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# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
""" MiniCPM model configuration"""
import os
from typing import Union
from transformers.utils import logging
from transformers import LlamaConfig, PretrainedConfig
from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionConfig
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(LlamaConfig):
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": "idefics2",
"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,
**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
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
if vision_config is None:
self.vision_config = Idefics2VisionConfig(**self.default_vision_config)
logger.info("vision_config is None, using default vision config")
elif isinstance(vision_config, dict):
self.vision_config = Idefics2VisionConfig(**vision_config)
elif isinstance(vision_config, Idefics2VisionConfig):
self.vision_config = vision_config
self.patch_size = self.vision_config.patch_size
super().__init__(**kwargs) |