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import torch |
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import os |
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from transformers import AutoConfig, PretrainedConfig, PreTrainedModel |
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from .siglip_encoder import SiglipVisionTower |
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from .context_provider import ContextProvider, ContextProviderConfig |
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def build_vision_tower( |
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model_name_or_path: str, config: PretrainedConfig |
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) -> PreTrainedModel: |
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if model_name_or_path is None: |
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return None |
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vision_tower_arch = None |
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if config.resume_path and "radio" not in model_name_or_path: |
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assert os.path.exists( |
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model_name_or_path |
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), f"Resume vision tower path {model_name_or_path} does not exist!" |
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vision_tower_cfg = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True) |
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vision_tower_arch = vision_tower_cfg.architectures[0].lower() |
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vision_tower_name = ( |
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vision_tower_arch if vision_tower_arch is not None else model_name_or_path |
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) |
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if "siglip" in vision_tower_name: |
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vision_tower = SiglipVisionTower(model_name_or_path, config) |
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else: |
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raise ValueError(f"Unknown vision tower: {model_name_or_path}") |
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config.mm_hidden_size = vision_tower.config.hidden_size |
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return vision_tower |
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def build_context_provider( |
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model_type_or_path: str, config: PretrainedConfig |
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) -> PreTrainedModel: |
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if model_type_or_path is None: |
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return None |
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if config.resume_path: |
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assert os.path.exists( |
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model_type_or_path |
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), f"Resume context provider path {model_type_or_path} does not exist!" |
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return ContextProvider.from_pretrained( |
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model_type_or_path, config, torch_dtype=eval(config.model_dtype) |
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) |
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else: |
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mm_projector_cfg = ContextProviderConfig(model_type_or_path) |
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mm_projector = ContextProvider(mm_projector_cfg, config).to( |
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eval(config.model_dtype) |
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) |
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return mm_projector |
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