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"""TaiVisionLM configuration""" |
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from transformers import PretrainedConfig |
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from transformers import logging, CONFIG_MAPPING |
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import warnings |
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import transformers |
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logger = logging.get_logger(__name__) |
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class TaiVisionLMConfig(PretrainedConfig): |
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model_type = "taivisionlm" |
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is_composition = False |
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def __init__( |
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self, |
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vision_config=None, |
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text_config=None, |
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ignore_index=-100, |
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image_token_idx=32000, |
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vocab_size=32001, |
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projection_dim=768, |
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hidden_size=2048, |
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**kwargs, |
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): |
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self.ignore_index = ignore_index |
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self.image_token_index = image_token_idx |
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self._vocab_size = vocab_size |
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self.projection_dim = projection_dim |
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self.hidden_size = hidden_size |
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self.vision_config = vision_config |
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self.is_encoder_decoder = False |
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if isinstance(self.vision_config, dict): |
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vision_config["model_type"] = ( |
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vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model" |
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) |
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self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) |
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elif vision_config is None: |
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self.vision_config = CONFIG_MAPPING["siglip_vision_model"]( |
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attention_dropout=0.0, |
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hidden_act="gelu_pytorch_tanh", |
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hidden_size=768, |
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image_size=224, |
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intermediate_size=3072, |
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layer_norm_eps=1e-06, |
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num_attention_heads=12, |
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num_channels=3, |
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num_hidden_layers=12, |
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patch_size=16, |
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) |
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self.vocab_size = vocab_size |
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self.text_config = text_config |
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if isinstance(self.text_config, dict): |
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text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "gpt2" |
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self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) |
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elif text_config is None: |
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self.text_config = CONFIG_MAPPING["llama"]( |
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architecture= ["LlamaForCausalLM"], |
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hidden_act = "silu", |
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attention_bias = False, |
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attention_dropout = 0.0, |
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bos_token_id = 1, |
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eos_token_id = 2, |
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hidden_size = 2048, |
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initializer_range = 0.02, |
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intermediate_size = 5632, |
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max_position_embeddings = 2048, |
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model_type = "llama", |
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num_attention_heads = 32, |
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num_hidden_layers = 22, |
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num_key_value_heads = 4, |
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pretraining_tp = 1, |
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rms_norm_eps = 1e-05, |
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rope_scaling = None, |
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rope_theta = 10000.0, |
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tie_word_embeddings = False, |
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torch_dtype = "bfloat16", |
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transformers_version = "4.40.2", |
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use_cache = True, |
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vocab_size = 32001 |
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) |
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self.num_image_tokens = (self.vision_config.image_size // self.vision_config.patch_size) ** 2 |
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self.pad_token_id = self.text_config.pad_token_id |
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self.vision_config.projection_dim = projection_dim |
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super().__init__(**kwargs) |
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@property |
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def vocab_size(self): |
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warnings.warn( |
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"The `vocab_size` attribute is deprecated and will be removed in v4.44, Please use `text_config.vocab_size` instead.", |
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FutureWarning, |
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) |
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return self._vocab_size |
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@vocab_size.setter |
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def vocab_size(self, value): |
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self._vocab_size = value |
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def to_dict(self): |
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output = super().to_dict() |
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output.pop("_vocab_size", None) |
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return output |
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