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xtuner_config.py

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+import torch
+from mmengine.dataset import DefaultSampler
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          BitsAndBytesConfig, 
+                          CLIPImageProcessor, CLIPVisionModel,
+                          SiglipVisionModel, SiglipImageProcessor, AutoProcessor)
+from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
+
+from peft import LoraConfig
+from torch.optim import AdamW
+from xtuner.dataset import LLaVADataset, CambrianDataset, ConcatDataset
+from xtuner.dataset.collate_fns import default_collate_fn
+from xtuner.dataset.map_fns import llava_map_fn, cambrian_map_fn, template_map_fn_factory
+from xtuner.dataset.samplers import LengthGroupedSampler
+from xtuner.engine import DatasetInfoHook, EvaluateChatHook
+from xtuner.model import LLaVAModel, PikaModel
+from xtuner.utils import PROMPT_TEMPLATE
+
+    
+#######################################################################
+#                          PART 1  Settings                           #
+#######################################################################
+# Model
+llm_name_or_path = 'meta-llama/Meta-Llama-3.1-8B-Instruct'
+visual_encoder_name_or_path = 'google/siglip-so400m-patch14-384'
+# pretrained_pth = '/data/wenhao/projects/xtuner/work_dirs/final_siglip_llama31_P/projector'
+
+prompt_template = PROMPT_TEMPLATE.llama3_chat
+max_length = 4096
+size = 378
+
+batch_size = 8  # per_device
+accumulative_counts = 2
+lr = 1e-3
+dataloader_num_workers = 0
+max_epochs = 1
+optim_type = AdamW
+betas = (0.9, 0.999)
+weight_decay = 0
+max_norm = 1  # grad clip
+warmup_ratio = 0.03
+
+# Save
+save_steps = 200
+save_total_limit = 2  # Maximum checkpoints to keep (-1 means unlimited)
+
+#######################################################################
+#            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='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k',
+    trust_remote_code=True,
+    size=size,
+    crop_size=size)
+
+model = dict(
+    type=PikaModel,
+    freeze_llm=True,
+    freeze_visual_encoder=True,
+    # pretrained_pth=pretrained_pth,
+    llm=dict(
+        type=AutoModelForCausalLM.from_pretrained,
+        pretrained_model_name_or_path=llm_name_or_path,
+        trust_remote_code=True,
+        torch_dtype=torch.float16,),
+    visual_encoder=dict(
+        type=SiglipVisionModel.from_pretrained,
+        pretrained_model_name_or_path=visual_encoder_name_or_path))
+
+#######################################################################
+#                      PART 3  Dataset & Dataloader                   #
+#######################################################################
+dense_data_root = '/data/wenhao/projects/xtuner/data/DenseFusion-1M/'
+dense_data_path = dense_data_root + 'DenseFusion-1M/DenseFusion-1M-instruct.jsonl'
+dense_image_folder = dense_data_root + '1M_data'
+dense_processed_text_folder = dense_data_root + 'pre_token_llama3'
+dense_dataset = dict(
+    type=CambrianDataset,
+    image_folder=dense_image_folder,
+    image_processor=image_processor,
+    # data_path=dense_data_path,
+    # tokenizer=tokenizer,
+    offline_processed_text_folder=dense_processed_text_folder,
+    dataset_map_fn=cambrian_map_fn,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    max_length=max_length,
+    pad_image_to_square=True)
+
+laion_data_root = '/data/wenhao/projects/xtuner/data/LLaVA-Pretrain/'
+laion_data_path = laion_data_root + 'laion_558k.jsonl'
+laion_image_folder = laion_data_root
+laion_dataset = dict(
+    type=CambrianDataset,
+    offline_processed_text_folder='/data/wenhao/projects/xtuner/data/LLaVA-Pretrain/pre_token_llama31',
+    image_folder=laion_image_folder,
+    image_processor=image_processor,
+    dataset_map_fn=cambrian_map_fn,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    max_length=max_length,
+    pad_image_to_square=True)
+
+face_data_root = '/data/wenhao/projects/xtuner/data/FaceCaption-15M/'
+face_data_path = face_data_root + 'FaceCaption-100K.jsonl'
+face_image_folder = face_data_root + 'full_data'
+face_processed_text_folder = face_data_root + 'pre_token_llama3'
+face_dataset = dict(
+    type=CambrianDataset,
+    offline_processed_text_folder=face_processed_text_folder,
+    image_folder=face_image_folder,
+    image_processor=image_processor,
+    dataset_map_fn=cambrian_map_fn,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    max_length=max_length,
+    pad_image_to_square=True)
+
+allava_data_root = '/data/wenhao/projects/xtuner/data/ALLaVA-4V'
+allava_cl_data_path = '/data/wenhao/projects/xtuner/data/ALLaVA-4V/ALLaVA-Caption-LAION-4V.jsonl'
+allava_cl_image_folder = allava_data_root
+allava_cl_dataset = dict(
+    type=CambrianDataset,
+    offline_processed_text_folder='/data/wenhao/projects/xtuner/data/ALLaVA-4V/pre_token_cl_llama31',
+    # tokenizer=tokenizer,
+    # data_path=allava_cl_data_path,
+    image_folder=allava_cl_image_folder,
+    image_processor=image_processor,
+    dataset_map_fn=cambrian_map_fn,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    max_length=max_length,
+    pad_image_to_square=True)
+
+allava_cv_data_path = '/data/wenhao/projects/xtuner/data/ALLaVA-4V/ALLaVA-Caption-VFLAN-4V.jsonl'
+allava_image_folder = allava_data_root
+allava_cv_dataset = dict(
+    type=CambrianDataset,
+    offline_processed_text_folder='/data/wenhao/projects/xtuner/data/ALLaVA-4V/pre_token_cv_llama31',
+    # tokenizer=tokenizer,
+    # data_path=allava_cv_data_path,
+    image_folder=allava_image_folder,
+    image_processor=image_processor,
+    dataset_map_fn=cambrian_map_fn,
+    template_map_fn=dict(
+        type=template_map_fn_factory, template=prompt_template),
+    max_length=max_length,
+    pad_image_to_square=True)
+
+sharept_data_root = '/data/wenhao/projects/xtuner/data/ShareGPT4V/'
+sharept_data_path = sharept_data_root + 'sharegpt4v_pt.jsonl'
+sharept_image_folder = '/data/wenhao/projects/xtuner/data/'
+sharept_dataset = dict(
+    type=CambrianDataset,
+    offline_processed_text_folder='/data/wenhao/projects/xtuner/data/ShareGPT4V/pre_token_llama31',
+    # tokenizer=tokenizer,
+    # data_path='/data/wenhao/projects/xtuner/data/ShareGPT4V/sharegpt4v_pt.jsonl',
+    image_folder=sharept_image_folder,
+    image_processor=image_processor,
+    dataset_map_fn=cambrian_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=[laion_dataset, dense_dataset, face_dataset, sharept_dataset, allava_cl_dataset, allava_cv_dataset],
+)
+
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=dataloader_num_workers,
+    dataset=train_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,
+        T_max=max_epochs,
+        convert_to_iter_based=True)
+]
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
+
+#######################################################################
+#                           PART 5  Runtime                           #
+#######################################################################
+# Evaluate the generation performance during the training
+evaluation_freq = 100
+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']
+
+
+# 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 100 iterations.
+    logger=dict(type=LoggerHook, interval=10),
+    # enable the parameter scheduler.
+    param_scheduler=dict(type=ParamSchedulerHook),
+    # save checkpoint per epoch.
+    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)