# ------------------------------- Phi-2 --------------------------------------------- # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. # https://huggingface.co/google/siglip-so400m-patch14-384 # # Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu. # Licensed under the BSD 3-Clause License. # ------------------------------- SigLIP -------------------------------------------- # Copyright 2024 Google AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ------------------------------- Llava --------------------------------------------- # Copyright 2023 Haotian Liu # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ----------------------------------------------------------------------------------- import os import math from typing import Optional, Union from transformers import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class PhiConfig(PretrainedConfig): """Phi configuration.""" model_type = "phi-msft" attribute_map = { "max_position_embeddings": "n_positions", "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size: int = 50304, n_positions: int = 2048, n_embd: int = 1024, n_layer: int = 20, n_inner: Optional[int] = None, n_head: int = 16, n_head_kv: Optional[int] = None, rotary_dim: Optional[int] = 32, activation_function: Optional[str] = "gelu_new", flash_attn: bool = False, flash_rotary: bool = False, fused_dense: bool = False, attn_pdrop: float = 0.0, embd_pdrop: float = 0.0, resid_pdrop: float = 0.0, layer_norm_epsilon: float = 1e-5, initializer_range: float = 0.02, tie_word_embeddings: bool = False, pad_vocab_size_multiple: int = 64, **kwargs ) -> None: self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple) self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_inner = n_inner self.n_head = n_head self.n_head_kv = n_head_kv self.rotary_dim = min(rotary_dim, n_embd // n_head) self.activation_function = activation_function self.flash_attn = flash_attn self.flash_rotary = flash_rotary self.fused_dense = fused_dense self.attn_pdrop = attn_pdrop self.embd_pdrop = embd_pdrop self.resid_pdrop = resid_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class SiglipVisionConfig(PretrainedConfig): model_type = "siglip_vision_model" def __init__( self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=16, hidden_act="gelu_pytorch_tanh", layer_norm_eps=1e-6, attention_dropout=0.0, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.image_size = image_size self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from SiglipConfig if config_dict.get("model_type") == "siglip": config_dict = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class ImpConfig(PhiConfig): model_type = "imp" def __init__(self, **kwargs): super().__init__(**kwargs) self.image_token_index = getattr(self, "image_token_index", 50296) self.image_token = getattr(self, "image_token", "") if not hasattr(self, "vision_tower_config") and hasattr(self, "mm_vision_tower"): vision_tower_config = SiglipVisionConfig.from_pretrained(self.mm_vision_tower) self.vision_tower_config = vision_tower_config.to_diff_dict() @property def vision_tower_cfg(self): cfg = SiglipVisionConfig.from_dict(self.vision_tower_config) # imp-v1 only supports `patch` feature for now w/o cls token # cfg.mm_vision_select_feature = self.mm_vision_select_feature cfg.mm_vision_select_layer = self.mm_vision_select_layer cfg.mm_vision_tower = self.mm_vision_tower return cfg