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import os |
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import math |
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from typing import Optional, Union |
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from transformers 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 PhiConfig(PretrainedConfig): |
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"""Phi configuration.""" |
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model_type = "phi-msft" |
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attribute_map = { |
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"max_position_embeddings": "n_positions", |
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"hidden_size": "n_embd", |
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"num_attention_heads": "n_head", |
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"num_hidden_layers": "n_layer", |
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} |
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def __init__( |
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self, |
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vocab_size: int = 50304, |
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n_positions: int = 2048, |
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n_embd: int = 1024, |
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n_layer: int = 20, |
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n_inner: Optional[int] = None, |
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n_head: int = 16, |
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n_head_kv: Optional[int] = None, |
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rotary_dim: Optional[int] = 32, |
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activation_function: Optional[str] = "gelu_new", |
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flash_attn: bool = False, |
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flash_rotary: bool = False, |
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fused_dense: bool = False, |
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attn_pdrop: float = 0.0, |
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embd_pdrop: float = 0.0, |
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resid_pdrop: float = 0.0, |
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layer_norm_epsilon: float = 1e-5, |
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initializer_range: float = 0.02, |
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tie_word_embeddings: bool = False, |
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pad_vocab_size_multiple: int = 64, |
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**kwargs |
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) -> None: |
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self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple) |
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self.n_positions = n_positions |
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self.n_embd = n_embd |
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self.n_layer = n_layer |
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self.n_inner = n_inner |
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self.n_head = n_head |
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self.n_head_kv = n_head_kv |
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self.rotary_dim = min(rotary_dim, n_embd // n_head) |
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self.activation_function = activation_function |
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self.flash_attn = flash_attn |
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self.flash_rotary = flash_rotary |
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self.fused_dense = fused_dense |
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self.attn_pdrop = attn_pdrop |
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self.embd_pdrop = embd_pdrop |
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self.resid_pdrop = resid_pdrop |
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self.layer_norm_epsilon = layer_norm_epsilon |
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self.initializer_range = initializer_range |
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) |
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class SiglipVisionConfig(PretrainedConfig): |
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model_type = "siglip_vision_model" |
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def __init__( |
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self, |
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hidden_size=768, |
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intermediate_size=3072, |
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num_hidden_layers=12, |
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num_attention_heads=12, |
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num_channels=3, |
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image_size=224, |
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patch_size=16, |
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hidden_act="gelu_pytorch_tanh", |
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layer_norm_eps=1e-6, |
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attention_dropout=0.0, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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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.attention_dropout = attention_dropout |
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self.layer_norm_eps = layer_norm_eps |
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self.hidden_act = hidden_act |
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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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cls._set_token_in_kwargs(kwargs) |
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
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if config_dict.get("model_type") == "siglip": |
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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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class ImpConfig(PhiConfig): |
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model_type = "imp" |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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self.image_token_index = getattr(self, "image_token_index", 50296) |
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self.image_token = getattr(self, "image_token", "<image>") |
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if not hasattr(self, "vision_tower_config") and hasattr(self, "mm_vision_tower"): |
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vision_tower_config = SiglipVisionConfig.from_pretrained(self.mm_vision_tower) |
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self.vision_tower_config = vision_tower_config.to_diff_dict() |
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@property |
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def vision_tower_cfg(self): |
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cfg = SiglipVisionConfig.from_dict(self.vision_tower_config) |
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cfg.mm_vision_select_layer = self.mm_vision_select_layer |
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cfg.mm_vision_tower = self.mm_vision_tower |
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return cfg |
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