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import copy |
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from transformers import LlamaConfig, Qwen2Config |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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from .configuration_intern_vit import InternVisionConfig |
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logger = logging.get_logger(__name__) |
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class InternVLChatConfig(PretrainedConfig): |
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model_type = "internvl_chat" |
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is_composition = True |
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def __init__( |
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self, |
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vision_config=None, |
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llm_config=None, |
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use_backbone_lora=0, |
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use_llm_lora=0, |
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select_layer=-1, |
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force_image_size=None, |
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downsample_ratio=0.5, |
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template=None, |
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dynamic_image_size=False, |
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use_thumbnail=False, |
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ps_version="v1", |
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min_dynamic_patch=1, |
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max_dynamic_patch=6, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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if vision_config is None: |
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vision_config = {} |
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logger.info("vision_config is None. Initializing the InternVisionConfig with default values.") |
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if llm_config is None: |
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llm_config = {"architectures": ["Qwen2ForCausalLM"]} |
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logger.info("llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).") |
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self.vision_config = InternVisionConfig(**vision_config) |
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if llm_config["architectures"][0] == "LlamaForCausalLM": |
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self.llm_config = LlamaConfig(**llm_config) |
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elif llm_config["architectures"][0] == "Qwen2ForCausalLM": |
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self.llm_config = Qwen2Config(**llm_config) |
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else: |
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raise ValueError("Unsupported architecture: {}".format(llm_config["architectures"][0])) |
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self.use_backbone_lora = use_backbone_lora |
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self.use_llm_lora = use_llm_lora |
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self.select_layer = select_layer |
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self.force_image_size = force_image_size |
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self.downsample_ratio = downsample_ratio |
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self.template = template |
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self.dynamic_image_size = dynamic_image_size |
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self.use_thumbnail = use_thumbnail |
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self.ps_version = ps_version |
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self.min_dynamic_patch = min_dynamic_patch |
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self.max_dynamic_patch = max_dynamic_patch |
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logger.info(f"vision_select_layer: {self.select_layer}") |
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logger.info(f"ps_version: {self.ps_version}") |
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logger.info(f"min_dynamic_patch: {self.min_dynamic_patch}") |
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logger.info(f"max_dynamic_patch: {self.max_dynamic_patch}") |
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def to_dict(self): |
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""" |
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. |
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Returns: |
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
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""" |
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output = copy.deepcopy(self.__dict__) |
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output["vision_config"] = self.vision_config.to_dict() |
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output["llm_config"] = self.llm_config.to_dict() |
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output["model_type"] = self.__class__.model_type |
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output["use_backbone_lora"] = self.use_backbone_lora |
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output["use_llm_lora"] = self.use_llm_lora |
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output["select_layer"] = self.select_layer |
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output["force_image_size"] = self.force_image_size |
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output["downsample_ratio"] = self.downsample_ratio |
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output["template"] = self.template |
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output["dynamic_image_size"] = self.dynamic_image_size |
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output["use_thumbnail"] = self.use_thumbnail |
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output["ps_version"] = self.ps_version |
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output["min_dynamic_patch"] = self.min_dynamic_patch |
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output["max_dynamic_patch"] = self.max_dynamic_patch |
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return output |
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