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'))