llama3.1 support

[yinjun622]

hope can use llama3.1 soon on ollama

[ds-kczerski]

Hey, yes, +1 for the above comment for llava + llama3.1.

[9Somboon]

Hey, yes, +1 for the above comment for llava + llama3.1.

+1024 ... I'm waiting llava + llama3.1

[mylesgoose]

it seems to work i ran this. however you have to upgrade transformers and pytorch and deepspeed

Copyright (c) OpenMMLab. All rights reserved.

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)