| | |
| | |
| | |
| | |
| | |
| | import os |
| | from typing import Union |
| | import json |
| |
|
| | from transformers.configuration_utils import PretrainedConfig |
| | from transformers.utils import logging |
| |
|
| | logger = logging.get_logger(__name__) |
| |
|
| |
|
| | class HolisticEmbeddingConfig(PretrainedConfig): |
| |
|
| | model_type = 'holistic_embedding' |
| |
|
| | def __init__( |
| | self, |
| | num_hidden_layers=32, |
| | initializer_factor=1e-5, |
| | use_autoregressive_loss=False, |
| | |
| | num_channels=3, |
| | patch_size=14, |
| | image_size=224, |
| | |
| | hidden_size=4096, |
| | num_attention_heads=32, |
| | num_key_value_heads=32, |
| | attention_bias=False, |
| | attention_dropout=0.0, |
| | max_position_embeddings=4096, |
| | rope_theta=10000.0, |
| | rope_scaling=None, |
| | |
| | intermediate_size=11008, |
| | mlp_bias=False, |
| | hidden_act='silu', |
| | |
| | rms_norm_eps=1e-5, |
| | |
| | pretraining_tp=1, |
| | use_ls=True, |
| | use_img_start_end_tokens=True, |
| | special_token_maps={}, |
| | llm_vocab_size=92553, |
| | llm_hidden_size=2048, |
| | attn_implementation='flash_attention_2', |
| | downsample_ratio=0.5, |
| | img_context_token_id=92546, |
| | pixel_shuffle_loc="pre", |
| | **kwargs, |
| | ): |
| | super().__init__(**kwargs) |
| |
|
| | self.num_hidden_layers = num_hidden_layers |
| | self.initializer_factor = initializer_factor |
| | self.use_autoregressive_loss = use_autoregressive_loss |
| |
|
| | self.num_channels = num_channels |
| | self.patch_size = patch_size |
| | self.image_size = image_size |
| |
|
| | self.hidden_size = hidden_size |
| | self.num_attention_heads = num_attention_heads |
| | self.num_key_value_heads = num_key_value_heads |
| | self.attention_bias = attention_bias |
| | self.attention_dropout = attention_dropout |
| | self.max_position_embeddings = max_position_embeddings |
| | self.rope_theta = rope_theta |
| | self.rope_scaling = rope_scaling |
| |
|
| | self.intermediate_size = intermediate_size |
| | self.mlp_bias = mlp_bias |
| | self.hidden_act = hidden_act |
| |
|
| | self.rms_norm_eps = rms_norm_eps |
| |
|
| | self.pretraining_tp = pretraining_tp |
| | self.use_ls = use_ls |
| | self.use_img_start_end_tokens = use_img_start_end_tokens |
| | |
| | self.special_token_maps = special_token_maps |
| | self.llm_vocab_size = llm_vocab_size |
| | self.llm_hidden_size = llm_hidden_size |
| | self.attn_implementation = attn_implementation |
| | self.downsample_ratio = downsample_ratio |
| | self.img_context_token_id = img_context_token_id |
| | self.pixel_shuffle_loc = pixel_shuffle_loc |
| |
|
| | @classmethod |
| | def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig': |
| | config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
| |
|
| | if 'vision_config' in config_dict: |
| | 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) |
| | |
| | @classmethod |
| | def from_dict_path(cls, config_path): |
| | with open(config_path, 'r') as f: |
| | config_dict = json.load(f) |
| |
|
| | return cls.from_dict(config_dict) |
| |
|
