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# --------------------------------------------------------
# InternVL
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
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,
# vision embedding
num_channels=3,
patch_size=14,
image_size=224,
# attention layer
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,
# mlp layer
intermediate_size=11008,
mlp_bias=False,
hidden_act='silu',
# rms norm
rms_norm_eps=1e-5,
# pretraining
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)
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