
handler.setLevel(logging.DEBUG)
|
|
logger.addHandler(handler)
|
|
logger.propagate = False
|
|
|
|
return logger
|
|
|
|
|
|
def log(rank='main'):
|
|
return logging.getLogger(f'process_{rank}')
|
|
|
|
|
|
def prepare_data(args, field, logger):
|
|
|
|
if field is None:
|
|
logger.info(f'Constructing field')
|
|
FIELD = torchtext.data.ReversibleField(batch_first=True, init_token='<init>', eos_token='<eos>', lower=args.lower, include_lengths=True)
|
|
else:
|
|
FIELD = field
|
|
|
|
if len(args.train_tasks) > 1 and args.use_curriculum:
|
|
logger.error('Curriculum learning is supported for one task only.')
|
|
train_sets, val_sets, aux_sets, vocab_sets = [], [], [], []
|
|
for task in args.train_tasks:
|
|
logger.info(f'Loading {task}')
|
|
kwargs = {'test': None}
|
|
kwargs['subsample'] = args.subsample
|
|
kwargs['validation'] = None
|
|
if args.use_curriculum:
|
|
kwargs['curriculum'] = True
|
|
logger.info(f'Adding {task} to training datasets')
|
|
split = get_splits(args, task, FIELD, **kwargs)
|
|
if args.use_curriculum:
|
|
assert len(split) == 2
|
|
train_sets.append(split[0])
|
|
aux_sets.append(split[1])
|
|
else:
|
|
assert len(split) == 1
|
|
train_sets.append(split[0])
|
|
logger.info(f'{task} has {len(split)} training examples')
|
|
|
|
if args.vocab_tasks is not None and task in args.vocab_tasks:
|
|
vocab_sets.extend(split)
|
|
|
|
for task in args.val_tasks:
|
|
logger.info(f'Loading {task}')
|
|
kwargs = {'test': None}
|
|
kwargs['subsample'] = args.subsample
|
|
kwargs['train'] = None
|
|
logger.info(f'Adding {task} to validation datasets')
|
|
split = get_splits(args, task, FIELD, **kwargs)
|
|
assert len(split) == 1
|
|
logger.info(f'{task} has {len(split)} validation examples')
|
|
val_sets.append(split[0])
|
|
if args.vocab_tasks is not None and task in args.vocab_tasks:
|
|
vocab_sets.extend(split)
|
|
|
|
for task, s in zip(args.train_tasks, train_sets):
|
|
for ex in s.examples[:10]:
|
|
logger.debug(f'examples***: {[token.strip() for token in ex.context]}')
|
|
|
|
if args.load is None:
|
|
logger.info(f'Getting pretrained word vectors')
|
|
char_vectors = torchtext.vocab.CharNGram(cache=args.embeddings)
|
|
if args.small_glove:
|
|
glove_vectors = torchtext.vocab.GloVe(cache=args.embeddings, name="6B", dim=50)
|
|
else:
|
|
glove_vectors = torchtext.vocab.GloVe(cache=args.embeddings)
|
|
vectors = [char_vectors, glove_vectors]
|
|
vocab_sets = (train_sets + val_sets + aux_sets) if len(vocab_sets) == 0 else vocab_sets
|
|
logger.info(f'Building vocabulary')
|
|
FIELD.build_vocab(*vocab_sets, max_size=args.max_effective_vocab, vectors=vectors)
|
|
|
|
FIELD.decoder_itos = FIELD.vocab.itos[:args.max_generative_vocab]
|
|
FIELD.decoder_stoi = {word: idx for idx, word in enumerate(FIELD.decoder_itos)}
|
|
FIELD.decoder_to_vocab = {idx: FIELD.vocab.stoi[word] for idx, word in enumerate(FIELD.decoder_itos)}
|
|
FIELD.vocab_to_decoder = {idx: FIELD.decoder_stoi[word] for idx, word in enumerate(FIELD.vocab.itos) if word in FIELD.decoder_stoi}
|
|
|
|
logger.info(f'Vocabulary has {len(FIELD.vocab)} tokens')
|
|
logger.debug(f'The first 200 tokens:')
|
|
logger.debug(FIELD.vocab.itos[:200])
|
|
|
|
if args.use_curriculum:
|
|
logger.info('Preprocessing auxiliary data for curriculum')
|
|
preprocess_examples(args, args.train_tasks, aux_sets, FIELD, logger, train=True)
|
|
logger.info('Preprocessing training data')
|
|
preprocess_examples(args, args.train_tasks, train_sets, FIELD, logger, train=True)
|
|
logger.info('Preprocessing validation data')
|
|
preprocess_examples(args, args.val_tasks, val_sets, FIELD, logger, train=args.val_filter)
|
|
|
|
return FIELD, train_sets, val_sets, aux_sets
|
|
|
|
|
|
def to_iter(args, world_size, val_batch_size, data, device, train=True, token_testing=False, sort=None):
|
|
sort = sort if not token_testing else True
|
|
shuffle = None if not token_testing else False
|
|
reverse = args.reverse
|
|
Iterator = torchtext.data.BucketIterator if train else torchtext.data.Iterator
|
|
repeat = False
|
|
it = Iterator(data, batch_size=val_batch_size,
|
|
device=device, batch_size_fn=batch_fn if train else None,
|
|
distributed=world_size>1, train=train, repeat=repeat, sort=sort,
|
|
shuffle=shuffle, reverse=args.reverse)
|
|
return it
|
|
|
|
|
|
def get_learning_rate(i, args):
|
|
transformer_lr = 1. / math.sqrt(args.dimension) * min(
|
|
1 / math.sqrt(i), i / (args.warmup * math.sqrt(args.warmup)))
|
|
if 'adam' not in args.optimizer.lower():
|
|
transformer_lr = transformer_lr * math.sqrt(args.dimension * args.warmup) * args.sgd_lr
|
|
return transformer_lr
|
|
|
|
|
|
def step(model, batch, opt, iteration, field, task, lr=None, grad_clip=None, writer=None, it=None):
|
|
model.train()
|
|
opt.zero_grad()
|
|
loss, predictions = model(batch, iteration)
|
|
loss.backward()
|
|
if lr is not None:
|
|
opt.param_groups[0]['lr'] = lr
|
|
if grad_clip > 0.0:
|
|
torch.nn.utils.clip_grad_norm_(model.params, grad_clip)
|
|
opt.step()
|
|
return loss.item(), {}
|
|
|
|
|
|
|
|
def create_mixed_set(args, train_sets, aux_sets, epoch):
|
|
|
|
assert len(aux_sets) == len(train_sets)
|
|
num_tasks = len(train_sets)
|
|
|
|
# mixed_set = deepcopy(train_sets)
|
|
mixed_set = train_sets
|
|
|
|
for i in range(num_tasks):
|
|
|
|
train_set = train_sets[i]
|
|
aux_set = aux_sets[i]
|
|
|
|
assert aux_set.fields == train_set.fields
|
|
|
|
train_examples = train_set.examples
|
|
aux_examples = aux_set.examples
|
|
|
|
train_size = len(train_examples)
|
|
aux_size = len(aux_examples)
|
|
|
|
total_size = train_size + aux_size
|
|
|
|
if args.curriculum_strategy == 'linear':
|
|
next_fraction = args.curriculum_rate * epoch
|
|
elif args. curriculum_strategy == 'exp':
|
|
next_fraction = args.curriculum_rate * np.exp(epoch)
|
|
|
|
fraction = min(args.curriculum_max_frac, next_fraction)
|
|
|
|
train_size_target = int((1 - fraction) * total_size)
|
|
aux_size_target = int(fraction * total_size)
|
|
if aux_size_target > aux_size:
|
|
aux_size_target = aux_size
|
|
train_size_target = int(aux_size * (1 - fraction) / fraction)
|
|
elif train_size_target > train_size:
|
|
train_size_target = train_size
|
|
aux_size_target = int(train_size * fraction / (1 - fraction))
|
|
|
|
logging.info(f'at epoch {epoch} we have {train_size_target} examples from training set and {aux_size_target} examples from auxiliary training set')
|
|
|
|
train_set_indices = np.random.choice(range(train_size_target), size=train_size_target, replace=False)
|
|
aux_set_indices = np.random.choice(range(aux_size_target), size=aux_size_target, replace=False)
|
|
|
|
setattr(mixed_set[i], 'examples', [train_examples[i] for i in train_set_indices] + [aux_examples[i] for i in aux_set_indices])
|
|
|
|
return mixed_set
|
|
|
|
|
|
|
|
def train(args, model, opt, train_sets, train_iterations, field, rank=0, world_size=1,
|
|
log_every=10, val_every=100, save_every=1000, rounds=False, val_sets=[], aux_sets=[], writer=None, start_iteration=1, rnd=1, best_decascore=None):
|
|
"""main training function"""
|
|
|
|
device = next(model.parameters()).device
|
|
|
|
logger = log(rank)
|
|
local_loss, num_examples, len_contexts, len_answers, iteration = 0, 0, 0, 0, start_iteration
|
|
|
|
train_iter_deep = deepcopy(train_iterations)
|
|
local_train_metric_dict = {}
|
|
|
|
task_iteration = defaultdict(int)
|
|
|
|
saver = Saver(args.log_dir, world_size, args.max_to_keep)
|
|
epoch = 0
|
|
|
|
while True:
|
|
logging.info(f'starting epoch {epoch}')
|
|
|
|
if epoch == 0:
|
|
logger.info(f'Preparing iterators')
|
|
train_iters = [(name, to_iter(args, world_size, tok, x, device, token_testing=args.token_testing))
|
|
for name, x, tok in zip(args.train_tasks, train_sets, args.train_batch_tokens)]
|
|
val_iters = [(name, to_iter(args, world_size, tok, x, device, train=False, token_testing=args.token_testing, sort=False if 'sql' in name else None))
|
|
for name, x, tok in zip(args.val_tasks, val_sets, args.val_batch_size)]
|
|
|
|
elif args.use_curriculum:
|
|
logger.info(f'Updating iterators for curriculum')
|
|
mixed_sets = create_mixed_set(args, train_sets, aux_sets, epoch)
|
|
train_iters = [(name, to_iter(args, world_size, tok, x, device, token_testing=args.token_testing))
|
|
for name, x, tok in zip(args.train_tasks, mixed_sets, args.train_batch_tokens)]
|
|
|
|
|
|
train_iters = [(task, iter(train_iter)) for task, train_iter in train_iters]
|
|
# For some number of rounds, we 'jump start' some subset of the tasks
|
|
# by training them and not others
|
|
# once the specified number of rounds is completed,
|
|
# switch to normal round robin training
|
|
if rnd < args.jump_start:
|
|
train_iterations = [0]*len(train_iterations)
|
|
for j in range(args.n_jump_start): train_iterations[j] = 1
|
|
else:
|
|
train_iterations = train_iter_deep
|
|
|
|
for task_idx, (task, train_iter) in enumerate(train_iters):
|
|
|
|
task_iterations = train_iterations[task_idx] if train_iterations is not None else None
|
|
if task_iterations == 0:
|
|
continue
|
|
task_iteration[task] = 1
|
|
for batch in train_iter:
|
|
if not args.resume or iteration > start_iteration:
|
|
task_progress = f'{task_iteration[task]}/{task_iterations}:' if task_iterations is not None else ''
|
|
round_progress = f'round_{rnd}:' if rounds else ''
|
|
|
|
# validate
|
|
|
|
deca_score = None
|
|
if (val_every is not None and
|
|
((iteration % args.val_every == 0 % args.val_every) or
|
|
(args.load and iteration == start_iteration + 1))):
|
|
|
|
deca_score = 0
|
|
for val_task_idx, (val_task, val_iter) in enumerate(val_iters):
|
|
val_loss, metric_dict = validate(val_task, val_iter, model, logger, field, world_size, rank, iteration, num_print=args.num_print, args=args)
|
|
if val_loss is not None:
|
|
log_entry = f'{args.timestamp}:{elapsed_time(logger)}:iteration_{iteration}:{round_progress}train_{task}:{task_progress}val_{val_task}:val_loss{val_loss.item():.4f}:'
|
|
writer.add_scalar(f'loss/{val_task}/val', val_loss.item(), iteration)
|
|
else:
|
|
log_entry = f'{args.timestamp}:{elapsed_time(logger)}:iteration_{iteration}:{round_progress}train_{task}:{task_progress}val_{val_task}:'
|
|
|
|
metric_entry = ''
|
|
for metric_key, metric_value in metric_dict.items():
|
|
metric_entry += f'{metric_key}_{metric_value:.2f}:'
|
|
metric_entry = metric_entry[:-1]
|
|
|
|
deca_score += metric_dict[args.task_to_metric[val_task]]
|
|
|
|
# val log
|
|
logger.info(log_entry + metric_entry)
|
|
if writer is not None:
|
|
for metric_key, metric_value in metric_dict.items():
|
|
writer.add_scalar(f'{metric_key}/{val_task}/val', metric_value, iteration)
|
|
writer.add_scalar(f'{val_task}/{metric_key}/val', metric_value, iteration)
|
|
writer.add_scalar('deca/val', deca_score, iteration)
|
|
logger.info(f'{args.timestamp}:{elapsed_time(logger)}:iteration_{iteration}:{round_progress}train_{task}:{task_progress}val_deca:deca_{deca_score:.2f}')
|
|
|
|
# saving
|
|
if save_every is not None and (iteration % args.save_every == 0):
|
|
if rank is not None and rank == 0:
|
|
should_save_best = False
|
|
if deca_score is not None and (best_decascore is None or best_decascore < deca_score):
|
|
best_decascore = deca_score
|
|
should_save_best = True
|
|
|
|
save_model_state_dict = {'model_state_dict': {k: v.cpu() for k, v in model.state_dict().items()}, 'field': field,
|
|
'best_decascore': best_decascore}
|
|
save_opt_state_dict = opt.state_dict()
|
|
save_opt_state_dict.update({'start_iteration': iteration})
|
|
|
|
if world_size > 1:
|
|
torch.distributed.barrier()
|
|
saver.save(save_model_state_dict, save_opt_state_dict, global_step=iteration)
|
|
if should_save_best:
|
|
logger.info(f'{args.timestamp}:{elapsed_time(logger)}:iteration_{iteration}:{round_progress}train_{task}:{task_progress}found new best model')
|
|
torch.save(save_model_state_dict, os.path.join(args.log_dir, 'best.pth'))
|
|
if world_size > 1:
|
|
torch.distributed.barrier()
|
|
torch.save(save_opt_state_dict, os.path.join(args.log_dir, 'best_optim.pth'))
|
|
if world_size > 1:
|
|
torch.distributed.barrier()
|
|
|
|
# lr update
|
|
lr = opt.param_groups[0]['lr']
|
|
if args.warmup > 0 and args.transformer_lr:
|
|
lr = get_learning_rate(iteration, args)
|
|
|
|
# param update
|
|
loss, train_metric_dict = step(model, batch, opt, iteration, field, task, lr=lr, grad_clip=args.grad_clip, writer=writer, it=train_iter)
|
|
|
|
# train metrics
|
|
local_loss += loss
|
|
for metric_name, metric_val in train_metric_dict.items():
|
|
if metric_name in local_train_metric_dict:
|
|
local_train_metric_dict[metric_name] += metric_val / args.log_every
|
|
else:
|
|
local_train_metric_dict[metric_name] = metric_val / args.log_every
|
|
|
|
# train logs
|
|
num_examples += batch.context.size(0)
|
|
len_contexts += batch.context.size(1)
|
|
len_answers += batch.answer.size(1)
|
|
|
|
if log_every is not None and (iteration % log_every == 0 % log_every):
|
|
local_loss /= args.log_every
|
|
num_examples /= args.log_every
|
|
len_contexts /= args.log_every
|
|
len_answers /= args.log_every
|
|
avg_batch_size = f'avbatch_{num_examples:.0f}_{len_contexts:.0f}_{len_answers:.0f}:'
|
|
metric_entry = ''
|
|
for metric_key, metric_value in local_train_metric_dict.items():
|
|
metric_entry += f'{metric_key}_{metric_value:.2f}:'
|
|
metric_entry = f'{metric_entry[:-1]}'
|
|
logger.info(f'{args.timestamp}:{elapsed_time(logger)}:iteration_{iteration}:{round_progress}train_{task}:{task_progress}{avg_batch_size}loss_{local_loss:.4f}{metric_entry}')
|
|
num_examples = 0
|
|
len_contexts = 0
|
|
len_answers = 0
|
|
|
|
if writer is not None:
|
|
writer.add_scalar(f'loss/{task}/train', local_loss, iteration)
|
|
for metric_key, metric_value in local_train_metric_dict.items():
|
|
writer.add_scalar(f'{metric_key}/{task}/train', metric_value, iteration)
|
|
writer.add_scalar(f'{task}/{metric_key}/train', metric_value, iteration)
|
|
|
|
|
|
local_loss = 0
|
|
local_train_metric_dict = {}
|
|
num_examples = 0
|
|
|
|
# book keeping
|
|
task_iteration[task] += 1
|
|
iteration += 1
|
|
if task_iterations is not None and task_iteration[task] > task_iterations:
|
|
break
|
|
|
|
# book keeping
|
|
epoch += 1
|
|
rnd += 1
|
|
# if not rounds or rnd > rounds:
|
|
# break
|
|
|
|
|
|
def run(args, run_args, rank=0, world_size=1):
|
|
device = set_seed(args, rank=rank)
|
|
logger = initialize_logger(args, rank)
|
|
field, train_sets, val_sets, aux_sets, save_dict = run_args
|
|
|
|
logger.start = time.time()
|
|
|
|
if hasattr(args, 'tensorboard') and args.tensorboard:
|
|
logger.info(f'Initializing Writer')
|
|
writer = SummaryWriter(log_dir=args.log_dir)
|
|
else:
|
|
writer = None
|
|
|
|
model = init_model(args, field, logger, world_size, device)
|
|
opt = init_opt(args, model)
|
|
start_iteration = 1
|
|
|
|
if save_dict is not None:
|
|
logger.info(f'Loading model from {os.path.join(args.save, args.load)}')
|
|
save_dict = torch.load(os.path.join(args.save, args.load))
|
|
model.load_state_dict(save_dict['model_state_dict'])
|
|
if args.resume:
|
|
logger.info(f'Resuming Training from {os.path.splitext(args.load)[0]}_optim.pth')
|
|
opt_state_dict = torch.load(os.path.join(args.save, f'{os.path.splitext(args.load)[0]}_optim.pth'))
|
|
start_iteration = opt_state_dict.pop('start_iteration')
|
|
logger.info(f'Starting iteration is {start_iteration}')
|
|
opt.load_state_dict(opt_state_dict)
|
|
# start_iteration = int(os.path.splitext(os.path.basename(args.load))[0].split('_')[1])
|
|
|
|
logger.info(f'Begin Training')
|
|
train(args, model, opt, train_sets, args.train_iterations, field, val_sets=val_sets, aux_sets=aux_sets,
|
|
rank=rank, world_size=world_size,
|
|
log_every=args.log_every, val_every=args.val_every, rounds=len(train_sets)>1,
|
|
writer=writer if rank==0 else None, save_every=args.save_every, start_iteration=start_iteration,
|
|
best_decascore=save_dict.get('best_decascore') if save_dict is not None else None)
|
|
|
|
|
|
def init_model(args, field, logger, world_size, device):
|
|
logger.info(f'Initializing {args.model}')
|
|
Model = getattr(models, args.model)
|
|
model = Model(field, args)
|
|
params = get_trainable_params(model)
|
|
num_param = count_params(params)
|
|
logger.info(f'{args.model} has {num_param:,} trainable parameters')
|
|
|
|
model.to(device)
|
|
if world_size > 1:
|
|
logger.info(f'Wrapping model for distributed')
|
|
model = DistributedDataParallel(model)
|
|
|
|
model.params = params
|
|
return model
|
|
|
|
|
|
def init_opt(args, model):
|
|
opt = None
|
|
if 'adam' in args.optimizer.lower():
|
|
if args.transformer_lr:
|
|
opt = torch.optim.Adam(model.params, lr=args.lr_rate, betas=(0.9, 0.98), eps=1e-9)
|
|
else:
|
|
opt = torch.optim.Adam(model.params, lr=args.lr_rate, betas=(args.beta0, 0.999))
|
|
else:
|
|
opt = torch.optim.SGD(model.params, lr=args.sgd_lr)
|
|
return opt
|
|
|
|
|
|
def main(argv=sys.argv):
|
|
args = arguments.parse(argv)
|
|
if args is None:
|
|
return
|
|
set_seed(args)
|
|
logger = initialize_logger(args)
|
|
logger.info(f'Arguments:\n{pformat(vars(args))}')
|
|
|
|
field, save_dict = None, None
|
|
if args.load is not None:
|
|
logger.info(f'Loading field from {os.path.join(args.save, args.load)}')
|
|
save_dict = torch.load(os.path.join(args.save, args.load))
|
|
field = save_dict['field']
|
|
field, train_sets, val_sets, aux_sets = prepare_data(args, field, logger)
|
|
if (args.use_curriculum and aux_sets is None) or (not args.use_curriculum and len(aux_sets)):
|
|
logging.error('sth unpleasant is happening with curriculum')
|
|
run_args = (field, train_sets, val_sets, aux_sets, save_dict)
|
|
if len(args.devices) > 1:
|
|
logger.info(f'Multiprocessing')
|
|
mp = Multiprocess(run, args)
|
|
mp.run(run_args)
|
|
else:
|
|
logger.info(f'Processing')
|
|
run(args, run_args, world_size=args.world_size)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|
|
|