| |
| |
| |
| |
| |
| 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) |
|
|
