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  1. .gitattributes +1 -0
  2. .ipynb_checkpoints/config-checkpoint.json +211 -0
  3. added_tokens.json +33 -0
  4. config.json +211 -0
  5. configuration_intern_vit.py +120 -0
  6. configuration_internlm2.py +150 -0
  7. configuration_internvl_chat.py +112 -0
  8. configuration_skywork_chat.py +92 -0
  9. configuration_skywork_lm2.py +139 -0
  10. configuration_skywork_vit.py +101 -0
  11. conversation.py +416 -0
  12. generation_config.json +8 -0
  13. inputs_stats.pth +3 -0
  14. merges.txt +0 -0
  15. modeling_intern_vit.py +430 -0
  16. modeling_internlm2.py +1415 -0
  17. modeling_internvl_chat.py +387 -0
  18. modeling_skywork_chat.py +354 -0
  19. modeling_skywork_lm2.py +1403 -0
  20. modeling_skywork_vit.py +424 -0
  21. outputs_stats.pth +3 -0
  22. pytorch_model-00001-of-00016.bin +3 -0
  23. pytorch_model-00002-of-00016.bin +3 -0
  24. pytorch_model-00003-of-00016.bin +3 -0
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  26. pytorch_model-00005-of-00016.bin +3 -0
  27. pytorch_model-00006-of-00016.bin +3 -0
  28. pytorch_model-00007-of-00016.bin +3 -0
  29. pytorch_model-00008-of-00016.bin +3 -0
  30. pytorch_model-00009-of-00016.bin +3 -0
  31. pytorch_model-00010-of-00016.bin +3 -0
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  33. pytorch_model-00012-of-00016.bin +3 -0
  34. pytorch_model-00013-of-00016.bin +3 -0
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  36. pytorch_model-00015-of-00016.bin +3 -0
  37. pytorch_model-00016-of-00016.bin +3 -0
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  39. special_tokens_map.json +40 -0
  40. tokenization_internlm2.py +235 -0
  41. tokenization_internlm2_fast.py +211 -0
  42. tokenizer.json +3 -0
  43. tokenizer_config.json +290 -0
  44. vocab.json +0 -0
  45. zero_to_fp32.py +604 -0
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+ "return_dict_in_generate": false,
194
+ "sep_token_id": null,
195
+ "suppress_tokens": null,
196
+ "task_specific_params": null,
197
+ "temperature": 1.0,
198
+ "tf_legacy_loss": false,
199
+ "tie_encoder_decoder": false,
200
+ "tie_word_embeddings": true,
201
+ "tokenizer_class": null,
202
+ "top_k": 50,
203
+ "top_p": 1.0,
204
+ "torch_dtype": "bfloat16",
205
+ "torchscript": false,
206
+ "transformers_version": "4.50.0",
207
+ "typical_p": 1.0,
208
+ "use_bfloat16": true,
209
+ "use_flash_attn": true
210
+ }
211
+ }
configuration_intern_vit.py ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
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
+ **kwargs,
85
+ ):
86
+ super().__init__(**kwargs)
87
+
88
+ self.hidden_size = hidden_size
89
+ self.intermediate_size = intermediate_size
90
+ self.dropout = dropout
91
+ self.drop_path_rate = drop_path_rate
92
+ self.num_hidden_layers = num_hidden_layers
93
+ self.num_attention_heads = num_attention_heads
94
+ self.num_channels = num_channels
95
+ self.patch_size = patch_size
96
+ self.image_size = image_size
97
+ self.initializer_range = initializer_range
98
+ self.initializer_factor = initializer_factor
99
+ self.attention_dropout = attention_dropout
100
+ self.layer_norm_eps = layer_norm_eps
101
+ self.hidden_act = hidden_act
102
+ self.norm_type = norm_type
103
+ self.qkv_bias = qkv_bias
104
+ self.qk_normalization = qk_normalization
105
+ self.use_flash_attn = use_flash_attn
106
+
107
+ @classmethod
108
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
109
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
110
+
111
+ if 'vision_config' in config_dict:
112
+ config_dict = config_dict['vision_config']
113
+
114
+ if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
115
+ logger.warning(
116
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
117
+ f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
118
+ )
119
+
120
+ return cls.from_dict(config_dict, **kwargs)
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_internvl_chat.py ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ from transformers import Qwen2Config, Qwen2ForCausalLM
16
+
17
+ logger = logging.get_logger(__name__)
18
+
19
+
20
+ class InternVLChatConfig(PretrainedConfig):
21
+ model_type = 'internvl_chat'
22
+ is_composition = True
23
+
24
+ def __init__(
25
+ self,
26
+ vision_config=None,
27
+ llm_config=None,
28
+ use_backbone_lora=0,
29
+ use_llm_lora=0,
30
+ select_layer=-1,
31
+ force_image_size=None,
32
+ downsample_ratio=0.5,
33
+ template=None,
34
+ dynamic_image_size=False,
35
+ use_thumbnail=False,
36
+ ps_version='v1',
37
+ min_dynamic_patch=1,
38
+ max_dynamic_patch=6,
39
+ **kwargs):
40
+ super().__init__(**kwargs)
41
+ if vision_config is None:
42
+ vision_config = {'architectures': ['InternVisionModel']}
43
+ logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
44
+
45
+ if llm_config is None:
46
+ llm_config = {'architectures': ['Qwen2ForCausalLM']}
47
+ logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
48
+
49
+ self.vision_config = InternVisionConfig(**vision_config)
50
+ if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
51
+ self.llm_config = LlamaConfig(**llm_config)
52
+ elif llm_config.get('architectures')[0] == 'Qwen2ForCausalLM':
53
+ self.llm_config = Qwen2Config(**llm_config)
54
+ else:
55
+ raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
56
+
57
+ # if vision_config is None:
58
+ # vision_config = {}
59
+ # logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
60
+
61
+ # if llm_config is None:
62
+ # llm_config = {}
63
+ # logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
64
+
65
+ # self.vision_config = InternVisionConfig(**vision_config)
66
+ # if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
67
+ # self.llm_config = LlamaConfig(**llm_config)
68
+ # elif llm_config.get('architectures')[0] == 'InternLM2ForCausalLM':
69
+ # self.llm_config = InternLM2Config(**llm_config)
70
+ # else:
71
+ # raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
72
+ self.use_backbone_lora = use_backbone_lora
73
+ self.use_llm_lora = use_llm_lora
74
+ self.select_layer = select_layer
75
+ self.force_image_size = force_image_size
76
+ self.downsample_ratio = downsample_ratio
77
+ self.template = template
78
+ self.dynamic_image_size = dynamic_image_size
79
+ self.use_thumbnail = use_thumbnail
80
+ self.ps_version = ps_version # pixel shuffle version
81
+ self.min_dynamic_patch = min_dynamic_patch
82
+ self.max_dynamic_patch = max_dynamic_patch
83
+
84
+ logger.info(f'vision_select_layer: {self.select_layer}')
85
+ logger.info(f'ps_version: {self.ps_version}')
86
+ logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
87
+ logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
88
+
89
+ def to_dict(self):
90
+ """
91
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
92
+
93
+ Returns:
94
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
95
+ """
96
+ output = copy.deepcopy(self.__dict__)
97
+ output['vision_config'] = self.vision_config.to_dict()
98
+ output['llm_config'] = self.llm_config.to_dict()
99
+ output['model_type'] = self.__class__.model_type
100
+ output['use_backbone_lora'] = self.use_backbone_lora
101
+ output['use_llm_lora'] = self.use_llm_lora
102
+ output['select_layer'] = self.select_layer
103
+ output['force_image_size'] = self.force_image_size
104
+ output['downsample_ratio'] = self.downsample_ratio
105
+ output['template'] = self.template
106
+ output['dynamic_image_size'] = self.dynamic_image_size
107
+ output['use_thumbnail'] = self.use_thumbnail
108
+ output['ps_version'] = self.ps_version
109
+ output['min_dynamic_patch'] = self.min_dynamic_patch
110
+ output['max_dynamic_patch'] = self.max_dynamic_patch
111
+
112
+ return output
configuration_skywork_chat.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+
3
+ from transformers import AutoConfig, LlamaConfig
4
+ from transformers.configuration_utils import PretrainedConfig
5
+ from transformers.utils import logging
6
+
7
+ from .configuration_skywork_vit import SkyworkVisionConfig
8
+ from .configuration_skywork_lm2 import SkyworkLM2Config
9
+ from transformers import Qwen2Config, Qwen2ForCausalLM
10
+
11
+ logger = logging.get_logger(__name__)
12
+
13
+
14
+ class SkyworkChatConfig(PretrainedConfig):
15
+ model_type = 'skywork_chat'
16
+ is_composition = True
17
+
18
+ def __init__(
19
+ self,
20
+ vision_config=None,
21
+ llm_config=None,
22
+ use_backbone_lora=0,
23
+ use_llm_lora=0,
24
+ select_layer=-1,
25
+ force_image_size=None,
26
+ downsample_ratio=0.5,
27
+ template=None,
28
+ dynamic_image_size=False,
29
+ use_thumbnail=False,
30
+ ps_version='v1',
31
+ min_dynamic_patch=1,
32
+ max_dynamic_patch=6,
33
+ **kwargs):
34
+ super().__init__(**kwargs)
35
+ if vision_config is None:
36
+ vision_config = {'architectures': ['SkyworkVisionModel']}
37
+ logger.info('vision_config is None. Initializing the SkyworkVisionConfig with default values.')
38
+
39
+ if llm_config is None:
40
+ llm_config = {'architectures': ['Qwen2ForCausalLM']}
41
+ logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
42
+
43
+ self.vision_config = SkyworkVisionConfig(**vision_config)
44
+ if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
45
+ self.llm_config = LlamaConfig(**llm_config)
46
+ elif llm_config.get('architectures')[0] == 'Qwen2ForCausalLM':
47
+ self.llm_config = Qwen2Config(**llm_config)
48
+ else:
49
+ raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
50
+
51
+
52
+ self.use_backbone_lora = use_backbone_lora
53
+ self.use_llm_lora = use_llm_lora
54
+ self.select_layer = select_layer
55
+ self.force_image_size = force_image_size
56
+ self.downsample_ratio = downsample_ratio
57
+ self.template = template
58
+ self.dynamic_image_size = dynamic_image_size
59
+ self.use_thumbnail = use_thumbnail
60
+ self.ps_version = ps_version # pixel shuffle version
61
+ self.min_dynamic_patch = min_dynamic_patch
62
+ self.max_dynamic_patch = max_dynamic_patch
63
+
64
+ logger.info(f'vision_select_layer: {self.select_layer}')
65
+ logger.info(f'ps_version: {self.ps_version}')
66
+ logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
67
+ logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
68
+
69
+ def to_dict(self):
70
+ """
71
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
72
+
73
+ Returns:
74
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
75
+ """
76
+ output = copy.deepcopy(self.__dict__)
77
+ output['vision_config'] = self.vision_config.to_dict()
78
+ output['llm_config'] = self.llm_config.to_dict()
79
+ output['model_type'] = self.__class__.model_type
80
+ output['use_backbone_lora'] = self.use_backbone_lora
81
+ output['use_llm_lora'] = self.use_llm_lora
82
+ output['select_layer'] = self.select_layer
83
+ output['force_image_size'] = self.force_image_size
84
+ output['downsample_ratio'] = self.downsample_ratio
85
+ output['template'] = self.template
86
+ output['dynamic_image_size'] = self.dynamic_image_size
87
+ output['use_thumbnail'] = self.use_thumbnail
88
+ output['ps_version'] = self.ps_version
89
+ output['min_dynamic_patch'] = self.min_dynamic_patch
90
+ output['max_dynamic_patch'] = self.max_dynamic_patch
91
+
92
+ return output
configuration_skywork_lm2.py ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The Skywork 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
+ """ SkyworkLM2 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
+
24
+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
25
+ class SkyworkLM2Config(PretrainedConfig):
26
+ r"""
27
+ Args:
28
+ vocab_size (`int`, *optional*, defaults to 32000):
29
+ Vocabulary size of the SkyworkLM2 model. Defines the number of different tokens that can be represented by the
30
+ `inputs_ids` passed when calling [`SkyworkLM2Model`]
31
+ hidden_size (`int`, *optional*, defaults to 4096):
32
+ Dimension of the hidden representations.
33
+ intermediate_size (`int`, *optional*, defaults to 11008):
34
+ Dimension of the MLP representations.
35
+ num_hidden_layers (`int`, *optional*, defaults to 32):
36
+ Number of hidden layers in the Transformer encoder.
37
+ num_attention_heads (`int`, *optional*, defaults to 32):
38
+ Number of attention heads for each attention layer in the Transformer encoder.
39
+ num_key_value_heads (`int`, *optional*):
40
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
41
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
42
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
43
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
44
+ by meanpooling all the original heads within that group. For more details checkout [this
45
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
46
+ `num_attention_heads`.
47
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
48
+ The non-linear activation function (function or string) in the decoder.
49
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
50
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
51
+ just in case (e.g., 512 or 1024 or 2048).
52
+ initializer_range (`float`, *optional*, defaults to 0.02):
53
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
54
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
55
+ The epsilon used by the rms normalization layers.
56
+ use_cache (`bool`, *optional*, defaults to `True`):
57
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
58
+ relevant if `config.is_decoder=True`.
59
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
60
+ Whether to tie weight embeddings
61
+ Example:
62
+
63
+ """
64
+ _auto_class = 'AutoConfig'
65
+
66
+ def __init__(
67
+ self,
68
+ vocab_size=103168,
69
+ hidden_size=4096,
70
+ intermediate_size=11008,
71
+ num_hidden_layers=32,
72
+ num_attention_heads=32,
73
+ num_key_value_heads=None,
74
+ hidden_act='silu',
75
+ max_position_embeddings=2048,
76
+ initializer_range=0.02,
77
+ rms_norm_eps=1e-6,
78
+ use_cache=True,
79
+ pad_token_id=0,
80
+ bos_token_id=1,
81
+ eos_token_id=2,
82
+ tie_word_embeddings=False,
83
+ bias=True,
84
+ rope_theta=10000,
85
+ rope_scaling=None,
86
+ attn_implementation='eager',
87
+ **kwargs,
88
+ ):
89
+ self.vocab_size = vocab_size
90
+ self.max_position_embeddings = max_position_embeddings
91
+ self.hidden_size = hidden_size
92
+ self.intermediate_size = intermediate_size
93
+ self.num_hidden_layers = num_hidden_layers
94
+ self.num_attention_heads = num_attention_heads
95
+ self.bias = bias
96
+
97
+ if num_key_value_heads is None:
98
+ num_key_value_heads = num_attention_heads
99
+ self.num_key_value_heads = num_key_value_heads
100
+
101
+ self.hidden_act = hidden_act
102
+ self.initializer_range = initializer_range
103
+ self.rms_norm_eps = rms_norm_eps
104
+ self.use_cache = use_cache
105
+ self.rope_theta = rope_theta
106
+ self.rope_scaling = rope_scaling
107
+ self._rope_scaling_validation()
108
+
109
+ self.attn_implementation = attn_implementation
110
+ if self.attn_implementation is None:
111
+ self.attn_implementation = 'eager'
112
+ super().__init__(
113
+ pad_token_id=pad_token_id,
114
+ bos_token_id=bos_token_id,
115
+ eos_token_id=eos_token_id,
116
+ tie_word_embeddings=tie_word_embeddings,
117
+ **kwargs,
118
+ )
119
+
120
+ def _rope_scaling_validation(self):
121
+ """
122
+ Validate the `rope_scaling` configuration.
123
+ """
124
+ if self.rope_scaling is None:
125
+ return
126
+
127
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
128
+ raise ValueError(
129
+ '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
130
+ f'got {self.rope_scaling}'
131
+ )
132
+ rope_scaling_type = self.rope_scaling.get('type', None)
133
+ rope_scaling_factor = self.rope_scaling.get('factor', None)
134
+ if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
135
+ raise ValueError(
136
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
137
+ )
138
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
139
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
configuration_skywork_vit.py ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from typing import Union
3
+
4
+ from transformers.configuration_utils import PretrainedConfig
5
+ from transformers.utils import logging
6
+
7
+ logger = logging.get_logger(__name__)
8
+
9
+
10
+ class SkyworkVisionConfig(PretrainedConfig):
11
+ r"""
12
+ Args:
13
+ num_channels (`int`, *optional*, defaults to 3):
14
+ Number of color channels in the input images (e.g., 3 for RGB).
15
+ patch_size (`int`, *optional*, defaults to 14):
16
+ The size (resolution) of each patch.
17
+ image_size (`int`, *optional*, defaults to 224):
18
+ The size (resolution) of each image.
19
+ qkv_bias (`bool`, *optional*, defaults to `False`):
20
+ Whether to add a bias to the queries and values in the self-attention layers.
21
+ hidden_size (`int`, *optional*, defaults to 3200):
22
+ Dimensionality of the encoder layers and the pooler layer.
23
+ num_attention_heads (`int`, *optional*, defaults to 25):
24
+ Number of attention heads for each attention layer in the Transformer encoder.
25
+ intermediate_size (`int`, *optional*, defaults to 12800):
26
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
27
+ qk_normalization (`bool`, *optional*, defaults to `True`):
28
+ Whether to normalize the queries and keys in the self-attention layers.
29
+ num_hidden_layers (`int`, *optional*, defaults to 48):
30
+ Number of hidden layers in the Transformer encoder.
31
+ use_flash_attn (`bool`, *optional*, defaults to `True`):
32
+ Whether to use flash attention mechanism.
33
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
34
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
35
+ `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
36
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
37
+ The epsilon used by the layer normalization layers.
38
+ dropout (`float`, *optional*, defaults to 0.0):
39
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
40
+ drop_path_rate (`float`, *optional*, defaults to 0.0):
41
+ Dropout rate for stochastic depth.
42
+ attention_dropout (`float`, *optional*, defaults to 0.0):
43
+ The dropout ratio for the attention probabilities.
44
+ initializer_range (`float`, *optional*, defaults to 0.02):
45
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
46
+ initializer_factor (`float`, *optional*, defaults to 0.1):
47
+ A factor for layer scale.
48
+ """
49
+
50
+
51
+ def __init__(
52
+ self,
53
+ num_channels=3,
54
+ patch_size=14,
55
+ image_size=224,
56
+ qkv_bias=False,
57
+ hidden_size=3200,
58
+ num_attention_heads=25,
59
+ intermediate_size=12800,
60
+ qk_normalization=True,
61
+ num_hidden_layers=48,
62
+ use_flash_attn=True,
63
+ hidden_act='gelu',
64
+ norm_type='rms_norm',
65
+ layer_norm_eps=1e-6,
66
+ dropout=0.0,
67
+ drop_path_rate=0.0,
68
+ attention_dropout=0.0,
69
+ initializer_range=0.02,
70
+ initializer_factor=0.1,
71
+ **kwargs,
72
+ ):
73
+ super().__init__(**kwargs)
74
+
75
+ self.hidden_size = hidden_size
76
+ self.intermediate_size = intermediate_size
77
+ self.dropout = dropout
78
+ self.drop_path_rate = drop_path_rate
79
+ self.num_hidden_layers = num_hidden_layers
80
+ self.num_attention_heads = num_attention_heads
81
+ self.num_channels = num_channels
82
+ self.patch_size = patch_size
83
+ self.image_size = image_size
84
+ self.initializer_range = initializer_range
85
+ self.initializer_factor = initializer_factor
86
+ self.attention_dropout = attention_dropout
87
+ self.layer_norm_eps = layer_norm_eps
88
+ self.hidden_act = hidden_act
89
+ self.norm_type = norm_type
90
+ self.qkv_bias = qkv_bias
91
+ self.qk_normalization = qk_normalization
92
+ self.use_flash_attn = use_flash_attn
93
+
94
+ @classmethod
95
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
96
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
97
+
98
+ if 'vision_config' in config_dict:
99
+ config_dict = config_dict['vision_config']
100
+
101
+ return cls.from_dict(config_dict, **kwargs)
conversation.py ADDED
@@ -0,0 +1,416 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+
8
+ import dataclasses
9
+ from enum import IntEnum, auto
10
+ from typing import Any, Dict, List, Tuple, Union
11
+
12
+
13
+ class SeparatorStyle(IntEnum):
14
+ """Separator styles."""
15
+
16
+ ADD_COLON_SINGLE = auto()
17
+ ADD_COLON_TWO = auto()
18
+ ADD_COLON_SPACE_SINGLE = auto()
19
+ NO_COLON_SINGLE = auto()
20
+ NO_COLON_TWO = auto()
21
+ ADD_NEW_LINE_SINGLE = auto()
22
+ LLAMA2 = auto()
23
+ CHATGLM = auto()
24
+ CHATML = auto()
25
+ CHATINTERN = auto()
26
+ DOLLY = auto()
27
+ RWKV = auto()
28
+ PHOENIX = auto()
29
+ ROBIN = auto()
30
+ FALCON_CHAT = auto()
31
+ CHATGLM3 = auto()
32
+ INTERNVL_ZH = auto()
33
+ MPT = auto()
34
+
35
+
36
+ @dataclasses.dataclass
37
+ class Conversation:
38
+ """A class that manages prompt templates and keeps all conversation history."""
39
+
40
+ # The name of this template
41
+ name: str
42
+ # The template of the system prompt
43
+ system_template: str = '{system_message}'
44
+ # The system message
45
+ system_message: str = ''
46
+ # The names of two roles
47
+ roles: Tuple[str] = ('USER', 'ASSISTANT')
48
+ # All messages. Each item is (role, message).
49
+ messages: List[List[str]] = ()
50
+ # The number of few shot examples
51
+ offset: int = 0
52
+ # The separator style and configurations
53
+ sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
54
+ sep: str = '\n'
55
+ sep2: str = None
56
+ # Stop criteria (the default one is EOS token)
57
+ stop_str: Union[str, List[str]] = None
58
+ # Stops generation if meeting any token in this list
59
+ stop_token_ids: List[int] = None
60
+
61
+ def get_prompt(self) -> str:
62
+ """Get the prompt for generation."""
63
+ system_prompt = self.system_template.format(system_message=self.system_message)
64
+ # ret = system_prompt + self.sep
65
+ # for role, message in self.messages:
66
+ # if type(message) is tuple:
67
+ # message, _, _ = message
68
+ # ret += role + message
69
+ # else:
70
+ # ret += role
71
+ # print(ret)
72
+
73
+ if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
74
+ ret = system_prompt + self.sep
75
+ for role, message in self.messages:
76
+ if message:
77
+ ret += role + ': ' + message + self.sep
78
+ else:
79
+ ret += role + ':'
80
+ return ret
81
+ elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
82
+ seps = [self.sep, self.sep2]
83
+ ret = system_prompt + seps[0]
84
+ for i, (role, message) in enumerate(self.messages):
85
+ if message:
86
+ ret += role + ': ' + message + seps[i % 2]
87
+ else:
88
+ ret += role + ':'
89
+ return ret
90
+ elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
91
+ ret = system_prompt + self.sep
92
+ for role, message in self.messages:
93
+ if message:
94
+ ret += role + ': ' + message + self.sep
95
+ else:
96
+ ret += role + ': ' # must be end with a space
97
+ return ret
98
+ elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
99
+ ret = '' if system_prompt == '' else system_prompt + self.sep
100
+ for role, message in self.messages:
101
+ if message:
102
+ ret += role + '\n' + message + self.sep
103
+ else:
104
+ ret += role + '\n'
105
+ return ret
106
+ elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
107
+ ret = system_prompt
108
+ for role, message in self.messages:
109
+ if message:
110
+ ret += role + message + self.sep
111
+ else:
112
+ ret += role
113
+ return ret
114
+ elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
115
+ seps = [self.sep, self.sep2]
116
+ ret = system_prompt
117
+ for i, (role, message) in enumerate(self.messages):
118
+ if message:
119
+ ret += role + message + seps[i % 2]
120
+ else:
121
+ ret += role
122
+ return ret
123
+ elif self.sep_style == SeparatorStyle.RWKV:
124
+ ret = system_prompt
125
+ for i, (role, message) in enumerate(self.messages):
126
+ if message:
127
+ ret += (
128
+ role
129
+ + ': '
130
+ + message.replace('\r\n', '\n').replace('\n\n', '\n')
131
+ )
132
+ ret += '\n\n'
133
+ else:
134
+ ret += role + ':'
135
+ return ret
136
+ elif self.sep_style == SeparatorStyle.LLAMA2:
137
+ seps = [self.sep, self.sep2]
138
+ if self.system_message:
139
+ ret = system_prompt
140
+ else:
141
+ ret = '[INST] '
142
+ for i, (role, message) in enumerate(self.messages):
143
+ tag = self.roles[i % 2]
144
+ if message:
145
+ if i == 0:
146
+ ret += message + ' '
147
+ else:
148
+ ret += tag + ' ' + message + seps[i % 2]
149
+ else:
150
+ ret += tag
151
+ return ret
152
+ elif self.sep_style == SeparatorStyle.CHATGLM:
153
+ # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
154
+ # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
155
+ round_add_n = 1 if self.name == 'chatglm2' else 0
156
+ if system_prompt:
157
+ ret = system_prompt + self.sep
158
+ else:
159
+ ret = ''
160
+
161
+ for i, (role, message) in enumerate(self.messages):
162
+ if i % 2 == 0:
163
+ ret += f'[Round {i//2 + round_add_n}]{self.sep}'
164
+
165
+ if message:
166
+ ret += f'{role}:{message}{self.sep}'
167
+ else:
168
+ ret += f'{role}:'
169
+ return ret
170
+ elif self.sep_style == SeparatorStyle.CHATML:
171
+ ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
172
+ for role, message in self.messages:
173
+ if message:
174
+ ret += role + '\n' + message + self.sep + '\n'
175
+ else:
176
+ ret += role + '\n'
177
+ return ret
178
+ elif self.sep_style == SeparatorStyle.CHATGLM3:
179
+ ret = ''
180
+ if self.system_message:
181
+ ret += system_prompt
182
+ for role, message in self.messages:
183
+ if message:
184
+ ret += role + '\n' + ' ' + message
185
+ else:
186
+ ret += role
187
+ return ret
188
+ elif self.sep_style == SeparatorStyle.CHATINTERN:
189
+ # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
190
+ seps = [self.sep, self.sep2]
191
+ ret = system_prompt
192
+ for i, (role, message) in enumerate(self.messages):
193
+ # if i % 2 == 0:
194
+ # ret += "<s>"
195
+ if message:
196
+ ret += role + ':' + message + seps[i % 2] + '\n'
197
+ else:
198
+ ret += role + ':'
199
+ return ret
200
+ elif self.sep_style == SeparatorStyle.DOLLY:
201
+ seps = [self.sep, self.sep2]
202
+ ret = system_prompt
203
+ for i, (role, message) in enumerate(self.messages):
204
+ if message:
205
+ ret += role + ':\n' + message + seps[i % 2]
206
+ if i % 2 == 1:
207
+ ret += '\n\n'
208
+ else:
209
+ ret += role + ':\n'
210
+ return ret
211
+ elif self.sep_style == SeparatorStyle.PHOENIX:
212
+ ret = system_prompt
213
+ for role, message in self.messages:
214
+ if message:
215
+ ret += role + ': ' + '<s>' + message + '</s>'
216
+ else:
217
+ ret += role + ': ' + '<s>'
218
+ return ret
219
+ elif self.sep_style == SeparatorStyle.ROBIN:
220
+ ret = system_prompt + self.sep
221
+ for role, message in self.messages:
222
+ if message:
223
+ ret += role + ':\n' + message + self.sep
224
+ else:
225
+ ret += role + ':\n'
226
+ return ret
227
+ elif self.sep_style == SeparatorStyle.FALCON_CHAT:
228
+ ret = ''
229
+ if self.system_message:
230
+ ret += system_prompt + self.sep
231
+ for role, message in self.messages:
232
+ if message:
233
+ ret += role + ': ' + message + self.sep
234
+ else:
235
+ ret += role + ':'
236
+
237
+ return ret
238
+ elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
239
+ seps = [self.sep2, self.sep]
240
+ ret = self.system_message + seps[0]
241
+ for i, (role, message) in enumerate(self.messages):
242
+ if message:
243
+ ret += role + ': ' + message + seps[i % 2]
244
+ else:
245
+ ret += role + ':'
246
+ return ret
247
+ elif self.sep_style == SeparatorStyle.MPT:
248
+ ret = system_prompt
249
+ for role, message in self.messages:
250
+ if message:
251
+ if type(message) is tuple:
252
+ message, _, _ = message
253
+ # ret += role + message + self.sep
254
+ ret += role + message
255
+ else:
256
+ ret += role
257
+
258
+ return ret
259
+ else:
260
+ raise ValueError(f'Invalid style: {self.sep_style}')
261
+
262
+ def set_system_message(self, system_message: str):
263
+ """Set the system message."""
264
+ self.system_message = system_message
265
+
266
+ def append_message(self, role: str, message: str):
267
+ """Append a new message."""
268
+ self.messages.append([role, message])
269
+
270
+ def update_last_message(self, message: str):
271
+ """Update the last output.
272
+
273
+ The last message is typically set to be None when constructing the prompt,
274
+ so we need to update it in-place after getting the response from a model.
275
+ """
276
+ self.messages[-1][1] = message
277
+
278
+ def to_gradio_chatbot(self):
279
+ """Convert the conversation to gradio chatbot format."""
280
+ ret = []
281
+ for i, (role, msg) in enumerate(self.messages[self.offset :]):
282
+ if i % 2 == 0:
283
+ ret.append([msg, None])
284
+ else:
285
+ ret[-1][-1] = msg
286
+ return ret
287
+
288
+ def to_openai_api_messages(self):
289
+ """Convert the conversation to OpenAI chat completion format."""
290
+ ret = [{'role': 'system', 'content': self.system_message}]
291
+
292
+ for i, (_, msg) in enumerate(self.messages[self.offset :]):
293
+ if i % 2 == 0:
294
+ ret.append({'role': 'user', 'content': msg})
295
+ else:
296
+ if msg is not None:
297
+ ret.append({'role': 'assistant', 'content': msg})
298
+ return ret
299
+
300
+ def copy(self):
301
+ return Conversation(
302
+ name=self.name,
303
+ system_template=self.system_template,
304
+ system_message=self.system_message,
305
+ roles=self.roles,
306
+ messages=[[x, y] for x, y in self.messages],
307
+ offset=self.offset,
308
+ sep_style=self.sep_style,
309
+ sep=self.sep,
310
+ sep2=self.sep2,
311
+ stop_str=self.stop_str,
312
+ stop_token_ids=self.stop_token_ids,
313
+ )
314
+
315
+ def dict(self):
316
+ return {
317
+ 'template_name': self.name,
318
+ 'system_message': self.system_message,
319
+ 'roles': self.roles,
320
+ 'messages': self.messages,
321
+ 'offset': self.offset,
322
+ }
323
+
324
+
325
+ # A global registry for all conversation templates
326
+ conv_templates: Dict[str, Conversation] = {}
327
+
328
+
329
+ def register_conv_template(template: Conversation, override: bool = False):
330
+ """Register a new conversation template."""
331
+ if not override:
332
+ assert (
333
+ template.name not in conv_templates
334
+ ), f'{template.name} has been registered.'
335
+
336
+ conv_templates[template.name] = template
337
+
338
+
339
+ def get_conv_template(name: str) -> Conversation:
340
+ """Get a conversation template."""
341
+ return conv_templates[name].copy()
342
+
343
+
344
+ # InternVL-Chat-V1-1 template
345
+ # register_conv_template(
346
+ # Conversation(
347
+ # name='internvl_zh',
348
+ # system_template='',
349
+ # roles=('<human>', '<bot>'),
350
+ # sep_style=SeparatorStyle.INTERNVL_ZH,
351
+ # sep='</s>',
352
+ # sep2=' ',
353
+ # )
354
+ # )
355
+
356
+
357
+ # Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
358
+ # is that during training, the preprocessing function for the Hermes-2 template doesn't add
359
+ # <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
360
+ # Therefore, they are completely equivalent during inference.
361
+ # register_conv_template(
362
+ # Conversation(
363
+ # name='Hermes-2',
364
+ # system_template='<|begin▁of▁sentence|>system\n{system_message}',
365
+ # # note: The new system prompt was not used here to avoid changes in benchmark performance.
366
+ # # 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。',
367
+ # system_message='你是SkyVL,是由昆仑万维开发的多模态大语言模型。',
368
+ # roles=('<|begin▁of▁sentence|><|User|>\n', '<|Assistant|>\n'),
369
+ # sep_style=SeparatorStyle.MPT,
370
+ # sep='<|end▁of▁sentence|>',
371
+ # stop_str='<|endoftext|>',
372
+ # )
373
+ # )
374
+
375
+
376
+ register_conv_template(
377
+ Conversation(
378
+ name='internlm2-chat',
379
+ system_template='<|begin▁of▁sentence|>{system_message}',
380
+ # note: The new system prompt was not used here to avoid changes in benchmark performance.
381
+ # 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。',
382
+ # system_message='你是SkyVL,是由昆仑万维开发的多模态大语言模型。',
383
+ system_message='',
384
+ # roles=('<|begin▁of▁sentence|>user\n', '<|begin▁of▁sentence|>assistant\n'),
385
+ roles=('<|User|>\n', '<|Assistant|><think>\n'),
386
+ sep_style=SeparatorStyle.MPT,
387
+ sep='<|end▁of▁sentence|>',
388
+ )
389
+ )
390
+
391
+
392
+ # register_conv_template(
393
+ # Conversation(
394
+ # name='phi3-chat',
395
+ # system_template='<|system|>\n{system_message}',
396
+ # # note: The new system prompt was not used here to avoid changes in benchmark performance.
397
+ # # 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。',
398
+ # system_message='你是SkyVL,是由昆仑万维开发的多模态大语言模型。',
399
+ # roles=('<|user|>\n', '<|assistant|>\n'),
400
+ # sep_style=SeparatorStyle.MPT,
401
+ # sep='<|end|>',
402
+ # )
403
+ # )
404
+ #
405
+ #
406
+ # register_conv_template(
407
+ # Conversation(
408
+ # name='internvl2_5',
409
+ # system_template='<|begin▁of▁sentence|>{system_message}',
410
+ # # system_message='你是SkyVL,是由昆仑万维开发的多模态大语言模型。',
411
+ # system_message = '',
412
+ # roles=('<|User|>\n', '<|Assistant|>\n'),
413
+ # sep_style=SeparatorStyle.MPT,
414
+ # sep='<|end▁of▁sentence|>\n',
415
+ # )
416
+ # )
generation_config.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "eos_token_id": [
4
+ 151644,
5
+ 151645
6
+ ],
7
+ "transformers_version": "4.43.0"
8
+ }
inputs_stats.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7facb22600e795d114a90d0ee02515d18df182747ba63ffd54fea415d000a137
3
+ size 37987294
merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
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_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"""<|begin▁of▁sentence|>system\n{meta_instruction}<|end▁of▁sentence|>\n"""
1167
+ for record in history:
1168
+ prompt += f"""<|begin▁of▁sentence|>user\n{record[0]}<|end▁of▁sentence|>\n<|begin▁of▁sentence|>assistant\n{record[1]}<|end▁of▁sentence|>\n"""
1169
+ prompt += f"""<|begin▁of▁sentence|>user\n{query}<|end▁of▁sentence|>\n<|begin▁of▁sentence|>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(['<|end▁of▁sentence|>'])[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('<|end▁of▁sentence|>')[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() != '<|end▁of▁sentence|>':
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_internvl_chat.py ADDED
@@ -0,0 +1,387 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_internvl_chat import InternVLChatConfig
21
+ from .conversation import get_conv_template
22
+ from .modeling_intern_vit import InternVisionModel, has_flash_attn
23
+ from .modeling_internlm2 import InternLM2ForCausalLM
24
+
25
+ from transformers import Qwen2Config, Qwen2ForCausalLM
26
+
27
+ logger = logging.get_logger(__name__)
28
+
29
+
30
+ def version_cmp(v1, v2, op='eq'):
31
+ import operator
32
+
33
+ from packaging import version
34
+ op_func = getattr(operator, op)
35
+ return op_func(version.parse(v1), version.parse(v2))
36
+
37
+
38
+ class InternVLChatModel(PreTrainedModel):
39
+ config_class = InternVLChatConfig
40
+ main_input_name = 'pixel_values'
41
+ base_model_prefix = 'language_model'
42
+ _supports_flash_attn_2 = True
43
+ _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
44
+
45
+ def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
46
+ super().__init__(config)
47
+
48
+ assert version_cmp(transformers.__version__, '4.36.2', 'ge')
49
+ image_size = config.force_image_size or config.vision_config.image_size
50
+ patch_size = config.vision_config.patch_size
51
+ self.patch_size = patch_size
52
+ self.select_layer = config.select_layer
53
+ self.template = config.template
54
+ self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
55
+ self.downsample_ratio = config.downsample_ratio
56
+ self.ps_version = config.ps_version
57
+ use_flash_attn = use_flash_attn if has_flash_attn else False
58
+ config.vision_config.use_flash_attn = True if use_flash_attn else False
59
+ config.llm_config.attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
60
+
61
+ logger.info(f'num_image_token: {self.num_image_token}')
62
+ logger.info(f'ps_version: {self.ps_version}')
63
+ if vision_model is not None:
64
+ self.vision_model = vision_model
65
+ else:
66
+ self.vision_model = InternVisionModel(config.vision_config)
67
+ if language_model is not None:
68
+ self.language_model = language_model
69
+ else:
70
+ if config.llm_config.architectures[0] == 'LlamaForCausalLM':
71
+ self.language_model = LlamaForCausalLM(config.llm_config)
72
+ elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
73
+ self.language_model = InternLM2ForCausalLM(config.llm_config)
74
+ elif config.llm_config.architectures[0] == 'Qwen2ForCausalLM':
75
+ self.language_model = Qwen2ForCausalLM(config.llm_config)
76
+ else:
77
+ raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
78
+
79
+ vit_hidden_size = config.vision_config.hidden_size
80
+ llm_hidden_size = config.llm_config.hidden_size
81
+
82
+ self.mlp1 = nn.Sequential(
83
+ nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
84
+ nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
85
+ nn.GELU(),
86
+ nn.Linear(llm_hidden_size, llm_hidden_size)
87
+ )
88
+
89
+ self.img_context_token_id = None
90
+ self.conv_template = get_conv_template(self.template)
91
+ self.system_message = self.conv_template.system_message
92
+
93
+ def forward(
94
+ self,
95
+ pixel_values: torch.FloatTensor,
96
+ input_ids: torch.LongTensor = None,
97
+ attention_mask: Optional[torch.Tensor] = None,
98
+ position_ids: Optional[torch.LongTensor] = None,
99
+ image_flags: Optional[torch.LongTensor] = None,
100
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
101
+ labels: Optional[torch.LongTensor] = None,
102
+ use_cache: Optional[bool] = None,
103
+ output_attentions: Optional[bool] = None,
104
+ output_hidden_states: Optional[bool] = None,
105
+ return_dict: Optional[bool] = None,
106
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
107
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
108
+
109
+ image_flags = image_flags.squeeze(-1)
110
+ input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
111
+
112
+ vit_embeds = self.extract_feature(pixel_values)
113
+ vit_embeds = vit_embeds[image_flags == 1]
114
+ vit_batch_size = pixel_values.shape[0]
115
+
116
+ B, N, C = input_embeds.shape
117
+ input_embeds = input_embeds.reshape(B * N, C)
118
+
119
+ if torch.distributed.get_rank() == 0:
120
+ print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
121
+
122
+ input_ids = input_ids.reshape(B * N)
123
+ selected = (input_ids == self.img_context_token_id)
124
+ try:
125
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
126
+ except Exception as e:
127
+ vit_embeds = vit_embeds.reshape(-1, C)
128
+ print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
129
+ f'vit_embeds.shape={vit_embeds.shape}')
130
+ n_token = selected.sum()
131
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
132
+
133
+ input_embeds = input_embeds.reshape(B, N, C)
134
+
135
+ outputs = self.language_model(
136
+ inputs_embeds=input_embeds,
137
+ attention_mask=attention_mask,
138
+ position_ids=position_ids,
139
+ past_key_values=past_key_values,
140
+ use_cache=use_cache,
141
+ output_attentions=output_attentions,
142
+ output_hidden_states=output_hidden_states,
143
+ return_dict=return_dict,
144
+ )
145
+ logits = outputs.logits
146
+
147
+ loss = None
148
+ if labels is not None:
149
+ # Shift so that tokens < n predict n
150
+ shift_logits = logits[..., :-1, :].contiguous()
151
+ shift_labels = labels[..., 1:].contiguous()
152
+ # Flatten the tokens
153
+ loss_fct = CrossEntropyLoss()
154
+ shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
155
+ shift_labels = shift_labels.view(-1)
156
+ # Enable model parallelism
157
+ shift_labels = shift_labels.to(shift_logits.device)
158
+ loss = loss_fct(shift_logits, shift_labels)
159
+
160
+ if not return_dict:
161
+ output = (logits,) + outputs[1:]
162
+ return (loss,) + output if loss is not None else output
163
+
164
+ return CausalLMOutputWithPast(
165
+ loss=loss,
166
+ logits=logits,
167
+ past_key_values=outputs.past_key_values,
168
+ hidden_states=outputs.hidden_states,
169
+ attentions=outputs.attentions,
170
+ )
171
+
172
+ def pixel_shuffle(self, x, scale_factor=0.5):
173
+ n, w, h, c = x.size()
174
+ # N, W, H, C --> N, W, H * scale, C // scale
175
+ x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
176
+ # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
177
+ x = x.permute(0, 2, 1, 3).contiguous()
178
+ # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
179
+ x = x.view(n, int(h * scale_factor), int(w * scale_factor),
180
+ int(c / (scale_factor * scale_factor)))
181
+ if self.ps_version == 'v1':
182
+ warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
183
+ 'which results in a transposed image.')
184
+ else:
185
+ x = x.permute(0, 2, 1, 3).contiguous()
186
+ return x
187
+
188
+ def extract_feature(self, pixel_values):
189
+ if self.select_layer == -1:
190
+ vit_embeds = self.vision_model(
191
+ pixel_values=pixel_values,
192
+ output_hidden_states=False,
193
+ return_dict=True).last_hidden_state
194
+ else:
195
+ vit_embeds = self.vision_model(
196
+ pixel_values=pixel_values,
197
+ output_hidden_states=True,
198
+ return_dict=True).hidden_states[self.select_layer]
199
+ vit_embeds = vit_embeds[:, 1:, :]
200
+
201
+ h = w = int(vit_embeds.shape[1] ** 0.5)
202
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
203
+ vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
204
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
205
+ vit_embeds = self.mlp1(vit_embeds)
206
+ return vit_embeds
207
+
208
+ def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
209
+ history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
210
+ IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
211
+ if history is not None or return_history:
212
+ print('Now multi-turn chat is not supported in batch_chat.')
213
+ raise NotImplementedError
214
+
215
+ if image_counts is not None:
216
+ num_patches_list = image_counts
217
+ print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
218
+
219
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
220
+ self.img_context_token_id = img_context_token_id
221
+ # print("##############1################")
222
+ # print(self.img_context_token_id)
223
+ # print("##############1################")
224
+ # exit()
225
+
226
+ if verbose and pixel_values is not None:
227
+ image_bs = pixel_values.shape[0]
228
+ print(f'dynamic ViT batch size: {image_bs}')
229
+
230
+ queries = []
231
+ for idx, num_patches in enumerate(num_patches_list):
232
+ question = questions[idx]
233
+ if pixel_values is not None and '<image>' not in question:
234
+ question = '<image>\n' + question
235
+ template = get_conv_template(self.template)
236
+ template.system_message = self.system_message
237
+ template.append_message(template.roles[0], question)
238
+ template.append_message(template.roles[1], None)
239
+ query = template.get_prompt()
240
+
241
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
242
+ query = query.replace('<image>', image_tokens, 1)
243
+ queries.append(query)
244
+
245
+ tokenizer.padding_side = 'left'
246
+ model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
247
+ input_ids = model_inputs['input_ids'].to(self.device)
248
+ attention_mask = model_inputs['attention_mask'].to(self.device)
249
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
250
+ generation_config['eos_token_id'] = eos_token_id
251
+ generation_output = self.generate(
252
+ pixel_values=pixel_values,
253
+ input_ids=input_ids,
254
+ attention_mask=attention_mask,
255
+ **generation_config
256
+ )
257
+ responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
258
+ responses = [response.split(template.sep.strip())[0].strip() for response in responses]
259
+ return responses
260
+
261
+ def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
262
+ num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
263
+ verbose=False):
264
+
265
+ if history is None and pixel_values is not None and '<image>' not in question:
266
+ question = '<image>\n' + question
267
+
268
+ if num_patches_list is None:
269
+ num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
270
+ assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
271
+
272
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
273
+ self.img_context_token_id = img_context_token_id
274
+ # print("##############2################")
275
+ # print(self.img_context_token_id)
276
+ # print("##############2################")
277
+
278
+ template = get_conv_template(self.template)
279
+ template.system_message = self.system_message
280
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
281
+ # print("##############2.5################")
282
+ # print(template.sep.strip())
283
+ # print(eos_token_id)
284
+ # print("##############2.5################")
285
+
286
+ history = [] if history is None else history
287
+ for (old_question, old_answer) in history:
288
+ template.append_message(template.roles[0], old_question)
289
+ template.append_message(template.roles[1], old_answer)
290
+ template.append_message(template.roles[0], question)
291
+ template.append_message(template.roles[1], None)
292
+ query = template.get_prompt()
293
+ # print("##############3################")
294
+ # print(query)
295
+ # print("##############3################")
296
+ # query = """<|begin▁of▁sentence|>user
297
+ # <image>
298
+ # 图片内容是什么?<|end▁of▁sentence|>
299
+ # <|begin▁of▁sentence|>assistant"""
300
+
301
+ if verbose and pixel_values is not None:
302
+ image_bs = pixel_values.shape[0]
303
+ print(f'dynamic ViT batch size: {image_bs}')
304
+
305
+ for num_patches in num_patches_list:
306
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
307
+ query = query.replace('<image>', image_tokens, 1)
308
+ # print("##############4################")
309
+ # # print(query)
310
+ # print("##############4################")
311
+
312
+ model_inputs = tokenizer(query, return_tensors='pt')
313
+ input_ids = model_inputs['input_ids'].to(self.device)
314
+ attention_mask = model_inputs['attention_mask'].to(self.device)
315
+ generation_config['eos_token_id'] = eos_token_id
316
+ generation_output = self.generate(
317
+ pixel_values=pixel_values,
318
+ input_ids=input_ids,
319
+ attention_mask=attention_mask,
320
+ **generation_config
321
+ )
322
+ response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
323
+ response = response.split(template.sep.strip())[0].strip()
324
+ history.append((question, response))
325
+ # print("###" + str(response))
326
+ if return_history:
327
+ return response, history
328
+ else:
329
+ query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
330
+ query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
331
+ if verbose:
332
+ print(query_to_print, response)
333
+ return response
334
+
335
+ @torch.no_grad()
336
+ def generate(
337
+ self,
338
+ pixel_values: Optional[torch.FloatTensor] = None,
339
+ input_ids: Optional[torch.FloatTensor] = None,
340
+ attention_mask: Optional[torch.LongTensor] = None,
341
+ visual_features: Optional[torch.FloatTensor] = None,
342
+ generation_config: Optional[GenerationConfig] = None,
343
+ output_hidden_states: Optional[bool] = None,
344
+ **generate_kwargs,
345
+ ) -> torch.LongTensor:
346
+
347
+ assert self.img_context_token_id is not None
348
+ if pixel_values is not None:
349
+ if visual_features is not None:
350
+ vit_embeds = visual_features
351
+ else:
352
+ vit_embeds = self.extract_feature(pixel_values)
353
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
354
+ B, N, C = input_embeds.shape
355
+ input_embeds = input_embeds.reshape(B * N, C)
356
+
357
+ input_ids = input_ids.reshape(B * N)
358
+ selected = (input_ids == self.img_context_token_id)
359
+ # print("#######################5####################")
360
+ # print(self.img_context_token_id)
361
+ # print(selected)
362
+ # print(selected.sum())
363
+ # print("#######################5####################")
364
+ # exit()
365
+ assert selected.sum() != 0
366
+ input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
367
+
368
+ input_embeds = input_embeds.reshape(B, N, C)
369
+ else:
370
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
371
+
372
+ # print("#######################6####################")
373
+ # print(attention_mask)
374
+ # print(attention_mask.sum())
375
+ # print(output_hidden_states)
376
+ # print("#######################6####################")
377
+
378
+ outputs = self.language_model.generate(
379
+ inputs_embeds=input_embeds,
380
+ attention_mask=attention_mask,
381
+ generation_config=generation_config,
382
+ output_hidden_states=output_hidden_states,
383
+ use_cache=True,
384
+ **generate_kwargs,
385
+ )
386
+
387
+ return outputs
modeling_skywork_chat.py ADDED
@@ -0,0 +1,354 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import warnings
2
+ from typing import List, Optional, Tuple, Union
3
+
4
+ import torch.utils.checkpoint
5
+ import transformers
6
+ from torch import nn
7
+ from torch.nn import CrossEntropyLoss
8
+ from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
9
+ LlamaTokenizer)
10
+ from transformers.modeling_outputs import CausalLMOutputWithPast
11
+ from transformers.modeling_utils import PreTrainedModel
12
+ from transformers.utils import ModelOutput, logging
13
+
14
+ from .configuration_skywork_chat import SkyworkChatConfig
15
+ from .conversation import get_conv_template
16
+ from .modeling_skywork_vit import SkyworkVisionModel, has_flash_attn
17
+ from .modeling_skywork_lm2 import SkyworkLM2ForCausalLM
18
+
19
+ from transformers import Qwen2Config, Qwen2ForCausalLM
20
+
21
+ logger = logging.get_logger(__name__)
22
+
23
+
24
+ def version_cmp(v1, v2, op='eq'):
25
+ import operator
26
+
27
+ from packaging import version
28
+ op_func = getattr(operator, op)
29
+ return op_func(version.parse(v1), version.parse(v2))
30
+
31
+
32
+ class SkyworkChatModel(PreTrainedModel):
33
+ config_class = SkyworkChatConfig
34
+ main_input_name = 'pixel_values'
35
+ base_model_prefix = 'language_model'
36
+ _supports_flash_attn_2 = True
37
+ _no_split_modules = ['SkyworkVisionModel', 'LlamaDecoderLayer', 'SkyworkLM2DecoderLayer']
38
+
39
+ def __init__(self, config: SkyworkChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
40
+ super().__init__(config)
41
+
42
+ assert version_cmp(transformers.__version__, '4.36.2', 'ge')
43
+ image_size = config.force_image_size or config.vision_config.image_size
44
+ patch_size = config.vision_config.patch_size
45
+ self.patch_size = patch_size
46
+ self.select_layer = config.select_layer
47
+ self.template = config.template
48
+ self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
49
+ self.downsample_ratio = config.downsample_ratio
50
+ self.ps_version = config.ps_version
51
+ use_flash_attn = use_flash_attn if has_flash_attn else False
52
+ config.vision_config.use_flash_attn = True if use_flash_attn else False
53
+ config.llm_config.attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
54
+
55
+ logger.info(f'num_image_token: {self.num_image_token}')
56
+ logger.info(f'ps_version: {self.ps_version}')
57
+ if vision_model is not None:
58
+ self.vision_model = vision_model
59
+ else:
60
+ self.vision_model = SkyworkVisionModel(config.vision_config)
61
+ if language_model is not None:
62
+ self.language_model = language_model
63
+ else:
64
+ if config.llm_config.architectures[0] == 'LlamaForCausalLM':
65
+ self.language_model = LlamaForCausalLM(config.llm_config)
66
+ elif config.llm_config.architectures[0] == 'SkyworkLM2ForCausalLM':
67
+ self.language_model = SkyworkLM2ForCausalLM(config.llm_config)
68
+ elif config.llm_config.architectures[0] == 'Qwen2ForCausalLM':
69
+ self.language_model = Qwen2ForCausalLM(config.llm_config)
70
+ else:
71
+ raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
72
+
73
+ vit_hidden_size = config.vision_config.hidden_size
74
+ llm_hidden_size = config.llm_config.hidden_size
75
+
76
+ self.mlp1 = nn.Sequential(
77
+ nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
78
+ nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
79
+ nn.GELU(),
80
+ nn.Linear(llm_hidden_size, llm_hidden_size)
81
+ )
82
+
83
+ self.img_context_token_id = None
84
+ self.conv_template = get_conv_template(self.template)
85
+ self.system_message = self.conv_template.system_message
86
+
87
+ def forward(
88
+ self,
89
+ pixel_values: torch.FloatTensor,
90
+ input_ids: torch.LongTensor = None,
91
+ attention_mask: Optional[torch.Tensor] = None,
92
+ position_ids: Optional[torch.LongTensor] = None,
93
+ image_flags: Optional[torch.LongTensor] = None,
94
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
95
+ labels: Optional[torch.LongTensor] = None,
96
+ use_cache: Optional[bool] = None,
97
+ output_attentions: Optional[bool] = None,
98
+ output_hidden_states: Optional[bool] = None,
99
+ return_dict: Optional[bool] = None,
100
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
101
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
102
+
103
+ image_flags = image_flags.squeeze(-1)
104
+ input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
105
+
106
+ vit_embeds = self.extract_feature(pixel_values)
107
+ vit_embeds = vit_embeds[image_flags == 1]
108
+ vit_batch_size = pixel_values.shape[0]
109
+
110
+ B, N, C = input_embeds.shape
111
+ input_embeds = input_embeds.reshape(B * N, C)
112
+
113
+ if torch.distributed.get_rank() == 0:
114
+ print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
115
+
116
+ input_ids = input_ids.reshape(B * N)
117
+ selected = (input_ids == self.img_context_token_id)
118
+ try:
119
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
120
+ except Exception as e:
121
+ vit_embeds = vit_embeds.reshape(-1, C)
122
+ print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
123
+ f'vit_embeds.shape={vit_embeds.shape}')
124
+ n_token = selected.sum()
125
+ input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
126
+
127
+ input_embeds = input_embeds.reshape(B, N, C)
128
+
129
+ outputs = self.language_model(
130
+ inputs_embeds=input_embeds,
131
+ attention_mask=attention_mask,
132
+ position_ids=position_ids,
133
+ past_key_values=past_key_values,
134
+ use_cache=use_cache,
135
+ output_attentions=output_attentions,
136
+ output_hidden_states=output_hidden_states,
137
+ return_dict=return_dict,
138
+ )
139
+ logits = outputs.logits
140
+
141
+ loss = None
142
+ if labels is not None:
143
+ # Shift so that tokens < n predict n
144
+ shift_logits = logits[..., :-1, :].contiguous()
145
+ shift_labels = labels[..., 1:].contiguous()
146
+ # Flatten the tokens
147
+ loss_fct = CrossEntropyLoss()
148
+ shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
149
+ shift_labels = shift_labels.view(-1)
150
+ # Enable model parallelism
151
+ shift_labels = shift_labels.to(shift_logits.device)
152
+ loss = loss_fct(shift_logits, shift_labels)
153
+
154
+ if not return_dict:
155
+ output = (logits,) + outputs[1:]
156
+ return (loss,) + output if loss is not None else output
157
+
158
+ return CausalLMOutputWithPast(
159
+ loss=loss,
160
+ logits=logits,
161
+ past_key_values=outputs.past_key_values,
162
+ hidden_states=outputs.hidden_states,
163
+ attentions=outputs.attentions,
164
+ )
165
+
166
+ def pixel_shuffle(self, x, scale_factor=0.5):
167
+ n, w, h, c = x.size()
168
+ # N, W, H, C --> N, W, H * scale, C // scale
169
+ x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
170
+ # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
171
+ x = x.permute(0, 2, 1, 3).contiguous()
172
+ # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
173
+ x = x.view(n, int(h * scale_factor), int(w * scale_factor),
174
+ int(c / (scale_factor * scale_factor)))
175
+ if self.ps_version == 'v1':
176
+ warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
177
+ 'which results in a transposed image.')
178
+ else:
179
+ x = x.permute(0, 2, 1, 3).contiguous()
180
+ return x
181
+
182
+ def extract_feature(self, pixel_values):
183
+ if self.select_layer == -1:
184
+ vit_embeds = self.vision_model(
185
+ pixel_values=pixel_values,
186
+ output_hidden_states=False,
187
+ return_dict=True).last_hidden_state
188
+ else:
189
+ vit_embeds = self.vision_model(
190
+ pixel_values=pixel_values,
191
+ output_hidden_states=True,
192
+ return_dict=True).hidden_states[self.select_layer]
193
+ vit_embeds = vit_embeds[:, 1:, :]
194
+
195
+ h = w = int(vit_embeds.shape[1] ** 0.5)
196
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
197
+ vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
198
+ vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
199
+ vit_embeds = self.mlp1(vit_embeds)
200
+ return vit_embeds
201
+
202
+ def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
203
+ history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
204
+ IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
205
+ if history is not None or return_history:
206
+ print('Now multi-turn chat is not supported in batch_chat.')
207
+ raise NotImplementedError
208
+
209
+ if image_counts is not None:
210
+ num_patches_list = image_counts
211
+ print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
212
+
213
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
214
+ self.img_context_token_id = img_context_token_id
215
+
216
+
217
+ if verbose and pixel_values is not None:
218
+ image_bs = pixel_values.shape[0]
219
+ print(f'dynamic ViT batch size: {image_bs}')
220
+
221
+ queries = []
222
+ for idx, num_patches in enumerate(num_patches_list):
223
+ question = questions[idx]
224
+ if pixel_values is not None and '<image>' not in question:
225
+ question = '<image>\n' + question
226
+ template = get_conv_template(self.template)
227
+ template.system_message = self.system_message
228
+ template.append_message(template.roles[0], question)
229
+ template.append_message(template.roles[1], None)
230
+ query = template.get_prompt()
231
+
232
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
233
+ query = query.replace('<image>', image_tokens, 1)
234
+ queries.append(query)
235
+
236
+ tokenizer.padding_side = 'left'
237
+ model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
238
+ input_ids = model_inputs['input_ids'].to(self.device)
239
+ attention_mask = model_inputs['attention_mask'].to(self.device)
240
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
241
+ generation_config['eos_token_id'] = eos_token_id
242
+ generation_output = self.generate(
243
+ pixel_values=pixel_values,
244
+ input_ids=input_ids,
245
+ attention_mask=attention_mask,
246
+ **generation_config
247
+ )
248
+ responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
249
+ responses = [response.split(template.sep.strip())[0].strip() for response in responses]
250
+ return responses
251
+
252
+ def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
253
+ num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
254
+ verbose=False):
255
+
256
+ if history is None and pixel_values is not None and '<image>' not in question:
257
+ question = '<image>\n' + question
258
+
259
+ if num_patches_list is None:
260
+ num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
261
+ assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
262
+
263
+ img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
264
+ self.img_context_token_id = img_context_token_id
265
+
266
+ template = get_conv_template(self.template)
267
+ template.system_message = self.system_message
268
+ eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
269
+
270
+
271
+ history = [] if history is None else history
272
+ for (old_question, old_answer) in history:
273
+ template.append_message(template.roles[0], old_question)
274
+ template.append_message(template.roles[1], old_answer)
275
+ template.append_message(template.roles[0], question)
276
+ template.append_message(template.roles[1], None)
277
+ query = template.get_prompt()
278
+
279
+
280
+ if verbose and pixel_values is not None:
281
+ image_bs = pixel_values.shape[0]
282
+ print(f'dynamic ViT batch size: {image_bs}')
283
+
284
+ for num_patches in num_patches_list:
285
+ image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
286
+ query = query.replace('<image>', image_tokens, 1)
287
+
288
+
289
+ model_inputs = tokenizer(query, return_tensors='pt')
290
+ input_ids = model_inputs['input_ids'].to(self.device)
291
+ attention_mask = model_inputs['attention_mask'].to(self.device)
292
+ generation_config['eos_token_id'] = eos_token_id
293
+ generation_output = self.generate(
294
+ pixel_values=pixel_values,
295
+ input_ids=input_ids,
296
+ attention_mask=attention_mask,
297
+ **generation_config
298
+ )
299
+ response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
300
+ response = response.split(template.sep.strip())[0].strip()
301
+ history.append((question, response))
302
+
303
+ if return_history:
304
+ return response, history
305
+ else:
306
+ query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
307
+ query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
308
+ if verbose:
309
+ print(query_to_print, response)
310
+ return response
311
+
312
+ @torch.no_grad()
313
+ def generate(
314
+ self,
315
+ pixel_values: Optional[torch.FloatTensor] = None,
316
+ input_ids: Optional[torch.FloatTensor] = None,
317
+ attention_mask: Optional[torch.LongTensor] = None,
318
+ visual_features: Optional[torch.FloatTensor] = None,
319
+ generation_config: Optional[GenerationConfig] = None,
320
+ output_hidden_states: Optional[bool] = None,
321
+ **generate_kwargs,
322
+ ) -> torch.LongTensor:
323
+
324
+ assert self.img_context_token_id is not None
325
+ if pixel_values is not None:
326
+ if visual_features is not None:
327
+ vit_embeds = visual_features
328
+ else:
329
+ vit_embeds = self.extract_feature(pixel_values)
330
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
331
+ B, N, C = input_embeds.shape
332
+ input_embeds = input_embeds.reshape(B * N, C)
333
+
334
+ input_ids = input_ids.reshape(B * N)
335
+ selected = (input_ids == self.img_context_token_id)
336
+
337
+ assert selected.sum() != 0
338
+ input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
339
+
340
+ input_embeds = input_embeds.reshape(B, N, C)
341
+ else:
342
+ input_embeds = self.language_model.get_input_embeddings()(input_ids)
343
+
344
+
345
+ outputs = self.language_model.generate(
346
+ inputs_embeds=input_embeds,
347
+ attention_mask=attention_mask,
348
+ generation_config=generation_config,
349
+ output_hidden_states=output_hidden_states,
350
+ use_cache=True,
351
+ **generate_kwargs,
352
+ )
353
+
354
+ return outputs
modeling_skywork_lm2.py ADDED
@@ -0,0 +1,1403 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The Skywork 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 SkyworkLM2 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:
41
+ BaseStreamer = None
42
+
43
+ from .configuration_skywork_lm2 import SkyworkLM2Config
44
+
45
+ logger = logging.get_logger(__name__)
46
+
47
+ _CONFIG_FOR_DOC = 'SkyworkLM2Config'
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->SkyworkLM2
129
+ class SkyworkLM2RMSNorm(nn.Module):
130
+ def __init__(self, hidden_size, eps=1e-6):
131
+ """
132
+ SkyworkLM2RMSNorm 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->SkyworkLM2
147
+ class SkyworkLM2RotaryEmbedding(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->SkyworkLM2
184
+ class SkyworkLM2LinearScalingRotaryEmbedding(SkyworkLM2RotaryEmbedding):
185
+
186
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
187
+ self.scaling_factor = scaling_factor
188
+ super().__init__(dim, max_position_embeddings, base, device)
189
+
190
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
191
+ self.max_seq_len_cached = seq_len
192
+ t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
193
+ t = t / self.scaling_factor
194
+
195
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
196
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
197
+ emb = torch.cat((freqs, freqs), dim=-1)
198
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
199
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
200
+
201
+
202
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->SkyworkLM2
203
+ class SkyworkLM2DynamicNTKScalingRotaryEmbedding(SkyworkLM2RotaryEmbedding):
204
+
205
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
206
+ self.scaling_factor = scaling_factor
207
+ super().__init__(dim, max_position_embeddings, base, device)
208
+
209
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
210
+ self.max_seq_len_cached = seq_len
211
+
212
+ if seq_len > self.max_position_embeddings:
213
+ base = self.base * (
214
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
215
+ ) ** (self.dim / (self.dim - 2))
216
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
217
+ self.register_buffer('inv_freq', inv_freq, persistent=False)
218
+
219
+ t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
220
+
221
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
222
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
223
+ emb = torch.cat((freqs, freqs), dim=-1)
224
+ self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
225
+ self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
226
+
227
+
228
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
229
+ def rotate_half(x):
230
+ """Rotates half the hidden dims of the input."""
231
+ x1 = x[..., : x.shape[-1] // 2]
232
+ x2 = x[..., x.shape[-1] // 2 :]
233
+ return torch.cat((-x2, x1), dim=-1)
234
+
235
+
236
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
237
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
238
+ """Applies Rotary Position Embedding to the query and key tensors."""
239
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
240
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
241
+ q_embed = (q * cos) + (rotate_half(q) * sin)
242
+ k_embed = (k * cos) + (rotate_half(k) * sin)
243
+ return q_embed, k_embed
244
+
245
+
246
+ class SkyworkLM2MLP(nn.Module):
247
+ def __init__(self, config):
248
+ super().__init__()
249
+ self.config = config
250
+ self.hidden_size = config.hidden_size
251
+ self.intermediate_size = config.intermediate_size
252
+ self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
253
+ self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
254
+ self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
255
+ self.act_fn = ACT2FN[config.hidden_act]
256
+
257
+ def forward(self, x):
258
+ down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
259
+
260
+ return down_proj
261
+
262
+
263
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
264
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
265
+ """
266
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
267
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
268
+ """
269
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
270
+ if n_rep == 1:
271
+ return hidden_states
272
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
273
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
274
+
275
+
276
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
277
+ class SkyworkLM2Attention(nn.Module):
278
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
279
+
280
+ def __init__(self, config: SkyworkLM2Config):
281
+ super().__init__()
282
+ self.config = config
283
+ self.hidden_size = config.hidden_size
284
+ self.num_heads = config.num_attention_heads
285
+ self.head_dim = self.hidden_size // self.num_heads
286
+ self.num_key_value_heads = config.num_key_value_heads
287
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
288
+ self.max_position_embeddings = config.max_position_embeddings
289
+ self.is_causal = True
290
+
291
+ if (self.head_dim * self.num_heads) != self.hidden_size:
292
+ raise ValueError(
293
+ f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
294
+ f' and `num_heads`: {self.num_heads}).'
295
+ )
296
+
297
+ self.wqkv = nn.Linear(
298
+ self.hidden_size,
299
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
300
+ bias=config.bias,
301
+ )
302
+
303
+ self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
304
+ self._init_rope()
305
+
306
+ def _init_rope(self):
307
+ if self.config.rope_scaling is None:
308
+ self.rotary_emb = SkyworkLM2RotaryEmbedding(
309
+ self.head_dim,
310
+ max_position_embeddings=self.max_position_embeddings,
311
+ base=self.config.rope_theta,
312
+ )
313
+ else:
314
+ scaling_type = self.config.rope_scaling['type']
315
+ scaling_factor = self.config.rope_scaling['factor']
316
+ if scaling_type == 'dynamic':
317
+ self.rotary_emb = SkyworkLM2DynamicNTKScalingRotaryEmbedding(
318
+ self.head_dim,
319
+ max_position_embeddings=self.max_position_embeddings,
320
+ base=self.config.rope_theta,
321
+ scaling_factor=scaling_factor,
322
+ )
323
+ elif scaling_type == 'linear':
324
+ self.rotary_emb = SkyworkLM2LinearScalingRotaryEmbedding(
325
+ self.head_dim,
326
+ max_position_embeddings=self.max_position_embeddings,
327
+ base=self.config.rope_theta,
328
+ scaling_factor=scaling_factor,
329
+ )
330
+ else:
331
+ raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
332
+ return self.rotary_emb
333
+
334
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
335
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
336
+
337
+ def forward(
338
+ self,
339
+ hidden_states: torch.Tensor,
340
+ attention_mask: Optional[torch.Tensor] = None,
341
+ position_ids: Optional[torch.LongTensor] = None,
342
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
343
+ output_attentions: bool = False,
344
+ use_cache: bool = False,
345
+ **kwargs,
346
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
347
+ if 'padding_mask' in kwargs:
348
+ warnings.warn(
349
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
350
+ 'Please make sure use `attention_mask` instead.`'
351
+ )
352
+
353
+ bsz, q_len, _ = hidden_states.size()
354
+
355
+ qkv_states = self.wqkv(hidden_states)
356
+
357
+ qkv_states = rearrange(
358
+ qkv_states,
359
+ 'b q (h gs d) -> b q h gs d',
360
+ gs=2 + self.num_key_value_groups,
361
+ d=self.head_dim,
362
+ )
363
+
364
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
365
+ query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
366
+ key_states = qkv_states[..., -2, :]
367
+ value_states = qkv_states[..., -1, :]
368
+
369
+ query_states = query_states.transpose(1, 2)
370
+ key_states = key_states.transpose(1, 2)
371
+ value_states = value_states.transpose(1, 2)
372
+
373
+ kv_seq_len = key_states.shape[-2]
374
+ if past_key_value is not None:
375
+ kv_seq_len += past_key_value[0].shape[-2]
376
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
377
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
378
+
379
+ if past_key_value is not None:
380
+ # reuse k, v, self_attention
381
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
382
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
383
+
384
+ past_key_value = (key_states, value_states) if use_cache else None
385
+
386
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
387
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
388
+
389
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
390
+
391
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
392
+ raise ValueError(
393
+ f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
394
+ f' {attn_weights.size()}'
395
+ )
396
+
397
+ if attention_mask is not None:
398
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
399
+ raise ValueError(
400
+ f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
401
+ )
402
+ attn_weights = attn_weights + attention_mask
403
+
404
+ # upcast attention to fp32
405
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
406
+ attn_output = torch.matmul(attn_weights, value_states)
407
+
408
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
409
+ raise ValueError(
410
+ f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
411
+ f' {attn_output.size()}'
412
+ )
413
+
414
+ attn_output = attn_output.transpose(1, 2).contiguous()
415
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
416
+
417
+ attn_output = self.wo(attn_output)
418
+
419
+ if not output_attentions:
420
+ attn_weights = None
421
+
422
+ return attn_output, attn_weights, past_key_value
423
+
424
+
425
+ # Modified from transformers.model.llama.modeling_llama.SkyworkLM2FlashAttention2
426
+ class SkyworkLM2FlashAttention2(SkyworkLM2Attention):
427
+
428
+ def forward(
429
+ self,
430
+ hidden_states: torch.Tensor,
431
+ attention_mask: Optional[torch.LongTensor] = None,
432
+ position_ids: Optional[torch.LongTensor] = None,
433
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
434
+ output_attentions: bool = False,
435
+ use_cache: bool = False,
436
+ **kwargs,
437
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
438
+ if 'padding_mask' in kwargs:
439
+ warnings.warn(
440
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
441
+ 'Please make sure use `attention_mask` instead.`'
442
+ )
443
+
444
+ # overwrite attention_mask with padding_mask
445
+ attention_mask = kwargs.pop('padding_mask')
446
+
447
+ output_attentions = False
448
+
449
+ bsz, q_len, _ = hidden_states.size()
450
+
451
+ qkv_states = self.wqkv(hidden_states)
452
+
453
+ qkv_states = rearrange(
454
+ qkv_states,
455
+ 'b q (h gs d) -> b q h gs d',
456
+ gs=2 + self.num_key_value_groups,
457
+ d=self.head_dim,
458
+ )
459
+
460
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
461
+ query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
462
+ key_states = qkv_states[..., -2, :]
463
+ value_states = qkv_states[..., -1, :]
464
+
465
+ query_states = query_states.transpose(1, 2)
466
+ key_states = key_states.transpose(1, 2)
467
+ value_states = value_states.transpose(1, 2)
468
+
469
+ kv_seq_len = key_states.shape[-2]
470
+ if past_key_value is not None:
471
+ kv_seq_len += past_key_value[0].shape[-2]
472
+
473
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
474
+
475
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
476
+
477
+ if past_key_value is not None:
478
+ # reuse k, v, self_attention
479
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
480
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
481
+
482
+ past_key_value = (key_states, value_states) if use_cache else None
483
+
484
+ query_states = query_states.transpose(1, 2)
485
+ key_states = key_states.transpose(1, 2)
486
+ value_states = value_states.transpose(1, 2)
487
+
488
+ attn_output = self._flash_attention_forward(
489
+ query_states, key_states, value_states, attention_mask, q_len
490
+ )
491
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
492
+ attn_output = self.wo(attn_output)
493
+
494
+ if not output_attentions:
495
+ attn_weights = None
496
+
497
+ return attn_output, attn_weights, past_key_value
498
+
499
+ def _flash_attention_forward(
500
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
501
+ ):
502
+ """
503
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
504
+ first unpad the input, then computes the attention scores and pad the final attention scores.
505
+
506
+ Args:
507
+ query_states (`torch.Tensor`):
508
+ Input query states to be passed to Flash Attention API
509
+ key_states (`torch.Tensor`):
510
+ Input key states to be passed to Flash Attention API
511
+ value_states (`torch.Tensor`):
512
+ Input value states to be passed to Flash Attention API
513
+ attention_mask (`torch.Tensor`):
514
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
515
+ position of padding tokens and 1 for the position of non-padding tokens.
516
+ dropout (`int`, *optional*):
517
+ Attention dropout
518
+ softmax_scale (`float`, *optional*):
519
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
520
+ """
521
+ # Contains at least one padding token in the sequence
522
+ causal = self.is_causal and query_length != 1
523
+ if attention_mask is not None:
524
+ batch_size = query_states.shape[0]
525
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
526
+ query_states, key_states, value_states, attention_mask, query_length
527
+ )
528
+
529
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
530
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
531
+
532
+ attn_output_unpad = flash_attn_varlen_func(
533
+ query_states,
534
+ key_states,
535
+ value_states,
536
+ cu_seqlens_q=cu_seqlens_q,
537
+ cu_seqlens_k=cu_seqlens_k,
538
+ max_seqlen_q=max_seqlen_in_batch_q,
539
+ max_seqlen_k=max_seqlen_in_batch_k,
540
+ dropout_p=dropout,
541
+ softmax_scale=softmax_scale,
542
+ causal=causal,
543
+ )
544
+
545
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
546
+ else:
547
+ attn_output = flash_attn_func(
548
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
549
+ )
550
+
551
+ return attn_output
552
+
553
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
554
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
555
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
556
+
557
+ key_layer = index_first_axis(
558
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
559
+ )
560
+ value_layer = index_first_axis(
561
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
562
+ )
563
+
564
+ if query_length == kv_seq_len:
565
+ query_layer = index_first_axis(
566
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
567
+ )
568
+ cu_seqlens_q = cu_seqlens_k
569
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
570
+ indices_q = indices_k
571
+ elif query_length == 1:
572
+ max_seqlen_in_batch_q = 1
573
+ cu_seqlens_q = torch.arange(
574
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
575
+ ) # There is a memcpy here, that is very bad.
576
+ indices_q = cu_seqlens_q[:-1]
577
+ query_layer = query_layer.squeeze(1)
578
+ else:
579
+ # The -q_len: slice assumes left padding.
580
+ attention_mask = attention_mask[:, -query_length:]
581
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
582
+
583
+ return (
584
+ query_layer,
585
+ key_layer,
586
+ value_layer,
587
+ indices_q.to(torch.int64),
588
+ (cu_seqlens_q, cu_seqlens_k),
589
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
590
+ )
591
+
592
+
593
+ INTERNLM2_ATTENTION_CLASSES = {
594
+ 'eager': SkyworkLM2Attention,
595
+ 'flash_attention_2': SkyworkLM2FlashAttention2,
596
+ }
597
+
598
+
599
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
600
+ class SkyworkLM2DecoderLayer(nn.Module):
601
+ def __init__(self, config: SkyworkLM2Config):
602
+ super().__init__()
603
+ self.hidden_size = config.hidden_size
604
+
605
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
606
+
607
+ self.feed_forward = SkyworkLM2MLP(config)
608
+ self.attention_norm = SkyworkLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
609
+ self.ffn_norm = SkyworkLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
610
+
611
+ def forward(
612
+ self,
613
+ hidden_states: torch.Tensor,
614
+ attention_mask: Optional[torch.Tensor] = None,
615
+ position_ids: Optional[torch.LongTensor] = None,
616
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
617
+ output_attentions: Optional[bool] = False,
618
+ use_cache: Optional[bool] = False,
619
+ **kwargs,
620
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
621
+ """
622
+ Args:
623
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
624
+ attention_mask (`torch.FloatTensor`, *optional*):
625
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
626
+ query_sequence_length, key_sequence_length)` if default attention is used.
627
+ output_attentions (`bool`, *optional*):
628
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
629
+ returned tensors for more detail.
630
+ use_cache (`bool`, *optional*):
631
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
632
+ (see `past_key_values`).
633
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
634
+ """
635
+ if 'padding_mask' in kwargs:
636
+ warnings.warn(
637
+ 'Passing `padding_mask` is deprecated and will be removed in v4.37. '
638
+ 'Please make sure use `attention_mask` instead.`'
639
+ )
640
+
641
+ residual = hidden_states
642
+
643
+ hidden_states = self.attention_norm(hidden_states)
644
+
645
+ # Self Attention
646
+ hidden_states, self_attn_weights, present_key_value = self.attention(
647
+ hidden_states=hidden_states,
648
+ attention_mask=attention_mask,
649
+ position_ids=position_ids,
650
+ past_key_value=past_key_value,
651
+ output_attentions=output_attentions,
652
+ use_cache=use_cache,
653
+ **kwargs,
654
+ )
655
+ hidden_states = residual + hidden_states
656
+
657
+ # Fully Connected
658
+ residual = hidden_states
659
+ hidden_states = self.ffn_norm(hidden_states)
660
+ hidden_states = self.feed_forward(hidden_states)
661
+ hidden_states = residual + hidden_states
662
+
663
+ outputs = (hidden_states,)
664
+
665
+ if output_attentions:
666
+ outputs += (self_attn_weights,)
667
+
668
+ if use_cache:
669
+ outputs += (present_key_value,)
670
+
671
+ return outputs
672
+
673
+
674
+ SkyworkLM2_START_DOCSTRING = r"""
675
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
676
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
677
+ etc.)
678
+
679
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
680
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
681
+ and behavior.
682
+
683
+ Parameters:
684
+ config ([`SkyworkLM2Config`]):
685
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
686
+ load the weights associated with the model, only the configuration. Check out the
687
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
688
+ """
689
+
690
+
691
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->SkyworkLM2
692
+ @add_start_docstrings(
693
+ 'The bare SkyworkLM2 Model outputting raw hidden-states without any specific head on top.',
694
+ SkyworkLM2_START_DOCSTRING,
695
+ )
696
+ class SkyworkLM2PreTrainedModel(PreTrainedModel):
697
+ config_class = SkyworkLM2Config
698
+ base_model_prefix = 'model'
699
+ supports_gradient_checkpointing = True
700
+ _no_split_modules = ['SkyworkLM2DecoderLayer']
701
+ _skip_keys_device_placement = 'past_key_values'
702
+ _supports_flash_attn_2 = True
703
+
704
+ def _init_weights(self, module):
705
+ std = self.config.initializer_range
706
+ if isinstance(module, nn.Linear):
707
+ module.weight.data.normal_(mean=0.0, std=std)
708
+ if module.bias is not None:
709
+ module.bias.data.zero_()
710
+ elif isinstance(module, nn.Embedding):
711
+ module.weight.data.normal_(mean=0.0, std=std)
712
+ if module.padding_idx is not None:
713
+ module.weight.data[module.padding_idx].zero_()
714
+
715
+
716
+ SkyworkLM2_INPUTS_DOCSTRING = r"""
717
+ Args:
718
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
719
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
720
+ it.
721
+
722
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
723
+ [`PreTrainedTokenizer.__call__`] for details.
724
+
725
+ [What are input IDs?](../glossary#input-ids)
726
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
727
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
728
+
729
+ - 1 for tokens that are **not masked**,
730
+ - 0 for tokens that are **masked**.
731
+
732
+ [What are attention masks?](../glossary#attention-mask)
733
+
734
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
735
+ [`PreTrainedTokenizer.__call__`] for details.
736
+
737
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
738
+ `past_key_values`).
739
+
740
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
741
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
742
+ information on the default strategy.
743
+
744
+ - 1 indicates the head is **not masked**,
745
+ - 0 indicates the head is **masked**.
746
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
747
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
748
+ config.n_positions - 1]`.
749
+
750
+ [What are position IDs?](../glossary#position-ids)
751
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
752
+ when `config.use_cache=True`):
753
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
754
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
755
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
756
+
757
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
758
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
759
+
760
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
761
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
762
+ of shape `(batch_size, sequence_length)`.
763
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
764
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
765
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
766
+ model's skywork embedding lookup matrix.
767
+ use_cache (`bool`, *optional*):
768
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
769
+ `past_key_values`).
770
+ output_attentions (`bool`, *optional*):
771
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
772
+ tensors for more detail.
773
+ output_hidden_states (`bool`, *optional*):
774
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
775
+ more detail.
776
+ return_dict (`bool`, *optional*):
777
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
778
+ """
779
+
780
+
781
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
782
+ @add_start_docstrings(
783
+ 'The bare SkyworkLM2 Model outputting raw hidden-states without any specific head on top.',
784
+ SkyworkLM2_START_DOCSTRING,
785
+ )
786
+ class SkyworkLM2Model(SkyworkLM2PreTrainedModel):
787
+ """
788
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`SkyworkLM2DecoderLayer`]
789
+
790
+ Args:
791
+ config: SkyworkLM2Config
792
+ """
793
+
794
+ _auto_class = 'AutoModel'
795
+
796
+ def __init__(self, config: SkyworkLM2Config):
797
+ super().__init__(config)
798
+ self.padding_idx = config.pad_token_id
799
+ self.vocab_size = config.vocab_size
800
+ self.config = config
801
+ if not has_flash_attn:
802
+ self.config.attn_implementation = 'eager'
803
+ print('Warning: Flash attention is not available, using eager attention instead.')
804
+
805
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
806
+
807
+ self.layers = nn.ModuleList([SkyworkLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
808
+ self.norm = SkyworkLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
809
+
810
+ self.gradient_checkpointing = False
811
+ # Initialize weights and apply final processing
812
+ self.post_init()
813
+
814
+ def get_input_embeddings(self):
815
+ return self.tok_embeddings
816
+
817
+ def set_input_embeddings(self, value):
818
+ self.tok_embeddings = value
819
+
820
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
821
+ # create causal mask
822
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
823
+ combined_attention_mask = None
824
+ if input_shape[-1] > 1:
825
+ combined_attention_mask = _make_causal_mask(
826
+ input_shape,
827
+ inputs_embeds.dtype,
828
+ device=inputs_embeds.device,
829
+ past_key_values_length=past_key_values_length,
830
+ )
831
+
832
+ if attention_mask is not None:
833
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
834
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
835
+ inputs_embeds.device
836
+ )
837
+ combined_attention_mask = (
838
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
839
+ )
840
+
841
+ return combined_attention_mask
842
+
843
+ @add_start_docstrings_to_model_forward(SkyworkLM2_INPUTS_DOCSTRING)
844
+ def forward(
845
+ self,
846
+ input_ids: torch.LongTensor = None,
847
+ attention_mask: Optional[torch.Tensor] = None,
848
+ position_ids: Optional[torch.LongTensor] = None,
849
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
850
+ inputs_embeds: Optional[torch.FloatTensor] = None,
851
+ use_cache: Optional[bool] = None,
852
+ output_attentions: Optional[bool] = None,
853
+ output_hidden_states: Optional[bool] = None,
854
+ return_dict: Optional[bool] = None,
855
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
856
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
857
+ output_hidden_states = (
858
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
859
+ )
860
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
861
+
862
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
863
+
864
+ if self.config.attn_implementation == 'flash_attention_2':
865
+ _import_flash_attn()
866
+
867
+ # retrieve input_ids and inputs_embeds
868
+ if input_ids is not None and inputs_embeds is not None:
869
+ raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
870
+ elif input_ids is not None:
871
+ batch_size, seq_length = input_ids.shape[:2]
872
+ elif inputs_embeds is not None:
873
+ batch_size, seq_length = inputs_embeds.shape[:2]
874
+ else:
875
+ raise ValueError('You have to specify either input_ids or inputs_embeds')
876
+
877
+ seq_length_with_past = seq_length
878
+ past_key_values_length = 0
879
+ if past_key_values is not None:
880
+ past_key_values_length = past_key_values[0][0].shape[2]
881
+ seq_length_with_past = seq_length_with_past + past_key_values_length
882
+
883
+ if position_ids is None:
884
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
885
+ position_ids = torch.arange(
886
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
887
+ )
888
+ position_ids = position_ids.unsqueeze(0)
889
+
890
+ if inputs_embeds is None:
891
+ inputs_embeds = self.tok_embeddings(input_ids)
892
+
893
+ if self.config.attn_implementation == 'flash_attention_2':
894
+ # 2d mask is passed through the layers
895
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
896
+ else:
897
+ if attention_mask is None:
898
+ attention_mask = torch.ones(
899
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
900
+ )
901
+ attention_mask = self._prepare_decoder_attention_mask(
902
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
903
+ )
904
+
905
+ # embed positions
906
+ hidden_states = inputs_embeds
907
+
908
+ if self.gradient_checkpointing and self.training:
909
+ if use_cache:
910
+ logger.warning_once(
911
+ '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
912
+ )
913
+ use_cache = False
914
+
915
+ # decoder layers
916
+ all_hidden_states = () if output_hidden_states else None
917
+ all_self_attns = () if output_attentions else None
918
+ next_decoder_cache = () if use_cache else None
919
+
920
+ for idx, decoder_layer in enumerate(self.layers):
921
+ if output_hidden_states:
922
+ all_hidden_states += (hidden_states,)
923
+
924
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
925
+
926
+ if self.gradient_checkpointing and self.training:
927
+
928
+ def create_custom_forward(module):
929
+ def custom_forward(*inputs):
930
+ # None for past_key_value
931
+ return module(*inputs, output_attentions, None)
932
+
933
+ return custom_forward
934
+
935
+ layer_outputs = torch.utils.checkpoint.checkpoint(
936
+ create_custom_forward(decoder_layer),
937
+ hidden_states,
938
+ attention_mask,
939
+ position_ids,
940
+ None,
941
+ )
942
+ else:
943
+ layer_outputs = decoder_layer(
944
+ hidden_states,
945
+ attention_mask=attention_mask,
946
+ position_ids=position_ids,
947
+ past_key_value=past_key_value,
948
+ output_attentions=output_attentions,
949
+ use_cache=use_cache,
950
+ )
951
+
952
+ hidden_states = layer_outputs[0]
953
+
954
+ if use_cache:
955
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
956
+
957
+ if output_attentions:
958
+ all_self_attns += (layer_outputs[1],)
959
+
960
+ hidden_states = self.norm(hidden_states)
961
+
962
+ # add hidden states from the last decoder layer
963
+ if output_hidden_states:
964
+ all_hidden_states += (hidden_states,)
965
+
966
+ next_cache = next_decoder_cache if use_cache else None
967
+ if not return_dict:
968
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
969
+ return BaseModelOutputWithPast(
970
+ last_hidden_state=hidden_states,
971
+ past_key_values=next_cache,
972
+ hidden_states=all_hidden_states,
973
+ attentions=all_self_attns,
974
+ )
975
+
976
+
977
+ # Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
978
+ class SkyworkLM2ForCausalLM(SkyworkLM2PreTrainedModel):
979
+ _auto_class = 'AutoModelForCausalLM'
980
+
981
+ _tied_weights_keys = ['output.weight']
982
+
983
+ def __init__(self, config):
984
+ super().__init__(config)
985
+ self.model = SkyworkLM2Model(config)
986
+ self.vocab_size = config.vocab_size
987
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
988
+
989
+ # Initialize weights and apply final processing
990
+ self.post_init()
991
+
992
+ def get_input_embeddings(self):
993
+ return self.model.tok_embeddings
994
+
995
+ def set_input_embeddings(self, value):
996
+ self.model.tok_embeddings = value
997
+
998
+ def get_output_embeddings(self):
999
+ return self.output
1000
+
1001
+ def set_output_embeddings(self, new_embeddings):
1002
+ self.output = new_embeddings
1003
+
1004
+ def set_decoder(self, decoder):
1005
+ self.model = decoder
1006
+
1007
+ def get_decoder(self):
1008
+ return self.model
1009
+
1010
+ @add_start_docstrings_to_model_forward(SkyworkLM2_INPUTS_DOCSTRING)
1011
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1012
+ def forward(
1013
+ self,
1014
+ input_ids: torch.LongTensor = None,
1015
+ attention_mask: Optional[torch.Tensor] = None,
1016
+ position_ids: Optional[torch.LongTensor] = None,
1017
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1018
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1019
+ labels: Optional[torch.LongTensor] = None,
1020
+ use_cache: Optional[bool] = None,
1021
+ output_attentions: Optional[bool] = None,
1022
+ output_hidden_states: Optional[bool] = None,
1023
+ return_dict: Optional[bool] = None,
1024
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1025
+ r"""
1026
+ Args:
1027
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1028
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1029
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1030
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1031
+
1032
+ Returns:
1033
+
1034
+ Example:
1035
+
1036
+ ```python
1037
+ >>> from transformers import AutoTokenizer, SkyworkLM2ForCausalLM
1038
+
1039
+ >>> model = SkyworkLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1040
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1041
+
1042
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1043
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1044
+
1045
+ >>> # Generate
1046
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1047
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1048
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1049
+ ```"""
1050
+
1051
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1052
+ output_hidden_states = (
1053
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1054
+ )
1055
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1056
+
1057
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1058
+ outputs = self.model(
1059
+ input_ids=input_ids,
1060
+ attention_mask=attention_mask,
1061
+ position_ids=position_ids,
1062
+ past_key_values=past_key_values,
1063
+ inputs_embeds=inputs_embeds,
1064
+ use_cache=use_cache,
1065
+ output_attentions=output_attentions,
1066
+ output_hidden_states=output_hidden_states,
1067
+ return_dict=return_dict,
1068
+ )
1069
+
1070
+ hidden_states = outputs[0]
1071
+ logits = self.output(hidden_states)
1072
+ logits = logits.float()
1073
+
1074
+ loss = None
1075
+ if labels is not None:
1076
+ # Shift so that tokens < n predict n
1077
+ shift_logits = logits[..., :-1, :].contiguous()
1078
+ shift_labels = labels[..., 1:].contiguous()
1079
+ # Flatten the tokens
1080
+ loss_fct = CrossEntropyLoss()
1081
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1082
+ shift_labels = shift_labels.view(-1)
1083
+ # Enable model parallelism
1084
+ shift_labels = shift_labels.to(shift_logits.device)
1085
+ loss = loss_fct(shift_logits, shift_labels)
1086
+
1087
+ if not return_dict:
1088
+ output = (logits,) + outputs[1:]
1089
+ return (loss,) + output if loss is not None else output
1090
+
1091
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1092
+ output = CausalLMOutputWithPast(
1093
+ loss=loss,
1094
+ logits=logits,
1095
+ past_key_values=outputs.past_key_values,
1096
+ hidden_states=outputs.hidden_states,
1097
+ attentions=outputs.attentions,
1098
+ )
1099
+ output['logits'] = output['logits'].to(device)
1100
+ return output
1101
+
1102
+ def prepare_inputs_for_generation(
1103
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1104
+ ):
1105
+ if past_key_values is not None:
1106
+ past_length = past_key_values[0][0].shape[2]
1107
+
1108
+ # Some generation methods already pass only the last input ID
1109
+ if input_ids.shape[1] > past_length:
1110
+ remove_prefix_length = past_length
1111
+ else:
1112
+ # Default to old behavior: keep only final ID
1113
+ remove_prefix_length = input_ids.shape[1] - 1
1114
+
1115
+ input_ids = input_ids[:, remove_prefix_length:]
1116
+
1117
+ position_ids = kwargs.get('position_ids', None)
1118
+ if attention_mask is not None and position_ids is None:
1119
+ # create position_ids on the fly for batch generation
1120
+ position_ids = attention_mask.long().cumsum(-1) - 1
1121
+ position_ids.masked_fill_(attention_mask == 0, 1)
1122
+ if past_key_values:
1123
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1124
+
1125
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1126
+ if inputs_embeds is not None and past_key_values is None:
1127
+ model_inputs = {'inputs_embeds': inputs_embeds}
1128
+ else:
1129
+ model_inputs = {'input_ids': input_ids}
1130
+
1131
+ model_inputs.update(
1132
+ {
1133
+ 'position_ids': position_ids,
1134
+ 'past_key_values': past_key_values,
1135
+ 'use_cache': kwargs.get('use_cache'),
1136
+ 'attention_mask': attention_mask,
1137
+ }
1138
+ )
1139
+ return model_inputs
1140
+
1141
+ @staticmethod
1142
+ def _reorder_cache(past_key_values, beam_idx):
1143
+ reordered_past = ()
1144
+ for layer_past in past_key_values:
1145
+ reordered_past += (
1146
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1147
+ )
1148
+ return reordered_past
1149
+
1150
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''): #TODO
1151
+ if tokenizer.add_bos_token:
1152
+ prompt = ''
1153
+ else:
1154
+ prompt = tokenizer.bos_token
1155
+ if meta_instruction:
1156
+ prompt += f"""<|begin▁of▁sentence|>system\n{meta_instruction}<|end▁of▁sentence|>\n"""
1157
+ for record in history:
1158
+ prompt += f"""<|begin▁of▁sentence��>user\n{record[0]}<|end▁of▁sentence|>\n<|begin▁of▁sentence|>assistant\n{record[1]}<|end▁of▁sentence|>\n"""
1159
+ prompt += f"""<|begin▁of▁sentence|>user\n{query}<|end▁of▁sentence|>\n<|begin▁of▁sentence|>assistant\n"""
1160
+ return tokenizer([prompt], return_tensors='pt')
1161
+
1162
+ @torch.no_grad()
1163
+ def chat(
1164
+ self,
1165
+ tokenizer,
1166
+ query: str,
1167
+ history: List[Tuple[str, str]] = [],
1168
+ streamer: Optional[BaseStreamer] = None,
1169
+ max_new_tokens: int = 1024,
1170
+ do_sample: bool = True,
1171
+ temperature: float = 0.8,
1172
+ top_p: float = 0.8,
1173
+ meta_instruction: str = '',
1174
+ **kwargs,
1175
+ ):
1176
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1177
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1178
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1179
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['<|end▁of▁sentence|>'])[0]]
1180
+ outputs = self.generate(
1181
+ **inputs,
1182
+ streamer=streamer,
1183
+ max_new_tokens=max_new_tokens,
1184
+ do_sample=do_sample,
1185
+ temperature=temperature,
1186
+ top_p=top_p,
1187
+ eos_token_id=eos_token_id,
1188
+ **kwargs,
1189
+ )
1190
+ outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]) :]
1191
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1192
+ response = response.split('<|end▁of▁sentence|>')[0]
1193
+ history = history + [(query, response)]
1194
+ return response, history
1195
+
1196
+ @torch.no_grad()
1197
+ def stream_chat(
1198
+ self,
1199
+ tokenizer,
1200
+ query: str,
1201
+ history: List[Tuple[str, str]] = [],
1202
+ max_new_tokens: int = 1024,
1203
+ do_sample: bool = True,
1204
+ temperature: float = 0.8,
1205
+ top_p: float = 0.8,
1206
+ **kwargs,
1207
+ ):
1208
+ """
1209
+ Return a generator in format: (response, history)
1210
+ Eg.
1211
+ ('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')])
1212
+ ('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?')])
1213
+ """
1214
+ if BaseStreamer is None:
1215
+ raise ModuleNotFoundError(
1216
+ 'The version of `transformers` is too low. Please make sure '
1217
+ 'that you have installed `transformers>=4.28.0`.'
1218
+ )
1219
+
1220
+ response_queue = queue.Queue(maxsize=20)
1221
+
1222
+ class ChatStreamer(BaseStreamer):
1223
+ def __init__(self, tokenizer) -> None:
1224
+ super().__init__()
1225
+ self.tokenizer = tokenizer
1226
+ self.queue = response_queue
1227
+ self.query = query
1228
+ self.history = history
1229
+ self.response = ''
1230
+ self.cache = []
1231
+ self.received_inputs = False
1232
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1233
+
1234
+ def put(self, value):
1235
+ if len(value.shape) > 1 and value.shape[0] > 1:
1236
+ raise ValueError('ChatStreamer only supports batch size 1')
1237
+ elif len(value.shape) > 1:
1238
+ value = value[0]
1239
+
1240
+ if not self.received_inputs:
1241
+ # The first received value is input_ids, ignore here
1242
+ self.received_inputs = True
1243
+ return
1244
+
1245
+ self.cache.extend(value.tolist())
1246
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1247
+ if token.strip() != '<|end▁of▁sentence|>':
1248
+ self.response = self.response + token
1249
+ history = self.history + [(self.query, self.response)]
1250
+ self.queue.put((self.response, history))
1251
+ self.cache = []
1252
+ else:
1253
+ self.end()
1254
+
1255
+ def end(self):
1256
+ self.queue.put(None)
1257
+
1258
+ def stream_producer():
1259
+ return self.chat(
1260
+ tokenizer=tokenizer,
1261
+ query=query,
1262
+ streamer=ChatStreamer(tokenizer=tokenizer),
1263
+ history=history,
1264
+ max_new_tokens=max_new_tokens,
1265
+ do_sample=do_sample,
1266
+ temperature=temperature,
1267
+ top_p=top_p,
1268
+ **kwargs,
1269
+ )
1270
+
1271
+ def consumer():
1272
+ producer = threading.Thread(target=stream_producer)
1273
+ producer.start()
1274
+ while True:
1275
+ res = response_queue.get()
1276
+ if res is None:
1277
+ return
1278
+ yield res
1279
+
1280
+ return consumer()
1281
+
1282
+
1283
+ # Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->SkyworkLM2
1284
+ @add_start_docstrings(
1285
+ """
1286
+ The SkyworkLM2 Model transformer with a sequence classification head on top (linear layer).
1287
+
1288
+ [`SkyworkLM2ForSequenceClassification`] uses the last token in order to do the classification,
1289
+ as other causal models (e.g. GPT-2) do.
1290
+
1291
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1292
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1293
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1294
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1295
+ each row of the batch).
1296
+ """,
1297
+ SkyworkLM2_START_DOCSTRING,
1298
+ )
1299
+ class SkyworkLM2ForSequenceClassification(SkyworkLM2PreTrainedModel):
1300
+ def __init__(self, config):
1301
+ super().__init__(config)
1302
+ self.num_labels = config.num_labels
1303
+ self.model = SkyworkLM2Model(config)
1304
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1305
+
1306
+ # Initialize weights and apply final processing
1307
+ self.post_init()
1308
+
1309
+ def get_input_embeddings(self):
1310
+ return self.model.tok_embeddings
1311
+
1312
+ def set_input_embeddings(self, value):
1313
+ self.model.tok_embeddings = value
1314
+
1315
+ @add_start_docstrings_to_model_forward(SkyworkLM2_INPUTS_DOCSTRING)
1316
+ def forward(
1317
+ self,
1318
+ input_ids: torch.LongTensor = None,
1319
+ attention_mask: Optional[torch.Tensor] = None,
1320
+ position_ids: Optional[torch.LongTensor] = None,
1321
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1322
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1323
+ labels: Optional[torch.LongTensor] = None,
1324
+ use_cache: Optional[bool] = None,
1325
+ output_attentions: Optional[bool] = None,
1326
+ output_hidden_states: Optional[bool] = None,
1327
+ return_dict: Optional[bool] = None,
1328
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1329
+ r"""
1330
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1331
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1332
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1333
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1334
+ """
1335
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1336
+
1337
+ transformer_outputs = self.model(
1338
+ input_ids,
1339
+ attention_mask=attention_mask,
1340
+ position_ids=position_ids,
1341
+ past_key_values=past_key_values,
1342
+ inputs_embeds=inputs_embeds,
1343
+ use_cache=use_cache,
1344
+ output_attentions=output_attentions,
1345
+ output_hidden_states=output_hidden_states,
1346
+ return_dict=return_dict,
1347
+ )
1348
+ hidden_states = transformer_outputs[0]
1349
+ logits = self.score(hidden_states)
1350
+
1351
+ if input_ids is not None:
1352
+ batch_size = input_ids.shape[0]
1353
+ else:
1354
+ batch_size = inputs_embeds.shape[0]
1355
+
1356
+ if self.config.pad_token_id is None and batch_size != 1:
1357
+ raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.')
1358
+ if self.config.pad_token_id is None:
1359
+ sequence_lengths = -1
1360
+ else:
1361
+ if input_ids is not None:
1362
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1363
+ logits.device
1364
+ )
1365
+ else:
1366
+ sequence_lengths = -1
1367
+
1368
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1369
+
1370
+ loss = None
1371
+ if labels is not None:
1372
+ labels = labels.to(logits.device)
1373
+ if self.config.problem_type is None:
1374
+ if self.num_labels == 1:
1375
+ self.config.problem_type = 'regression'
1376
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1377
+ self.config.problem_type = 'single_label_classification'
1378
+ else:
1379
+ self.config.problem_type = 'multi_label_classification'
1380
+
1381
+ if self.config.problem_type == 'regression':
1382
+ loss_fct = MSELoss()
1383
+ if self.num_labels == 1:
1384
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1385
+ else:
1386
+ loss = loss_fct(pooled_logits, labels)
1387
+ elif self.config.problem_type == 'single_label_classification':
1388
+ loss_fct = CrossEntropyLoss()
1389
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1390
+ elif self.config.problem_type == 'multi_label_classification':
1391
+ loss_fct = BCEWithLogitsLoss()
1392
+ loss = loss_fct(pooled_logits, labels)
1393
+ if not return_dict:
1394
+ output = (pooled_logits,) + transformer_outputs[1:]
1395
+ return ((loss,) + output) if loss is not None else output
1396
+
1397
+ return SequenceClassifierOutputWithPast(
1398
+ loss=loss,
1399
+ logits=pooled_logits,
1400
+ past_key_values=transformer_outputs.past_key_values,
1401
+ hidden_states=transformer_outputs.hidden_states,
1402
+ attentions=transformer_outputs.attentions,
1403
+ )
modeling_skywork_vit.py ADDED
@@ -0,0 +1,424 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Optional, Tuple, Union
2
+
3
+ import torch
4
+ import torch.nn.functional as F
5
+ import torch.utils.checkpoint
6
+ from einops import rearrange
7
+ from timm.models.layers import DropPath
8
+ from torch import nn
9
+ from transformers.activations import ACT2FN
10
+ from transformers.modeling_outputs import (BaseModelOutput,
11
+ BaseModelOutputWithPooling)
12
+ from transformers.modeling_utils import PreTrainedModel
13
+ from transformers.utils import logging
14
+
15
+ from .configuration_skywork_vit import SkyworkVisionConfig
16
+
17
+ try:
18
+ from flash_attn.bert_padding import pad_input, unpad_input
19
+ from flash_attn.flash_attn_interface import \
20
+ flash_attn_varlen_qkvpacked_func
21
+ has_flash_attn = True
22
+ except:
23
+ print('FlashAttention2 is not installed.')
24
+ has_flash_attn = False
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+
29
+ class FlashAttention(nn.Module):
30
+ """Implement the scaled dot product attention with softmax.
31
+ Arguments
32
+ ---------
33
+ softmax_scale: The temperature to use for the softmax attention.
34
+ (default: 1/sqrt(d_keys) where d_keys is computed at
35
+ runtime)
36
+ attention_dropout: The dropout rate to apply to the attention
37
+ (default: 0.0)
38
+ """
39
+
40
+ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
41
+ super().__init__()
42
+ self.softmax_scale = softmax_scale
43
+ self.dropout_p = attention_dropout
44
+
45
+ def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
46
+ max_s=None, need_weights=False):
47
+ """Implements the multihead softmax attention.
48
+ Arguments
49
+ ---------
50
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
51
+ if unpadded: (nnz, 3, h, d)
52
+ key_padding_mask: a bool tensor of shape (B, S)
53
+ """
54
+ assert not need_weights
55
+ assert qkv.dtype in [torch.float16, torch.bfloat16]
56
+ assert qkv.is_cuda
57
+
58
+ if cu_seqlens is None:
59
+ batch_size = qkv.shape[0]
60
+ seqlen = qkv.shape[1]
61
+ if key_padding_mask is None:
62
+ qkv = rearrange(qkv, 'b s ... -> (b s) ...')
63
+ max_s = seqlen
64
+ cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
65
+ device=qkv.device)
66
+ output = flash_attn_varlen_qkvpacked_func(
67
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
68
+ softmax_scale=self.softmax_scale, causal=causal
69
+ )
70
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
71
+ else:
72
+ nheads = qkv.shape[-2]
73
+ x = rearrange(qkv, 'b s three h d -> b s (three h d)')
74
+ x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
75
+ x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
76
+ output_unpad = flash_attn_varlen_qkvpacked_func(
77
+ x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
78
+ softmax_scale=self.softmax_scale, causal=causal
79
+ )
80
+ output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
81
+ indices, batch_size, seqlen),
82
+ 'b s (h d) -> b s h d', h=nheads)
83
+ else:
84
+ assert max_s is not None
85
+ output = flash_attn_varlen_qkvpacked_func(
86
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
87
+ softmax_scale=self.softmax_scale, causal=causal
88
+ )
89
+
90
+ return output, None
91
+
92
+
93
+ class SkyworkRMSNorm(nn.Module):
94
+ def __init__(self, hidden_size, eps=1e-6):
95
+ super().__init__()
96
+ self.weight = nn.Parameter(torch.ones(hidden_size))
97
+ self.variance_epsilon = eps
98
+
99
+ def forward(self, hidden_states):
100
+ input_dtype = hidden_states.dtype
101
+ hidden_states = hidden_states.to(torch.float32)
102
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
103
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
104
+ return self.weight * hidden_states.to(input_dtype)
105
+
106
+
107
+ try:
108
+ from apex.normalization import FusedRMSNorm
109
+
110
+ SkyworkRMSNorm = FusedRMSNorm # noqa
111
+
112
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead ofSkyworkRMSNorm')
113
+ except ImportError:
114
+ # using the normal SkyworkRMSNorm
115
+ pass
116
+ except Exception:
117
+ logger.warning('discovered apex but it failed to load, falling back to SkyworkRMSNorm')
118
+ pass
119
+
120
+
121
+ NORM2FN = {
122
+ 'rms_norm': SkyworkRMSNorm,
123
+ 'layer_norm': nn.LayerNorm,
124
+ }
125
+
126
+
127
+ class SkyworkVisionEmbeddings(nn.Module):
128
+ def __init__(self, config: SkyworkVisionConfig):
129
+ super().__init__()
130
+ self.config = config
131
+ self.embed_dim = config.hidden_size
132
+ self.image_size = config.image_size
133
+ self.patch_size = config.patch_size
134
+
135
+ self.class_embedding = nn.Parameter(
136
+ torch.randn(1, 1, self.embed_dim),
137
+ )
138
+
139
+ self.patch_embedding = nn.Conv2d(
140
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
141
+ )
142
+
143
+ self.num_patches = (self.image_size // self.patch_size) ** 2
144
+ self.num_positions = self.num_patches + 1
145
+
146
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
147
+
148
+ def _get_pos_embed(self, pos_embed, H, W):
149
+ target_dtype = pos_embed.dtype
150
+ pos_embed = pos_embed.float().reshape(
151
+ 1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
152
+ pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
153
+ reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
154
+ return pos_embed
155
+
156
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
157
+ target_dtype = self.patch_embedding.weight.dtype
158
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height]
159
+ batch_size, _, height, width = patch_embeds.shape
160
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
161
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
162
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
163
+ position_embedding = torch.cat([
164
+ self.position_embedding[:, :1, :],
165
+ self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
166
+ ], dim=1)
167
+ embeddings = embeddings + position_embedding.to(target_dtype)
168
+ return embeddings
169
+
170
+
171
+ class SkyworkAttention(nn.Module):
172
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
173
+
174
+ def __init__(self, config: SkyworkVisionConfig):
175
+ super().__init__()
176
+ self.config = config
177
+ self.embed_dim = config.hidden_size
178
+ self.num_heads = config.num_attention_heads
179
+ self.use_flash_attn = config.use_flash_attn and has_flash_attn
180
+ if config.use_flash_attn and not has_flash_attn:
181
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
182
+ self.head_dim = self.embed_dim // self.num_heads
183
+ if self.head_dim * self.num_heads != self.embed_dim:
184
+ raise ValueError(
185
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
186
+ f' {self.num_heads}).'
187
+ )
188
+
189
+ self.scale = self.head_dim ** -0.5
190
+ self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
191
+ self.attn_drop = nn.Dropout(config.attention_dropout)
192
+ self.proj_drop = nn.Dropout(config.dropout)
193
+
194
+ self.qk_normalization = config.qk_normalization
195
+
196
+ if self.qk_normalization:
197
+ self.q_norm = SkyworkRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
198
+ self.k_norm = SkyworkRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
199
+
200
+ if self.use_flash_attn:
201
+ self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
202
+ self.proj = nn.Linear(self.embed_dim, self.embed_dim)
203
+
204
+ def _naive_attn(self, x):
205
+ B, N, C = x.shape
206
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
207
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
208
+
209
+ if self.qk_normalization:
210
+ B_, H_, N_, D_ = q.shape
211
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
212
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
213
+
214
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
215
+ attn = attn.softmax(dim=-1)
216
+ attn = self.attn_drop(attn)
217
+
218
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
219
+ x = self.proj(x)
220
+ x = self.proj_drop(x)
221
+ return x
222
+
223
+ def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
224
+ qkv = self.qkv(x)
225
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
226
+
227
+ if self.qk_normalization:
228
+ q, k, v = qkv.unbind(2)
229
+ q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
230
+ k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
231
+ qkv = torch.stack([q, k, v], dim=2)
232
+
233
+ context, _ = self.inner_attn(
234
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
235
+ )
236
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
237
+ outs = self.proj_drop(outs)
238
+ return outs
239
+
240
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
241
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
242
+ return x
243
+
244
+
245
+ class SkyworkMLP(nn.Module):
246
+ def __init__(self, config: SkyworkVisionConfig):
247
+ super().__init__()
248
+ self.config = config
249
+ self.act = ACT2FN[config.hidden_act]
250
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
251
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
252
+
253
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
254
+ hidden_states = self.fc1(hidden_states)
255
+ hidden_states = self.act(hidden_states)
256
+ hidden_states = self.fc2(hidden_states)
257
+ return hidden_states
258
+
259
+
260
+ class SkyworkVisionEncoderLayer(nn.Module):
261
+ def __init__(self, config: SkyworkVisionConfig, drop_path_rate: float):
262
+ super().__init__()
263
+ self.embed_dim = config.hidden_size
264
+ self.intermediate_size = config.intermediate_size
265
+ self.norm_type = config.norm_type
266
+
267
+ self.attn = SkyworkAttention(config)
268
+ self.mlp = SkyworkMLP(config)
269
+ self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
270
+ self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
271
+
272
+ self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
273
+ self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
274
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
275
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
276
+
277
+ def forward(
278
+ self,
279
+ hidden_states: torch.Tensor,
280
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
281
+ """
282
+ Args:
283
+ hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
284
+ """
285
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1)
286
+
287
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2)
288
+
289
+ return hidden_states
290
+
291
+
292
+ class SkyworkVisionEncoder(nn.Module):
293
+ """
294
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
295
+ [`SkyworkEncoderLayer`].
296
+
297
+ Args:
298
+ config (`SkyworkConfig`):
299
+ The corresponding vision configuration for the `SkyworkEncoder`.
300
+ """
301
+
302
+ def __init__(self, config: SkyworkVisionConfig):
303
+ super().__init__()
304
+ self.config = config
305
+ # stochastic depth decay rule
306
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
307
+ self.layers = nn.ModuleList([
308
+ SkyworkVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
309
+ self.gradient_checkpointing = True
310
+
311
+ def forward(
312
+ self,
313
+ inputs_embeds,
314
+ output_hidden_states: Optional[bool] = None,
315
+ return_dict: Optional[bool] = None,
316
+ ) -> Union[Tuple, BaseModelOutput]:
317
+ r"""
318
+ Args:
319
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
320
+ Embedded representation of the inputs. Should be float, not int tokens.
321
+ output_hidden_states (`bool`, *optional*):
322
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
323
+ for more detail.
324
+ return_dict (`bool`, *optional*):
325
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
326
+ """
327
+ output_hidden_states = (
328
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
329
+ )
330
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
331
+
332
+ encoder_states = () if output_hidden_states else None
333
+ hidden_states = inputs_embeds
334
+
335
+ for idx, encoder_layer in enumerate(self.layers):
336
+ if output_hidden_states:
337
+ encoder_states = encoder_states + (hidden_states,)
338
+ if self.gradient_checkpointing and self.training:
339
+ layer_outputs = torch.utils.checkpoint.checkpoint(
340
+ encoder_layer,
341
+ hidden_states)
342
+ else:
343
+ layer_outputs = encoder_layer(
344
+ hidden_states,
345
+ )
346
+ hidden_states = layer_outputs
347
+
348
+ if output_hidden_states:
349
+ encoder_states = encoder_states + (hidden_states,)
350
+
351
+ if not return_dict:
352
+ return tuple(v for v in [hidden_states, encoder_states] if v is not None)
353
+ return BaseModelOutput(
354
+ last_hidden_state=hidden_states, hidden_states=encoder_states
355
+ )
356
+
357
+
358
+ class SkyworkVisionModel(PreTrainedModel):
359
+ main_input_name = 'pixel_values'
360
+ _supports_flash_attn_2 = True
361
+ config_class = SkyworkVisionConfig
362
+ _no_split_modules = ['SkyworkVisionEncoderLayer']
363
+
364
+ def __init__(self, config: SkyworkVisionConfig):
365
+ super().__init__(config)
366
+ self.config = config
367
+
368
+ self.embeddings = SkyworkVisionEmbeddings(config)
369
+ self.encoder = SkyworkVisionEncoder(config)
370
+
371
+ def resize_pos_embeddings(self, old_size, new_size, patch_size):
372
+ pos_emb = self.embeddings.position_embedding
373
+ _, num_positions, embed_dim = pos_emb.shape
374
+ cls_emb = pos_emb[:, :1, :]
375
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
376
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
377
+ pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
378
+ pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
379
+ self.embeddings.position_embedding = nn.Parameter(pos_emb)
380
+ self.embeddings.image_size = new_size
381
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
382
+
383
+ def get_input_embeddings(self):
384
+ return self.embeddings
385
+
386
+ def forward(
387
+ self,
388
+ pixel_values: Optional[torch.FloatTensor] = None,
389
+ output_hidden_states: Optional[bool] = None,
390
+ return_dict: Optional[bool] = None,
391
+ pixel_embeds: Optional[torch.FloatTensor] = None,
392
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
393
+ output_hidden_states = (
394
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
395
+ )
396
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
397
+
398
+ if pixel_values is None and pixel_embeds is None:
399
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
400
+
401
+ if pixel_embeds is not None:
402
+ hidden_states = pixel_embeds
403
+ else:
404
+ if len(pixel_values.shape) == 4:
405
+ hidden_states = self.embeddings(pixel_values)
406
+ else:
407
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
408
+ encoder_outputs = self.encoder(
409
+ inputs_embeds=hidden_states,
410
+ output_hidden_states=output_hidden_states,
411
+ return_dict=return_dict,
412
+ )
413
+ last_hidden_state = encoder_outputs.last_hidden_state
414
+ pooled_output = last_hidden_state[:, 0, :]
415
+
416
+ if not return_dict:
417
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
418
+
419
+ return BaseModelOutputWithPooling(
420
+ last_hidden_state=last_hidden_state,
421
+ pooler_output=pooled_output,
422
+ hidden_states=encoder_outputs.hidden_states,
423
+ attentions=encoder_outputs.attentions,
424
+ )
outputs_stats.pth ADDED
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pytorch_model-00008-of-00016.bin ADDED
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pytorch_model-00012-of-00016.bin ADDED
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pytorch_model.bin.index.json ADDED
The diff for this file is too large to render. See raw diff
 
special_tokens_map.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>",
5
+ "<|object_ref_start|>",
6
+ "<|object_ref_end|>",
7
+ "<|box_start|>",
8
+ "<|box_end|>",
9
+ "<|quad_start|>",
10
+ "<|quad_end|>",
11
+ "<|vision_start|>",
12
+ "<|vision_end|>",
13
+ "<|vision_pad|>",
14
+ "<|image_pad|>",
15
+ "<|video_pad|>",
16
+ "<img>",
17
+ "</img>",
18
+ "<IMG_CONTEXT>",
19
+ "<quad>",
20
+ "</quad>",
21
+ "<ref>",
22
+ "</ref>",
23
+ "<box>",
24
+ "</box>"
25
+ ],
26
+ "eos_token": {
27
+ "content": "<|im_end|>",
28
+ "lstrip": false,
29
+ "normalized": false,
30
+ "rstrip": false,
31
+ "single_word": false
32
+ },
33
+ "pad_token": {
34
+ "content": "<|endoftext|>",
35
+ "lstrip": false,
36
+ "normalized": false,
37
+ "rstrip": false,
38
+ "single_word": false
39
+ }
40
+ }
tokenization_internlm2.py ADDED
@@ -0,0 +1,235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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.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 classes for InternLM."""
18
+ import os
19
+ from shutil import copyfile
20
+ from typing import Any, Dict, List, Optional, Tuple
21
+
22
+ import sentencepiece as spm
23
+ from transformers.tokenization_utils import PreTrainedTokenizer
24
+ from transformers.utils import logging
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
29
+
30
+ PRETRAINED_VOCAB_FILES_MAP = {}
31
+
32
+
33
+ # Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
34
+ class InternLM2Tokenizer(PreTrainedTokenizer):
35
+ """
36
+ Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
37
+
38
+ Args:
39
+ vocab_file (`str`):
40
+ Path to the vocabulary file.
41
+ """
42
+
43
+ vocab_files_names = VOCAB_FILES_NAMES
44
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
45
+ model_input_names = ['input_ids', 'attention_mask']
46
+ _auto_class = 'AutoTokenizer'
47
+
48
+ def __init__(
49
+ self,
50
+ vocab_file,
51
+ unk_token='<unk>',
52
+ bos_token='<s>',
53
+ eos_token='</s>',
54
+ pad_token='</s>',
55
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
56
+ add_bos_token=True,
57
+ add_eos_token=False,
58
+ decode_with_prefix_space=False,
59
+ clean_up_tokenization_spaces=False,
60
+ **kwargs,
61
+ ):
62
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
63
+ self.vocab_file = vocab_file
64
+ self.add_bos_token = add_bos_token
65
+ self.add_eos_token = add_eos_token
66
+ self.decode_with_prefix_space = decode_with_prefix_space
67
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
68
+ self.sp_model.Load(vocab_file)
69
+ self._no_prefix_space_tokens = None
70
+ super().__init__(
71
+ bos_token=bos_token,
72
+ eos_token=eos_token,
73
+ unk_token=unk_token,
74
+ pad_token=pad_token,
75
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
76
+ **kwargs,
77
+ )
78
+
79
+ @property
80
+ def no_prefix_space_tokens(self):
81
+ if self._no_prefix_space_tokens is None:
82
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
83
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith('▁')}
84
+ return self._no_prefix_space_tokens
85
+
86
+ @property
87
+ def vocab_size(self):
88
+ """Returns vocab size"""
89
+ return self.sp_model.get_piece_size()
90
+
91
+ @property
92
+ def bos_token_id(self) -> Optional[int]:
93
+ return self.sp_model.bos_id()
94
+
95
+ @property
96
+ def eos_token_id(self) -> Optional[int]:
97
+ return self.sp_model.eos_id()
98
+
99
+ def get_vocab(self):
100
+ """Returns vocab as a dict"""
101
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
102
+ vocab.update(self.added_tokens_encoder)
103
+ return vocab
104
+
105
+ def _tokenize(self, text):
106
+ """Returns a tokenized string."""
107
+ return self.sp_model.encode(text, out_type=str)
108
+
109
+ def _convert_token_to_id(self, token):
110
+ """Converts a token (str) in an id using the vocab."""
111
+ return self.sp_model.piece_to_id(token)
112
+
113
+ def _convert_id_to_token(self, index):
114
+ """Converts an index (integer) in a token (str) using the vocab."""
115
+ token = self.sp_model.IdToPiece(index)
116
+ return token
117
+
118
+ def _maybe_add_prefix_space(self, tokens, decoded):
119
+ if tokens and tokens[0] not in self.no_prefix_space_tokens:
120
+ return ' ' + decoded
121
+ else:
122
+ return decoded
123
+
124
+ def convert_tokens_to_string(self, tokens):
125
+ """Converts a sequence of tokens (string) in a single string."""
126
+ current_sub_tokens = []
127
+ out_string = ''
128
+ prev_is_special = False
129
+ for token in tokens:
130
+ # make sure that special tokens are not decoded using sentencepiece model
131
+ if token in self.all_special_tokens:
132
+ if not prev_is_special:
133
+ out_string += ' '
134
+ out_string += self.sp_model.decode(current_sub_tokens) + token
135
+ prev_is_special = True
136
+ current_sub_tokens = []
137
+ else:
138
+ current_sub_tokens.append(token)
139
+ prev_is_special = False
140
+ out_string += self.sp_model.decode(current_sub_tokens)
141
+ out_string = self.clean_up_tokenization(out_string)
142
+ out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
143
+ return out_string[1:]
144
+
145
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
146
+ """
147
+ Save the vocabulary and special tokens file to a directory.
148
+
149
+ Args:
150
+ save_directory (`str`):
151
+ The directory in which to save the vocabulary.
152
+
153
+ Returns:
154
+ `Tuple(str)`: Paths to the files saved.
155
+ """
156
+ if not os.path.isdir(save_directory):
157
+ logger.error(f'Vocabulary path ({save_directory}) should be a directory')
158
+ return
159
+ out_vocab_file = os.path.join(
160
+ save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
161
+ )
162
+
163
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
164
+ copyfile(self.vocab_file, out_vocab_file)
165
+ elif not os.path.isfile(self.vocab_file):
166
+ with open(out_vocab_file, 'wb') as fi:
167
+ content_spiece_model = self.sp_model.serialized_model_proto()
168
+ fi.write(content_spiece_model)
169
+
170
+ return (out_vocab_file,)
171
+
172
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
173
+ if self.add_bos_token:
174
+ bos_token_ids = [self.bos_token_id]
175
+ else:
176
+ bos_token_ids = []
177
+
178
+ output = bos_token_ids + token_ids_0
179
+
180
+ if token_ids_1 is not None:
181
+ output = output + token_ids_1
182
+
183
+ if self.add_eos_token:
184
+ output = output + [self.eos_token_id]
185
+
186
+ return output
187
+
188
+ def get_special_tokens_mask(
189
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
190
+ ) -> List[int]:
191
+ """
192
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
193
+ special tokens using the tokenizer `prepare_for_model` method.
194
+
195
+ Args:
196
+ token_ids_0 (`List[int]`):
197
+ List of IDs.
198
+ token_ids_1 (`List[int]`, *optional*):
199
+ Optional second list of IDs for sequence pairs.
200
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
201
+ Whether or not the token list is already formatted with special tokens for the model.
202
+
203
+ Returns:
204
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
205
+ """
206
+ if already_has_special_tokens:
207
+ return super().get_special_tokens_mask(
208
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
209
+ )
210
+
211
+ if token_ids_1 is None:
212
+ return [1] + ([0] * len(token_ids_0)) + [1]
213
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
214
+
215
+ def create_token_type_ids_from_sequences(
216
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
217
+ ) -> List[int]:
218
+ """
219
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
220
+ use of token type ids, therefore a list of zeros is returned.
221
+
222
+ Args:
223
+ token_ids_0 (`List[int]`):
224
+ List of IDs.
225
+ token_ids_1 (`List[int]`, *optional*):
226
+ Optional second list of IDs for sequence pairs.
227
+
228
+ Returns:
229
+ `List[int]`: List of zeros.
230
+ """
231
+ eos = [self.eos_token_id]
232
+
233
+ if token_ids_1 is None:
234
+ return len(token_ids_0 + eos) * [0]
235
+ 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.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:34a2790c1c37a3f4774fef44480b2b50e3c0f40f2122d26e057f249460b8735d
3
+ size 11423542
tokenizer_config.json ADDED
@@ -0,0 +1,290 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
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6
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+ "single_word": false,
236
+ "special": true
237
+ },
238
+ "151672": {
239
+ "content": "<box>",
240
+ "lstrip": false,
241
+ "normalized": false,
242
+ "rstrip": false,
243
+ "single_word": false,
244
+ "special": true
245
+ },
246
+ "151673": {
247
+ "content": "</box>",
248
+ "lstrip": false,
249
+ "normalized": false,
250
+ "rstrip": false,
251
+ "single_word": false,
252
+ "special": true
253
+ }
254
+ },
255
+ "additional_special_tokens": [
256
+ "<|im_start|>",
257
+ "<|im_end|>",
258
+ "<|object_ref_start|>",
259
+ "<|object_ref_end|>",
260
+ "<|box_start|>",
261
+ "<|box_end|>",
262
+ "<|quad_start|>",
263
+ "<|quad_end|>",
264
+ "<|vision_start|>",
265
+ "<|vision_end|>",
266
+ "<|vision_pad|>",
267
+ "<|image_pad|>",
268
+ "<|video_pad|>",
269
+ "<img>",
270
+ "</img>",
271
+ "<IMG_CONTEXT>",
272
+ "<quad>",
273
+ "</quad>",
274
+ "<ref>",
275
+ "</ref>",
276
+ "<box>",
277
+ "</box>"
278
+ ],
279
+ "bos_token": null,
280
+ "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" and not message.tool_calls %}\n {%- set content = message.content %}\n {%- if not loop.last %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- if not loop.last %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}\n",
281
+ "clean_up_tokenization_spaces": false,
282
+ "eos_token": "<|im_end|>",
283
+ "errors": "replace",
284
+ "extra_special_tokens": {},
285
+ "model_max_length": 12000,
286
+ "pad_token": "<|endoftext|>",
287
+ "split_special_tokens": false,
288
+ "tokenizer_class": "Qwen2Tokenizer",
289
+ "unk_token": null
290
+ }
vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
zero_to_fp32.py ADDED
@@ -0,0 +1,604 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+
3
+ # Copyright (c) Microsoft Corporation.
4
+ # SPDX-License-Identifier: Apache-2.0
5
+
6
+ # DeepSpeed Team
7
+
8
+ # This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
9
+ # copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
10
+ # the future. Once extracted, the weights don't require DeepSpeed and can be used in any
11
+ # application.
12
+ #
13
+ # example: python zero_to_fp32.py . pytorch_model.bin
14
+
15
+ import argparse
16
+ import torch
17
+ import glob
18
+ import math
19
+ import os
20
+ import re
21
+ from collections import OrderedDict
22
+ from dataclasses import dataclass
23
+
24
+ # while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
25
+ # DeepSpeed data structures it has to be available in the current python environment.
26
+ from deepspeed.utils import logger
27
+ from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
28
+ FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
29
+ FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
30
+
31
+
32
+ @dataclass
33
+ class zero_model_state:
34
+ buffers: dict()
35
+ param_shapes: dict()
36
+ shared_params: list
37
+ ds_version: int
38
+ frozen_param_shapes: dict()
39
+ frozen_param_fragments: dict()
40
+
41
+
42
+ debug = 0
43
+
44
+ # load to cpu
45
+ device = torch.device('cpu')
46
+
47
+
48
+ def atoi(text):
49
+ return int(text) if text.isdigit() else text
50
+
51
+
52
+ def natural_keys(text):
53
+ '''
54
+ alist.sort(key=natural_keys) sorts in human order
55
+ http://nedbatchelder.com/blog/200712/human_sorting.html
56
+ (See Toothy's implementation in the comments)
57
+ '''
58
+ return [atoi(c) for c in re.split(r'(\d+)', text)]
59
+
60
+
61
+ def get_model_state_file(checkpoint_dir, zero_stage):
62
+ if not os.path.isdir(checkpoint_dir):
63
+ raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
64
+
65
+ # there should be only one file
66
+ if zero_stage <= 2:
67
+ file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
68
+ elif zero_stage == 3:
69
+ file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
70
+
71
+ if not os.path.exists(file):
72
+ raise FileNotFoundError(f"can't find model states file at '{file}'")
73
+
74
+ return file
75
+
76
+
77
+ def get_checkpoint_files(checkpoint_dir, glob_pattern):
78
+ # XXX: need to test that this simple glob rule works for multi-node setup too
79
+ ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
80
+
81
+ if len(ckpt_files) == 0:
82
+ raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
83
+
84
+ return ckpt_files
85
+
86
+
87
+ def get_optim_files(checkpoint_dir):
88
+ return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
89
+
90
+
91
+ def get_model_state_files(checkpoint_dir):
92
+ return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
93
+
94
+
95
+ def parse_model_states(files):
96
+ zero_model_states = []
97
+ for file in files:
98
+ state_dict = torch.load(file, map_location=device)
99
+
100
+ if BUFFER_NAMES not in state_dict:
101
+ raise ValueError(f"{file} is not a model state checkpoint")
102
+ buffer_names = state_dict[BUFFER_NAMES]
103
+ if debug:
104
+ print("Found buffers:", buffer_names)
105
+
106
+ # recover just the buffers while restoring them to fp32 if they were saved in fp16
107
+ buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
108
+ param_shapes = state_dict[PARAM_SHAPES]
109
+
110
+ # collect parameters that are included in param_shapes
111
+ param_names = []
112
+ for s in param_shapes:
113
+ for name in s.keys():
114
+ param_names.append(name)
115
+
116
+ # update with frozen parameters
117
+ frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
118
+ if frozen_param_shapes is not None:
119
+ if debug:
120
+ print(f"Found frozen_param_shapes: {frozen_param_shapes}")
121
+ param_names += list(frozen_param_shapes.keys())
122
+
123
+ # handle shared params
124
+ shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
125
+
126
+ ds_version = state_dict.get(DS_VERSION, None)
127
+
128
+ frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
129
+
130
+ z_model_state = zero_model_state(buffers=buffers,
131
+ param_shapes=param_shapes,
132
+ shared_params=shared_params,
133
+ ds_version=ds_version,
134
+ frozen_param_shapes=frozen_param_shapes,
135
+ frozen_param_fragments=frozen_param_fragments)
136
+ zero_model_states.append(z_model_state)
137
+
138
+ return zero_model_states
139
+
140
+
141
+ def parse_optim_states(files, ds_checkpoint_dir):
142
+
143
+ total_files = len(files)
144
+ state_dicts = []
145
+ for f in files:
146
+ state_dict = torch.load(f, map_location=device)
147
+ # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
148
+ # and also handle the case where it was already removed by another helper script
149
+ state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
150
+ state_dicts.append(state_dict)
151
+
152
+ if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
153
+ raise ValueError(f"{files[0]} is not a zero checkpoint")
154
+ zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
155
+ world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
156
+
157
+ # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
158
+ # parameters can be different from data parallelism for non-expert parameters. So we can just
159
+ # use the max of the partition_count to get the dp world_size.
160
+
161
+ if type(world_size) is list:
162
+ world_size = max(world_size)
163
+
164
+ if world_size != total_files:
165
+ raise ValueError(
166
+ f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
167
+ "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
168
+ )
169
+
170
+ # the groups are named differently in each stage
171
+ if zero_stage <= 2:
172
+ fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
173
+ elif zero_stage == 3:
174
+ fp32_groups_key = FP32_FLAT_GROUPS
175
+ else:
176
+ raise ValueError(f"unknown zero stage {zero_stage}")
177
+
178
+ if zero_stage <= 2:
179
+ fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
180
+ elif zero_stage == 3:
181
+ # if there is more than one param group, there will be multiple flattened tensors - one
182
+ # flattened tensor per group - for simplicity merge them into a single tensor
183
+ #
184
+ # XXX: could make the script more memory efficient for when there are multiple groups - it
185
+ # will require matching the sub-lists of param_shapes for each param group flattened tensor
186
+
187
+ fp32_flat_groups = [
188
+ torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
189
+ ]
190
+
191
+ return zero_stage, world_size, fp32_flat_groups
192
+
193
+
194
+ def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
195
+ """
196
+ Returns fp32 state_dict reconstructed from ds checkpoint
197
+
198
+ Args:
199
+ - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
200
+
201
+ """
202
+ print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
203
+
204
+ optim_files = get_optim_files(ds_checkpoint_dir)
205
+ zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
206
+ print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
207
+
208
+ model_files = get_model_state_files(ds_checkpoint_dir)
209
+
210
+ zero_model_states = parse_model_states(model_files)
211
+ print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
212
+
213
+ if zero_stage <= 2:
214
+ return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
215
+ exclude_frozen_parameters)
216
+ elif zero_stage == 3:
217
+ return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
218
+ exclude_frozen_parameters)
219
+
220
+
221
+ def _zero2_merge_frozen_params(state_dict, zero_model_states):
222
+ if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
223
+ return
224
+
225
+ frozen_param_shapes = zero_model_states[0].frozen_param_shapes
226
+ frozen_param_fragments = zero_model_states[0].frozen_param_fragments
227
+
228
+ if debug:
229
+ num_elem = sum(s.numel() for s in frozen_param_shapes.values())
230
+ print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
231
+
232
+ wanted_params = len(frozen_param_shapes)
233
+ wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
234
+ avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
235
+ print(f'Frozen params: Have {avail_numel} numels to process.')
236
+ print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
237
+
238
+ total_params = 0
239
+ total_numel = 0
240
+ for name, shape in frozen_param_shapes.items():
241
+ total_params += 1
242
+ unpartitioned_numel = shape.numel()
243
+ total_numel += unpartitioned_numel
244
+
245
+ state_dict[name] = frozen_param_fragments[name]
246
+
247
+ if debug:
248
+ print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
249
+
250
+ print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
251
+
252
+
253
+ def _has_callable(obj, fn):
254
+ attr = getattr(obj, fn, None)
255
+ return callable(attr)
256
+
257
+
258
+ def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
259
+ param_shapes = zero_model_states[0].param_shapes
260
+
261
+ # Reconstruction protocol:
262
+ #
263
+ # XXX: document this
264
+
265
+ if debug:
266
+ for i in range(world_size):
267
+ for j in range(len(fp32_flat_groups[0])):
268
+ print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
269
+
270
+ # XXX: memory usage doubles here (zero2)
271
+ num_param_groups = len(fp32_flat_groups[0])
272
+ merged_single_partition_of_fp32_groups = []
273
+ for i in range(num_param_groups):
274
+ merged_partitions = [sd[i] for sd in fp32_flat_groups]
275
+ full_single_fp32_vector = torch.cat(merged_partitions, 0)
276
+ merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
277
+ avail_numel = sum(
278
+ [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
279
+
280
+ if debug:
281
+ wanted_params = sum([len(shapes) for shapes in param_shapes])
282
+ wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
283
+ # not asserting if there is a mismatch due to possible padding
284
+ print(f"Have {avail_numel} numels to process.")
285
+ print(f"Need {wanted_numel} numels in {wanted_params} params.")
286
+
287
+ # params
288
+ # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
289
+ # out-of-core computing solution
290
+ total_numel = 0
291
+ total_params = 0
292
+ for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
293
+ offset = 0
294
+ avail_numel = full_single_fp32_vector.numel()
295
+ for name, shape in shapes.items():
296
+
297
+ unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
298
+ total_numel += unpartitioned_numel
299
+ total_params += 1
300
+
301
+ if debug:
302
+ print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
303
+ state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
304
+ offset += unpartitioned_numel
305
+
306
+ # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
307
+ # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
308
+ # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
309
+ # live optimizer object, so we are checking that the numbers are within the right range
310
+ align_to = 2 * world_size
311
+
312
+ def zero2_align(x):
313
+ return align_to * math.ceil(x / align_to)
314
+
315
+ if debug:
316
+ print(f"original offset={offset}, avail_numel={avail_numel}")
317
+
318
+ offset = zero2_align(offset)
319
+ avail_numel = zero2_align(avail_numel)
320
+
321
+ if debug:
322
+ print(f"aligned offset={offset}, avail_numel={avail_numel}")
323
+
324
+ # Sanity check
325
+ if offset != avail_numel:
326
+ raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
327
+
328
+ print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
329
+
330
+
331
+ def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
332
+ exclude_frozen_parameters):
333
+ state_dict = OrderedDict()
334
+
335
+ # buffers
336
+ buffers = zero_model_states[0].buffers
337
+ state_dict.update(buffers)
338
+ if debug:
339
+ print(f"added {len(buffers)} buffers")
340
+
341
+ if not exclude_frozen_parameters:
342
+ _zero2_merge_frozen_params(state_dict, zero_model_states)
343
+
344
+ _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
345
+
346
+ # recover shared parameters
347
+ for pair in zero_model_states[0].shared_params:
348
+ if pair[1] in state_dict:
349
+ state_dict[pair[0]] = state_dict[pair[1]]
350
+
351
+ return state_dict
352
+
353
+
354
+ def zero3_partitioned_param_info(unpartitioned_numel, world_size):
355
+ remainder = unpartitioned_numel % world_size
356
+ padding_numel = (world_size - remainder) if remainder else 0
357
+ partitioned_numel = math.ceil(unpartitioned_numel / world_size)
358
+ return partitioned_numel, padding_numel
359
+
360
+
361
+ def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
362
+ if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
363
+ return
364
+
365
+ if debug:
366
+ for i in range(world_size):
367
+ num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
368
+ print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
369
+
370
+ frozen_param_shapes = zero_model_states[0].frozen_param_shapes
371
+ wanted_params = len(frozen_param_shapes)
372
+ wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
373
+ avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
374
+ print(f'Frozen params: Have {avail_numel} numels to process.')
375
+ print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
376
+
377
+ total_params = 0
378
+ total_numel = 0
379
+ for name, shape in zero_model_states[0].frozen_param_shapes.items():
380
+ total_params += 1
381
+ unpartitioned_numel = shape.numel()
382
+ total_numel += unpartitioned_numel
383
+
384
+ param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
385
+ state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
386
+
387
+ partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
388
+
389
+ if debug:
390
+ print(
391
+ f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
392
+ )
393
+
394
+ print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
395
+
396
+
397
+ def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
398
+ param_shapes = zero_model_states[0].param_shapes
399
+ avail_numel = fp32_flat_groups[0].numel() * world_size
400
+ # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
401
+ # param, re-consolidating each param, while dealing with padding if any
402
+
403
+ # merge list of dicts, preserving order
404
+ param_shapes = {k: v for d in param_shapes for k, v in d.items()}
405
+
406
+ if debug:
407
+ for i in range(world_size):
408
+ print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
409
+
410
+ wanted_params = len(param_shapes)
411
+ wanted_numel = sum(shape.numel() for shape in param_shapes.values())
412
+ # not asserting if there is a mismatch due to possible padding
413
+ avail_numel = fp32_flat_groups[0].numel() * world_size
414
+ print(f"Trainable params: Have {avail_numel} numels to process.")
415
+ print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
416
+
417
+ # params
418
+ # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
419
+ # out-of-core computing solution
420
+ offset = 0
421
+ total_numel = 0
422
+ total_params = 0
423
+ for name, shape in param_shapes.items():
424
+
425
+ unpartitioned_numel = shape.numel()
426
+ total_numel += unpartitioned_numel
427
+ total_params += 1
428
+
429
+ partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
430
+
431
+ if debug:
432
+ print(
433
+ f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
434
+ )
435
+
436
+ # XXX: memory usage doubles here
437
+ state_dict[name] = torch.cat(
438
+ tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
439
+ 0).narrow(0, 0, unpartitioned_numel).view(shape)
440
+ offset += partitioned_numel
441
+
442
+ offset *= world_size
443
+
444
+ # Sanity check
445
+ if offset != avail_numel:
446
+ raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
447
+
448
+ print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
449
+
450
+
451
+ def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
452
+ exclude_frozen_parameters):
453
+ state_dict = OrderedDict()
454
+
455
+ # buffers
456
+ buffers = zero_model_states[0].buffers
457
+ state_dict.update(buffers)
458
+ if debug:
459
+ print(f"added {len(buffers)} buffers")
460
+
461
+ if not exclude_frozen_parameters:
462
+ _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
463
+
464
+ _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
465
+
466
+ # recover shared parameters
467
+ for pair in zero_model_states[0].shared_params:
468
+ if pair[1] in state_dict:
469
+ state_dict[pair[0]] = state_dict[pair[1]]
470
+
471
+ return state_dict
472
+
473
+
474
+ def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):
475
+ """
476
+ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
477
+ ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
478
+ via a model hub.
479
+
480
+ Args:
481
+ - ``checkpoint_dir``: path to the desired checkpoint folder
482
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
483
+ - ``exclude_frozen_parameters``: exclude frozen parameters
484
+
485
+ Returns:
486
+ - pytorch ``state_dict``
487
+
488
+ Note: this approach may not work if your application doesn't have sufficient free CPU memory and
489
+ you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
490
+ the checkpoint.
491
+
492
+ A typical usage might be ::
493
+
494
+ from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
495
+ # do the training and checkpoint saving
496
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
497
+ model = model.cpu() # move to cpu
498
+ model.load_state_dict(state_dict)
499
+ # submit to model hub or save the model to share with others
500
+
501
+ In this example the ``model`` will no longer be usable in the deepspeed context of the same
502
+ application. i.e. you will need to re-initialize the deepspeed engine, since
503
+ ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
504
+
505
+ If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
506
+
507
+ """
508
+ if tag is None:
509
+ latest_path = os.path.join(checkpoint_dir, 'latest')
510
+ if os.path.isfile(latest_path):
511
+ with open(latest_path, 'r') as fd:
512
+ tag = fd.read().strip()
513
+ else:
514
+ raise ValueError(f"Unable to find 'latest' file at {latest_path}")
515
+
516
+ ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
517
+
518
+ if not os.path.isdir(ds_checkpoint_dir):
519
+ raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
520
+
521
+ return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
522
+
523
+
524
+ def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):
525
+ """
526
+ Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
527
+ loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
528
+
529
+ Args:
530
+ - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
531
+ - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
532
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
533
+ - ``exclude_frozen_parameters``: exclude frozen parameters
534
+ """
535
+
536
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
537
+ print(f"Saving fp32 state dict to {output_file}")
538
+ torch.save(state_dict, output_file)
539
+
540
+
541
+ def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
542
+ """
543
+ 1. Put the provided model to cpu
544
+ 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
545
+ 3. Load it into the provided model
546
+
547
+ Args:
548
+ - ``model``: the model object to update
549
+ - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
550
+ - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
551
+
552
+ Returns:
553
+ - ``model`: modified model
554
+
555
+ Make sure you have plenty of CPU memory available before you call this function. If you don't
556
+ have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
557
+ conveniently placed for you in the checkpoint folder.
558
+
559
+ A typical usage might be ::
560
+
561
+ from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
562
+ model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
563
+ # submit to model hub or save the model to share with others
564
+
565
+ Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
566
+ of the same application. i.e. you will need to re-initialize the deepspeed engine, since
567
+ ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
568
+
569
+ """
570
+ logger.info(f"Extracting fp32 weights")
571
+ state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
572
+
573
+ logger.info(f"Overwriting model with fp32 weights")
574
+ model = model.cpu()
575
+ model.load_state_dict(state_dict, strict=False)
576
+
577
+ return model
578
+
579
+
580
+ if __name__ == "__main__":
581
+
582
+ parser = argparse.ArgumentParser()
583
+ parser.add_argument("checkpoint_dir",
584
+ type=str,
585
+ help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
586
+ parser.add_argument(
587
+ "output_file",
588
+ type=str,
589
+ help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
590
+ parser.add_argument("-t",
591
+ "--tag",
592
+ type=str,
593
+ default=None,
594
+ help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
595
+ parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
596
+ parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
597
+ args = parser.parse_args()
598
+
599
+ debug = args.debug
600
+
601
+ convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
602
+ args.output_file,
603
+ tag=args.tag,
604
+ exclude_frozen_parameters=args.exclude_frozen_parameters)