File size: 6,411 Bytes
0a0d29d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
from transformers import PretrainedConfig
from transformers import logging
from transformers import CONFIG_MAPPING

logger = logging.get_logger(__name__)

class XGenMMVisionEncoderConfig(PretrainedConfig):
    model_type = "xgenmm_vision_encoder"
    
    def __init__(self, 
                 model_name: str = 'google/siglip-so400m-patch14-384',
                 anyres_grids: list[int] = [[384, 768],[768, 384],[768, 768],[1152, 384],[384,1152]],
                 **kwargs):
        self.model_name = model_name
        self.anyres_grids = anyres_grids
        super().__init__(**kwargs)
    

class XGenMMVisionTokenizerConfig(PretrainedConfig):
    model_type = "xgenmm_vision_tokenizer"
    
    def __init__(self, 
                 vis_feature_dim: int = 1152,
                 lang_embedding_dim: int = 3072,
                 num_vis_tokens: int = 128,
                 image_aspect_ratio: str = 'anyres',
                **kwargs):
        self.vis_feature_dim = vis_feature_dim
        self.lang_embedding_dim = lang_embedding_dim
        self.num_vis_tokens = num_vis_tokens
        self.image_aspect_ratio = image_aspect_ratio
        super().__init__(**kwargs)
        
        
class XGenMMConfig(PretrainedConfig):
    model_type = "xgenmm"
    
    def __init__(self, 
                 vision_encoder_config: dict = None,
                 vision_tokenizer_config: dict = None,
                 text_config: dict = None,
                 **kwargs):
        
        if vision_encoder_config is None:
            vision_encoder_config = {'image_aspect_ratio': 'anyres', 'anyres_patch_sampling': True}
            logger.info("vision_encoder_config is None. initializing the XGenMMVisionEncoderConfig with default values.")

        if vision_tokenizer_config is None:
            vision_tokenizer_config = {}
            logger.info("vision_tokenizer_config is None. Initializing the XGenMMVisionTokenizerConfig with default values.")

        if text_config is None:
            text_config = {
                'initial_tokenizer_len':32012,
                'pad_token_id':32011, 
                'bos_token_id':1, 
                'eos_token_id':32000,
                'vocab_size': 32064,
                'hidden_size': 3072,
                'intermediate_size': 8192,
                'num_hidden_layers': 32,
                'num_attention_heads': 32,
                'num_key_value_heads': 32,
                'resid_pdrop': 0.0,
                'embd_pdrop': 0.0,
                'attention_dropout': 0.0,
                'hidden_act': 'silu',
                'max_position_embeddings': 4096,
                'original_max_position_embeddings': 4096,
                'initializer_range': 0.02,
                'rms_norm_eps': 1e-05,
                'use_cache': True,
                'rope_theta': 10000.0,
                'rope_scaling': None,
                'sliding_window': 2047,
                'return_dict': True,
                'output_hidden_states': False,
                'output_attentions': False,
                'torchscript': False,
                'torch_dtype': 'bfloat16',
                'use_bfloat16': False,
                'tf_legacy_loss': False,
                'pruned_heads': {},
                'tie_word_embeddings': False,
                'chunk_size_feed_forward': 0,
                'is_encoder_decoder': False,
                'is_decoder': False,
                'cross_attention_hidden_size': None,
                'add_cross_attention': False,
                'tie_encoder_decoder': False,
                'max_length': 20,
                'min_length': 0,
                'do_sample': False,
                'early_stopping': False,
                'num_beams': 1,
                'num_beam_groups': 1,
                'diversity_penalty': 0.0,
                'temperature': 1.0,
                'top_k': 50,
                'top_p': 1.0,
                'typical_p': 1.0,
                'repetition_penalty': 1.0,
                'length_penalty': 1.0,
                'no_repeat_ngram_size': 0,
                'encoder_no_repeat_ngram_size': 0,
                'bad_words_ids': None,
                'num_return_sequences': 1,
                'output_scores': False,
                'return_dict_in_generate': False,
                'forced_bos_token_id': None,
                'forced_eos_token_id': None,
                'remove_invalid_values': False,
                'exponential_decay_length_penalty': None,
                'suppress_tokens': None,
                'begin_suppress_tokens': None,
                'finetuning_task': None,
                'id2label': {0: 'LABEL_0', 1: 'LABEL_1'},
                'label2id': {'LABEL_0': 0, 'LABEL_1': 1},
                'tokenizer_class': None,
                'prefix': None,
                'bos_token_id': 1,
                'pad_token_id': 32000,
                'eos_token_id': 32000,
                'sep_token_id': None,
                'decoder_start_token_id': None,
                'task_specific_params': None,
                'problem_type': None,
                'model_type': 'phi3'
                }
            logger.info("text_config is None. Initializing the text config with default values (`Phi3Config`).")
        
        self.vision_encoder_config = XGenMMVisionEncoderConfig(**vision_encoder_config)
        
        self.vision_tokenizer_config = XGenMMVisionTokenizerConfig(**vision_tokenizer_config)
        
        text_model_type = text_config["model_type"] if "model_type" in text_config else "phi3"
        self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
        
        for key in ['initial_tokenizer_len', 'pad_token_id']:
            if key not in self.text_config.to_dict():
                raise ValueError(f"The key `{key}` is missing in the text_config.")
            
        super().__init__(**kwargs)
        
    @classmethod
    def from_vision_encoder_vision_tokenizer_text_configs(
        cls,
        vision_encoder_config: XGenMMVisionEncoderConfig,
        vision_tokenizer_config: XGenMMVisionTokenizerConfig,
        text_config: PretrainedConfig,
        **kwargs):
        
        return cls(
            vision_encoder_config=vision_encoder_config.to_dict(),
            vision_tokenizer_config=vision_tokenizer_config.to_dict(),
            text_config=text_config.to_dict(),
            **kwargs,
        )