InternLM
llama3.1 support
Hey, yes, +1 for the above comment for llava + llama3.1.
+1024 ... I'm waiting llava + llama3.1
it seems to work i ran this. however you have to upgrade transformers and pytorch and deepspeed
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from mmengine.dataset import DefaultSampler 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, SiglipImageProcessor, SiglipVisionModel)
from xtuner.dataset import 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.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 = 'Meta-Llama/Meta-Llama-3.1-8B-Instruct-abliterated' visual_encoder_name_or_path = 'google/siglip-so400m-patch14-384'
Data
data_root = './data/llava_data/' data_path = data_root + 'LLaVA-Pretrain/blip_laion_cc_sbu_558k.json' image_folder = data_root + 'LLaVA-Pretrain/images' prompt_template = PROMPT_TEMPLATE.llama3_chat max_length = int(131072 - (336 / 14)**2)
Scheduler & Optimizer
batch_size = 1 # per_device accumulative_counts = 1 dataloader_num_workers = 5 max_epochs = 1 optim_type = AdamW lr = 1e-3 betas = (0.9, 0.999) weight_decay = 0 max_norm = 1 # grad clip warmup_ratio = 0.03
Save
save_steps = 500 save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
Evaluate the generation performance during the training
evaluation_freq = 500 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=SiglipImageProcessor.from_pretrained, pretrained_model_name_or_path=visual_encoder_name_or_path, trust_remote_code=True)
model = dict( type=LLaVAModel, freeze_llm=True, freeze_visual_encoder=True, llm=dict( type=AutoModelForCausalLM.from_pretrained, pretrained_model_name_or_path=llm_name_or_path, trust_remote_code=True), visual_encoder=dict( type=SiglipVisionModel.from_pretrained, pretrained_model_name_or_path=visual_encoder_name_or_path))
#######################################################################
PART 3 Dataset & Dataloader
####################################################################### llava_dataset = dict( type=LLaVADataset, data_path=data_path, image_folder=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=False)
train_dataloader = dict( batch_size=batch_size, num_workers=dataloader_num_workers, pin_memory=True, dataset=llava_dataset, sampler=dict(type=DefaultSampler, shuffle=True), 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 environment.
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)
