/
configuration_llava_next.py
141 lines (117 loc) 路 6.07 KB
/
configuration_llava_next.py
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
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# 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.
""" Llava-NeXT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
LLAVA_NEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"llava-hf/llava-v1.6-mistral-7b-hf": "https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf/resolve/main/config.json",
}
class LlavaNextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaNextForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)
model.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
The config object or dictionary of the text backbone.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the vision backbone.
Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
If `"full"`, the full vision features are used.
vision_feature_layer (`int`, *optional*, defaults to -2):
The index of the layer to select the vision feature.
image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
of the form `(height, width)`.
Example:
```python
>>> from transformers import LlavaNextForConditionalGeneration, LlavaNextConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a Llava-Next llava-hf/llava-v1.6-mistral-7b-hf style configuration
>>> configuration = LlavaNextConfig(vision_config, text_config)
>>> # Initializing a model from the llava-hf/llava-v1.6-mistral-7b-hf style configuration
>>> model = LlavaNextForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "llava_next"
is_composition = False
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
projector_hidden_act="gelu",
vision_feature_select_strategy="default",
vision_feature_layer=-2,
image_grid_pinpoints=None,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
if vision_feature_select_strategy not in ["default", "full"]:
raise ValueError(
"vision_feature_select_strategy should be one of 'default', 'full'."
f"Got: {vision_feature_select_strategy}"
)
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
image_grid_pinpoints = (
image_grid_pinpoints
if image_grid_pinpoints is not None
else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
)
self.image_grid_pinpoints = image_grid_pinpoints
if isinstance(vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
)
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
vision_config = CONFIG_MAPPING["clip_vision_model"](
intermediate_size=4096,
hidden_size=1024,
patch_size=14,
image_size=336,
num_hidden_layers=24,
num_attention_heads=16,
vocab_size=32000,
projection_dim=768,
)
self.vision_config = vision_config
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["llama"]()
self.text_config = text_config
super().__init__(**kwargs)