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import os |
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from typing import Union |
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import json |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class HolisticEmbeddingConfig(PretrainedConfig): |
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model_type = 'holistic_embedding' |
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def __init__( |
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self, |
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num_hidden_layers=32, |
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initializer_factor=1e-5, |
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use_autoregressive_loss=False, |
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num_channels=3, |
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patch_size=14, |
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image_size=224, |
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hidden_size=4096, |
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num_attention_heads=32, |
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num_key_value_heads=32, |
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attention_bias=False, |
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attention_dropout=0.0, |
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max_position_embeddings=4096, |
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rope_theta=10000.0, |
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rope_scaling=None, |
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intermediate_size=11008, |
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mlp_bias=False, |
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hidden_act='silu', |
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rms_norm_eps=1e-5, |
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pretraining_tp=1, |
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use_ls=True, |
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use_img_start_end_tokens=True, |
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special_token_maps={}, |
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llm_vocab_size=92553, |
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llm_hidden_size=2048, |
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attn_implementation='flash_attention_2', |
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downsample_ratio=0.5, |
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img_context_token_id=92546, |
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pixel_shuffle_loc="pre", |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.num_hidden_layers = num_hidden_layers |
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self.initializer_factor = initializer_factor |
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self.use_autoregressive_loss = use_autoregressive_loss |
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self.num_channels = num_channels |
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self.patch_size = patch_size |
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self.image_size = image_size |
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self.hidden_size = hidden_size |
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self.num_attention_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.attention_bias = attention_bias |
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self.attention_dropout = attention_dropout |
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self.max_position_embeddings = max_position_embeddings |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.intermediate_size = intermediate_size |
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self.mlp_bias = mlp_bias |
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self.hidden_act = hidden_act |
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self.rms_norm_eps = rms_norm_eps |
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self.pretraining_tp = pretraining_tp |
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self.use_ls = use_ls |
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self.use_img_start_end_tokens = use_img_start_end_tokens |
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self.special_token_maps = special_token_maps |
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self.llm_vocab_size = llm_vocab_size |
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self.llm_hidden_size = llm_hidden_size |
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self.attn_implementation = attn_implementation |
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self.downsample_ratio = downsample_ratio |
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self.img_context_token_id = img_context_token_id |
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self.pixel_shuffle_loc = pixel_shuffle_loc |
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@classmethod |
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig': |
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
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if 'vision_config' in config_dict: |
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config_dict = config_dict['vision_config'] |
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if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type: |
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logger.warning( |
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " |
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f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' |
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) |
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return cls.from_dict(config_dict, **kwargs) |
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@classmethod |
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def from_dict_path(cls, config_path): |
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with open(config_path, 'r') as f: |
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config_dict = json.load(f) |
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return cls.from_dict(config_dict) |
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