cyyang822 commited on
Commit
87b74fc
1 Parent(s): 4fe9c30

upload checkpoint

Browse files
added_tokens.json ADDED
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config.json ADDED
@@ -0,0 +1,172 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "_commit_hash": null,
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+ "architectures": [
4
+ "PVCInternVLModel"
5
+ ],
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_pvc_internvl.PVCInternVLConfig",
8
+ "AutoModel": "modeling_pvc_internvl.PVCInternVLModel",
9
+ "AutoModelForCausalLM": "modeling_pvc_internvl.PVCInternVLModel"
10
+ },
11
+ "downsample_ratio": 0.25,
12
+ "dynamic_image_size": true,
13
+ "force_image_size": 448,
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+ "image_repeat_time": 4,
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+ "llm_config": {
16
+ "_name_or_path": "internlm/internlm2_5-7b-chat",
17
+ "add_cross_attention": false,
18
+ "architectures": [
19
+ "InternLM2ForCausalLM"
20
+ ],
21
+ "attn_implementation": "flash_attention_2",
22
+ "auto_map": {
23
+ "AutoConfig": "configuration_internlm2.InternLM2Config",
24
+ "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
25
+ "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
26
+ },
27
+ "bad_words_ids": null,
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+ "begin_suppress_tokens": null,
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+ "bias": false,
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+ "bos_token_id": 1,
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+ "chunk_size_feed_forward": 0,
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+ "cross_attention_hidden_size": null,
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+ "decoder_start_token_id": null,
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+ "diversity_penalty": 0.0,
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+ "do_sample": false,
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+ "early_stopping": false,
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+ "encoder_no_repeat_ngram_size": 0,
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+ "eos_token_id": 2,
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+ "exponential_decay_length_penalty": null,
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+ "finetuning_task": null,
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+ "forced_bos_token_id": null,
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+ "forced_eos_token_id": null,
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+ "hidden_act": "silu",
44
+ "hidden_size": 4096,
45
+ "id2label": {
46
+ "0": "LABEL_0",
47
+ "1": "LABEL_1"
48
+ },
49
+ "initializer_range": 0.02,
50
+ "intermediate_size": 14336,
51
+ "is_decoder": false,
52
+ "is_encoder_decoder": false,
53
+ "label2id": {
54
+ "LABEL_0": 0,
55
+ "LABEL_1": 1
56
+ },
57
+ "length_penalty": 1.0,
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+ "max_length": 20,
59
+ "max_position_embeddings": 32768,
60
+ "min_length": 0,
61
+ "model_type": "internlm2",
62
+ "no_repeat_ngram_size": 0,
63
+ "num_attention_heads": 32,
64
+ "num_beam_groups": 1,
65
+ "num_beams": 1,
66
+ "num_hidden_layers": 32,
67
+ "num_key_value_heads": 8,
68
+ "num_return_sequences": 1,
69
+ "output_attentions": false,
70
+ "output_hidden_states": false,
71
+ "output_scores": false,
72
+ "pad_token_id": 2,
73
+ "prefix": null,
74
+ "pretraining_tp": 1,
75
+ "problem_type": null,
76
+ "pruned_heads": {},
77
+ "remove_invalid_values": false,
78
+ "repetition_penalty": 1.0,
79
+ "return_dict": true,
80
+ "return_dict_in_generate": false,
81
+ "rms_norm_eps": 1e-05,
82
+ "rope_scaling": {
83
+ "factor": 2.0,
84
+ "type": "dynamic"
85
+ },
86
+ "rope_theta": 1000000,
87
+ "sep_token_id": null,
88
+ "suppress_tokens": null,
89
+ "task_specific_params": null,
90
+ "temperature": 1.0,
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+ "tf_legacy_loss": false,
92
+ "tie_encoder_decoder": false,
93
+ "tie_word_embeddings": false,
94
+ "tokenizer_class": null,
95
+ "top_k": 50,
96
+ "top_p": 1.0,
97
+ "torch_dtype": "bfloat16",
98
+ "torchscript": false,
99
+ "transformers_version": "4.43.3",
100
+ "typical_p": 1.0,
101
+ "use_bfloat16": true,
102
+ "use_cache": true,
103
+ "vocab_size": 92553
104
+ },
105
+ "max_dynamic_patch": 12,
106
+ "min_dynamic_patch": 1,
107
+ "mlp_add_ops": "adaln_temporal_rel",
108
+ "model_type": "pvc_internvl",
109
+ "pad2square": false,
110
+ "ps_version": "v2",
111
+ "select_layer": -1,
112
+ "template": "internlm2-chat",
113
+ "tile_repeat_way": "cycle",
114
+ "torch_dtype": "bfloat16",
115
+ "use_backbone_lora": 0,
116
+ "use_llm_lora": 0,
117
+ "use_temporal": true,
118
+ "use_thumbnail": true,
119
+ "video_repeat_time": 1,
120
+ "vision_config": {
121
+ "architectures": [
122
+ "InternVisionTemporalModel"
123
+ ],
124
+ "attention_dropout": 0.0,
125
+ "auto_map": {
126
+ "AutoConfig": "configuration_intern_vit.InternVisionConfig",
127
+ "AutoModel": "modeling_intern_vit_pvc.InternVisionTemporalModel"
128
+ },
129
+ "drop_path_rate": 0.0,
130
+ "dropout": 0.0,
131
+ "hidden_act": "gelu",
132
+ "hidden_size": 1024,
133
+ "image_size": 448,
134
+ "initializer_factor": 1.0,
135
+ "initializer_range": 0.02,
136
+ "intermediate_size": 4096,
137
+ "layer_norm_eps": 1e-06,
138
+ "max_temporal_length": 96,
139
+ "model_type": "intern_vit_6b",
140
+ "norm_type": "layer_norm",
141
+ "num_attention_heads": 16,
142
+ "num_channels": 3,
143
+ "num_hidden_layers": 24,
144
+ "output_attentions": false,
145
+ "output_hidden_states": false,
146
+ "patch_size": 14,
147
+ "qk_normalization": false,
148
+ "qkv_bias": true,
149
+ "relative_timestep_scale": 100,
150
+ "return_dict": true,
151
+ "temporal_adaln_hidden_condition": true,
152
+ "temporal_adaln_zero_init": true,
153
+ "temporal_alpha_channelwise": true,
154
+ "temporal_alpha_init": 0.0,
155
+ "temporal_causal": true,
156
+ "temporal_layer_ids": [
157
+ 16,
158
+ 17,
159
+ 18,
160
+ 19,
161
+ 20,
162
+ 21,
163
+ 22,
164
+ 23
165
+ ],
166
+ "temporal_module_place": "after_self_attn",
167
+ "torch_dtype": "bfloat16",
168
+ "transformers_version": "4.43.3",
169
+ "use_bfloat16": true,
170
+ "use_flash_attn": true
171
+ }
172
+ }
configuration_intern_vit.py ADDED
@@ -0,0 +1,147 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2024 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ import os
8
+ from typing import Union, Optional
9
+
10
+ from transformers.configuration_utils import PretrainedConfig
11
+ from transformers.utils import logging
12
+
13
+ logger = logging.get_logger(__name__)
14
+
15
+
16
+ class InternVisionConfig(PretrainedConfig):
17
+ r"""
18
+ This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
19
+ instantiate a vision encoder according to the specified arguments, defining the model architecture.
20
+
21
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
22
+ documentation from [`PretrainedConfig`] for more information.
23
+
24
+ Args:
25
+ num_channels (`int`, *optional*, defaults to 3):
26
+ Number of color channels in the input images (e.g., 3 for RGB).
27
+ patch_size (`int`, *optional*, defaults to 14):
28
+ The size (resolution) of each patch.
29
+ image_size (`int`, *optional*, defaults to 224):
30
+ The size (resolution) of each image.
31
+ qkv_bias (`bool`, *optional*, defaults to `False`):
32
+ Whether to add a bias to the queries and values in the self-attention layers.
33
+ hidden_size (`int`, *optional*, defaults to 3200):
34
+ Dimensionality of the encoder layers and the pooler layer.
35
+ num_attention_heads (`int`, *optional*, defaults to 25):
36
+ Number of attention heads for each attention layer in the Transformer encoder.
37
+ intermediate_size (`int`, *optional*, defaults to 12800):
38
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
39
+ qk_normalization (`bool`, *optional*, defaults to `True`):
40
+ Whether to normalize the queries and keys in the self-attention layers.
41
+ num_hidden_layers (`int`, *optional*, defaults to 48):
42
+ Number of hidden layers in the Transformer encoder.
43
+ use_flash_attn (`bool`, *optional*, defaults to `True`):
44
+ Whether to use flash attention mechanism.
45
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
46
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
47
+ `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
48
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
49
+ The epsilon used by the layer normalization layers.
50
+ dropout (`float`, *optional*, defaults to 0.0):
51
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
52
+ drop_path_rate (`float`, *optional*, defaults to 0.0):
53
+ Dropout rate for stochastic depth.
54
+ attention_dropout (`float`, *optional*, defaults to 0.0):
55
+ The dropout ratio for the attention probabilities.
56
+ initializer_range (`float`, *optional*, defaults to 0.02):
57
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
58
+ initializer_factor (`float`, *optional*, defaults to 0.1):
59
+ A factor for layer scale.
60
+ """
61
+
62
+ model_type = 'intern_vit_6b'
63
+
64
+ def __init__(
65
+ self,
66
+ num_channels=3,
67
+ patch_size=14,
68
+ image_size=224,
69
+ qkv_bias=False,
70
+ hidden_size=3200,
71
+ num_attention_heads=25,
72
+ intermediate_size=12800,
73
+ qk_normalization=True,
74
+ num_hidden_layers=48,
75
+ use_flash_attn=True,
76
+ hidden_act='gelu',
77
+ norm_type='rms_norm',
78
+ layer_norm_eps=1e-6,
79
+ dropout=0.0,
80
+ drop_path_rate=0.0,
81
+ attention_dropout=0.0,
82
+ initializer_range=0.02,
83
+ initializer_factor=0.1,
84
+ max_temporal_length: int = 16,
85
+ temporal_causal: bool = False,
86
+ temporal_adaln_zero_init: bool = False,
87
+ temporal_adaln_hidden_condition: bool = False,
88
+ relative_timestep_scale: float = 100,
89
+ temporal_alpha_init: float = 0.0,
90
+ temporal_alpha_channelwise: bool = False,
91
+ temporal_module_place: Union[str, list] = "after_self_attn",
92
+ temporal_layer_ids: Optional[list] = None,
93
+ **kwargs,
94
+ ):
95
+ super().__init__(**kwargs)
96
+
97
+ self.hidden_size = hidden_size
98
+ self.intermediate_size = intermediate_size
99
+ self.dropout = dropout
100
+ self.drop_path_rate = drop_path_rate
101
+ self.num_hidden_layers = num_hidden_layers
102
+ self.num_attention_heads = num_attention_heads
103
+ self.num_channels = num_channels
104
+ self.patch_size = patch_size
105
+ self.image_size = image_size
106
+ self.initializer_range = initializer_range
107
+ self.initializer_factor = initializer_factor
108
+ self.attention_dropout = attention_dropout
109
+ self.layer_norm_eps = layer_norm_eps
110
+ self.hidden_act = hidden_act
111
+ self.norm_type = norm_type
112
+ self.qkv_bias = qkv_bias
113
+ self.qk_normalization = qk_normalization
114
+ self.use_flash_attn = use_flash_attn
115
+
116
+ self.max_temporal_length = max_temporal_length
117
+ self.temporal_causal = temporal_causal
118
+ self.temporal_adaln_zero_init = temporal_adaln_zero_init
119
+ self.temporal_adaln_hidden_condition = temporal_adaln_hidden_condition
120
+ self.relative_timestep_scale = relative_timestep_scale
121
+ self.temporal_alpha_init = temporal_alpha_init
122
+ self.temporal_alpha_channelwise = temporal_alpha_channelwise
123
+ self.temporal_module_place = temporal_module_place
124
+ self.temporal_layer_ids = temporal_layer_ids
125
+
126
+ @classmethod
127
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
128
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
129
+
130
+ if 'vision_config' in config_dict:
131
+ config_dict = config_dict['vision_config']
132
+
133
+ if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
134
+ logger.warning(
135
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
136
+ f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
137
+ )
138
+
139
+ return cls.from_dict(config_dict, **kwargs)
140
+
141
+ def to_dict(self):
142
+ output = super().to_dict()
143
+ # override temporal_config
144
+ if hasattr(self, 'temporal_config'):
145
+ temporal_config_dict = self.temporal_config.to_dict()
146
+ output['temporal_config'] = temporal_config_dict
147
+ return output
configuration_internlm2.py ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/configuration_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ InternLM2 model configuration"""
17
+
18
+ from transformers.configuration_utils import PretrainedConfig
19
+ from transformers.utils import logging
20
+
21
+ logger = logging.get_logger(__name__)
22
+
23
+ INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
24
+
25
+
26
+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
27
+ class InternLM2Config(PretrainedConfig):
28
+ r"""
29
+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
30
+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
31
+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
32
+
33
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
34
+ documentation from [`PretrainedConfig`] for more information.
35
+
36
+
37
+ Args:
38
+ vocab_size (`int`, *optional*, defaults to 32000):
39
+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
40
+ `inputs_ids` passed when calling [`InternLM2Model`]
41
+ hidden_size (`int`, *optional*, defaults to 4096):
42
+ Dimension of the hidden representations.
43
+ intermediate_size (`int`, *optional*, defaults to 11008):
44
+ Dimension of the MLP representations.
45
+ num_hidden_layers (`int`, *optional*, defaults to 32):
46
+ Number of hidden layers in the Transformer encoder.
47
+ num_attention_heads (`int`, *optional*, defaults to 32):
48
+ Number of attention heads for each attention layer in the Transformer encoder.
49
+ num_key_value_heads (`int`, *optional*):
50
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
51
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
52
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
53
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
54
+ by meanpooling all the original heads within that group. For more details checkout [this
55
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
56
+ `num_attention_heads`.
57
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
58
+ The non-linear activation function (function or string) in the decoder.
59
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
60
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
61
+ just in case (e.g., 512 or 1024 or 2048).
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
63
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
65
+ The epsilon used by the rms normalization layers.
66
+ use_cache (`bool`, *optional*, defaults to `True`):
67
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
68
+ relevant if `config.is_decoder=True`.
69
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
70
+ Whether to tie weight embeddings
71
+ Example:
72
+
73
+ """
74
+ model_type = 'internlm2'
75
+ _auto_class = 'AutoConfig'
76
+
77
+ def __init__( # pylint: disable=W0102
78
+ self,
79
+ vocab_size=103168,
80
+ hidden_size=4096,
81
+ intermediate_size=11008,
82
+ num_hidden_layers=32,
83
+ num_attention_heads=32,
84
+ num_key_value_heads=None,
85
+ hidden_act='silu',
86
+ max_position_embeddings=2048,
87
+ initializer_range=0.02,
88
+ rms_norm_eps=1e-6,
89
+ use_cache=True,
90
+ pad_token_id=0,
91
+ bos_token_id=1,
92
+ eos_token_id=2,
93
+ tie_word_embeddings=False,
94
+ bias=True,
95
+ rope_theta=10000,
96
+ rope_scaling=None,
97
+ attn_implementation='eager',
98
+ **kwargs,
99
+ ):
100
+ self.vocab_size = vocab_size
101
+ self.max_position_embeddings = max_position_embeddings
102
+ self.hidden_size = hidden_size
103
+ self.intermediate_size = intermediate_size
104
+ self.num_hidden_layers = num_hidden_layers
105
+ self.num_attention_heads = num_attention_heads
106
+ self.bias = bias
107
+
108
+ if num_key_value_heads is None:
109
+ num_key_value_heads = num_attention_heads
110
+ self.num_key_value_heads = num_key_value_heads
111
+
112
+ self.hidden_act = hidden_act
113
+ self.initializer_range = initializer_range
114
+ self.rms_norm_eps = rms_norm_eps
115
+ self.use_cache = use_cache
116
+ self.rope_theta = rope_theta
117
+ self.rope_scaling = rope_scaling
118
+ self._rope_scaling_validation()
119
+
120
+ self.attn_implementation = attn_implementation
121
+ if self.attn_implementation is None:
122
+ self.attn_implementation = 'eager'
123
+ super().__init__(
124
+ pad_token_id=pad_token_id,
125
+ bos_token_id=bos_token_id,
126
+ eos_token_id=eos_token_id,
127
+ tie_word_embeddings=tie_word_embeddings,
128
+ **kwargs,
129
+ )
130
+
131
+ def _rope_scaling_validation(self):
132
+ """
133
+ Validate the `rope_scaling` configuration.
134
+ """
135
+ if self.rope_scaling is None:
136
+ return
137
+
138
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
139
+ raise ValueError(
140
+ '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
141
+ f'got {self.rope_scaling}'
142
+ )
143
+ rope_scaling_type = self.rope_scaling.get('type', None)
144
+ rope_scaling_factor = self.rope_scaling.get('factor', None)
145
+ if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
146
+ raise ValueError(
147
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
148
+ )
149
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
150
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
configuration_pvc_internvl.py ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2024 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ import copy
8
+
9
+ from transformers import AutoConfig, LlamaConfig
10
+ from transformers.configuration_utils import PretrainedConfig
11
+ from transformers.utils import logging
12
+
13
+ from .configuration_intern_vit import InternVisionConfig
14
+ from .configuration_internlm2 import InternLM2Config
15
+
16
+ logger = logging.get_logger(__name__)
17
+
18
+
19
+ class PVCInternVLConfig(PretrainedConfig):
20
+ model_type = 'pvc_internvl'
21
+ is_composition = True
22
+
23
+ def __init__(
24
+ self,
25
+ vision_config=None,
26
+ llm_config=None,
27
+ use_backbone_lora=0,
28
+ use_llm_lora=0,
29
+ use_temporal=False,
30
+ pad2square=False,
31
+ select_layer=-1,
32
+ force_image_size=None,
33
+ downsample_ratio=0.5,
34
+ template=None,
35
+ dynamic_image_size=False,
36
+ use_thumbnail=False,
37
+ ps_version='v1',
38
+ min_dynamic_patch=1,
39
+ max_dynamic_patch=6,
40
+ image_repeat_time=1,
41
+ video_repeat_time=1,
42
+ tile_repeat_way=None,
43
+ mlp_add_ops=None,
44
+ **kwargs):
45
+ super().__init__(**kwargs)
46
+
47
+ if vision_config is None:
48
+ vision_config = {'architectures': ['InternVisionTemporalModel']}
49
+ logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
50
+
51
+ if llm_config is None:
52
+ llm_config = {'architectures': ['InternLM2ForCausalLM']}
53
+ logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
54
+
55
+ self.vision_config = InternVisionConfig(**vision_config)
56
+ if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
57
+ self.llm_config = LlamaConfig(**llm_config)
58
+ elif llm_config.get('architectures')[0] == 'InternLM2ForCausalLM':
59
+ self.llm_config = InternLM2Config(**llm_config)
60
+ else:
61
+ raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
62
+ self.use_backbone_lora = use_backbone_lora
63
+ self.use_llm_lora = use_llm_lora
64
+ self.use_temporal = use_temporal
65
+ self.pad2square = pad2square
66
+ self.select_layer = select_layer
67
+ self.force_image_size = force_image_size
68
+ self.downsample_ratio = downsample_ratio
69
+ self.template = template
70
+ self.dynamic_image_size = dynamic_image_size
71
+ self.use_thumbnail = use_thumbnail
72
+ self.ps_version = ps_version # pixel shuffle version
73
+ self.min_dynamic_patch = min_dynamic_patch
74
+ self.max_dynamic_patch = max_dynamic_patch
75
+ self.image_repeat_time = image_repeat_time
76
+ self.video_repeat_time = video_repeat_time
77
+ self.tile_repeat_way = tile_repeat_way
78
+ self.mlp_add_ops = mlp_add_ops
79
+
80
+ logger.info(f'vision_select_layer: {self.select_layer}')
81
+ logger.info(f'mlp_add_ops: {self.mlp_add_ops}')
82
+ logger.info(f'ps_version: {self.ps_version}')
83
+ logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
84
+ logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
85
+ logger.info(f'image_repeat_time: {self.image_repeat_time}')
86
+ logger.info(f'video_repeat_time: {self.video_repeat_time}')
87
+ logger.info(f'tile_repeat_way: {self.tile_repeat_way}')
88
+
89
+
90
+ def to_dict(self):
91
+ """
92
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
93
+
94
+ Returns:
95
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
96
+ """
97
+ output = copy.deepcopy(self.__dict__)
98
+ output['vision_config'] = self.vision_config.to_dict()
99
+ output['llm_config'] = self.llm_config.to_dict()
100
+ output['model_type'] = self.__class__.model_type
101
+ output['use_backbone_lora'] = self.use_backbone_lora
102
+ output['use_llm_lora'] = self.use_llm_lora
103
+ output['use_temporal'] = self.use_temporal
104
+ output['pad2square'] = self.pad2square
105
+ output['select_layer'] = self.select_layer
106
+ output['force_image_size'] = self.force_image_size
107
+ output['downsample_ratio'] = self.downsample_ratio
108
+ output['template'] = self.template
109
+ output['dynamic_image_size'] = self.dynamic_image_size
110
+ output['use_thumbnail'] = self.use_thumbnail
111
+ output['ps_version'] = self.ps_version
112
+ output['min_dynamic_patch'] = self.min_dynamic_patch
113
+ output['max_dynamic_patch'] = self.max_dynamic_patch
114
+ output['image_repeat_time'] = self.image_repeat_time
115
+ output['video_repeat_time'] = self.video_repeat_time
116
+ output['tile_repeat_way'] = self.tile_repeat_way
117
+ output['mlp_add_ops'] = self.mlp_add_ops
118
+
119
+ return output
conversation.py ADDED
@@ -0,0 +1,391 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Conversation prompt templates.
3
+
4
+ We kindly request that you import fastchat instead of copying this file if you wish to use it.
5
+ If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
6
+
7
+ Modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
8
+ """
9
+
10
+ import dataclasses
11
+ from enum import IntEnum, auto
12
+ from typing import Dict, List, Tuple, Union
13
+
14
+
15
+ class SeparatorStyle(IntEnum):
16
+ """Separator styles."""
17
+
18
+ ADD_COLON_SINGLE = auto()
19
+ ADD_COLON_TWO = auto()
20
+ ADD_COLON_SPACE_SINGLE = auto()
21
+ NO_COLON_SINGLE = auto()
22
+ NO_COLON_TWO = auto()
23
+ ADD_NEW_LINE_SINGLE = auto()
24
+ LLAMA2 = auto()
25
+ CHATGLM = auto()
26
+ CHATML = auto()
27
+ CHATINTERN = auto()
28
+ DOLLY = auto()
29
+ RWKV = auto()
30
+ PHOENIX = auto()
31
+ ROBIN = auto()
32
+ FALCON_CHAT = auto()
33
+ CHATGLM3 = auto()
34
+ INTERNVL_ZH = auto()
35
+ MPT = auto()
36
+
37
+
38
+ @dataclasses.dataclass
39
+ class Conversation:
40
+ """A class that manages prompt templates and keeps all conversation history."""
41
+
42
+ # The name of this template
43
+ name: str
44
+ # The template of the system prompt
45
+ system_template: str = '{system_message}'
46
+ # The system message
47
+ system_message: str = ''
48
+ # The names of two roles
49
+ roles: Tuple[str] = ('USER', 'ASSISTANT')
50
+ # All messages. Each item is (role, message).
51
+ messages: List[List[str]] = ()
52
+ # The number of few shot examples
53
+ offset: int = 0
54
+ # The separator style and configurations
55
+ sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
56
+ sep: str = '\n'
57
+ sep2: str = None
58
+ # Stop criteria (the default one is EOS token)
59
+ stop_str: Union[str, List[str]] = None
60
+ # Stops generation if meeting any token in this list
61
+ stop_token_ids: List[int] = None
62
+
63
+ def get_prompt(self) -> str:
64
+ """Get the prompt for generation."""
65
+ system_prompt = self.system_template.format(system_message=self.system_message)
66
+ if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
67
+ ret = system_prompt + self.sep
68
+ for role, message in self.messages:
69
+ if message:
70
+ ret += role + ': ' + message + self.sep
71
+ else:
72
+ ret += role + ':'
73
+ return ret
74
+ elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
75
+ seps = [self.sep, self.sep2]
76
+ ret = system_prompt + seps[0]
77
+ for i, (role, message) in enumerate(self.messages):
78
+ if message:
79
+ ret += role + ': ' + message + seps[i % 2]
80
+ else:
81
+ ret += role + ':'
82
+ return ret
83
+ elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
84
+ ret = system_prompt + self.sep
85
+ for role, message in self.messages:
86
+ if message:
87
+ ret += role + ': ' + message + self.sep
88
+ else:
89
+ ret += role + ': ' # must be end with a space
90
+ return ret
91
+ elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
92
+ ret = '' if system_prompt == '' else system_prompt + self.sep
93
+ for role, message in self.messages:
94
+ if message:
95
+ ret += role + '\n' + message + self.sep
96
+ else:
97
+ ret += role + '\n'
98
+ return ret
99
+ elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
100
+ ret = system_prompt
101
+ for role, message in self.messages:
102
+ if message:
103
+ ret += role + message + self.sep
104
+ else:
105
+ ret += role
106
+ return ret
107
+ elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
108
+ seps = [self.sep, self.sep2]
109
+ ret = system_prompt
110
+ for i, (role, message) in enumerate(self.messages):
111
+ if message:
112
+ ret += role + message + seps[i % 2]
113
+ else:
114
+ ret += role
115
+ return ret
116
+ elif self.sep_style == SeparatorStyle.RWKV:
117
+ ret = system_prompt
118
+ for i, (role, message) in enumerate(self.messages):
119
+ if message:
120
+ ret += (
121
+ role
122
+ + ': '
123
+ + message.replace('\r\n', '\n').replace('\n\n', '\n')
124
+ )
125
+ ret += '\n\n'
126
+ else:
127
+ ret += role + ':'
128
+ return ret
129
+ elif self.sep_style == SeparatorStyle.LLAMA2:
130
+ seps = [self.sep, self.sep2]
131
+ if self.system_message:
132
+ ret = system_prompt
133
+ else:
134
+ ret = '[INST] '
135
+ for i, (role, message) in enumerate(self.messages):
136
+ tag = self.roles[i % 2]
137
+ if message:
138
+ if i == 0:
139
+ ret += message + ' '
140
+ else:
141
+ ret += tag + ' ' + message + seps[i % 2]
142
+ else:
143
+ ret += tag
144
+ return ret
145
+ elif self.sep_style == SeparatorStyle.CHATGLM:
146
+ # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
147
+ # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
148
+ round_add_n = 1 if self.name == 'chatglm2' else 0
149
+ if system_prompt:
150
+ ret = system_prompt + self.sep
151
+ else:
152
+ ret = ''
153
+
154
+ for i, (role, message) in enumerate(self.messages):
155
+ if i % 2 == 0:
156
+ ret += f'[Round {i//2 + round_add_n}]{self.sep}'
157
+
158
+ if message:
159
+ ret += f'{role}:{message}{self.sep}'
160
+ else:
161
+ ret += f'{role}:'
162
+ return ret
163
+ elif self.sep_style == SeparatorStyle.CHATML:
164
+ ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
165
+ for role, message in self.messages:
166
+ if message:
167
+ ret += role + '\n' + message + self.sep + '\n'
168
+ else:
169
+ ret += role + '\n'
170
+ return ret
171
+ elif self.sep_style == SeparatorStyle.CHATGLM3:
172
+ ret = ''
173
+ if self.system_message:
174
+ ret += system_prompt
175
+ for role, message in self.messages:
176
+ if message:
177
+ ret += role + '\n' + ' ' + message
178
+ else:
179
+ ret += role
180
+ return ret
181
+ elif self.sep_style == SeparatorStyle.CHATINTERN:
182
+ # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
183
+ seps = [self.sep, self.sep2]
184
+ ret = system_prompt
185
+ for i, (role, message) in enumerate(self.messages):
186
+ # if i % 2 == 0:
187
+ # ret += "<s>"
188
+ if message:
189
+ ret += role + ':' + message + seps[i % 2] + '\n'
190
+ else:
191
+ ret += role + ':'
192
+ return ret
193
+ elif self.sep_style == SeparatorStyle.DOLLY:
194
+ seps = [self.sep, self.sep2]
195
+ ret = system_prompt
196
+ for i, (role, message) in enumerate(self.messages):
197
+ if message:
198
+ ret += role + ':\n' + message + seps[i % 2]
199
+ if i % 2 == 1:
200
+ ret += '\n\n'
201
+ else:
202
+ ret += role + ':\n'
203
+ return ret
204
+ elif self.sep_style == SeparatorStyle.PHOENIX:
205
+ ret = system_prompt
206
+ for role, message in self.messages:
207
+ if message:
208
+ ret += role + ': ' + '<s>' + message + '</s>'
209
+ else:
210
+ ret += role + ': ' + '<s>'
211
+ return ret
212
+ elif self.sep_style == SeparatorStyle.ROBIN:
213
+ ret = system_prompt + self.sep
214
+ for role, message in self.messages:
215
+ if message:
216
+ ret += role + ':\n' + message + self.sep
217
+ else:
218
+ ret += role + ':\n'
219
+ return ret
220
+ elif self.sep_style == SeparatorStyle.FALCON_CHAT:
221
+ ret = ''
222
+ if self.system_message:
223
+ ret += system_prompt + self.sep
224
+ for role, message in self.messages:
225
+ if message:
226
+ ret += role + ': ' + message + self.sep
227
+ else:
228
+ ret += role + ':'
229
+
230
+ return ret
231
+ elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
232
+ seps = [self.sep, self.sep2]
233
+ ret = self.system_message + seps[0]
234
+ for i, (role, message) in enumerate(self.messages):
235
+ if message:
236
+ ret += role + ': ' + message + seps[i % 2]
237
+ else:
238
+ ret += role + ':'
239
+ return ret
240
+ elif self.sep_style == SeparatorStyle.MPT:
241
+ ret = system_prompt + self.sep
242
+ for role, message in self.messages:
243
+ if message:
244
+ if type(message) is tuple:
245
+ message, _, _ = message
246
+ ret += role + message + self.sep
247
+ else:
248
+ ret += role
249
+ return ret
250
+ else:
251
+ raise ValueError(f'Invalid style: {self.sep_style}')
252
+
253
+ def set_system_message(self, system_message: str):
254
+ """Set the system message."""
255
+ self.system_message = system_message
256
+
257
+ def append_message(self, role: str, message: str):
258
+ """Append a new message."""
259
+ self.messages.append([role, message])
260
+
261
+ def update_last_message(self, message: str):
262
+ """Update the last output.
263
+
264
+ The last message is typically set to be None when constructing the prompt,
265
+ so we need to update it in-place after getting the response from a model.
266
+ """
267
+ self.messages[-1][1] = message
268
+
269
+ def to_gradio_chatbot(self):
270
+ """Convert the conversation to gradio chatbot format."""
271
+ ret = []
272
+ for i, (role, msg) in enumerate(self.messages[self.offset :]):
273
+ if i % 2 == 0:
274
+ ret.append([msg, None])
275
+ else:
276
+ ret[-1][-1] = msg
277
+ return ret
278
+
279
+ def to_openai_api_messages(self):
280
+ """Convert the conversation to OpenAI chat completion format."""
281
+ ret = [{'role': 'system', 'content': self.system_message}]
282
+
283
+ for i, (_, msg) in enumerate(self.messages[self.offset :]):
284
+ if i % 2 == 0:
285
+ ret.append({'role': 'user', 'content': msg})
286
+ else:
287
+ if msg is not None:
288
+ ret.append({'role': 'assistant', 'content': msg})
289
+ return ret
290
+
291
+ def copy(self):
292
+ return Conversation(
293
+ name=self.name,
294
+ system_template=self.system_template,
295
+ system_message=self.system_message,
296
+ roles=self.roles,
297
+ messages=[[x, y] for x, y in self.messages],
298
+ offset=self.offset,
299
+ sep_style=self.sep_style,
300
+ sep=self.sep,
301
+ sep2=self.sep2,
302
+ stop_str=self.stop_str,
303
+ stop_token_ids=self.stop_token_ids,
304
+ )
305
+
306
+ def dict(self):
307
+ return {
308
+ 'template_name': self.name,
309
+ 'system_message': self.system_message,
310
+ 'roles': self.roles,
311
+ 'messages': self.messages,
312
+ 'offset': self.offset,
313
+ }
314
+
315
+
316
+ # A global registry for all conversation templates
317
+ conv_templates: Dict[str, Conversation] = {}
318
+
319
+
320
+ def register_conv_template(template: Conversation, override: bool = False):
321
+ """Register a new conversation template."""
322
+ if not override:
323
+ assert (
324
+ template.name not in conv_templates
325
+ ), f'{template.name} has been registered.'
326
+
327
+ conv_templates[template.name] = template
328
+
329
+
330
+ def get_conv_template(name: str) -> Conversation:
331
+ """Get a conversation template."""
332
+ return conv_templates[name].copy()
333
+
334
+
335
+ # Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
336
+ # is that during training, the preprocessing function for the Hermes-2 template doesn't add
337
+ # <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
338
+ # Therefore, they are completely equivalent during inference.
339
+ register_conv_template(
340
+ Conversation(
341
+ name='Hermes-2',
342
+ system_template='<|im_start|>system\n{system_message}',
343
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
344
+ # system_message='我是书生·万象,英文名是InternVL,是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
345
+ system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型,英文名叫InternVL, 是一个有用无害的人工智能助手。',
346
+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
347
+ sep_style=SeparatorStyle.MPT,
348
+ sep='<|im_end|>',
349
+ stop_str='<|endoftext|>',
350
+ )
351
+ )
352
+
353
+
354
+ register_conv_template(
355
+ Conversation(
356
+ name='internlm2-chat',
357
+ system_template='<|im_start|>system\n{system_message}',
358
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
359
+ # system_message='我是书生·万象,英文名是InternVL,是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
360
+ system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型,英文名叫InternVL, 是一个有用无害的人工智能助手。',
361
+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
362
+ sep_style=SeparatorStyle.MPT,
363
+ sep='<|im_end|>',
364
+ )
365
+ )
366
+
367
+
368
+ register_conv_template(
369
+ Conversation(
370
+ name='phi3-chat',
371
+ system_template='<|system|>\n{system_message}',
372
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
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+ name='internvl2_5',
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+ system_template='<|im_start|>system\n{system_message}',
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+ system_message='你是书生·万象,英文名是InternVL,是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
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+ roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
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+ sep_style=SeparatorStyle.MPT,
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+ sep='<|im_end|>\n',
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+ )
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+ )
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+ }
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+ }
modeling_intern_vit.py ADDED
@@ -0,0 +1,430 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2024 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ from typing import Optional, Tuple, Union
8
+
9
+ import torch
10
+ import torch.nn.functional as F
11
+ import torch.utils.checkpoint
12
+ from einops import rearrange
13
+ from timm.models.layers import DropPath
14
+ from torch import nn
15
+ from transformers.activations import ACT2FN
16
+ from transformers.modeling_outputs import (BaseModelOutput,
17
+ BaseModelOutputWithPooling)
18
+ from transformers.modeling_utils import PreTrainedModel
19
+ from transformers.utils import logging
20
+
21
+ from .configuration_intern_vit import InternVisionConfig
22
+
23
+ try:
24
+ from flash_attn.bert_padding import pad_input, unpad_input
25
+ from flash_attn.flash_attn_interface import \
26
+ flash_attn_varlen_qkvpacked_func
27
+ has_flash_attn = True
28
+ except:
29
+ print('FlashAttention2 is not installed.')
30
+ has_flash_attn = False
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+
35
+ class FlashAttention(nn.Module):
36
+ """Implement the scaled dot product attention with softmax.
37
+ Arguments
38
+ ---------
39
+ softmax_scale: The temperature to use for the softmax attention.
40
+ (default: 1/sqrt(d_keys) where d_keys is computed at
41
+ runtime)
42
+ attention_dropout: The dropout rate to apply to the attention
43
+ (default: 0.0)
44
+ """
45
+
46
+ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
47
+ super().__init__()
48
+ self.softmax_scale = softmax_scale
49
+ self.dropout_p = attention_dropout
50
+
51
+ def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
52
+ max_s=None, need_weights=False):
53
+ """Implements the multihead softmax attention.
54
+ Arguments
55
+ ---------
56
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
57
+ if unpadded: (nnz, 3, h, d)
58
+ key_padding_mask: a bool tensor of shape (B, S)
59
+ """
60
+ assert not need_weights
61
+ assert qkv.dtype in [torch.float16, torch.bfloat16]
62
+ assert qkv.is_cuda
63
+
64
+ if cu_seqlens is None:
65
+ batch_size = qkv.shape[0]
66
+ seqlen = qkv.shape[1]
67
+ if key_padding_mask is None:
68
+ qkv = rearrange(qkv, 'b s ... -> (b s) ...')
69
+ max_s = seqlen
70
+ cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
71
+ device=qkv.device)
72
+ output = flash_attn_varlen_qkvpacked_func(
73
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
74
+ softmax_scale=self.softmax_scale, causal=causal
75
+ )
76
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
77
+ else:
78
+ nheads = qkv.shape[-2]
79
+ x = rearrange(qkv, 'b s three h d -> b s (three h d)')
80
+ x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
81
+ x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
82
+ output_unpad = flash_attn_varlen_qkvpacked_func(
83
+ x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
84
+ softmax_scale=self.softmax_scale, causal=causal
85
+ )
86
+ output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
87
+ indices, batch_size, seqlen),
88
+ 'b s (h d) -> b s h d', h=nheads)
89
+ else:
90
+ assert max_s is not None
91
+ output = flash_attn_varlen_qkvpacked_func(
92
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
93
+ softmax_scale=self.softmax_scale, causal=causal
94
+ )
95
+
96
+ return output, None
97
+
98
+
99
+ class InternRMSNorm(nn.Module):
100
+ def __init__(self, hidden_size, eps=1e-6):
101
+ super().__init__()
102
+ self.weight = nn.Parameter(torch.ones(hidden_size))
103
+ self.variance_epsilon = eps
104
+
105
+ def forward(self, hidden_states):
106
+ input_dtype = hidden_states.dtype
107
+ hidden_states = hidden_states.to(torch.float32)
108
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
109
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
110
+ return self.weight * hidden_states.to(input_dtype)
111
+
112
+
113
+ try:
114
+ from apex.normalization import FusedRMSNorm
115
+
116
+ InternRMSNorm = FusedRMSNorm # noqa
117
+
118
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
119
+ except ImportError:
120
+ # using the normal InternRMSNorm
121
+ pass
122
+ except Exception:
123
+ logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
124
+ pass
125
+
126
+
127
+ NORM2FN = {
128
+ 'rms_norm': InternRMSNorm,
129
+ 'layer_norm': nn.LayerNorm,
130
+ }
131
+
132
+
133
+ class InternVisionEmbeddings(nn.Module):
134
+ def __init__(self, config: InternVisionConfig):
135
+ super().__init__()
136
+ self.config = config
137
+ self.embed_dim = config.hidden_size
138
+ self.image_size = config.image_size
139
+ self.patch_size = config.patch_size
140
+
141
+ self.class_embedding = nn.Parameter(
142
+ torch.randn(1, 1, self.embed_dim),
143
+ )
144
+
145
+ self.patch_embedding = nn.Conv2d(
146
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
147
+ )
148
+
149
+ self.num_patches = (self.image_size // self.patch_size) ** 2
150
+ self.num_positions = self.num_patches + 1
151
+
152
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
153
+
154
+ def _get_pos_embed(self, pos_embed, H, W):
155
+ target_dtype = pos_embed.dtype
156
+ pos_embed = pos_embed.float().reshape(
157
+ 1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
158
+ pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
159
+ reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
160
+ return pos_embed
161
+
162
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
163
+ target_dtype = self.patch_embedding.weight.dtype
164
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height]
165
+ batch_size, _, height, width = patch_embeds.shape
166
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
167
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
168
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
169
+ position_embedding = torch.cat([
170
+ self.position_embedding[:, :1, :],
171
+ self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
172
+ ], dim=1)
173
+ embeddings = embeddings + position_embedding.to(target_dtype)
174
+ return embeddings
175
+
176
+
177
+ class InternAttention(nn.Module):
178
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
179
+
180
+ def __init__(self, config: InternVisionConfig):
181
+ super().__init__()
182
+ self.config = config
183
+ self.embed_dim = config.hidden_size
184
+ self.num_heads = config.num_attention_heads
185
+ self.use_flash_attn = config.use_flash_attn and has_flash_attn
186
+ if config.use_flash_attn and not has_flash_attn:
187
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
188
+ self.head_dim = self.embed_dim // self.num_heads
189
+ if self.head_dim * self.num_heads != self.embed_dim:
190
+ raise ValueError(
191
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
192
+ f' {self.num_heads}).'
193
+ )
194
+
195
+ self.scale = self.head_dim ** -0.5
196
+ self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
197
+ self.attn_drop = nn.Dropout(config.attention_dropout)
198
+ self.proj_drop = nn.Dropout(config.dropout)
199
+
200
+ self.qk_normalization = config.qk_normalization
201
+
202
+ if self.qk_normalization:
203
+ self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
204
+ self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
205
+
206
+ if self.use_flash_attn:
207
+ self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
208
+ self.proj = nn.Linear(self.embed_dim, self.embed_dim)
209
+
210
+ def _naive_attn(self, x):
211
+ B, N, C = x.shape
212
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
213
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
214
+
215
+ if self.qk_normalization:
216
+ B_, H_, N_, D_ = q.shape
217
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
218
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
219
+
220
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
221
+ attn = attn.softmax(dim=-1)
222
+ attn = self.attn_drop(attn)
223
+
224
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
225
+ x = self.proj(x)
226
+ x = self.proj_drop(x)
227
+ return x
228
+
229
+ def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
230
+ qkv = self.qkv(x)
231
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
232
+
233
+ if self.qk_normalization:
234
+ q, k, v = qkv.unbind(2)
235
+ q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
236
+ k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
237
+ qkv = torch.stack([q, k, v], dim=2)
238
+
239
+ context, _ = self.inner_attn(
240
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
241
+ )
242
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
243
+ outs = self.proj_drop(outs)
244
+ return outs
245
+
246
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
247
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
248
+ return x
249
+
250
+
251
+ class InternMLP(nn.Module):
252
+ def __init__(self, config: InternVisionConfig):
253
+ super().__init__()
254
+ self.config = config
255
+ self.act = ACT2FN[config.hidden_act]
256
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
257
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
258
+
259
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
260
+ hidden_states = self.fc1(hidden_states)
261
+ hidden_states = self.act(hidden_states)
262
+ hidden_states = self.fc2(hidden_states)
263
+ return hidden_states
264
+
265
+
266
+ class InternVisionEncoderLayer(nn.Module):
267
+ def __init__(self, config: InternVisionConfig, drop_path_rate: float):
268
+ super().__init__()
269
+ self.embed_dim = config.hidden_size
270
+ self.intermediate_size = config.intermediate_size
271
+ self.norm_type = config.norm_type
272
+
273
+ self.attn = InternAttention(config)
274
+ self.mlp = InternMLP(config)
275
+ self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
276
+ self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
277
+
278
+ self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
279
+ self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
280
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
281
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
282
+
283
+ def forward(
284
+ self,
285
+ hidden_states: torch.Tensor,
286
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
287
+ """
288
+ Args:
289
+ hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
290
+ """
291
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1)
292
+
293
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2)
294
+
295
+ return hidden_states
296
+
297
+
298
+ class InternVisionEncoder(nn.Module):
299
+ """
300
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
301
+ [`InternEncoderLayer`].
302
+
303
+ Args:
304
+ config (`InternConfig`):
305
+ The corresponding vision configuration for the `InternEncoder`.
306
+ """
307
+
308
+ def __init__(self, config: InternVisionConfig):
309
+ super().__init__()
310
+ self.config = config
311
+ # stochastic depth decay rule
312
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
313
+ self.layers = nn.ModuleList([
314
+ InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
315
+ self.gradient_checkpointing = True
316
+
317
+ def forward(
318
+ self,
319
+ inputs_embeds,
320
+ output_hidden_states: Optional[bool] = None,
321
+ return_dict: Optional[bool] = None,
322
+ ) -> Union[Tuple, BaseModelOutput]:
323
+ r"""
324
+ Args:
325
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
326
+ Embedded representation of the inputs. Should be float, not int tokens.
327
+ output_hidden_states (`bool`, *optional*):
328
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
329
+ for more detail.
330
+ return_dict (`bool`, *optional*):
331
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
332
+ """
333
+ output_hidden_states = (
334
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
335
+ )
336
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
337
+
338
+ encoder_states = () if output_hidden_states else None
339
+ hidden_states = inputs_embeds
340
+
341
+ for idx, encoder_layer in enumerate(self.layers):
342
+ if output_hidden_states:
343
+ encoder_states = encoder_states + (hidden_states,)
344
+ if self.gradient_checkpointing and self.training:
345
+ layer_outputs = torch.utils.checkpoint.checkpoint(
346
+ encoder_layer,
347
+ hidden_states)
348
+ else:
349
+ layer_outputs = encoder_layer(
350
+ hidden_states,
351
+ )
352
+ hidden_states = layer_outputs
353
+
354
+ if output_hidden_states:
355
+ encoder_states = encoder_states + (hidden_states,)
356
+
357
+ if not return_dict:
358
+ return tuple(v for v in [hidden_states, encoder_states] if v is not None)
359
+ return BaseModelOutput(
360
+ last_hidden_state=hidden_states, hidden_states=encoder_states
361
+ )
362
+
363
+
364
+ class InternVisionModel(PreTrainedModel):
365
+ main_input_name = 'pixel_values'
366
+ _supports_flash_attn_2 = True
367
+ config_class = InternVisionConfig
368
+ _no_split_modules = ['InternVisionEncoderLayer']
369
+
370
+ def __init__(self, config: InternVisionConfig):
371
+ super().__init__(config)
372
+ self.config = config
373
+
374
+ self.embeddings = InternVisionEmbeddings(config)
375
+ self.encoder = InternVisionEncoder(config)
376
+
377
+ def resize_pos_embeddings(self, old_size, new_size, patch_size):
378
+ pos_emb = self.embeddings.position_embedding
379
+ _, num_positions, embed_dim = pos_emb.shape
380
+ cls_emb = pos_emb[:, :1, :]
381
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
382
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
383
+ pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
384
+ pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
385
+ self.embeddings.position_embedding = nn.Parameter(pos_emb)
386
+ self.embeddings.image_size = new_size
387
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
388
+
389
+ def get_input_embeddings(self):
390
+ return self.embeddings
391
+
392
+ def forward(
393
+ self,
394
+ pixel_values: Optional[torch.FloatTensor] = None,
395
+ output_hidden_states: Optional[bool] = None,
396
+ return_dict: Optional[bool] = None,
397
+ pixel_embeds: Optional[torch.FloatTensor] = None,
398
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
399
+ output_hidden_states = (
400
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
401
+ )
402
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
403
+
404
+ if pixel_values is None and pixel_embeds is None:
405
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
406
+
407
+ if pixel_embeds is not None:
408
+ hidden_states = pixel_embeds
409
+ else:
410
+ if len(pixel_values.shape) == 4:
411
+ hidden_states = self.embeddings(pixel_values)
412
+ else:
413
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
414
+ encoder_outputs = self.encoder(
415
+ inputs_embeds=hidden_states,
416
+ output_hidden_states=output_hidden_states,
417
+ return_dict=return_dict,
418
+ )
419
+ last_hidden_state = encoder_outputs.last_hidden_state
420
+ pooled_output = last_hidden_state[:, 0, :]
421
+
422
+ if not return_dict:
423
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
424
+
425
+ return BaseModelOutputWithPooling(
426
+ last_hidden_state=last_hidden_state,
427
+ pooler_output=pooled_output,
428
+ hidden_states=encoder_outputs.hidden_states,
429
+ attentions=encoder_outputs.attentions,
430
+ )
modeling_intern_vit_pvc.py ADDED
@@ -0,0 +1,770 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2024 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ from typing import Optional, Tuple, Union
8
+
9
+ import math
10
+ import torch
11
+ import torch.nn.functional as F
12
+ import torch.utils.checkpoint
13
+ from einops import rearrange
14
+ from timm.models.layers import DropPath
15
+ from torch import nn
16
+ from transformers.activations import ACT2FN
17
+ from transformers.modeling_outputs import (BaseModelOutput,
18
+ BaseModelOutputWithPooling)
19
+ from transformers.modeling_utils import PreTrainedModel
20
+ from transformers.utils import logging
21
+
22
+ from .configuration_intern_vit import InternVisionConfig
23
+
24
+ try:
25
+ from flash_attn.bert_padding import pad_input, unpad_input
26
+ from flash_attn.flash_attn_interface import \
27
+ flash_attn_varlen_qkvpacked_func, flash_attn_varlen_func
28
+ has_flash_attn = True
29
+ except:
30
+ print('FlashAttention2 is not installed.')
31
+ has_flash_attn = False
32
+
33
+ logger = logging.get_logger(__name__)
34
+
35
+
36
+ class FlashAttention(nn.Module):
37
+ """Implement the scaled dot product attention with softmax.
38
+ Arguments
39
+ ---------
40
+ softmax_scale: The temperature to use for the softmax attention.
41
+ (default: 1/sqrt(d_keys) where d_keys is computed at
42
+ runtime)
43
+ attention_dropout: The dropout rate to apply to the attention
44
+ (default: 0.0)
45
+ """
46
+
47
+ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
48
+ super().__init__()
49
+ self.softmax_scale = softmax_scale
50
+ self.dropout_p = attention_dropout
51
+
52
+ def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
53
+ max_s=None, need_weights=False):
54
+ """Implements the multihead softmax attention.
55
+ Arguments
56
+ ---------
57
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
58
+ if unpadded: (nnz, 3, h, d)
59
+ key_padding_mask: a bool tensor of shape (B, S)
60
+ """
61
+ assert not need_weights
62
+ assert qkv.dtype in [torch.float16, torch.bfloat16]
63
+ assert qkv.is_cuda
64
+
65
+ if cu_seqlens is None:
66
+ batch_size = qkv.shape[0]
67
+ seqlen = qkv.shape[1]
68
+ if key_padding_mask is None:
69
+ qkv = rearrange(qkv, 'b s ... -> (b s) ...')
70
+ max_s = seqlen
71
+ cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
72
+ device=qkv.device)
73
+ output = flash_attn_varlen_qkvpacked_func(
74
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
75
+ softmax_scale=self.softmax_scale, causal=causal
76
+ )
77
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
78
+ else:
79
+ nheads = qkv.shape[-2]
80
+ x = rearrange(qkv, 'b s three h d -> b s (three h d)')
81
+ x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
82
+ x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
83
+ output_unpad = flash_attn_varlen_qkvpacked_func(
84
+ x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
85
+ softmax_scale=self.softmax_scale, causal=causal
86
+ )
87
+ output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
88
+ indices, batch_size, seqlen),
89
+ 'b s (h d) -> b s h d', h=nheads)
90
+ else:
91
+ assert max_s is not None
92
+ output = flash_attn_varlen_qkvpacked_func(
93
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
94
+ softmax_scale=self.softmax_scale, causal=causal
95
+ )
96
+
97
+ return output, None
98
+
99
+
100
+ class InternRMSNorm(nn.Module):
101
+ def __init__(self, hidden_size, eps=1e-6):
102
+ super().__init__()
103
+ self.weight = nn.Parameter(torch.ones(hidden_size))
104
+ self.variance_epsilon = eps
105
+
106
+ def forward(self, hidden_states):
107
+ input_dtype = hidden_states.dtype
108
+ hidden_states = hidden_states.to(torch.float32)
109
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
110
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
111
+ return self.weight * hidden_states.to(input_dtype)
112
+
113
+
114
+ try:
115
+ from apex.normalization import FusedRMSNorm
116
+
117
+ InternRMSNorm = FusedRMSNorm # noqa
118
+
119
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
120
+ except ImportError:
121
+ # using the normal InternRMSNorm
122
+ pass
123
+ except Exception:
124
+ logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
125
+ pass
126
+
127
+
128
+ NORM2FN = {
129
+ 'rms_norm': InternRMSNorm,
130
+ 'layer_norm': nn.LayerNorm,
131
+ }
132
+
133
+
134
+ class InternVisionEmbeddings(nn.Module):
135
+ def __init__(self, config: InternVisionConfig):
136
+ super().__init__()
137
+ self.config = config
138
+ self.embed_dim = config.hidden_size
139
+ self.image_size = config.image_size
140
+ self.patch_size = config.patch_size
141
+
142
+ self.class_embedding = nn.Parameter(
143
+ torch.randn(1, 1, self.embed_dim),
144
+ )
145
+
146
+ self.patch_embedding = nn.Conv2d(
147
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
148
+ )
149
+
150
+ self.num_patches = (self.image_size // self.patch_size) ** 2
151
+ self.num_positions = self.num_patches + 1
152
+
153
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
154
+
155
+ def _get_pos_embed(self, pos_embed, H, W):
156
+ target_dtype = pos_embed.dtype
157
+ pos_embed = pos_embed.float().reshape(
158
+ 1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
159
+ pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
160
+ reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
161
+ return pos_embed
162
+
163
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
164
+ target_dtype = self.patch_embedding.weight.dtype
165
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height]
166
+ batch_size, _, height, width = patch_embeds.shape
167
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
168
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
169
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
170
+ position_embedding = torch.cat([
171
+ self.position_embedding[:, :1, :],
172
+ self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
173
+ ], dim=1)
174
+ embeddings = embeddings + position_embedding.to(target_dtype)
175
+ return embeddings
176
+
177
+
178
+ class InternAttention(nn.Module):
179
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
180
+
181
+ def __init__(self, config: InternVisionConfig):
182
+ super().__init__()
183
+ self.config = config
184
+ self.embed_dim = config.hidden_size
185
+ self.num_heads = config.num_attention_heads
186
+ self.use_flash_attn = config.use_flash_attn and has_flash_attn
187
+ if config.use_flash_attn and not has_flash_attn:
188
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
189
+ self.head_dim = self.embed_dim // self.num_heads
190
+ if self.head_dim * self.num_heads != self.embed_dim:
191
+ raise ValueError(
192
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
193
+ f' {self.num_heads}).'
194
+ )
195
+
196
+ self.scale = self.head_dim ** -0.5
197
+ self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
198
+ self.attn_drop = nn.Dropout(config.attention_dropout)
199
+ self.proj_drop = nn.Dropout(config.dropout)
200
+
201
+ self.qk_normalization = config.qk_normalization
202
+
203
+ if self.qk_normalization:
204
+ self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
205
+ self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
206
+
207
+ if self.use_flash_attn:
208
+ self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
209
+ self.proj = nn.Linear(self.embed_dim, self.embed_dim)
210
+
211
+ def _naive_attn(self, x):
212
+ B, N, C = x.shape
213
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
214
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
215
+
216
+ if self.qk_normalization:
217
+ B_, H_, N_, D_ = q.shape
218
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
219
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
220
+
221
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
222
+ attn = attn.softmax(dim=-1)
223
+ attn = self.attn_drop(attn)
224
+
225
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
226
+ x = self.proj(x)
227
+ x = self.proj_drop(x)
228
+ return x
229
+
230
+ def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
231
+ qkv = self.qkv(x)
232
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
233
+
234
+ if self.qk_normalization:
235
+ q, k, v = qkv.unbind(2)
236
+ q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
237
+ k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
238
+ qkv = torch.stack([q, k, v], dim=2)
239
+
240
+ context, _ = self.inner_attn(
241
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
242
+ )
243
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
244
+ outs = self.proj_drop(outs)
245
+ return outs
246
+
247
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
248
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
249
+ return x
250
+
251
+
252
+ class InternMLP(nn.Module):
253
+ def __init__(self, config: InternVisionConfig):
254
+ super().__init__()
255
+ self.config = config
256
+ self.act = ACT2FN[config.hidden_act]
257
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
258
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
259
+
260
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
261
+ hidden_states = self.fc1(hidden_states)
262
+ hidden_states = self.act(hidden_states)
263
+ hidden_states = self.fc2(hidden_states)
264
+ return hidden_states
265
+
266
+
267
+ def generate_batch_temporal_mask(split_sizes, device='cpu'):
268
+ """
269
+ generate the temporal (padding) mask of a batch
270
+ Args:
271
+ split_sizes: List[num frames]
272
+ Returns:
273
+ temporal_mask: BoolTensor(B, T), `True` means taking, `False` means padding
274
+ """
275
+ B, T = len(split_sizes), max(split_sizes)
276
+ split_sizes = torch.tensor(split_sizes, dtype=torch.long, device=device)
277
+ temporal_idx = torch.arange(T, dtype=torch.long, device=device)[None].repeat((B, 1))
278
+ temporal_mask = temporal_idx < split_sizes[:, None]
279
+ return temporal_mask
280
+
281
+ def concat_batch_frames(images, split_sizes=None, temporal_mask=None):
282
+ """
283
+ B, T, L, D -> concat(T), L, D
284
+ """
285
+ if temporal_mask is None:
286
+ assert split_sizes is not None
287
+ temporal_mask = generate_batch_temporal_mask(split_sizes, device=images.device)
288
+ return images[temporal_mask]
289
+
290
+ def stack_batch_frames(images, split_sizes, return_mask=False):
291
+ """
292
+ concat(T), L, D -> B, T, L, D
293
+ """
294
+ B, T = len(split_sizes), max(split_sizes)
295
+ images_stack = images.new_zeros((B, T, *images.shape[1:]))
296
+ temporal_mask = generate_batch_temporal_mask(split_sizes, device=images.device)
297
+ images_stack[temporal_mask] = images
298
+ if return_mask:
299
+ return images_stack, temporal_mask
300
+ return images_stack
301
+
302
+ def temporal_idx_abs_to_rel(temporal_idx, split_sizes):
303
+ stacked_temporal_idx = stack_batch_frames(temporal_idx, split_sizes)
304
+ length = stacked_temporal_idx.max(dim=-1, keepdim=True)[0]
305
+ length = length.clip(min=1)
306
+ rel_temporal_idx = stacked_temporal_idx.float() / length.float()
307
+ rel_temporal_idx = concat_batch_frames(rel_temporal_idx, split_sizes)
308
+ return rel_temporal_idx
309
+
310
+
311
+ def get_timestep_embedding(
312
+ timesteps: torch.Tensor,
313
+ embedding_dim: int,
314
+ flip_sin_to_cos: bool = False,
315
+ downscale_freq_shift: float = 1,
316
+ scale: float = 1,
317
+ max_period: int = 10000,
318
+ ):
319
+ """
320
+ This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
321
+
322
+ Args
323
+ timesteps (torch.Tensor):
324
+ a 1-D Tensor of N indices, one per batch element. These may be fractional.
325
+ embedding_dim (int):
326
+ the dimension of the output.
327
+ flip_sin_to_cos (bool):
328
+ Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
329
+ downscale_freq_shift (float):
330
+ Controls the delta between frequencies between dimensions
331
+ scale (float):
332
+ Scaling factor applied to the embeddings.
333
+ max_period (int):
334
+ Controls the maximum frequency of the embeddings
335
+ Returns
336
+ torch.Tensor: an [N x dim] Tensor of positional embeddings.
337
+ """
338
+ assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
339
+ original_dtype = timesteps.dtype
340
+
341
+ half_dim = embedding_dim // 2
342
+ exponent = -math.log(max_period) * torch.arange(
343
+ start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
344
+ )
345
+ exponent = exponent / (half_dim - downscale_freq_shift)
346
+
347
+ emb = torch.exp(exponent)
348
+ emb = timesteps[:, None].float() * emb[None, :]
349
+
350
+ # scale embeddings
351
+ emb = scale * emb
352
+
353
+ # concat sine and cosine embeddings
354
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
355
+
356
+ # flip sine and cosine embeddings
357
+ if flip_sin_to_cos:
358
+ emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
359
+
360
+ # zero pad
361
+ if embedding_dim % 2 == 1:
362
+ emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
363
+ return emb.to(original_dtype)
364
+
365
+ class Timesteps(nn.Module):
366
+ def __init__(self, num_channels: int, flip_sin_to_cos: bool = False, downscale_freq_shift: float = 0, scale: int = 1):
367
+ super().__init__()
368
+ self.num_channels = num_channels
369
+ self.flip_sin_to_cos = flip_sin_to_cos
370
+ self.downscale_freq_shift = downscale_freq_shift
371
+ self.scale = scale
372
+
373
+ def forward(self, timesteps):
374
+ t_emb = get_timestep_embedding(
375
+ timesteps,
376
+ self.num_channels,
377
+ flip_sin_to_cos=self.flip_sin_to_cos,
378
+ downscale_freq_shift=self.downscale_freq_shift,
379
+ scale=self.scale,
380
+ )
381
+ return t_emb
382
+
383
+ class AdaLayerNorm(nn.Module):
384
+ def __init__(
385
+ self,
386
+ embedding_dim: int,
387
+ conditioning_embedding_dim: int,
388
+ elementwise_affine=False,
389
+ eps=1e-5,
390
+ bias=True,
391
+ norm_type="layer_norm",
392
+ zero_init=False,
393
+ ):
394
+ super().__init__()
395
+ self.silu = nn.SiLU()
396
+ self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
397
+ if zero_init:
398
+ nn.init.zeros_(self.linear.weight)
399
+ nn.init.zeros_(self.linear.bias)
400
+ print('AdaLN zero init')
401
+ if norm_type == "layer_norm":
402
+ self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine, bias)
403
+ else:
404
+ raise ValueError(f"unknown norm_type {norm_type}")
405
+
406
+ def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
407
+ emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
408
+ scale, shift = torch.chunk(emb, 2, dim=-1)
409
+ x = self.norm(x) * (1 + scale) + shift
410
+ return x
411
+
412
+
413
+ class TokenTemporalAttention(nn.Module):
414
+ def __init__(self, config: InternVisionConfig):
415
+ super().__init__()
416
+ self.config = config
417
+
418
+ d_model = config.hidden_size
419
+ temporal_num_heads = config.num_attention_heads
420
+ self.temporal_attn = nn.MultiheadAttention(d_model, temporal_num_heads, batch_first=True)
421
+
422
+ self.timestep_scale = self.config.relative_timestep_scale
423
+ self.time_embed = nn.Sequential(
424
+ Timesteps(num_channels=256),
425
+ nn.Linear(256, d_model),
426
+ nn.SiLU(),
427
+ nn.Linear(d_model, d_model),
428
+ )
429
+ self.adaln = AdaLayerNorm(d_model, d_model, eps=config.layer_norm_eps,
430
+ zero_init=self.config.temporal_adaln_zero_init)
431
+ if self.config.temporal_adaln_hidden_condition:
432
+ self.hidden_condition_proj = nn.Sequential(
433
+ nn.Linear(d_model, d_model),
434
+ nn.SiLU(), # default use `SiLU`
435
+ nn.Linear(d_model, d_model)
436
+ )
437
+
438
+ if self.config.temporal_alpha_channelwise:
439
+ self.alpha_xattn = nn.Parameter(self.config.temporal_alpha_init * torch.ones(d_model),
440
+ requires_grad=True)
441
+ else:
442
+ self.alpha_xattn = nn.Parameter(torch.tensor(self.config.temporal_alpha_init), requires_grad=True)
443
+
444
+ def forward(self,
445
+ hidden_states: torch.Tensor,
446
+ split_sizes: Optional[list] = None,
447
+ place: Optional[str] = None,
448
+ temporal_id: Optional[torch.LongTensor] = None,
449
+ ):
450
+ # use flash attention 2
451
+ if self.config.use_flash_attn:
452
+ return self._forward_flash_attention_2(hidden_states, split_sizes, place, temporal_id)
453
+
454
+ # stack temporal dim
455
+ hidden_states = stack_batch_frames(hidden_states, split_sizes) # concat(T) L D -> B T L D
456
+ residual = hidden_states
457
+ B, T, L, D = hidden_states.shape
458
+ x = hidden_states.transpose(1, 2).flatten(0, 1) # B T L D -> B*L, T, D
459
+
460
+ # attn & padding mask
461
+ temporal_mask = generate_batch_temporal_mask(split_sizes, device=hidden_states.device) # (B, T), 0 indicate masked out
462
+ temporal_mask = temporal_mask.unsqueeze(1).expand(B, L, T).flatten(0, 1) # B T -> B L T -> B*L, T
463
+ if self.config.temporal_causal:
464
+ attn_mask = torch.ones(T, T, dtype=torch.bool, device=hidden_states.device).tril(diagonal=0) # (T, T), 0 indicate masked out
465
+ else:
466
+ attn_mask = None
467
+
468
+ # temporal AdaLN
469
+ timestep = temporal_idx_abs_to_rel(temporal_id, split_sizes)
470
+ timestep = timestep * self.timestep_scale
471
+ time_condition = self.time_embed(timestep.to(hidden_states.dtype)) # N D
472
+ time_condition = stack_batch_frames(time_condition, split_sizes) # N D -> B T D
473
+ time_condition = time_condition.unsqueeze(1).repeat(1, L, 1, 1).flatten(0, 1) # B T D -> B L T D -> B*L, T, D
474
+ condition = time_condition
475
+ if self.config.temporal_adaln_hidden_condition:
476
+ condition = condition + self.hidden_condition_proj(x)
477
+ x = self.adaln(x, condition)
478
+
479
+ # pass attention
480
+ q = k = v = x
481
+ attn_mask = ~attn_mask if attn_mask is not None else None
482
+ temporal_mask = ~temporal_mask
483
+ # attn_mask, temporal_mask = ~attn_mask, ~temporal_mask, MHSA use 1 to indicate masked out
484
+ attn_out = self.temporal_attn(q, k, v, attn_mask=attn_mask, key_padding_mask=temporal_mask)
485
+ x = attn_out[0]
486
+
487
+ # add to residual
488
+ x = x.view(B, L, T, D).transpose(1, 2) # B*L, T, D -> B T L D
489
+ hidden_states = residual + x * self.alpha_xattn
490
+
491
+ # concat temporal dim
492
+ hidden_states = concat_batch_frames(hidden_states, split_sizes) # B T L D -> concat(T) L D
493
+
494
+ return hidden_states
495
+
496
+ def _forward_flash_attention_2(self,
497
+ hidden_states: torch.Tensor,
498
+ split_sizes: Optional[list] = None,
499
+ place: Optional[str] = None,
500
+ temporal_id: Optional[torch.LongTensor] = None,
501
+ ):
502
+ B, T = len(split_sizes), max(split_sizes)
503
+ N, L, D = hidden_states.shape
504
+ residual = hidden_states
505
+ hidden_states = hidden_states.transpose(0, 1).flatten(0, 1) # (N, L, D) -> (L, N, D) -> (L*N, D)
506
+
507
+ # temporal AdaLN
508
+ timestep = temporal_idx_abs_to_rel(temporal_id, split_sizes)
509
+ timestep = timestep * self.timestep_scale
510
+ time_condition = self.time_embed(timestep.to(hidden_states.dtype)) # (N, D)
511
+ time_condition = time_condition.unsqueeze(0).repeat(L, 1, 1).flatten(0, 1) # (L*N, D)
512
+ condition = time_condition
513
+ if self.config.temporal_adaln_hidden_condition:
514
+ condition = condition + self.hidden_condition_proj(hidden_states)
515
+ hidden_states = self.adaln(hidden_states, condition)
516
+
517
+ q = k = v = hidden_states # (L*N, D)
518
+ w_q, w_k, w_v = self.temporal_attn.in_proj_weight.chunk(3)
519
+ b_q, b_k, b_v = self.temporal_attn.in_proj_bias.chunk(3)
520
+ q = F.linear(q, w_q, b_q)
521
+ k = F.linear(k, w_k, b_k)
522
+ v = F.linear(v, w_v, b_v)
523
+
524
+ num_heads, head_dim = self.temporal_attn.num_heads, self.temporal_attn.head_dim
525
+ q = q.view(q.shape[0], num_heads, head_dim)
526
+ k = k.view(k.shape[0], num_heads, head_dim)
527
+ v = v.view(v.shape[0], num_heads, head_dim)
528
+
529
+ cu_len = torch.cumsum(torch.tensor(split_sizes, dtype=torch.int, device=hidden_states.device), dim=0)
530
+ cu_lens = [cu_len + i * N for i in range(L)]
531
+ cu_lens = torch.cat([torch.zeros((1, ), device=hidden_states.device)] + cu_lens).to(torch.int)
532
+ max_len = max(split_sizes)
533
+
534
+ out = flash_attn_varlen_func(
535
+ q=q, k=k, v=v,
536
+ cu_seqlens_q=cu_lens,
537
+ cu_seqlens_k=cu_lens,
538
+ max_seqlen_q=max_len,
539
+ max_seqlen_k=max_len,
540
+ causal=self.config.temporal_causal,
541
+ )
542
+
543
+ out = out.view(q.shape[0], num_heads*head_dim)
544
+ out = self.temporal_attn.out_proj(out) # (L*N, D)
545
+ out = out.view(L, N, D).transpose(0, 1).contiguous() # (L*N, D) -> (L, N, D) -> (N, L, D)
546
+
547
+ # add to residual
548
+ hidden_states = residual + out * self.alpha_xattn
549
+ return hidden_states
550
+
551
+
552
+ class InternVisionTemporalEncoderLayer(nn.Module):
553
+ def __init__(self, config: InternVisionConfig, drop_path_rate: float, layer_idx: int=None):
554
+ super().__init__()
555
+ self.config = config
556
+ self.layer_idx = layer_idx
557
+ self.embed_dim = config.hidden_size
558
+ self.intermediate_size = config.intermediate_size
559
+ self.norm_type = config.norm_type
560
+
561
+ self.attn = InternAttention(config)
562
+ self.mlp = InternMLP(config)
563
+ self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
564
+ self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
565
+
566
+ self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
567
+ self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
568
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
569
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
570
+
571
+ def initialize_temporal_module(self):
572
+ temporal_layer_ids = self.config.temporal_layer_ids
573
+ if (temporal_layer_ids is not None) and self.layer_idx not in temporal_layer_ids:
574
+ self.temporal_module = None
575
+ return
576
+
577
+ self.temporal_module = TokenTemporalAttention(self.config)
578
+ self.temporal_module_place = self.config.temporal_module_place
579
+ param_names = [k for k, v in self.temporal_module.named_parameters()]
580
+ print(f"[vision temporal model] layer {self.layer_idx} initialize temporal module. "
581
+ f"Place: {self.temporal_module_place}. Parameters: {param_names}")
582
+
583
+ def forward(
584
+ self,
585
+ hidden_states: torch.Tensor,
586
+ split_sizes: Optional[list] = None,
587
+ temporal_id: Optional[torch.LongTensor] = None,
588
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
589
+ """
590
+ Args:
591
+ hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
592
+ """
593
+ if (self.temporal_module is not None) and ('before_self_attn' in self.temporal_module_place):
594
+ hidden_states = self.temporal_module(hidden_states, split_sizes, temporal_id=temporal_id, place='before_self_attn')
595
+
596
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1)
597
+
598
+ # default: pass temporal module (between self-attn and MLP)
599
+ if (self.temporal_module is not None) and ('after_self_attn' in self.temporal_module_place):
600
+ hidden_states = self.temporal_module(hidden_states, split_sizes, temporal_id=temporal_id, place='after_self_attn')
601
+
602
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2)
603
+
604
+ if (self.temporal_module is not None) and ('after_mlp' in self.temporal_module_place):
605
+ hidden_states = self.temporal_module(hidden_states, split_sizes, temporal_id=temporal_id, place='after_mlp')
606
+
607
+ return hidden_states
608
+
609
+
610
+ class InternVisionTemporalEncoder(nn.Module):
611
+ """
612
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
613
+ [`InternEncoderLayer`].
614
+
615
+ Args:
616
+ config (`InternConfig`):
617
+ The corresponding vision configuration for the `InternEncoder`.
618
+ """
619
+
620
+ def __init__(self, config: InternVisionConfig):
621
+ super().__init__()
622
+ self.config = config
623
+ # stochastic depth decay rule
624
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
625
+ self.layers = nn.ModuleList([
626
+ InternVisionTemporalEncoderLayer(config, dpr[idx], layer_idx=idx)
627
+ for idx in range(config.num_hidden_layers)
628
+ ])
629
+ self.gradient_checkpointing = True
630
+
631
+ def forward(
632
+ self,
633
+ inputs_embeds,
634
+ output_hidden_states: Optional[bool] = None,
635
+ return_dict: Optional[bool] = None,
636
+ split_sizes: Optional[list] = None,
637
+ temporal_id: Optional[torch.LongTensor] = None,
638
+ ) -> Union[Tuple, BaseModelOutput]:
639
+ r"""
640
+ Args:
641
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
642
+ Embedded representation of the inputs. Should be float, not int tokens.
643
+ output_hidden_states (`bool`, *optional*):
644
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
645
+ for more detail.
646
+ return_dict (`bool`, *optional*):
647
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
648
+ """
649
+ output_hidden_states = (
650
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
651
+ )
652
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
653
+
654
+ encoder_states = () if output_hidden_states else None
655
+ hidden_states = inputs_embeds
656
+
657
+ for idx, encoder_layer in enumerate(self.layers):
658
+ if output_hidden_states:
659
+ encoder_states = encoder_states + (hidden_states,)
660
+ if self.gradient_checkpointing and self.training:
661
+ layer_outputs = torch.utils.checkpoint.checkpoint(
662
+ encoder_layer,
663
+ hidden_states,
664
+ split_sizes,
665
+ temporal_id)
666
+ else:
667
+ layer_outputs = encoder_layer(
668
+ hidden_states,
669
+ split_sizes=split_sizes,
670
+ temporal_id=temporal_id,
671
+ )
672
+ hidden_states = layer_outputs
673
+
674
+ if output_hidden_states:
675
+ encoder_states = encoder_states + (hidden_states,)
676
+
677
+ if not return_dict:
678
+ return tuple(v for v in [hidden_states, encoder_states] if v is not None)
679
+ return BaseModelOutput(
680
+ last_hidden_state=hidden_states, hidden_states=encoder_states
681
+ )
682
+
683
+
684
+ class InternVisionTemporalModel(PreTrainedModel):
685
+ main_input_name = 'pixel_values'
686
+ _supports_flash_attn_2 = True
687
+ config_class = InternVisionConfig
688
+ _no_split_modules = ['InternVisionTemporalEncoderLayer']
689
+
690
+ def __init__(self, config: InternVisionConfig, delay_init_new_param=False):
691
+ super().__init__(config)
692
+ self.config = config
693
+
694
+ self.embeddings = InternVisionEmbeddings(config)
695
+ self.encoder = InternVisionTemporalEncoder(config)
696
+
697
+ self.new_param_inited = False
698
+ if delay_init_new_param:
699
+ print(f"[vision temporal model] delay_init_new_param={delay_init_new_param}, temporal module should be initalized later")
700
+ else:
701
+ print(f"[vision temporal model] delay_init_new_param={delay_init_new_param}")
702
+ self.initialize_temporal_module()
703
+
704
+ def initialize_temporal_module(self):
705
+ if self.new_param_inited:
706
+ print("[vision temporal model] Warning!!! temporal modules have been initialized, skip.")
707
+ return
708
+ print("[vision temporal model] Initializing temporal modules...")
709
+ for layer in self.encoder.layers:
710
+ layer.initialize_temporal_module()
711
+ self.new_param_inited = True
712
+
713
+ def resize_pos_embeddings(self, old_size, new_size, patch_size):
714
+ pos_emb = self.embeddings.position_embedding
715
+ _, num_positions, embed_dim = pos_emb.shape
716
+ cls_emb = pos_emb[:, :1, :]
717
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
718
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
719
+ pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
720
+ pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
721
+ self.embeddings.position_embedding = nn.Parameter(pos_emb)
722
+ self.embeddings.image_size = new_size
723
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
724
+
725
+ def get_input_embeddings(self):
726
+ return self.embeddings
727
+
728
+ def forward(
729
+ self,
730
+ pixel_values: Optional[torch.FloatTensor] = None,
731
+ output_hidden_states: Optional[bool] = None,
732
+ return_dict: Optional[bool] = None,
733
+ pixel_embeds: Optional[torch.FloatTensor] = None,
734
+ split_sizes: Optional[list] = None,
735
+ temporal_id: Optional[torch.LongTensor] = None,
736
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
737
+ output_hidden_states = (
738
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
739
+ )
740
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
741
+
742
+ if pixel_values is None and pixel_embeds is None:
743
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
744
+
745
+ if pixel_embeds is not None:
746
+ hidden_states = pixel_embeds
747
+ else:
748
+ if len(pixel_values.shape) == 4:
749
+ hidden_states = self.embeddings(pixel_values)
750
+ else:
751
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
752
+ encoder_outputs = self.encoder(
753
+ inputs_embeds=hidden_states,
754
+ output_hidden_states=output_hidden_states,
755
+ return_dict=return_dict,
756
+ split_sizes=split_sizes,
757
+ temporal_id=temporal_id,
758
+ )
759
+ last_hidden_state = encoder_outputs.last_hidden_state
760
+ pooled_output = last_hidden_state[:, 0, :]
761
+
762
+ if not return_dict:
763
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
764
+
765
+ return BaseModelOutputWithPooling(
766
+ last_hidden_state=last_hidden_state,
767
+ pooler_output=pooled_output,
768
+ hidden_states=encoder_outputs.hidden_states,
769
+ attentions=encoder_outputs.attentions,
770
+ )
modeling_internlm2.py ADDED
@@ -0,0 +1,1415 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/modeling_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """ PyTorch InternLM2 model."""
17
+ import math
18
+ import queue
19
+ import threading
20
+ import warnings
21
+ from typing import List, Optional, Tuple, Union
22
+
23
+ import torch
24
+ import torch.nn.functional as F
25
+ import torch.utils.checkpoint
26
+ from einops import rearrange
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+ from transformers.activations import ACT2FN
30
+ from transformers.modeling_outputs import (BaseModelOutputWithPast,
31
+ CausalLMOutputWithPast,
32
+ SequenceClassifierOutputWithPast)
33
+ from transformers.modeling_utils import PreTrainedModel
34
+ from transformers.utils import (add_start_docstrings,
35
+ add_start_docstrings_to_model_forward, logging,
36
+ replace_return_docstrings)
37
+
38
+ try:
39
+ from transformers.generation.streamers import BaseStreamer
40
+ except: # noqa # pylint: disable=bare-except
41
+ BaseStreamer = None
42
+
43
+ from .configuration_internlm2 import InternLM2Config
44
+
45
+ logger = logging.get_logger(__name__)
46
+
47
+ _CONFIG_FOR_DOC = 'InternLM2Config'
48
+
49
+ flash_attn_func, flash_attn_varlen_func = None, None
50
+ pad_input, index_first_axis, unpad_input = None, None, None
51
+ try:
52
+ from flash_attn import flash_attn_func as _flash_attn_func
53
+ from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
54
+ from flash_attn.bert_padding import index_first_axis as _index_first_axis
55
+ from flash_attn.bert_padding import pad_input as _pad_input
56
+ from flash_attn.bert_padding import unpad_input as _unpad_input
57
+
58
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
59
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
60
+ has_flash_attn = True
61
+ except:
62
+ has_flash_attn = False
63
+
64
+
65
+ def _import_flash_attn():
66
+ global flash_attn_func, flash_attn_varlen_func
67
+ global pad_input, index_first_axis, unpad_input
68
+ try:
69
+ from flash_attn import flash_attn_func as _flash_attn_func
70
+ from flash_attn import \
71
+ flash_attn_varlen_func as _flash_attn_varlen_func
72
+ from flash_attn.bert_padding import \
73
+ index_first_axis as _index_first_axis
74
+ from flash_attn.bert_padding import pad_input as _pad_input
75
+ from flash_attn.bert_padding import unpad_input as _unpad_input
76
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
77
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
78
+ except ImportError:
79
+ raise ImportError('flash_attn is not installed.')
80
+
81
+
82
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
83
+ def _get_unpad_data(attention_mask):
84
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
85
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
86
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
87
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
88
+ return (
89
+ indices,
90
+ cu_seqlens,
91
+ max_seqlen_in_batch,
92
+ )
93
+
94
+
95
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
96
+ def _make_causal_mask(
97
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
98
+ ):
99
+ """
100
+ Make causal mask used for bi-directional self-attention.
101
+ """
102
+ bsz, tgt_len = input_ids_shape
103
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
104
+ mask_cond = torch.arange(mask.size(-1), device=device)
105
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
106
+ mask = mask.to(dtype)
107
+
108
+ if past_key_values_length > 0:
109
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
110
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
111
+
112
+
113
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
114
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
115
+ """
116
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
117
+ """
118
+ bsz, src_len = mask.size()
119
+ tgt_len = tgt_len if tgt_len is not None else src_len
120
+
121
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
122
+
123
+ inverted_mask = 1.0 - expanded_mask
124
+
125
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
126
+
127
+
128
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
129
+ class InternLM2RMSNorm(nn.Module):
130
+ def __init__(self, hidden_size, eps=1e-6):
131
+ """
132
+ InternLM2RMSNorm is equivalent to T5LayerNorm
133
+ """
134
+ super().__init__()
135
+ self.weight = nn.Parameter(torch.ones(hidden_size))
136
+ self.variance_epsilon = eps
137
+
138
+ def forward(self, hidden_states):
139
+ input_dtype = hidden_states.dtype
140
+ hidden_states = hidden_states.to(torch.float32)
141
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
142
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
143
+ return self.weight * hidden_states.to(input_dtype)
144
+
145
+
146
+ # Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
147
+ class InternLM2RotaryEmbedding(nn.Module):
148
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
149
+ super().__init__()
150
+
151
+ self.dim = dim
152
+ self.max_position_embeddings = max_position_embeddings
153
+ self.base = base
154
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
155
+ self.register_buffer('inv_freq', inv_freq, persistent=False)
156
+
157
+ # Build here to make `torch.jit.trace` work.
158
+ self._set_cos_sin_cache(
159
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
160
+ )
161
+
162
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
163
+ self.max_seq_len_cached = seq_len
164
+ t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
165
+
166
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
167
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
168
+ emb = torch.cat((freqs, freqs), dim=-1)
169
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
170
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
171
+
172
+ def forward(self, x, seq_len=None):
173
+ # x: [bs, num_attention_heads, seq_len, head_size]
174
+ if seq_len > self.max_seq_len_cached:
175
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
176
+
177
+ return (
178
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
179
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
180
+ )
181
+
182
+
183
+ # Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
184
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
185
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
186
+
187
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
188
+ self.scaling_factor = scaling_factor
189
+ super().__init__(dim, max_position_embeddings, base, device)
190
+
191
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
192
+ self.max_seq_len_cached = seq_len
193
+ t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
194
+ t = t / self.scaling_factor
195
+
196
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
197
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
198
+ emb = torch.cat((freqs, freqs), dim=-1)
199
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
200
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
201
+
202
+
203
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
204
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
205
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
206
+ Credits to the Reddit users /u/bloc97 and /u/emozilla.
207
+ """
208
+
209
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
210
+ self.scaling_factor = scaling_factor
211
+ super().__init__(dim, max_position_embeddings, base, device)
212
+
213
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
214
+ self.max_seq_len_cached = seq_len
215
+
216
+ if seq_len > self.max_position_embeddings:
217
+ base = self.base * (
218
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
219
+ ) ** (self.dim / (self.dim - 2))
220
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
221
+ self.register_buffer('inv_freq', inv_freq, persistent=False)
222
+
223
+ t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
224
+
225
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
226
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
227
+ emb = torch.cat((freqs, freqs), dim=-1)
228
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
229
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
230
+
231
+
232
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
233
+ def rotate_half(x):
234
+ """Rotates half the hidden dims of the input."""
235
+ x1 = x[..., : x.shape[-1] // 2]
236
+ x2 = x[..., x.shape[-1] // 2 :]
237
+ return torch.cat((-x2, x1), dim=-1)
238
+
239
+
240
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
241
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
242
+ """Applies Rotary Position Embedding to the query and key tensors."""
243
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
244
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
245
+ q_embed = (q * cos) + (rotate_half(q) * sin)
246
+ k_embed = (k * cos) + (rotate_half(k) * sin)
247
+ return q_embed, k_embed
248
+
249
+
250
+ class InternLM2MLP(nn.Module):
251
+ def __init__(self, config):
252
+ super().__init__()
253
+ self.config = config
254
+ self.hidden_size = config.hidden_size
255
+ self.intermediate_size = config.intermediate_size
256
+ self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
257
+ self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
258
+ self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
259
+ self.act_fn = ACT2FN[config.hidden_act]
260
+
261
+ def forward(self, x):
262
+ down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
263
+
264
+ return down_proj
265
+
266
+
267
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
268
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
269
+ """
270
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
271
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
272
+ """
273
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
274
+ if n_rep == 1:
275
+ return hidden_states
276
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
277
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
278
+
279
+
280
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
281
+ class InternLM2Attention(nn.Module):
282
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
283
+
284
+ def __init__(self, config: InternLM2Config):
285
+ super().__init__()
286
+ self.config = config
287
+ self.hidden_size = config.hidden_size
288
+ self.num_heads = config.num_attention_heads
289
+ self.head_dim = self.hidden_size // self.num_heads
290
+ self.num_key_value_heads = config.num_key_value_heads
291
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
292
+ self.max_position_embeddings = config.max_position_embeddings
293
+ self.is_causal = True
294
+
295
+ if (self.head_dim * self.num_heads) != self.hidden_size:
296
+ raise ValueError(
297
+ f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
298
+ f' and `num_heads`: {self.num_heads}).'
299
+ )
300
+
301
+ self.wqkv = nn.Linear(
302
+ self.hidden_size,
303
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
304
+ bias=config.bias,
305
+ )
306
+
307
+ self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
308
+ self._init_rope()
309
+
310
+ def _init_rope(self):
311
+ if self.config.rope_scaling is None:
312
+ self.rotary_emb = InternLM2RotaryEmbedding(
313
+ self.head_dim,
314
+ max_position_embeddings=self.max_position_embeddings,
315
+ base=self.config.rope_theta,
316
+ )
317
+ else:
318
+ scaling_type = self.config.rope_scaling['type']
319
+ scaling_factor = self.config.rope_scaling['factor']
320
+ if scaling_type == 'dynamic':
321
+ self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
322
+ self.head_dim,
323
+ max_position_embeddings=self.max_position_embeddings,
324
+ base=self.config.rope_theta,
325
+ scaling_factor=scaling_factor,
326
+ )
327
+ elif scaling_type == 'linear':
328
+ self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
329
+ self.head_dim,
330
+ max_position_embeddings=self.max_position_embeddings,
331
+ base=self.config.rope_theta,
332
+ scaling_factor=scaling_factor,
333
+ )
334
+ else:
335
+ raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
336
+ return self.rotary_emb
337
+
338
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
339
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
340
+
341
+ def forward(
342
+ self,
343
+ hidden_states: torch.Tensor,
344
+ attention_mask: Optional[torch.Tensor] = None,
345
+ position_ids: Optional[torch.LongTensor] = None,
346
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
347
+ output_attentions: bool = False,
348
+ use_cache: bool = False,
349
+ **kwargs,
350
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
351
+ if 'padding_mask' in kwargs:
352
+ warnings.warn(
353
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
354
+ 'Please make sure use `attention_mask` instead.`'
355
+ )
356
+
357
+ bsz, q_len, _ = hidden_states.size()
358
+
359
+ qkv_states = self.wqkv(hidden_states)
360
+
361
+ qkv_states = rearrange(
362
+ qkv_states,
363
+ 'b q (h gs d) -> b q h gs d',
364
+ gs=2 + self.num_key_value_groups,
365
+ d=self.head_dim,
366
+ )
367
+
368
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
369
+ query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
370
+ key_states = qkv_states[..., -2, :]
371
+ value_states = qkv_states[..., -1, :]
372
+
373
+ query_states = query_states.transpose(1, 2)
374
+ key_states = key_states.transpose(1, 2)
375
+ value_states = value_states.transpose(1, 2)
376
+
377
+ kv_seq_len = key_states.shape[-2]
378
+ if past_key_value is not None:
379
+ kv_seq_len += past_key_value[0].shape[-2]
380
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
381
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
382
+
383
+ if past_key_value is not None:
384
+ # reuse k, v, self_attention
385
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
386
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
387
+
388
+ past_key_value = (key_states, value_states) if use_cache else None
389
+
390
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
391
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
392
+
393
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
394
+
395
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
396
+ raise ValueError(
397
+ f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
398
+ f' {attn_weights.size()}'
399
+ )
400
+
401
+ if attention_mask is not None:
402
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
403
+ raise ValueError(
404
+ f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
405
+ )
406
+ attn_weights = attn_weights + attention_mask
407
+
408
+ # upcast attention to fp32
409
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
410
+ attn_output = torch.matmul(attn_weights, value_states)
411
+
412
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
413
+ raise ValueError(
414
+ f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
415
+ f' {attn_output.size()}'
416
+ )
417
+
418
+ attn_output = attn_output.transpose(1, 2).contiguous()
419
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
420
+
421
+ attn_output = self.wo(attn_output)
422
+
423
+ if not output_attentions:
424
+ attn_weights = None
425
+
426
+ return attn_output, attn_weights, past_key_value
427
+
428
+
429
+ # Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
430
+ class InternLM2FlashAttention2(InternLM2Attention):
431
+ """
432
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
433
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
434
+ flash attention and deal with padding tokens in case the input contains any of them.
435
+ """
436
+
437
+ def forward(
438
+ self,
439
+ hidden_states: torch.Tensor,
440
+ attention_mask: Optional[torch.LongTensor] = None,
441
+ position_ids: Optional[torch.LongTensor] = None,
442
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
443
+ output_attentions: bool = False,
444
+ use_cache: bool = False,
445
+ **kwargs,
446
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
447
+ # InternLM2FlashAttention2 attention does not support output_attentions
448
+ if 'padding_mask' in kwargs:
449
+ warnings.warn(
450
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
451
+ 'Please make sure use `attention_mask` instead.`'
452
+ )
453
+
454
+ # overwrite attention_mask with padding_mask
455
+ attention_mask = kwargs.pop('padding_mask')
456
+
457
+ output_attentions = False
458
+
459
+ bsz, q_len, _ = hidden_states.size()
460
+
461
+ qkv_states = self.wqkv(hidden_states)
462
+
463
+ qkv_states = rearrange(
464
+ qkv_states,
465
+ 'b q (h gs d) -> b q h gs d',
466
+ gs=2 + self.num_key_value_groups,
467
+ d=self.head_dim,
468
+ )
469
+
470
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
471
+ query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
472
+ key_states = qkv_states[..., -2, :]
473
+ value_states = qkv_states[..., -1, :]
474
+
475
+ query_states = query_states.transpose(1, 2)
476
+ key_states = key_states.transpose(1, 2)
477
+ value_states = value_states.transpose(1, 2)
478
+
479
+ kv_seq_len = key_states.shape[-2]
480
+ if past_key_value is not None:
481
+ kv_seq_len += past_key_value[0].shape[-2]
482
+
483
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
484
+
485
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
486
+
487
+ if past_key_value is not None:
488
+ # reuse k, v, self_attention
489
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
490
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
491
+
492
+ past_key_value = (key_states, value_states) if use_cache else None
493
+
494
+ query_states = query_states.transpose(1, 2)
495
+ key_states = key_states.transpose(1, 2)
496
+ value_states = value_states.transpose(1, 2)
497
+
498
+ attn_output = self._flash_attention_forward(
499
+ query_states, key_states, value_states, attention_mask, q_len
500
+ )
501
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
502
+ attn_output = self.wo(attn_output)
503
+
504
+ if not output_attentions:
505
+ attn_weights = None
506
+
507
+ return attn_output, attn_weights, past_key_value
508
+
509
+ def _flash_attention_forward(
510
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
511
+ ):
512
+ """
513
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
514
+ first unpad the input, then computes the attention scores and pad the final attention scores.
515
+
516
+ Args:
517
+ query_states (`torch.Tensor`):
518
+ Input query states to be passed to Flash Attention API
519
+ key_states (`torch.Tensor`):
520
+ Input key states to be passed to Flash Attention API
521
+ value_states (`torch.Tensor`):
522
+ Input value states to be passed to Flash Attention API
523
+ attention_mask (`torch.Tensor`):
524
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
525
+ position of padding tokens and 1 for the position of non-padding tokens.
526
+ dropout (`int`, *optional*):
527
+ Attention dropout
528
+ softmax_scale (`float`, *optional*):
529
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
530
+ """
531
+ # Contains at least one padding token in the sequence
532
+ causal = self.is_causal and query_length != 1
533
+ if attention_mask is not None:
534
+ batch_size = query_states.shape[0]
535
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
536
+ query_states, key_states, value_states, attention_mask, query_length
537
+ )
538
+
539
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
540
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
541
+
542
+ attn_output_unpad = flash_attn_varlen_func(
543
+ query_states,
544
+ key_states,
545
+ value_states,
546
+ cu_seqlens_q=cu_seqlens_q,
547
+ cu_seqlens_k=cu_seqlens_k,
548
+ max_seqlen_q=max_seqlen_in_batch_q,
549
+ max_seqlen_k=max_seqlen_in_batch_k,
550
+ dropout_p=dropout,
551
+ softmax_scale=softmax_scale,
552
+ causal=causal,
553
+ )
554
+
555
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
556
+ else:
557
+ attn_output = flash_attn_func(
558
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
559
+ )
560
+
561
+ return attn_output
562
+
563
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
564
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
565
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
566
+
567
+ key_layer = index_first_axis(
568
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
569
+ )
570
+ value_layer = index_first_axis(
571
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
572
+ )
573
+
574
+ if query_length == kv_seq_len:
575
+ query_layer = index_first_axis(
576
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
577
+ )
578
+ cu_seqlens_q = cu_seqlens_k
579
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
580
+ indices_q = indices_k
581
+ elif query_length == 1:
582
+ max_seqlen_in_batch_q = 1
583
+ cu_seqlens_q = torch.arange(
584
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
585
+ ) # There is a memcpy here, that is very bad.
586
+ indices_q = cu_seqlens_q[:-1]
587
+ query_layer = query_layer.squeeze(1)
588
+ else:
589
+ # The -q_len: slice assumes left padding.
590
+ attention_mask = attention_mask[:, -query_length:]
591
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
592
+
593
+ return (
594
+ query_layer,
595
+ key_layer,
596
+ value_layer,
597
+ indices_q.to(torch.int64),
598
+ (cu_seqlens_q, cu_seqlens_k),
599
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
600
+ )
601
+
602
+
603
+ INTERNLM2_ATTENTION_CLASSES = {
604
+ 'eager': InternLM2Attention,
605
+ 'flash_attention_2': InternLM2FlashAttention2,
606
+ }
607
+
608
+
609
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
610
+ class InternLM2DecoderLayer(nn.Module):
611
+ def __init__(self, config: InternLM2Config):
612
+ super().__init__()
613
+ self.hidden_size = config.hidden_size
614
+
615
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
616
+
617
+ self.feed_forward = InternLM2MLP(config)
618
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
619
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
620
+
621
+ def forward(
622
+ self,
623
+ hidden_states: torch.Tensor,
624
+ attention_mask: Optional[torch.Tensor] = None,
625
+ position_ids: Optional[torch.LongTensor] = None,
626
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
627
+ output_attentions: Optional[bool] = False,
628
+ use_cache: Optional[bool] = False,
629
+ **kwargs,
630
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
631
+ """
632
+ Args:
633
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
634
+ attention_mask (`torch.FloatTensor`, *optional*):
635
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
636
+ query_sequence_length, key_sequence_length)` if default attention is used.
637
+ output_attentions (`bool`, *optional*):
638
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
639
+ returned tensors for more detail.
640
+ use_cache (`bool`, *optional*):
641
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
642
+ (see `past_key_values`).
643
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
644
+ """
645
+ if 'padding_mask' in kwargs:
646
+ warnings.warn(
647
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
648
+ 'Please make sure use `attention_mask` instead.`'
649
+ )
650
+
651
+ residual = hidden_states
652
+
653
+ hidden_states = self.attention_norm(hidden_states)
654
+
655
+ # Self Attention
656
+ hidden_states, self_attn_weights, present_key_value = self.attention(
657
+ hidden_states=hidden_states,
658
+ attention_mask=attention_mask,
659
+ position_ids=position_ids,
660
+ past_key_value=past_key_value,
661
+ output_attentions=output_attentions,
662
+ use_cache=use_cache,
663
+ **kwargs,
664
+ )
665
+ hidden_states = residual + hidden_states
666
+
667
+ # Fully Connected
668
+ residual = hidden_states
669
+ hidden_states = self.ffn_norm(hidden_states)
670
+ hidden_states = self.feed_forward(hidden_states)
671
+ hidden_states = residual + hidden_states
672
+
673
+ outputs = (hidden_states,)
674
+
675
+ if output_attentions:
676
+ outputs += (self_attn_weights,)
677
+
678
+ if use_cache:
679
+ outputs += (present_key_value,)
680
+
681
+ return outputs
682
+
683
+
684
+ InternLM2_START_DOCSTRING = r"""
685
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
686
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
687
+ etc.)
688
+
689
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
690
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
691
+ and behavior.
692
+
693
+ Parameters:
694
+ config ([`InternLM2Config`]):
695
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
696
+ load the weights associated with the model, only the configuration. Check out the
697
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
698
+ """
699
+
700
+
701
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
702
+ @add_start_docstrings(
703
+ 'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
704
+ InternLM2_START_DOCSTRING,
705
+ )
706
+ class InternLM2PreTrainedModel(PreTrainedModel):
707
+ config_class = InternLM2Config
708
+ base_model_prefix = 'model'
709
+ supports_gradient_checkpointing = True
710
+ _no_split_modules = ['InternLM2DecoderLayer']
711
+ _skip_keys_device_placement = 'past_key_values'
712
+ _supports_flash_attn_2 = True
713
+
714
+ def _init_weights(self, module):
715
+ std = self.config.initializer_range
716
+ if isinstance(module, nn.Linear):
717
+ module.weight.data.normal_(mean=0.0, std=std)
718
+ if module.bias is not None:
719
+ module.bias.data.zero_()
720
+ elif isinstance(module, nn.Embedding):
721
+ module.weight.data.normal_(mean=0.0, std=std)
722
+ if module.padding_idx is not None:
723
+ module.weight.data[module.padding_idx].zero_()
724
+
725
+
726
+ InternLM2_INPUTS_DOCSTRING = r"""
727
+ Args:
728
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
729
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
730
+ it.
731
+
732
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
733
+ [`PreTrainedTokenizer.__call__`] for details.
734
+
735
+ [What are input IDs?](../glossary#input-ids)
736
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
737
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
738
+
739
+ - 1 for tokens that are **not masked**,
740
+ - 0 for tokens that are **masked**.
741
+
742
+ [What are attention masks?](../glossary#attention-mask)
743
+
744
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
745
+ [`PreTrainedTokenizer.__call__`] for details.
746
+
747
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
748
+ `past_key_values`).
749
+
750
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
751
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
752
+ information on the default strategy.
753
+
754
+ - 1 indicates the head is **not masked**,
755
+ - 0 indicates the head is **masked**.
756
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
757
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
758
+ config.n_positions - 1]`.
759
+
760
+ [What are position IDs?](../glossary#position-ids)
761
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
762
+ when `config.use_cache=True`):
763
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
764
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
765
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
766
+
767
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
768
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
769
+
770
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
771
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
772
+ of shape `(batch_size, sequence_length)`.
773
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
774
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
775
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
776
+ model's internal embedding lookup matrix.
777
+ use_cache (`bool`, *optional*):
778
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
779
+ `past_key_values`).
780
+ output_attentions (`bool`, *optional*):
781
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
782
+ tensors for more detail.
783
+ output_hidden_states (`bool`, *optional*):
784
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
785
+ more detail.
786
+ return_dict (`bool`, *optional*):
787
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
788
+ """
789
+
790
+
791
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
792
+ @add_start_docstrings(
793
+ 'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
794
+ InternLM2_START_DOCSTRING,
795
+ )
796
+ class InternLM2Model(InternLM2PreTrainedModel):
797
+ """
798
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
799
+
800
+ Args:
801
+ config: InternLM2Config
802
+ """
803
+
804
+ _auto_class = 'AutoModel'
805
+
806
+ def __init__(self, config: InternLM2Config):
807
+ super().__init__(config)
808
+ self.padding_idx = config.pad_token_id
809
+ self.vocab_size = config.vocab_size
810
+ self.config = config
811
+ if not has_flash_attn:
812
+ self.config.attn_implementation = 'eager'
813
+ print('Warning: Flash attention is not available, using eager attention instead.')
814
+
815
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
816
+
817
+ self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
818
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
819
+
820
+ self.gradient_checkpointing = False
821
+ # Initialize weights and apply final processing
822
+ self.post_init()
823
+
824
+ def get_input_embeddings(self):
825
+ return self.tok_embeddings
826
+
827
+ def set_input_embeddings(self, value):
828
+ self.tok_embeddings = value
829
+
830
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
831
+ # create causal mask
832
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
833
+ combined_attention_mask = None
834
+ if input_shape[-1] > 1:
835
+ combined_attention_mask = _make_causal_mask(
836
+ input_shape,
837
+ inputs_embeds.dtype,
838
+ device=inputs_embeds.device,
839
+ past_key_values_length=past_key_values_length,
840
+ )
841
+
842
+ if attention_mask is not None:
843
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
844
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
845
+ inputs_embeds.device
846
+ )
847
+ combined_attention_mask = (
848
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
849
+ )
850
+
851
+ return combined_attention_mask
852
+
853
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
854
+ def forward(
855
+ self,
856
+ input_ids: torch.LongTensor = None,
857
+ attention_mask: Optional[torch.Tensor] = None,
858
+ position_ids: Optional[torch.LongTensor] = None,
859
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
860
+ inputs_embeds: Optional[torch.FloatTensor] = None,
861
+ use_cache: Optional[bool] = None,
862
+ output_attentions: Optional[bool] = None,
863
+ output_hidden_states: Optional[bool] = None,
864
+ return_dict: Optional[bool] = None,
865
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
866
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
867
+ output_hidden_states = (
868
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
869
+ )
870
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
871
+
872
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
873
+
874
+ if self.config.attn_implementation == 'flash_attention_2':
875
+ _import_flash_attn()
876
+
877
+ # retrieve input_ids and inputs_embeds
878
+ if input_ids is not None and inputs_embeds is not None:
879
+ raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
880
+ elif input_ids is not None:
881
+ batch_size, seq_length = input_ids.shape[:2]
882
+ elif inputs_embeds is not None:
883
+ batch_size, seq_length = inputs_embeds.shape[:2]
884
+ else:
885
+ raise ValueError('You have to specify either input_ids or inputs_embeds')
886
+
887
+ seq_length_with_past = seq_length
888
+ past_key_values_length = 0
889
+ if past_key_values is not None:
890
+ past_key_values_length = past_key_values[0][0].shape[2]
891
+ seq_length_with_past = seq_length_with_past + past_key_values_length
892
+
893
+ if position_ids is None:
894
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
895
+ position_ids = torch.arange(
896
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
897
+ )
898
+ position_ids = position_ids.unsqueeze(0)
899
+
900
+ if inputs_embeds is None:
901
+ inputs_embeds = self.tok_embeddings(input_ids)
902
+
903
+ if self.config.attn_implementation == 'flash_attention_2':
904
+ # 2d mask is passed through the layers
905
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
906
+ else:
907
+ if attention_mask is None:
908
+ attention_mask = torch.ones(
909
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
910
+ )
911
+ attention_mask = self._prepare_decoder_attention_mask(
912
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
913
+ )
914
+
915
+ # embed positions
916
+ hidden_states = inputs_embeds
917
+
918
+ if self.gradient_checkpointing and self.training:
919
+ if use_cache:
920
+ logger.warning_once(
921
+ '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
922
+ )
923
+ use_cache = False
924
+
925
+ # decoder layers
926
+ all_hidden_states = () if output_hidden_states else None
927
+ all_self_attns = () if output_attentions else None
928
+ next_decoder_cache = () if use_cache else None
929
+
930
+ for idx, decoder_layer in enumerate(self.layers):
931
+ if output_hidden_states:
932
+ all_hidden_states += (hidden_states,)
933
+
934
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
935
+
936
+ if self.gradient_checkpointing and self.training:
937
+
938
+ def create_custom_forward(module):
939
+ def custom_forward(*inputs):
940
+ # None for past_key_value
941
+ return module(*inputs, output_attentions, None)
942
+
943
+ return custom_forward
944
+
945
+ layer_outputs = torch.utils.checkpoint.checkpoint(
946
+ create_custom_forward(decoder_layer),
947
+ hidden_states,
948
+ attention_mask,
949
+ position_ids,
950
+ None,
951
+ )
952
+ else:
953
+ layer_outputs = decoder_layer(
954
+ hidden_states,
955
+ attention_mask=attention_mask,
956
+ position_ids=position_ids,
957
+ past_key_value=past_key_value,
958
+ output_attentions=output_attentions,
959
+ use_cache=use_cache,
960
+ )
961
+
962
+ hidden_states = layer_outputs[0]
963
+
964
+ if use_cache:
965
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
966
+
967
+ if output_attentions:
968
+ all_self_attns += (layer_outputs[1],)
969
+
970
+ hidden_states = self.norm(hidden_states)
971
+
972
+ # add hidden states from the last decoder layer
973
+ if output_hidden_states:
974
+ all_hidden_states += (hidden_states,)
975
+
976
+ next_cache = next_decoder_cache if use_cache else None
977
+ if not return_dict:
978
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
979
+ return BaseModelOutputWithPast(
980
+ last_hidden_state=hidden_states,
981
+ past_key_values=next_cache,
982
+ hidden_states=all_hidden_states,
983
+ attentions=all_self_attns,
984
+ )
985
+
986
+
987
+ # Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
988
+ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
989
+ _auto_class = 'AutoModelForCausalLM'
990
+
991
+ _tied_weights_keys = ['output.weight']
992
+
993
+ def __init__(self, config):
994
+ super().__init__(config)
995
+ self.model = InternLM2Model(config)
996
+ self.vocab_size = config.vocab_size
997
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
998
+
999
+ # Initialize weights and apply final processing
1000
+ self.post_init()
1001
+
1002
+ def get_input_embeddings(self):
1003
+ return self.model.tok_embeddings
1004
+
1005
+ def set_input_embeddings(self, value):
1006
+ self.model.tok_embeddings = value
1007
+
1008
+ def get_output_embeddings(self):
1009
+ return self.output
1010
+
1011
+ def set_output_embeddings(self, new_embeddings):
1012
+ self.output = new_embeddings
1013
+
1014
+ def set_decoder(self, decoder):
1015
+ self.model = decoder
1016
+
1017
+ def get_decoder(self):
1018
+ return self.model
1019
+
1020
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1021
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1022
+ def forward(
1023
+ self,
1024
+ input_ids: torch.LongTensor = None,
1025
+ attention_mask: Optional[torch.Tensor] = None,
1026
+ position_ids: Optional[torch.LongTensor] = None,
1027
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1028
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1029
+ labels: Optional[torch.LongTensor] = None,
1030
+ use_cache: Optional[bool] = None,
1031
+ output_attentions: Optional[bool] = None,
1032
+ output_hidden_states: Optional[bool] = None,
1033
+ return_dict: Optional[bool] = None,
1034
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1035
+ r"""
1036
+ Args:
1037
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1038
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1039
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1040
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1041
+
1042
+ Returns:
1043
+
1044
+ Example:
1045
+
1046
+ ```python
1047
+ >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
1048
+
1049
+ >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1050
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1051
+
1052
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1053
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1054
+
1055
+ >>> # Generate
1056
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1057
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1058
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1059
+ ```"""
1060
+
1061
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1062
+ output_hidden_states = (
1063
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1064
+ )
1065
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1066
+
1067
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1068
+ outputs = self.model(
1069
+ input_ids=input_ids,
1070
+ attention_mask=attention_mask,
1071
+ position_ids=position_ids,
1072
+ past_key_values=past_key_values,
1073
+ inputs_embeds=inputs_embeds,
1074
+ use_cache=use_cache,
1075
+ output_attentions=output_attentions,
1076
+ output_hidden_states=output_hidden_states,
1077
+ return_dict=return_dict,
1078
+ )
1079
+
1080
+ hidden_states = outputs[0]
1081
+ logits = self.output(hidden_states)
1082
+ logits = logits.float()
1083
+
1084
+ loss = None
1085
+ if labels is not None:
1086
+ # Shift so that tokens < n predict n
1087
+ shift_logits = logits[..., :-1, :].contiguous()
1088
+ shift_labels = labels[..., 1:].contiguous()
1089
+ # Flatten the tokens
1090
+ loss_fct = CrossEntropyLoss()
1091
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1092
+ shift_labels = shift_labels.view(-1)
1093
+ # Enable model parallelism
1094
+ shift_labels = shift_labels.to(shift_logits.device)
1095
+ loss = loss_fct(shift_logits, shift_labels)
1096
+
1097
+ if not return_dict:
1098
+ output = (logits,) + outputs[1:]
1099
+ return (loss,) + output if loss is not None else output
1100
+
1101
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1102
+ output = CausalLMOutputWithPast(
1103
+ loss=loss,
1104
+ logits=logits,
1105
+ past_key_values=outputs.past_key_values,
1106
+ hidden_states=outputs.hidden_states,
1107
+ attentions=outputs.attentions,
1108
+ )
1109
+ output['logits'] = output['logits'].to(device)
1110
+ return output
1111
+
1112
+ def prepare_inputs_for_generation(
1113
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1114
+ ):
1115
+ if past_key_values is not None:
1116
+ past_length = past_key_values[0][0].shape[2]
1117
+
1118
+ # Some generation methods already pass only the last input ID
1119
+ if input_ids.shape[1] > past_length:
1120
+ remove_prefix_length = past_length
1121
+ else:
1122
+ # Default to old behavior: keep only final ID
1123
+ remove_prefix_length = input_ids.shape[1] - 1
1124
+
1125
+ input_ids = input_ids[:, remove_prefix_length:]
1126
+
1127
+ position_ids = kwargs.get('position_ids', None)
1128
+ if attention_mask is not None and position_ids is None:
1129
+ # create position_ids on the fly for batch generation
1130
+ position_ids = attention_mask.long().cumsum(-1) - 1
1131
+ position_ids.masked_fill_(attention_mask == 0, 1)
1132
+ if past_key_values:
1133
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1134
+
1135
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1136
+ if inputs_embeds is not None and past_key_values is None:
1137
+ model_inputs = {'inputs_embeds': inputs_embeds}
1138
+ else:
1139
+ model_inputs = {'input_ids': input_ids}
1140
+
1141
+ model_inputs.update(
1142
+ {
1143
+ 'position_ids': position_ids,
1144
+ 'past_key_values': past_key_values,
1145
+ 'use_cache': kwargs.get('use_cache'),
1146
+ 'attention_mask': attention_mask,
1147
+ }
1148
+ )
1149
+ return model_inputs
1150
+
1151
+ @staticmethod
1152
+ def _reorder_cache(past_key_values, beam_idx):
1153
+ reordered_past = ()
1154
+ for layer_past in past_key_values:
1155
+ reordered_past += (
1156
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1157
+ )
1158
+ return reordered_past
1159
+
1160
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''):
1161
+ if tokenizer.add_bos_token:
1162
+ prompt = ''
1163
+ else:
1164
+ prompt = tokenizer.bos_token
1165
+ if meta_instruction:
1166
+ prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
1167
+ for record in history:
1168
+ prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
1169
+ prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
1170
+ return tokenizer([prompt], return_tensors='pt')
1171
+
1172
+ @torch.no_grad()
1173
+ def chat(
1174
+ self,
1175
+ tokenizer,
1176
+ query: str,
1177
+ history: List[Tuple[str, str]] = [],
1178
+ streamer: Optional[BaseStreamer] = None,
1179
+ max_new_tokens: int = 1024,
1180
+ do_sample: bool = True,
1181
+ temperature: float = 0.8,
1182
+ top_p: float = 0.8,
1183
+ meta_instruction: str = 'You are an AI assistant whose name is InternLM (书生·浦语).\n'
1184
+ '- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
1185
+ '- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.',
1186
+ **kwargs,
1187
+ ):
1188
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1189
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1190
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1191
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['<|im_end|>'])[0]]
1192
+ outputs = self.generate(
1193
+ **inputs,
1194
+ streamer=streamer,
1195
+ max_new_tokens=max_new_tokens,
1196
+ do_sample=do_sample,
1197
+ temperature=temperature,
1198
+ top_p=top_p,
1199
+ eos_token_id=eos_token_id,
1200
+ **kwargs,
1201
+ )
1202
+ outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]) :]
1203
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1204
+ response = response.split('<|im_end|>')[0]
1205
+ history = history + [(query, response)]
1206
+ return response, history
1207
+
1208
+ @torch.no_grad()
1209
+ def stream_chat(
1210
+ self,
1211
+ tokenizer,
1212
+ query: str,
1213
+ history: List[Tuple[str, str]] = [],
1214
+ max_new_tokens: int = 1024,
1215
+ do_sample: bool = True,
1216
+ temperature: float = 0.8,
1217
+ top_p: float = 0.8,
1218
+ **kwargs,
1219
+ ):
1220
+ """
1221
+ Return a generator in format: (response, history)
1222
+ Eg.
1223
+ ('你好,有什么可以帮助您的吗', [('你好', '你好,有什么可以帮助您的吗')])
1224
+ ('你好,有什么可以帮助您的吗?', [('你好', '你好,有什么可以帮助您的吗?')])
1225
+ """
1226
+ if BaseStreamer is None:
1227
+ raise ModuleNotFoundError(
1228
+ 'The version of `transformers` is too low. Please make sure '
1229
+ 'that you have installed `transformers>=4.28.0`.'
1230
+ )
1231
+
1232
+ response_queue = queue.Queue(maxsize=20)
1233
+
1234
+ class ChatStreamer(BaseStreamer):
1235
+ def __init__(self, tokenizer) -> None:
1236
+ super().__init__()
1237
+ self.tokenizer = tokenizer
1238
+ self.queue = response_queue
1239
+ self.query = query
1240
+ self.history = history
1241
+ self.response = ''
1242
+ self.cache = []
1243
+ self.received_inputs = False
1244
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1245
+
1246
+ def put(self, value):
1247
+ if len(value.shape) > 1 and value.shape[0] > 1:
1248
+ raise ValueError('ChatStreamer only supports batch size 1')
1249
+ elif len(value.shape) > 1:
1250
+ value = value[0]
1251
+
1252
+ if not self.received_inputs:
1253
+ # The first received value is input_ids, ignore here
1254
+ self.received_inputs = True
1255
+ return
1256
+
1257
+ self.cache.extend(value.tolist())
1258
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1259
+ if token.strip() != '<|im_end|>':
1260
+ self.response = self.response + token
1261
+ history = self.history + [(self.query, self.response)]
1262
+ self.queue.put((self.response, history))
1263
+ self.cache = []
1264
+ else:
1265
+ self.end()
1266
+
1267
+ def end(self):
1268
+ self.queue.put(None)
1269
+
1270
+ def stream_producer():
1271
+ return self.chat(
1272
+ tokenizer=tokenizer,
1273
+ query=query,
1274
+ streamer=ChatStreamer(tokenizer=tokenizer),
1275
+ history=history,
1276
+ max_new_tokens=max_new_tokens,
1277
+ do_sample=do_sample,
1278
+ temperature=temperature,
1279
+ top_p=top_p,
1280
+ **kwargs,
1281
+ )
1282
+
1283
+ def consumer():
1284
+ producer = threading.Thread(target=stream_producer)
1285
+ producer.start()
1286
+ while True:
1287
+ res = response_queue.get()
1288
+ if res is None:
1289
+ return
1290
+ yield res
1291
+
1292
+ return consumer()
1293
+
1294
+
1295
+ # Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
1296
+ @add_start_docstrings(
1297
+ """
1298
+ The InternLM2 Model transformer with a sequence classification head on top (linear layer).
1299
+
1300
+ [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
1301
+ as other causal models (e.g. GPT-2) do.
1302
+
1303
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1304
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1305
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1306
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1307
+ each row of the batch).
1308
+ """,
1309
+ InternLM2_START_DOCSTRING,
1310
+ )
1311
+ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
1312
+ def __init__(self, config):
1313
+ super().__init__(config)
1314
+ self.num_labels = config.num_labels
1315
+ self.model = InternLM2Model(config)
1316
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1317
+
1318
+ # Initialize weights and apply final processing
1319
+ self.post_init()
1320
+
1321
+ def get_input_embeddings(self):
1322
+ return self.model.tok_embeddings
1323
+
1324
+ def set_input_embeddings(self, value):
1325
+ self.model.tok_embeddings = value
1326
+
1327
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1328
+ def forward(
1329
+ self,
1330
+ input_ids: torch.LongTensor = None,
1331
+ attention_mask: Optional[torch.Tensor] = None,
1332
+ position_ids: Optional[torch.LongTensor] = None,
1333
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1334
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1335
+ labels: Optional[torch.LongTensor] = None,
1336
+ use_cache: Optional[bool] = None,
1337
+ output_attentions: Optional[bool] = None,
1338
+ output_hidden_states: Optional[bool] = None,
1339
+ return_dict: Optional[bool] = None,
1340
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1341
+ r"""
1342
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1343
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1344
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1345
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1346
+ """
1347
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1348
+
1349
+ transformer_outputs = self.model(
1350
+ input_ids,
1351
+ attention_mask=attention_mask,
1352
+ position_ids=position_ids,
1353
+ past_key_values=past_key_values,
1354
+ inputs_embeds=inputs_embeds,
1355
+ use_cache=use_cache,
1356
+ output_attentions=output_attentions,
1357
+ output_hidden_states=output_hidden_states,
1358
+ return_dict=return_dict,
1359
+ )
1360
+ hidden_states = transformer_outputs[0]
1361
+ logits = self.score(hidden_states)
1362
+
1363
+ if input_ids is not None:
1364
+ batch_size = input_ids.shape[0]
1365
+ else:
1366
+ batch_size = inputs_embeds.shape[0]
1367
+
1368
+ if self.config.pad_token_id is None and batch_size != 1:
1369
+ raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.')
1370
+ if self.config.pad_token_id is None:
1371
+ sequence_lengths = -1
1372
+ else:
1373
+ if input_ids is not None:
1374
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1375
+ logits.device
1376
+ )
1377
+ else:
1378
+ sequence_lengths = -1
1379
+
1380
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1381
+
1382
+ loss = None
1383
+ if labels is not None:
1384
+ labels = labels.to(logits.device)
1385
+ if self.config.problem_type is None:
1386
+ if self.num_labels == 1:
1387
+ self.config.problem_type = 'regression'
1388
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1389
+ self.config.problem_type = 'single_label_classification'
1390
+ else:
1391
+ self.config.problem_type = 'multi_label_classification'
1392
+
1393
+ if self.config.problem_type == 'regression':
1394
+ loss_fct = MSELoss()
1395
+ if self.num_labels == 1:
1396
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1397
+ else:
1398
+ loss = loss_fct(pooled_logits, labels)
1399
+ elif self.config.problem_type == 'single_label_classification':
1400
+ loss_fct = CrossEntropyLoss()
1401
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1402
+ elif self.config.problem_type == 'multi_label_classification':
1403
+ loss_fct = BCEWithLogitsLoss()
1404
+ loss = loss_fct(pooled_logits, labels)
1405
+ if not return_dict:
1406
+ output = (pooled_logits,) + transformer_outputs[1:]
1407
+ return ((loss,) + output) if loss is not None else output
1408
+
1409
+ return SequenceClassifierOutputWithPast(
1410
+ loss=loss,
1411
+ logits=pooled_logits,
1412
+ past_key_values=transformer_outputs.past_key_values,
1413
+ hidden_states=transformer_outputs.hidden_states,
1414
+ attentions=transformer_outputs.attentions,
1415
+ )
modeling_pvc_internvl.py ADDED
@@ -0,0 +1,497 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2024 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ import warnings
8
+ from typing import List, Optional, Tuple, Union
9
+
10
+ import torch.utils.checkpoint
11
+ import transformers
12
+ from torch import nn
13
+ from torch.nn import CrossEntropyLoss
14
+ from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
15
+ LlamaTokenizer)
16
+ from transformers.modeling_outputs import CausalLMOutputWithPast
17
+ from transformers.modeling_utils import PreTrainedModel
18
+ from transformers.utils import ModelOutput, logging
19
+
20
+ from .configuration_pvc_internvl import PVCInternVLConfig
21
+ from .conversation import get_conv_template
22
+ from .modeling_intern_vit import InternVisionModel, has_flash_attn
23
+ from .modeling_intern_vit_pvc import InternVisionTemporalModel, AdaLayerNorm, Timesteps, temporal_idx_abs_to_rel
24
+ from .modeling_internlm2 import InternLM2ForCausalLM
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+
29
+ def version_cmp(v1, v2, op='eq'):
30
+ import operator
31
+
32
+ from packaging import version
33
+ op_func = getattr(operator, op)
34
+ return op_func(version.parse(v1), version.parse(v2))
35
+
36
+
37
+ class AdaLNMLP(nn.Module):
38
+ def __init__(self, input_dim, output_dim, use_temporal_condition=False,
39
+ use_rel_timestep=False, rel_timestep_scale=100):
40
+ super().__init__()
41
+ # condition proj
42
+ self.condition_proj = nn.Sequential(
43
+ nn.Linear(input_dim, input_dim),
44
+ nn.SiLU(), # default use `SiLU`
45
+ nn.Linear(input_dim, input_dim)
46
+ )
47
+ self.use_temporal_condition = use_temporal_condition
48
+ self.use_rel_timestep = use_rel_timestep
49
+ self.rel_timestep_scale = rel_timestep_scale
50
+ # from Stable Diffusion v3
51
+ if use_temporal_condition:
52
+ self.time_embed = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
53
+ self.time_proj = nn.Sequential(
54
+ nn.Linear(256, input_dim),
55
+ nn.SiLU(),
56
+ nn.Linear(input_dim, input_dim)
57
+ )
58
+
59
+ # adaln
60
+ self.adaln = AdaLayerNorm(input_dim, input_dim)
61
+ # original mlp
62
+ self.mlp = nn.Sequential(
63
+ nn.Linear(input_dim, output_dim),
64
+ nn.GELU(),
65
+ nn.Linear(output_dim, output_dim)
66
+ )
67
+ self.gradient_checkpointing = False
68
+
69
+ def forward(self, x, split_sizes, temporal_id=None):
70
+ condition = self.condition_proj(x)
71
+ # from Stable Diffusion v3
72
+ if self.use_temporal_condition:
73
+ t = temporal_id
74
+ if self.use_rel_timestep:
75
+ t = temporal_idx_abs_to_rel(temporal_id, split_sizes)
76
+ t = t * self.rel_timestep_scale
77
+ t_embed = self.time_embed(t)
78
+ t_embed = self.time_proj(t_embed.to(x.dtype))
79
+ condition = condition + t_embed.unsqueeze(1)
80
+ x = self.adaln(x, condition)
81
+ x = self.mlp(x)
82
+ return x
83
+
84
+
85
+ def build_projector_module(config: PVCInternVLConfig):
86
+ vit_hidden_size = config.vision_config.hidden_size
87
+ llm_hidden_size = config.llm_config.hidden_size
88
+
89
+ if config.mlp_add_ops is not None and 'adaln' in config.mlp_add_ops:
90
+ mlp_input_dim = vit_hidden_size * int(1 / config.downsample_ratio) ** 2
91
+ use_temporal_condition = ('temporal' in config.mlp_add_ops)
92
+ use_rel_timestep = ('rel' in config.mlp_add_ops)
93
+ mlp1 = AdaLNMLP(mlp_input_dim, llm_hidden_size,
94
+ use_temporal_condition=use_temporal_condition,
95
+ use_rel_timestep=use_rel_timestep)
96
+ else:
97
+ mlp1 = nn.Sequential(
98
+ nn.LayerNorm(vit_hidden_size * int(1 / config.downsample_ratio) ** 2),
99
+ nn.Linear(vit_hidden_size * int(1 / config.downsample_ratio) ** 2, llm_hidden_size),
100
+ nn.GELU(),
101
+ nn.Linear(llm_hidden_size, llm_hidden_size)
102
+ )
103
+ return mlp1
104
+
105
+
106
+ def forward_projector(projector, x, **kwargs):
107
+ if isinstance(projector, nn.Sequential):
108
+ return projector(x)
109
+ else:
110
+ return projector(x, **kwargs)
111
+
112
+
113
+ class PVCInternVLModel(PreTrainedModel):
114
+ config_class = PVCInternVLConfig
115
+ main_input_name = 'pixel_values'
116
+ base_model_prefix = 'language_model'
117
+ _supports_flash_attn_2 = True
118
+ _no_split_modules = ['InternVisionModel', 'InternVisionTemporalModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
119
+
120
+ def __init__(self, config: PVCInternVLConfig, vision_model=None, language_model=None, delay_init_new_param=False, use_flash_attn=True):
121
+ super().__init__(config)
122
+
123
+ assert version_cmp(transformers.__version__, '4.37.0', 'ge')
124
+ image_size = config.force_image_size or config.vision_config.image_size
125
+ patch_size = config.vision_config.patch_size
126
+ self.patch_size = patch_size
127
+ self.select_layer = config.select_layer
128
+ self.template = config.template
129
+ self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
130
+ self.num_frame_token = self.num_image_token
131
+ self.downsample_ratio = config.downsample_ratio
132
+ self.ps_version = config.ps_version
133
+ use_flash_attn = use_flash_attn if has_flash_attn else False
134
+ config.vision_config.use_flash_attn = True if use_flash_attn else False
135
+ config.llm_config.attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
136
+
137
+ logger.info(f'num_image_token: {self.num_image_token}')
138
+ logger.info(f'num_frame_token: {self.num_frame_token}')
139
+ logger.info(f'ps_version: {self.ps_version}')
140
+
141
+ if vision_model is not None:
142
+ self.vision_model = vision_model
143
+ else:
144
+ if config.use_temporal:
145
+ self.vision_model = InternVisionTemporalModel(config.vision_config, delay_init_new_param=delay_init_new_param)
146
+ else:
147
+ self.vision_model = InternVisionModel(config.vision_config)
148
+ if language_model is not None:
149
+ self.language_model = language_model
150
+ else:
151
+ if config.llm_config.architectures[0] == 'LlamaForCausalLM':
152
+ self.language_model = LlamaForCausalLM(config.llm_config)
153
+ elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
154
+ self.language_model = InternLM2ForCausalLM(config.llm_config)
155
+ else:
156
+ raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
157
+
158
+ self.mlp1 = build_projector_module(config)
159
+
160
+ self.img_context_token_id = None
161
+ self.conv_template = get_conv_template(self.template)
162
+ self.system_message = self.conv_template.system_message
163
+
164
+ def forward(
165
+ self,
166
+ pixel_values: torch.FloatTensor,
167
+ input_ids: torch.LongTensor = None,
168
+ attention_mask: Optional[torch.Tensor] = None,
169
+ position_ids: Optional[torch.LongTensor] = None,
170
+ image_flags: Optional[torch.LongTensor] = None,
171
+ split_sizes: Optional[torch.LongTensor] = None,
172
+ temporal_id: Optional[torch.LongTensor] = None,
173
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
174
+ labels: Optional[torch.LongTensor] = None,
175
+ use_cache: Optional[bool] = None,
176
+ output_attentions: Optional[bool] = None,
177
+ output_hidden_states: Optional[bool] = None,
178
+ return_dict: Optional[bool] = None,
179
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
180
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
181
+
182
+ image_flags = image_flags.squeeze(-1)
183
+ input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
184
+
185
+ vit_embeds = self.extract_feature(pixel_values, split_sizes=split_sizes, temporal_id=temporal_id)
186
+ vit_embeds = vit_embeds[image_flags == 1]
187
+ vit_batch_size = pixel_values.shape[0]
188
+
189
+ B, N, C = input_embeds.shape
190
+ input_embeds = input_embeds.reshape(B * N, C)
191
+
192
+ if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
193
+ print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
194
+
195
+ input_ids = input_ids.reshape(B * N)
196
+ selected = (input_ids == self.img_context_token_id)
197
+ try:
198
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
199
+ except Exception as e:
200
+ vit_embeds = vit_embeds.reshape(-1, C)
201
+ print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
202
+ f'vit_embeds.shape={vit_embeds.shape}')
203
+ n_token = selected.sum()
204
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
205
+
206
+ input_embeds = input_embeds.reshape(B, N, C)
207
+
208
+ outputs = self.language_model(
209
+ inputs_embeds=input_embeds,
210
+ attention_mask=attention_mask,
211
+ position_ids=position_ids,
212
+ past_key_values=past_key_values,
213
+ use_cache=use_cache,
214
+ output_attentions=output_attentions,
215
+ output_hidden_states=output_hidden_states,
216
+ return_dict=return_dict,
217
+ )
218
+ logits = outputs.logits
219
+
220
+ loss = None
221
+ if labels is not None:
222
+ # Shift so that tokens < n predict n
223
+ shift_logits = logits[..., :-1, :].contiguous()
224
+ shift_labels = labels[..., 1:].contiguous()
225
+ # Flatten the tokens
226
+ loss_fct = CrossEntropyLoss()
227
+ shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
228
+ shift_labels = shift_labels.view(-1)
229
+ # Enable model parallelism
230
+ shift_labels = shift_labels.to(shift_logits.device)
231
+ loss = loss_fct(shift_logits, shift_labels)
232
+
233
+ if not return_dict:
234
+ output = (logits,) + outputs[1:]
235
+ return (loss,) + output if loss is not None else output
236
+
237
+ return CausalLMOutputWithPast(
238
+ loss=loss,
239
+ logits=logits,
240
+ past_key_values=outputs.past_key_values,
241
+ hidden_states=outputs.hidden_states,
242
+ attentions=outputs.attentions,
243
+ )
244
+
245
+ def pixel_shuffle(self, x, scale_factor=0.5):
246
+ n, w, h, c = x.size()
247
+ # N, W, H, C --> N, W, H * scale, C // scale
248
+ x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
249
+ # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
250
+ x = x.permute(0, 2, 1, 3).contiguous()
251
+ # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
252
+ x = x.view(n, int(h * scale_factor), int(w * scale_factor),
253
+ int(c / (scale_factor * scale_factor)))
254
+ if self.ps_version == 'v1':
255
+ warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
256
+ 'which results in a transposed image.')
257
+ else:
258
+ x = x.permute(0, 2, 1, 3).contiguous()
259
+ return x
260
+
261
+ def extract_feature(self, pixel_values, split_sizes=None, temporal_id=None):
262
+ kwargs = {}
263
+ # add split_sizes for temporal module
264
+ if self.config.use_temporal:
265
+ if split_sizes is not None:
266
+ if isinstance(split_sizes, torch.Tensor):
267
+ split_sizes = split_sizes.tolist()
268
+ else:
269
+ split_sizes = [pixel_values.shape[0]]
270
+ assert sum(split_sizes) == pixel_values.shape[0]
271
+ kwargs['split_sizes'] = split_sizes
272
+ kwargs['temporal_id'] = temporal_id
273
+
274
+ if self.select_layer == -1:
275
+ vit_embeds = self.vision_model(
276
+ pixel_values=pixel_values,
277
+ output_hidden_states=False,
278
+ return_dict=True,
279
+ **kwargs
280
+ ).last_hidden_state
281
+ else:
282
+ vit_embeds = self.vision_model(
283
+ pixel_values=pixel_values,
284
+ output_hidden_states=True,
285
+ return_dict=True,
286
+ **kwargs
287
+ ).hidden_states[self.select_layer]
288
+ vit_embeds = vit_embeds[:, 1:, :]
289
+
290
+ h = w = int(vit_embeds.shape[1] ** 0.5)
291
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
292
+ vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
293
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
294
+ vit_embeds = forward_projector(self.mlp1, vit_embeds, split_sizes=split_sizes, temporal_id=temporal_id)
295
+ return vit_embeds
296
+
297
+ def batch_chat(self, tokenizer, pixel_values, questions, generation_config, split_sizes=None, data_flag=None,
298
+ num_patches_list=None, history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
299
+ IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
300
+ if history is not None or return_history:
301
+ print('Now multi-turn chat is not supported in batch_chat.')
302
+ raise NotImplementedError
303
+
304
+ if image_counts is not None:
305
+ num_patches_list = image_counts
306
+ print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
307
+
308
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
309
+ self.img_context_token_id = img_context_token_id
310
+
311
+ if verbose and pixel_values is not None:
312
+ image_bs = pixel_values.shape[0]
313
+ print(f'dynamic ViT batch size: {image_bs}')
314
+
315
+ queries = []
316
+ for idx, num_patches in enumerate(num_patches_list):
317
+ question = questions[idx]
318
+ if pixel_values is not None and '<image>' not in question:
319
+ question = '<image>\n' + question
320
+ template = get_conv_template(self.template)
321
+ template.system_message = self.system_message
322
+ template.append_message(template.roles[0], question)
323
+ template.append_message(template.roles[1], None)
324
+ query = template.get_prompt()
325
+
326
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
327
+ query = query.replace('<image>', image_tokens, 1)
328
+ queries.append(query)
329
+
330
+ tokenizer.padding_side = 'left'
331
+ model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
332
+ input_ids = model_inputs['input_ids'].to(self.device)
333
+ attention_mask = model_inputs['attention_mask'].to(self.device)
334
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
335
+ generation_config['eos_token_id'] = eos_token_id
336
+ generation_output = self.generate(
337
+ pixel_values=pixel_values,
338
+ input_ids=input_ids,
339
+ attention_mask=attention_mask,
340
+ split_sizes=split_sizes,
341
+ **generation_config
342
+ )
343
+ responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
344
+ responses = [response.split(template.sep)[0].strip() for response in responses]
345
+ return responses
346
+
347
+ def chat(self, tokenizer, pixel_values, question, generation_config, num_patches_list=None,
348
+ split_sizes=None, data_flag=None, history=None, return_history=False,
349
+ IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False):
350
+ # data flag: 0: pure text; 1: single image; 2: multi image; 3 video
351
+ flag = data_flag[0].item() if data_flag is not None else 1 # default as single image
352
+
353
+ if history is None and pixel_values is not None and '<image>' not in question:
354
+ question = '<image>\n' + question
355
+
356
+ if num_patches_list is None:
357
+ num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
358
+ assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
359
+ # default as `tile id`: [0, 1, ..., n_tile]
360
+ temporal_id = torch.arange(len(pixel_values), dtype=torch.long, device=pixel_values.device)
361
+ if self.config.tile_repeat_way == 'cycle':
362
+ new_temporal_id = []
363
+ for tid, n_tile in enumerate(num_patches_list):
364
+ new_temporal_id.append(torch.tensor([tid] * n_tile, dtype=torch.long, device=pixel_values.device))
365
+ temporal_id = torch.cat(new_temporal_id)
366
+
367
+ if (flag == 1 or flag == 2) and self.config.image_repeat_time > 1:
368
+ if self.config.tile_repeat_way == 'cycle':
369
+ cur_st = 0
370
+ new_pixel_values, new_temporal_id = [], []
371
+ for img_idx, n_tile in enumerate(num_patches_list):
372
+ image = pixel_values[cur_st:cur_st+n_tile]
373
+ new_pixel_values.append(torch.cat([image for _ in range(self.config.image_repeat_time)], dim=0))
374
+ new_temporal_id.append(torch.arange(img_idx * self.config.image_repeat_time, (img_idx + 1) * self.config.image_repeat_time,
375
+ dtype=torch.long, device=temporal_id.device).repeat_interleave(n_tile, dim=0))
376
+ cur_st += n_tile
377
+ new_pixel_values = torch.cat(new_pixel_values, dim=0)
378
+ new_temporal_id = torch.cat(new_temporal_id, dim=0)
379
+ assert cur_st == len(pixel_values)
380
+ assert len(new_pixel_values) == len(new_temporal_id) == len(pixel_values) * self.config.image_repeat_time
381
+ pixel_values, temporal_id = new_pixel_values, new_temporal_id
382
+ else:
383
+ pixel_values = pixel_values.repeat_interleave(self.config.image_repeat_time, dim=0)
384
+ temporal_id = torch.arange(len(pixel_values), dtype=torch.long, device=pixel_values.device)
385
+ split_sizes = [s * self.config.image_repeat_time for s in split_sizes] if split_sizes is not None else None
386
+ num_patches_list = [n * self.config.image_repeat_time for n in num_patches_list] if num_patches_list is not None else None
387
+ if flag == 3 and self.config.video_repeat_time > 1:
388
+ pixel_values = pixel_values.repeat_interleave(self.config.video_repeat_time, dim=0)
389
+ if self.config.tile_repeat_way == 'cycle':
390
+ new_temporal_id = []
391
+ for img_idx, n_tile in enumerate(num_patches_list):
392
+ new_temporal_id.append(torch.arange(img_idx * self.config.video_repeat_time, (img_idx + 1) * self.config.video_repeat_time,
393
+ dtype=torch.long, device=temporal_id.device).repeat_interleave(n_tile, dim=0))
394
+ temporal_id = torch.cat(new_temporal_id, dim=0)
395
+ else:
396
+ temporal_id = torch.arange(len(pixel_values), dtype=torch.long, device=pixel_values.device)
397
+ split_sizes = [s * self.config.video_repeat_time for s in split_sizes] if split_sizes is not None else None
398
+ num_patches_list = [n * self.config.video_repeat_time for n in num_patches_list] if num_patches_list is not None else None
399
+
400
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
401
+ self.img_context_token_id = img_context_token_id
402
+
403
+ template = get_conv_template(self.template)
404
+ template.system_message = self.system_message
405
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
406
+
407
+ history = [] if history is None else history
408
+ for (old_question, old_answer) in history:
409
+ template.append_message(template.roles[0], old_question)
410
+ template.append_message(template.roles[1], old_answer)
411
+ template.append_message(template.roles[0], question)
412
+ template.append_message(template.roles[1], None)
413
+ query = template.get_prompt()
414
+
415
+ if verbose and pixel_values is not None:
416
+ image_bs = pixel_values.shape[0]
417
+ print(f'dynamic ViT batch size: {image_bs}')
418
+
419
+ for num_patches in num_patches_list:
420
+ if flag == 0:
421
+ num_image_token = 0
422
+ elif (flag == 1 or flag == 2):
423
+ num_image_token = self.num_image_token * num_patches
424
+ else:
425
+ num_image_token = self.num_frame_token * num_patches
426
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * num_image_token + IMG_END_TOKEN
427
+ query = query.replace('<image>', image_tokens, 1)
428
+
429
+ model_inputs = tokenizer(query, return_tensors='pt')
430
+ input_ids = model_inputs['input_ids'].to(self.device)
431
+ attention_mask = model_inputs['attention_mask'].to(self.device)
432
+ generation_config['eos_token_id'] = eos_token_id
433
+ generation_output = self.generate(
434
+ pixel_values=pixel_values,
435
+ input_ids=input_ids,
436
+ attention_mask=attention_mask,
437
+ split_sizes=split_sizes,
438
+ temporal_id=temporal_id,
439
+ **generation_config
440
+ )
441
+ response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
442
+ response = response.split(template.sep.strip())[0].strip()
443
+ history.append((question, response))
444
+ if return_history:
445
+ return response, history
446
+ else:
447
+ query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
448
+ query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
449
+ if verbose:
450
+ print(query_to_print, response)
451
+ return response
452
+
453
+ @torch.no_grad()
454
+ def generate(
455
+ self,
456
+ pixel_values: Optional[torch.FloatTensor] = None,
457
+ input_ids: Optional[torch.FloatTensor] = None,
458
+ attention_mask: Optional[torch.LongTensor] = None,
459
+ visual_features: Optional[torch.FloatTensor] = None,
460
+ generation_config: Optional[GenerationConfig] = None,
461
+ output_hidden_states: Optional[bool] = None,
462
+ return_dict: Optional[bool] = None,
463
+ split_sizes: Optional[torch.LongTensor] = None,
464
+ temporal_id: Optional[torch.LongTensor] = None,
465
+ **generate_kwargs,
466
+ ) -> torch.LongTensor:
467
+
468
+ assert self.img_context_token_id is not None
469
+ if pixel_values is not None:
470
+ if visual_features is not None:
471
+ vit_embeds = visual_features
472
+ else:
473
+ vit_embeds = self.extract_feature(pixel_values, split_sizes=split_sizes, temporal_id=temporal_id)
474
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
475
+ B, N, C = input_embeds.shape
476
+ input_embeds = input_embeds.reshape(B * N, C)
477
+
478
+ input_ids = input_ids.reshape(B * N)
479
+ selected = (input_ids == self.img_context_token_id)
480
+ assert selected.sum() != 0
481
+ input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
482
+
483
+ input_embeds = input_embeds.reshape(B, N, C)
484
+ else:
485
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
486
+
487
+ outputs = self.language_model.generate(
488
+ inputs_embeds=input_embeds,
489
+ attention_mask=attention_mask,
490
+ generation_config=generation_config,
491
+ output_hidden_states=output_hidden_states,
492
+ return_dict=return_dict,
493
+ use_cache=True,
494
+ **generate_kwargs,
495
+ )
496
+
497
+ return outputs
special_tokens_map.json ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>",
5
+ "<|action_start|>",
6
+ "<|action_end|>",
7
+ "<|interpreter|>",
8
+ "<|plugin|>",
9
+ "<img>",
10
+ "</img>",
11
+ "<IMG_CONTEXT>",
12
+ "<quad>",
13
+ "</quad>",
14
+ "<ref>",
15
+ "</ref>",
16
+ "<box>",
17
+ "</box>"
18
+ ],
19
+ "bos_token": {
20
+ "content": "<s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false
25
+ },
26
+ "eos_token": {
27
+ "content": "</s>",
28
+ "lstrip": false,
29
+ "normalized": false,
30
+ "rstrip": false,
31
+ "single_word": false
32
+ },
33
+ "pad_token": {
34
+ "content": "</s>",
35
+ "lstrip": false,
36
+ "normalized": false,
37
+ "rstrip": false,
38
+ "single_word": false
39
+ },
40
+ "unk_token": {
41
+ "content": "<unk>",
42
+ "lstrip": false,
43
+ "normalized": false,
44
+ "rstrip": false,
45
+ "single_word": false
46
+ }
47
+ }
tokenization_internlm2.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+
18
+ """Tokenization classes for InternLM."""
19
+ import os
20
+ from shutil import copyfile
21
+ from typing import Any, Dict, List, Optional, Tuple
22
+
23
+ import sentencepiece as spm
24
+ from transformers.tokenization_utils import PreTrainedTokenizer
25
+ from transformers.utils import logging
26
+
27
+ logger = logging.get_logger(__name__)
28
+
29
+ VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
30
+
31
+ PRETRAINED_VOCAB_FILES_MAP = {}
32
+
33
+
34
+ # Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
35
+ class InternLM2Tokenizer(PreTrainedTokenizer):
36
+ """
37
+ Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
38
+
39
+ Args:
40
+ vocab_file (`str`):
41
+ Path to the vocabulary file.
42
+ """
43
+
44
+ vocab_files_names = VOCAB_FILES_NAMES
45
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
46
+ model_input_names = ["input_ids", "attention_mask"]
47
+ _auto_class = "AutoTokenizer"
48
+
49
+ def __init__(
50
+ self,
51
+ vocab_file,
52
+ unk_token="<unk>",
53
+ bos_token="<s>",
54
+ eos_token="</s>",
55
+ pad_token="</s>",
56
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
57
+ add_bos_token=True,
58
+ add_eos_token=False,
59
+ decode_with_prefix_space=False,
60
+ clean_up_tokenization_spaces=False,
61
+ **kwargs,
62
+ ):
63
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
64
+ self.vocab_file = vocab_file
65
+ self.add_bos_token = add_bos_token
66
+ self.add_eos_token = add_eos_token
67
+ self.decode_with_prefix_space = decode_with_prefix_space
68
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
69
+ self.sp_model.Load(vocab_file)
70
+ self._no_prefix_space_tokens = None
71
+ super().__init__(
72
+ bos_token=bos_token,
73
+ eos_token=eos_token,
74
+ unk_token=unk_token,
75
+ pad_token=pad_token,
76
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
77
+ **kwargs,
78
+ )
79
+
80
+ @property
81
+ def no_prefix_space_tokens(self):
82
+ if self._no_prefix_space_tokens is None:
83
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
84
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
85
+ return self._no_prefix_space_tokens
86
+
87
+ @property
88
+ def vocab_size(self):
89
+ """Returns vocab size"""
90
+ return self.sp_model.get_piece_size()
91
+
92
+ @property
93
+ def bos_token_id(self) -> Optional[int]:
94
+ return self.sp_model.bos_id()
95
+
96
+ @property
97
+ def eos_token_id(self) -> Optional[int]:
98
+ return self.sp_model.eos_id()
99
+
100
+ def get_vocab(self):
101
+ """Returns vocab as a dict"""
102
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
103
+ vocab.update(self.added_tokens_encoder)
104
+ return vocab
105
+
106
+ def _tokenize(self, text):
107
+ """Returns a tokenized string."""
108
+ return self.sp_model.encode(text, out_type=str)
109
+
110
+ def _convert_token_to_id(self, token):
111
+ """Converts a token (str) in an id using the vocab."""
112
+ return self.sp_model.piece_to_id(token)
113
+
114
+ def _convert_id_to_token(self, index):
115
+ """Converts an index (integer) in a token (str) using the vocab."""
116
+ token = self.sp_model.IdToPiece(index)
117
+ return token
118
+
119
+ def _maybe_add_prefix_space(self, tokens, decoded):
120
+ if tokens and tokens[0] not in self.no_prefix_space_tokens:
121
+ return " " + decoded
122
+ else:
123
+ return decoded
124
+
125
+ def convert_tokens_to_string(self, tokens):
126
+ """Converts a sequence of tokens (string) in a single string."""
127
+ current_sub_tokens = []
128
+ out_string = ""
129
+ prev_is_special = False
130
+ for token in tokens:
131
+ # make sure that special tokens are not decoded using sentencepiece model
132
+ if token in self.all_special_tokens:
133
+ if not prev_is_special:
134
+ out_string += " "
135
+ out_string += self.sp_model.decode(current_sub_tokens) + token
136
+ prev_is_special = True
137
+ current_sub_tokens = []
138
+ else:
139
+ current_sub_tokens.append(token)
140
+ prev_is_special = False
141
+ out_string += self.sp_model.decode(current_sub_tokens)
142
+ out_string = self.clean_up_tokenization(out_string)
143
+ out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
144
+ return out_string[1:]
145
+
146
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
147
+ """
148
+ Save the vocabulary and special tokens file to a directory.
149
+
150
+ Args:
151
+ save_directory (`str`):
152
+ The directory in which to save the vocabulary.
153
+
154
+ Returns:
155
+ `Tuple(str)`: Paths to the files saved.
156
+ """
157
+ if not os.path.isdir(save_directory):
158
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
159
+ return
160
+ out_vocab_file = os.path.join(
161
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
162
+ )
163
+
164
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
165
+ copyfile(self.vocab_file, out_vocab_file)
166
+ elif not os.path.isfile(self.vocab_file):
167
+ with open(out_vocab_file, "wb") as fi:
168
+ content_spiece_model = self.sp_model.serialized_model_proto()
169
+ fi.write(content_spiece_model)
170
+
171
+ return (out_vocab_file,)
172
+
173
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
174
+ if self.add_bos_token:
175
+ bos_token_ids = [self.bos_token_id]
176
+ else:
177
+ bos_token_ids = []
178
+
179
+ output = bos_token_ids + token_ids_0
180
+
181
+ if token_ids_1 is not None:
182
+ output = output + token_ids_1
183
+
184
+ if self.add_eos_token:
185
+ output = output + [self.eos_token_id]
186
+
187
+ return output
188
+
189
+ def get_special_tokens_mask(
190
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
191
+ ) -> List[int]:
192
+ """
193
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
194
+ special tokens using the tokenizer `prepare_for_model` method.
195
+
196
+ Args:
197
+ token_ids_0 (`List[int]`):
198
+ List of IDs.
199
+ token_ids_1 (`List[int]`, *optional*):
200
+ Optional second list of IDs for sequence pairs.
201
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
202
+ Whether or not the token list is already formatted with special tokens for the model.
203
+
204
+ Returns:
205
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
206
+ """
207
+ if already_has_special_tokens:
208
+ return super().get_special_tokens_mask(
209
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
210
+ )
211
+
212
+ if token_ids_1 is None:
213
+ return [1] + ([0] * len(token_ids_0)) + [1]
214
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
215
+
216
+ def create_token_type_ids_from_sequences(
217
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
218
+ ) -> List[int]:
219
+ """
220
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
221
+ use of token type ids, therefore a list of zeros is returned.
222
+
223
+ Args:
224
+ token_ids_0 (`List[int]`):
225
+ List of IDs.
226
+ token_ids_1 (`List[int]`, *optional*):
227
+ Optional second list of IDs for sequence pairs.
228
+
229
+ Returns:
230
+ `List[int]`: List of zeros.
231
+ """
232
+ eos = [self.eos_token_id]
233
+
234
+ if token_ids_1 is None:
235
+ return len(token_ids_0 + eos) * [0]
236
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
tokenization_internlm2_fast.py ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+
17
+ """Tokenization Fast class for InternLM."""
18
+ import os
19
+ from shutil import copyfile
20
+ from typing import Any, Dict, Optional, Tuple
21
+
22
+ from tokenizers import Tokenizer, decoders, normalizers, processors
23
+ from tokenizers.models import BPE
24
+ from transformers.convert_slow_tokenizer import (SLOW_TO_FAST_CONVERTERS,
25
+ SentencePieceExtractor,
26
+ SpmConverter)
27
+ from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
28
+ from transformers.utils import logging
29
+
30
+ from .tokenization_internlm2 import InternLM2Tokenizer
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
35
+
36
+
37
+ # Modified from transformers.convert_slow_tokenizer.LlamaConverter
38
+ class InternLM2Converter(SpmConverter):
39
+ handle_byte_fallback = True
40
+
41
+ def vocab(self, proto):
42
+ vocab = [
43
+ ('<unk>', 0.0),
44
+ ('<s>', 0.0),
45
+ ('</s>', 0.0),
46
+ ]
47
+ vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
48
+ return vocab
49
+
50
+ def unk_id(self, proto):
51
+ unk_id = 0
52
+ return unk_id
53
+
54
+ def decoder(self, replacement, add_prefix_space):
55
+ return decoders.Sequence(
56
+ [
57
+ decoders.Replace('▁', ' '),
58
+ decoders.ByteFallback(),
59
+ decoders.Fuse(),
60
+ decoders.Strip(content=' ', left=1),
61
+ ]
62
+ )
63
+
64
+ def tokenizer(self, proto):
65
+ model_type = proto.trainer_spec.model_type
66
+ vocab_scores = self.vocab(proto)
67
+ # special tokens
68
+ added_tokens = self.original_tokenizer.added_tokens_decoder
69
+ for i in range(len(vocab_scores)):
70
+ piece, score = vocab_scores[i]
71
+ if i in added_tokens:
72
+ vocab_scores[i] = (added_tokens[i].content, score)
73
+ if model_type == 1:
74
+ raise RuntimeError('InternLM2 is supposed to be a BPE model!')
75
+
76
+ elif model_type == 2:
77
+ _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
78
+ bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
79
+ tokenizer = Tokenizer(
80
+ BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
81
+ )
82
+ tokenizer.add_special_tokens(
83
+ [ added_token for index, added_token in added_tokens.items()]
84
+ )
85
+ else:
86
+ raise Exception(
87
+ "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
88
+ )
89
+
90
+ return tokenizer
91
+
92
+ def normalizer(self, proto):
93
+ normalizers_list = []
94
+ if proto.normalizer_spec.add_dummy_prefix:
95
+ normalizers_list.append(normalizers.Prepend(prepend='▁'))
96
+ normalizers_list.append(normalizers.Replace(pattern=' ', content='▁'))
97
+ return normalizers.Sequence(normalizers_list)
98
+
99
+ def pre_tokenizer(self, replacement, add_prefix_space):
100
+ return None
101
+
102
+
103
+ SLOW_TO_FAST_CONVERTERS['InternLM2Tokenizer'] = InternLM2Converter
104
+
105
+
106
+ # Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
107
+ class InternLM2TokenizerFast(PreTrainedTokenizerFast):
108
+ vocab_files_names = VOCAB_FILES_NAMES
109
+ slow_tokenizer_class = InternLM2Tokenizer
110
+ padding_side = 'left'
111
+ model_input_names = ['input_ids', 'attention_mask']
112
+ _auto_class = 'AutoTokenizer'
113
+
114
+ def __init__(
115
+ self,
116
+ vocab_file,
117
+ unk_token='<unk>',
118
+ bos_token='<s>',
119
+ eos_token='</s>',
120
+ pad_token='</s>',
121
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
122
+ add_bos_token=True,
123
+ add_eos_token=False,
124
+ decode_with_prefix_space=False,
125
+ clean_up_tokenization_spaces=False,
126
+ **kwargs,
127
+ ):
128
+ super().__init__(
129
+ vocab_file=vocab_file,
130
+ unk_token=unk_token,
131
+ bos_token=bos_token,
132
+ eos_token=eos_token,
133
+ pad_token=pad_token,
134
+ sp_model_kwargs=sp_model_kwargs,
135
+ add_bos_token=add_bos_token,
136
+ add_eos_token=add_eos_token,
137
+ decode_with_prefix_space=decode_with_prefix_space,
138
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
139
+ **kwargs,
140
+ )
141
+ self._add_bos_token = add_bos_token
142
+ self._add_eos_token = add_eos_token
143
+ self.update_post_processor()
144
+ self.vocab_file = vocab_file
145
+
146
+ @property
147
+ def can_save_slow_tokenizer(self) -> bool:
148
+ return os.path.isfile(self.vocab_file) if self.vocab_file else False
149
+
150
+ def update_post_processor(self):
151
+ """
152
+ Updates the underlying post processor with the current `bos_token` and `eos_token`.
153
+ """
154
+ bos = self.bos_token
155
+ bos_token_id = self.bos_token_id
156
+ if bos is None and self.add_bos_token:
157
+ raise ValueError('add_bos_token = True but bos_token = None')
158
+
159
+ eos = self.eos_token
160
+ eos_token_id = self.eos_token_id
161
+ if eos is None and self.add_eos_token:
162
+ raise ValueError('add_eos_token = True but eos_token = None')
163
+
164
+ single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
165
+ pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
166
+
167
+ special_tokens = []
168
+ if self.add_bos_token:
169
+ special_tokens.append((bos, bos_token_id))
170
+ if self.add_eos_token:
171
+ special_tokens.append((eos, eos_token_id))
172
+ self._tokenizer.post_processor = processors.TemplateProcessing(
173
+ single=single, pair=pair, special_tokens=special_tokens
174
+ )
175
+
176
+ @property
177
+ def add_eos_token(self):
178
+ return self._add_eos_token
179
+
180
+ @property
181
+ def add_bos_token(self):
182
+ return self._add_bos_token
183
+
184
+ @add_eos_token.setter
185
+ def add_eos_token(self, value):
186
+ self._add_eos_token = value
187
+ self.update_post_processor()
188
+
189
+ @add_bos_token.setter
190
+ def add_bos_token(self, value):
191
+ self._add_bos_token = value
192
+ self.update_post_processor()
193
+
194
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
195
+ if not self.can_save_slow_tokenizer:
196
+ raise ValueError(
197
+ 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
198
+ 'tokenizer.'
199
+ )
200
+
201
+ if not os.path.isdir(save_directory):
202
+ logger.error(f'Vocabulary path ({save_directory}) should be a directory')
203
+ return
204
+ out_vocab_file = os.path.join(
205
+ save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
206
+ )
207
+
208
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
209
+ copyfile(self.vocab_file, out_vocab_file)
210
+
211
+ return (out_vocab_file,)
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
3
+ size 1477754
tokenizer_config.json ADDED
@@ -0,0 +1,179 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<unk>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<s>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "92538": {
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+ "content": "<|plugin|>",
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+ "special": true
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+ },
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+ "92539": {
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+ "content": "<|interpreter|>",
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+ "lstrip": false,
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+ "rstrip": false,
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+ },
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+ "special": true
74
+ },
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+ "92544": {
76
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77
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81
+ "special": true
82
+ },
83
+ "92545": {
84
+ "content": "</img>",
85
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+ "normalized": false,
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+ "rstrip": false,
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+ "special": true
90
+ },
91
+ "92546": {
92
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93
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96
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+ "special": true
98
+ },
99
+ "92547": {
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101
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106
+ },
107
+ "92548": {
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109
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113
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114
+ },
115
+ "92549": {
116
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117
+ "lstrip": false,
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+ "normalized": false,
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122
+ },
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+ "92550": {
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+ },
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+ "92551": {
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146
+ }
147
+ },
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+ "additional_special_tokens": [
149
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150
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151
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+ "<img>",
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+ "</img>",
157
+ "<IMG_CONTEXT>",
158
+ "<quad>",
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+ "</quad>",
160
+ "<ref>",
161
+ "</ref>",
162
+ "<box>",
163
+ "</box>"
164
+ ],
165
+ "auto_map": {
166
+ "AutoTokenizer": [
167
+ "tokenization_internlm2.InternLM2Tokenizer",
168
+ null
169
+ ]
170
+ },
171
+ "bos_token": "<s>",
172
+ "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
173
+ "clean_up_tokenization_spaces": false,
174
+ "eos_token": "</s>",
175
+ "model_max_length": 8192,
176
+ "pad_token": "</s>",
177
+ "tokenizer_class": "InternLM2Tokenizer",
178
+ "unk_token": "<unk>"
179
+ }