LLaVa phi-3 sft 报错 ConnectionResetError: [Errno 104] Connection reset by peer

[Yu-Yang-Li]

指令：

Copyright (c) OpenMMLab. All rights reserved.

from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
CLIPImageProcessor, CLIPVisionModel)

from xtuner.dataset import ConcatDataset, LLaVADataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory
from xtuner.dataset.samplers import LengthGroupedSampler
from xtuner.engine.hooks import DatasetInfoHook, EvaluateChatHook
from xtuner.engine.runner import TrainLoop
from xtuner.model import LLaVAModel
from xtuner.utils import PROMPT_TEMPLATE

#######################################################################

PART 1 Settings

#######################################################################

Model

llm_name_or_path = 'microsoft/Phi-3-mini-4k-instruct'
visual_encoder_name_or_path = 'openai/clip-vit-large-patch14-336'

Specify the pretrained pth

pretrained_pth = './work_dirs/llava_phi3_mini_4k_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain/iter_9742.pth' # noqa: E501

Data

data_root = './data/internvl_sft/'

sharegpt4v_caption_data_path = data_root + 'sharegpt4v_instruct_gpt4-vision_cap100k.jsonl' # noqa: E501
sharegpt4v_caption_image_folder = data_root + 'data'

llava_data_path = data_root + 'llava_instruct_150k_zh.jsonl'
llava_image_folder = data_root + 'data/coco'

sharegpt4v_data_path = data_root + 'sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.jsonl' # noqa: E501
sharegpt4v_image_folder = data_root + 'data'

dvqa_data_path = data_root + 'dvqa_train_200k.jsonl'
dvqa_image_folder = data_root + 'data/dvqa'

chartqa_data_path = data_root + 'chartqa_train_18k.jsonl'
chartqa_image_folder = data_root + 'data/chartqa'

ai2d_data_path = data_root + 'ai2d_train_12k.jsonl'
ai2d_image_folder = data_root + 'data/ai2d'

docvqa_data_path = data_root + 'docvqa_train_10k.jsonl'
docvqa_image_folder = data_root + 'data/docvqa'

geoqa_data_path = data_root + 'geoqa+.jsonl'
geoqa_image_folder = data_root + 'data/geoqa+'

synthdog_data_path = data_root + 'synthdog_en.jsonl'
synthdog_image_folder = data_root + 'data/synthdog-en'

prompt_template = PROMPT_TEMPLATE.phi3_chat
max_length = int(4096 - (336 / 14)**2)

Scheduler & Optimizer

batch_size = 8 # per_device
accumulative_counts = 2
dataloader_num_workers = 4
max_epochs = 2
optim_type = AdamW
lr = 2e-5
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1 # grad clip
warmup_ratio = 0.03

Save

save_steps = 5000
save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)

Evaluate the generation performance during the training

evaluation_freq = 5000
SYSTEM = ''
evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
evaluation_inputs = ['请描述一下这张照片', 'Please describe this picture']

#######################################################################

PART 2 Model & Tokenizer & Image Processor

#######################################################################
tokenizer = dict(
type=AutoTokenizer.from_pretrained,
pretrained_model_name_or_path=llm_name_or_path,
trust_remote_code=True,
padding_side='right')

image_processor = dict(
type=CLIPImageProcessor.from_pretrained,
pretrained_model_name_or_path=visual_encoder_name_or_path,
trust_remote_code=True)

model = dict(
type=LLaVAModel,
freeze_llm=False,
freeze_visual_encoder=False,
pretrained_pth=pretrained_pth,
llm=dict(
type=AutoModelForCausalLM.from_pretrained,
pretrained_model_name_or_path=llm_name_or_path,
trust_remote_code=True),
visual_encoder=dict(
type=CLIPVisionModel.from_pretrained,
pretrained_model_name_or_path=visual_encoder_name_or_path))

#######################################################################

PART 3 Dataset & Dataloader

#######################################################################
sharegpt4v_caption_dataset = dict(
type=LLaVADataset,
data_path=sharegpt4v_caption_data_path,
image_folder=sharegpt4v_caption_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

llava_dataset = dict(
type=LLaVADataset,
data_path=llava_data_path,
image_folder=llava_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

sharegpt4v_dataset = dict(
type=LLaVADataset,
data_path=sharegpt4v_data_path,
image_folder=sharegpt4v_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

dvqa_dataset = dict(
type=LLaVADataset,
data_path=dvqa_data_path,
image_folder=dvqa_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

chartqa_dataset = dict(
type=LLaVADataset,
data_path=chartqa_data_path,
image_folder=chartqa_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

ai2d_dataset = dict(
type=LLaVADataset,
data_path=ai2d_data_path,
image_folder=ai2d_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

docvqa_dataset = dict(
type=LLaVADataset,
data_path=docvqa_data_path,
image_folder=docvqa_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

geoqa_dataset = dict(
type=LLaVADataset,
data_path=geoqa_data_path,
image_folder=geoqa_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

synthdog_dataset = dict(
type=LLaVADataset,
data_path=synthdog_data_path,
image_folder=synthdog_image_folder,
tokenizer=tokenizer,
image_processor=image_processor,
dataset_map_fn=llava_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
max_length=max_length,
pad_image_to_square=True)

train_dataset = dict(
type=ConcatDataset,
datasets=[
sharegpt4v_caption_dataset, llava_dataset, sharegpt4v_dataset,
dvqa_dataset, chartqa_dataset, ai2d_dataset, docvqa_dataset,
geoqa_dataset, synthdog_dataset
])

train_dataloader = dict(
batch_size=batch_size,
num_workers=dataloader_num_workers,
pin_memory=True,
dataset=train_dataset,
sampler=dict(
type=LengthGroupedSampler,
length_property='modality_length',
per_device_batch_size=batch_size * accumulative_counts),
collate_fn=dict(type=default_collate_fn))

#######################################################################

PART 4 Scheduler & Optimizer

#######################################################################

optimizer

optim_wrapper = dict(
type=AmpOptimWrapper,
optimizer=dict(
type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
accumulative_counts=accumulative_counts,
loss_scale='dynamic',
dtype='float16')

learning policy

More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501

param_scheduler = [
dict(
type=LinearLR,
start_factor=1e-5,
by_epoch=True,
begin=0,
end=warmup_ratio * max_epochs,
convert_to_iter_based=True),
dict(
type=CosineAnnealingLR,
eta_min=0.0,
by_epoch=True,
begin=warmup_ratio * max_epochs,
end=max_epochs,
convert_to_iter_based=True)
]

train, val, test setting

train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

#######################################################################

PART 5 Runtime

#######################################################################

Log the dialogue periodically during the training process, optional

custom_hooks = [
dict(type=DatasetInfoHook, tokenizer=tokenizer),
dict(
type=EvaluateChatHook,
tokenizer=tokenizer,
image_processor=image_processor,
every_n_iters=evaluation_freq,
evaluation_inputs=evaluation_inputs,
evaluation_images=evaluation_images,
system=SYSTEM,
prompt_template=prompt_template)
]

configure default hooks

default_hooks = dict(
# record the time of every iteration.
timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per save_steps.
checkpoint=dict(
type=CheckpointHook,
by_epoch=False,
interval=save_steps,
max_keep_ckpts=save_total_limit),
# set sampler seed in distributed evrionment.
sampler_seed=dict(type=DistSamplerSeedHook),
)

configure environment

env_cfg = dict(
# whether to enable cudnn benchmark
cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),
)

set visualizer

visualizer = None

set log level

log_level = 'INFO'

load from which checkpoint

load_from = None

whether to resume training from the loaded checkpoint

resume = False

Defaults to use random seed and disable deterministic

randomness = dict(seed=None, deterministic=False)

set log processor

log_processor = dict(by_epoch=False)

报错：

[rank4]: Traceback (most recent call last):
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 789, in urlopen
[rank4]: response = self._make_request(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 490, in _make_request
[rank4]: raise new_e
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 466, in _make_request
[rank4]: self._validate_conn(conn)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1095, in _validate_conn
[rank4]: conn.connect()
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connection.py", line 652, in connect
[rank4]: sock_and_verified = _ssl_wrap_socket_and_match_hostname(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connection.py", line 805, in ssl_wrap_socket_and_match_hostname
[rank4]: ssl_sock = ssl_wrap_socket(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/util/ssl.py", line 465, in ssl_wrap_socket
[rank4]: ssl_sock = ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/util/ssl.py", line 509, in _ssl_wrap_socket_impl
[rank4]: return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/ssl.py", line 513, in wrap_socket
[rank4]: return self.sslsocket_class._create(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/ssl.py", line 1104, in _create
[rank4]: self.do_handshake()
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/ssl.py", line 1375, in do_handshake
[rank4]: self._sslobj.do_handshake()
[rank4]: ConnectionResetError: [Errno 104] Connection reset by peer

[rank4]: During handling of the above exception, another exception occurred:

[rank4]: Traceback (most recent call last):
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/requests/adapters.py", line 667, in send
[rank4]: resp = conn.urlopen(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 843, in urlopen
[rank4]: retries = retries.increment(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/util/retry.py", line 474, in increment
[rank4]: raise reraise(type(error), error, _stacktrace)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/util/util.py", line 38, in reraise
[rank4]: raise value.with_traceback(tb)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 789, in urlopen
[rank4]: response = self._make_request(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 490, in _make_request
[rank4]: raise new_e
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 466, in _make_request
[rank4]: self._validate_conn(conn)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1095, in _validate_conn
[rank4]: conn.connect()
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connection.py", line 652, in connect
[rank4]: sock_and_verified = _ssl_wrap_socket_and_match_hostname(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/connection.py", line 805, in ssl_wrap_socket_and_match_hostname
[rank4]: ssl_sock = ssl_wrap_socket(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/util/ssl.py", line 465, in ssl_wrap_socket
[rank4]: ssl_sock = ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/site-packages/urllib3/util/ssl.py", line 509, in _ssl_wrap_socket_impl
[rank4]: return ssl_context.wrap_socket(sock, server_hostname=server_hostname)
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/ssl.py", line 513, in wrap_socket
[rank4]: return self.sslsocket_class._create(
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/ssl.py", line 1104, in _create
[rank4]: self.do_handshake()
[rank4]: File "/root/anaconda3/envs/xtuner-env/lib/python3.10/ssl.py", line 1375, in do_handshake
[rank4]: self._sslobj.do_handshake()
[rank4]: urllib3.exceptions.ProtocolError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))