File size: 3,280 Bytes
f3ba819 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# coding=utf-8
""" MiniCPMV model configuration"""
import os
from typing import Union
from transformers.utils import logging
from transformers import Qwen2Config, PretrainedConfig
from .modeling_navit_siglip import SiglipVisionConfig
logger = logging.get_logger(__name__)
class MiniCPMVSliceConfig(PretrainedConfig):
model_type = "minicpmv"
def __init__(
self,
patch_size=14,
max_slice_nums=9,
scale_resolution=448,
**kwargs,
):
super().__init__(**kwargs)
self.patch_size = patch_size
self.max_slice_nums = max_slice_nums
self.scale_resolution = scale_resolution
@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)
if config_dict.get("model_type") == "minicpmv":
config_dict = config_dict["slice_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 MiniCPMVConfig(Qwen2Config):
model_type = "minicpmv"
keys_to_ignore_at_inference = ["past_key_values"]
default_vision_config = {
"hidden_size": 1152,
"image_size": 980,
"intermediate_size": 4304,
"model_type": "siglip",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"patch_size": 14,
}
def __init__(
self,
use_cache=True,
query_num=64,
image_size=448,
drop_vision_last_layer=True,
batch_vision_input=True,
slice_config=None,
vision_config=None,
use_image_id=True,
vision_batch_size=16,
**kwargs,
):
self.use_cache = use_cache
self.query_num = query_num
self.image_size = image_size
self.drop_vision_last_layer = drop_vision_last_layer
self.batch_vision_input = batch_vision_input
self.use_image_id = use_image_id
self.vision_batch_size = vision_batch_size
if slice_config is None:
self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
else:
self.slice_config = MiniCPMVSliceConfig(**slice_config)
self.slice_mode = True
# same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
if vision_config is None:
self.vision_config = SiglipVisionConfig(**self.default_vision_config)
logger.info("vision_config is None, using default vision config")
elif isinstance(vision_config, dict):
self.vision_config = SiglipVisionConfig(**vision_config)
elif isinstance(vision_config, SiglipVisionConfig):
self.vision_config = vision_config
self.patch_size = self.vision_config.patch_size
super().__init__(**kwargs)
|