Delete xtuner_config.py

xtuner_config.py

@@ -1,208 +0,0 @@
-SYSTEM = ''
-accumulative_counts = 1
-batch_size = 16
-betas = (
-    0.9,
-    0.999,
-)
-custom_hooks = [
-    dict(
-        tokenizer=dict(
-            padding_side='right',
-            pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
-            trust_remote_code=True,
-            type='transformers.AutoTokenizer.from_pretrained'),
-        type='xtuner.engine.DatasetInfoHook'),
-    dict(
-        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',
-        ],
-        every_n_iters=500,
-        image_processor=dict(
-            pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
-            trust_remote_code=True,
-            type='transformers.CLIPImageProcessor.from_pretrained'),
-        prompt_template='xtuner.utils.PROMPT_TEMPLATE.vicuna',
-        system='',
-        tokenizer=dict(
-            padding_side='right',
-            pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
-            trust_remote_code=True,
-            type='transformers.AutoTokenizer.from_pretrained'),
-        type='xtuner.engine.EvaluateChatHook'),
-]
-data_path = './data/llava_data/LLaVA-Instruct-150K/llava_v1_5_mix665k.json'
-dataloader_num_workers = 0
-default_hooks = dict(
-    checkpoint=dict(interval=1, type='mmengine.hooks.CheckpointHook'),
-    logger=dict(interval=10, type='mmengine.hooks.LoggerHook'),
-    param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'),
-    sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'),
-    timer=dict(type='mmengine.hooks.IterTimerHook'))
-env_cfg = dict(
-    cudnn_benchmark=False,
-    dist_cfg=dict(backend='nccl'),
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
-evaluation_freq = 500
-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',
-]
-image_folder = './data/llava_data/llava_images'
-launcher = 'pytorch'
-llava_data_root = './data/llava_data/'
-llava_dataset = dict(
-    data_path='./data/llava_data/LLaVA-Instruct-150K/llava_v1_5_mix665k.json',
-    dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn',
-    image_folder='./data/llava_data/llava_images',
-    max_length=1472,
-    pad_image_to_square=True,
-    image_processor=dict(
-        pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
-        trust_remote_code=True,
-        type='transformers.CLIPImageProcessor.from_pretrained'),
-    template_map_fn=dict(
-        template='xtuner.utils.PROMPT_TEMPLATE.vicuna',
-        type='xtuner.dataset.map_fns.template_map_fn_factory'),
-    tokenizer=dict(
-        padding_side='right',
-        pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
-        trust_remote_code=True,
-        type='transformers.AutoTokenizer.from_pretrained'),
-    type='xtuner.dataset.LLaVADataset')
-llm_name_or_path = 'lmsys/vicuna-7b-v1.5'
-load_from = None
-log_level = 'INFO'
-lr = 0.0002
-max_epochs = 1
-max_length = 1472
-max_norm = 1
-model = dict(
-    freeze_llm=True,
-    freeze_visual_encoder=True,
-    llm=dict(
-        pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
-        quantization_config=dict(
-            bnb_4bit_compute_dtype='torch.float16',
-            bnb_4bit_quant_type='nf4',
-            bnb_4bit_use_double_quant=True,
-            llm_int8_has_fp16_weight=False,
-            llm_int8_threshold=6.0,
-            load_in_4bit=True,
-            load_in_8bit=False,
-            type='transformers.BitsAndBytesConfig'),
-        torch_dtype='torch.float16',
-        trust_remote_code=True,
-        type='transformers.AutoModelForCausalLM.from_pretrained'),
-    llm_lora=dict(
-        bias='none',
-        lora_alpha=256,
-        lora_dropout=0.05,
-        r=512,
-        task_type='CAUSAL_LM',
-        type='peft.LoraConfig'),
-    pretrained_pth=
-    './work_dirs/llava_vicuna_7b_v15_clip_vit_large_p14_336_e1_gpu8_pretrain/epoch_1.pth',
-    type='xtuner.model.LLaVAModel',
-    visual_encoder=dict(
-        pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
-        type='transformers.CLIPVisionModel.from_pretrained'),
-    visual_encoder_lora=dict(
-        bias='none',
-        lora_alpha=16,
-        lora_dropout=0.05,
-        r=64,
-        type='peft.LoraConfig'))
-optim_type = 'torch.optim.AdamW'
-optim_wrapper = dict(
-    optimizer=dict(
-        betas=(
-            0.9,
-            0.999,
-        ),
-        lr=0.0002,
-        type='torch.optim.AdamW',
-        weight_decay=0),
-    type='DeepSpeedOptimWrapper')
-param_scheduler = [
-    dict(
-        begin=0,
-        by_epoch=True,
-        convert_to_iter_based=True,
-        end=0.03,
-        start_factor=1e-05,
-        type='mmengine.optim.LinearLR'),
-    dict(
-        T_max=1,
-        begin=0.03,
-        by_epoch=True,
-        convert_to_iter_based=True,
-        eta_min=0.0,
-        type='mmengine.optim.CosineAnnealingLR'),
-]
-pretrained_pth = './work_dirs/llava_vicuna_7b_v15_clip_vit_large_p14_336_e1_gpu8_pretrain/epoch_1.pth'
-image_processor = dict(
-    pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
-    trust_remote_code=True,
-    type='transformers.CLIPImageProcessor.from_pretrained')
-prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.vicuna'
-randomness = dict(deterministic=False, seed=None)
-resume = False
-runner_type = 'FlexibleRunner'
-strategy = dict(
-    config=dict(
-        bf16=dict(enabled=True),
-        fp16=dict(enabled=False, initial_scale_power=16),
-        gradient_accumulation_steps='auto',
-        gradient_clipping='auto',
-        train_micro_batch_size_per_gpu='auto',
-        zero_allow_untested_optimizer=True,
-        zero_force_ds_cpu_optimizer=False,
-        zero_optimization=dict(overlap_comm=True, stage=2)),
-    exclude_frozen_parameters=True,
-    gradient_accumulation_steps=1,
-    gradient_clipping=1,
-    train_micro_batch_size_per_gpu=16,
-    type='xtuner.engine.DeepSpeedStrategy')
-tokenizer = dict(
-    padding_side='right',
-    pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
-    trust_remote_code=True,
-    type='transformers.AutoTokenizer.from_pretrained')
-train_cfg = dict(by_epoch=True, max_epochs=1, val_interval=1)
-train_dataloader = dict(
-    batch_size=16,
-    collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'),
-    dataset=dict(
-        data_path=
-        './data/llava_data/LLaVA-Instruct-150K/llava_v1_5_mix665k.json',
-        dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn',
-        image_folder='./data/llava_data/llava_images',
-        max_length=1472,
-        pad_image_to_square=True,
-        image_processor=dict(
-            pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
-            trust_remote_code=True,
-            type='transformers.CLIPImageProcessor.from_pretrained'),
-        template_map_fn=dict(
-            template='xtuner.utils.PROMPT_TEMPLATE.vicuna',
-            type='xtuner.dataset.map_fns.template_map_fn_factory'),
-        tokenizer=dict(
-            padding_side='right',
-            pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
-            trust_remote_code=True,
-            type='transformers.AutoTokenizer.from_pretrained'),
-        type='xtuner.dataset.LLaVADataset'),
-    num_workers=0,
-    sampler=dict(
-        length_property='modality_length',
-        per_device_batch_size=16,
-        type='xtuner.dataset.samplers.LengthGroupedSampler'))
-visual_encoder_name_or_path = 'openai/clip-vit-large-patch14-336'
-visualizer = None
-warmup_ratio = 0.03
-weight_decay = 0
-work_dir = './work_dirs/llava_vicuna_7b_v15_qlora_clip_vit_large_p14_336_lora_e1_gpu8_finetune'