DLight1551 commited on
Commit
744e428
1 Parent(s): ba9b884
added_tokens.json ADDED
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+ {
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+ "<|action_end|>": 92547,
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+ "<|action_start|>": 92546,
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+ "<|im_end|>": 92545,
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+ "<|im_start|>": 92544,
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+ "<|interpreter|>": 92548,
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+ "<|plugin|>": 92549
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+ }
build_mlp.py ADDED
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1
+ import torch
2
+ import torch.nn as nn
3
+ import re
4
+ import math
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+ from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
6
+
7
+
8
+ def build_vision_tower():
9
+ #vision_tower = '/mnt/petrelfs/share_data/dongxiaoyi/share_models/clip_l_336'
10
+ vision_tower = '/mnt/hwfile/mllm/zhangpan/share/from/xiaoyi/clip_l_336'
11
+ return CLIPVisionTower(vision_tower)
12
+
13
+
14
+ def build_vision_projector():
15
+ projector_type = 'mlp2x_gelu'
16
+ mm_hidden_size = 4096
17
+ mid_hidden_size = 4096
18
+ hidden_size = 4096
19
+
20
+ mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
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+ if mlp_gelu_match:
22
+ mlp_depth = int(mlp_gelu_match.group(1))
23
+ modules = [nn.Linear(mm_hidden_size, mid_hidden_size)]
24
+ for _ in range(1, mlp_depth):
25
+ modules.append(nn.GELU())
26
+ modules.append(nn.Linear(mid_hidden_size, mid_hidden_size))
27
+
28
+ return nn.Sequential(*modules)
29
+
30
+ if projector_type == 'identity':
31
+ return IdentityMap()
32
+
33
+ raise ValueError(f'Unknown projector type: {projector_type}')
34
+
35
+ class IdentityMap(nn.Module):
36
+ def __init__(self):
37
+ super().__init__()
38
+
39
+ def forward(self, x, *args, **kwargs):
40
+ return x
41
+
42
+ @property
43
+ def config(self):
44
+ return {"mm_projector_type": 'identity'}
45
+
46
+
47
+ class CLIPVisionTower(nn.Module):
48
+ def __init__(self, vision_tower):
49
+ super().__init__()
50
+
51
+ self.is_loaded = False
52
+
53
+ self.vision_tower_name = vision_tower
54
+ #self.conv_dim = 8192
55
+ #self.conv = torch.nn.Conv2d(1024, self.conv_dim,3,2,1)
56
+ self.select_layer = -1
57
+ self.select_feature = 'patch'
58
+ self.load_model()
59
+
60
+ def load_model(self):
61
+ self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
62
+ self.vision_tower.requires_grad_(False)
63
+
64
+ self.is_loaded = True
65
+
66
+ def resize_pos(self):
67
+ print ('Dummy Resized')
68
+
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+ def feature_select(self, image_forward_outs):
70
+ image_features = image_forward_outs.hidden_states[self.select_layer]
71
+ if self.select_feature == 'patch':
72
+ image_features = image_features[:, 1:]
73
+ elif self.select_feature == 'cls_patch':
74
+ image_features = image_features
75
+ else:
76
+ raise ValueError(f'Unexpected select feature: {self.select_feature}')
77
+ return image_features
78
+
79
+ def forward(self, images, glb_GN, sub_GN):
80
+ if not self.is_loaded:
81
+ self.load_model()
82
+ assert type(images) is list
83
+ shapes = []
84
+ input_imgs = []
85
+ for img in images:
86
+ _, C, H, W = img.shape
87
+ shapes.append([H//336, W//336])
88
+ sub_img = img.reshape(1,3,H//336,336,W//336,336).permute(0,2,4,1,3,5).reshape(-1,3,336,336).contiguous()
89
+ glb_img = torch.nn.functional.interpolate(img.float(), size=(336,336), mode='bicubic',).to(sub_img.dtype)
90
+ input_imgs.append(glb_img)
91
+ input_imgs.append(sub_img)
92
+ input_imgs = torch.cat(input_imgs, dim=0)
93
+
94
+ image_forward_outs = self.vision_tower(input_imgs.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
95
+ image_features = self.feature_select(image_forward_outs).to(input_imgs.dtype) ### B*?, N, C
96
+ _, N, C = image_features.shape
97
+ H = int(math.sqrt(N))
98
+ assert N == 24 ** 2
99
+
100
+ output_imgs = []
101
+ output_len = []
102
+ for [h, w] in shapes:
103
+ B_ = h*w
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+ glb_img = image_features[:1] ### 1, N, C
105
+ glb_img = glb_img.reshape(1,H,H,C).reshape(1,H//2,2,H//2,2,C).contiguous().permute(0,1,3,2,4,5).reshape(1,H//2,H//2,4*C).contiguous()
106
+ temp_glb_GN = sub_GN.repeat(1, H//2, 1, 1)
107
+ glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,4*C)
108
+
109
+ sub_img = image_features[1:1+B_] ### ?, N, C
110
+ sub_img = sub_img.reshape(B_,H,H,C).reshape(B_,H//2,2,H//2,2,C).contiguous().permute(0,1,3,2,4,5).reshape(B_,-1,4*C).contiguous()
111
+ sub_img = sub_img.reshape(1, h, w, 12, 12, -1).permute(0,1,3,2,4,5).reshape(1,h*12,w*12,4*C)
112
+ temp_sub_GN = sub_GN.repeat(1, h*12, 1, 1)
113
+ sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,4*C)
114
+
115
+ output_imgs.append(torch.cat([glb_img, glb_GN, sub_img], dim=1))
116
+ temp_len = int((h*w+1)*144 + 1 + (h+1)*12)
117
+ assert temp_len == output_imgs[-1].shape[1]
118
+ output_len.append(temp_len)
119
+
120
+ image_features = image_features[1+h*w:]
121
+
122
+ output_imgs = torch.cat(output_imgs, dim=1)
123
+
124
+ return output_imgs, output_len
125
+
126
+ @property
127
+ def dummy_feature(self):
128
+ return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
129
+
130
+ @property
131
+ def dtype(self):
132
+ return self.vision_tower.dtype
133
+
134
+ @property
135
+ def device(self):
136
+ return self.vision_tower.device
137
+
138
+ @property
139
+ def config(self):
140
+ if self.is_loaded:
141
+ return self.vision_tower.config
142
+ else:
143
+ return self.cfg_only
144
+
145
+ @property
146
+ def hidden_size(self):
147
+ return self.config.hidden_size
148
+
149
+ @property
150
+ def num_patches(self):
151
+ return (self.config.image_size // self.config.patch_size) ** 2
152
+
153
+ class PLoRA(nn.Linear):
154
+ def __init__(self,
155
+ in_features: int,
156
+ out_features: int,
157
+ bias: bool = True,
158
+ device=None,
159
+ dtype=None,
160
+ lora_r=8,
161
+ lora_alpha=16,
162
+ lora_dropout=0.05,
163
+ lora_len=0,
164
+ **kwargs) -> None:
165
+ super().__init__(in_features, out_features, bias, device, dtype)
166
+ self.lora_r = lora_r
167
+ self.lora_alpha = lora_alpha
168
+ self.lora_len = lora_len
169
+ if lora_dropout > 0.:
170
+ self.lora_dropout = nn.Dropout(p=lora_dropout)
171
+ else:
172
+ self.lora_dropout = lambda x: x
173
+ self.lora_scaling = self.lora_alpha / self.lora_r
174
+
175
+ self.Plora_A = nn.Linear(in_features,
176
+ self.lora_r,
177
+ bias=False,
178
+ device=device,
179
+ dtype=dtype)
180
+ self.Plora_B = nn.Linear(self.lora_r,
181
+ out_features,
182
+ bias=False,
183
+ device=device,
184
+ dtype=dtype)
185
+
186
+ self.reset_parameters()
187
+
188
+ def reset_parameters(self):
189
+ if hasattr(self, 'lora_A'):
190
+ # initialize A the same way as the default for nn.Linear and B to zero
191
+ nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
192
+ nn.init.zeros_(self.lora_B.weight)
193
+ #print ("lora weight init {} {}".format(torch.mean(self.lora_A.weight), torch.mean(self.lora_B.weight)))
194
+
195
+ def forward(self, x, im_mask=None):
196
+ B, N, C = x.shape
197
+ x = x.reshape(-1, C)
198
+ res = super().forward(x)
199
+ if im_mask is not None:
200
+ if torch.sum(im_mask) > 0:
201
+ part_x = x[im_mask]
202
+ res[im_mask] += self.Plora_B(self.Plora_A(
203
+ self.lora_dropout(part_x))) * self.lora_scaling
204
+ else:
205
+ part_x = x[:1]
206
+ res[:1] += self.Plora_B(self.Plora_A(
207
+ self.lora_dropout(part_x))) * 0
208
+
209
+ return res.reshape(B, N, -1)
config.json ADDED
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1
+ {
2
+ "_name_or_path": "/mnt/petrelfs/share_data/zhangpan/share/from/zhangpan/output_web/IXC2_4K_WST12/checkpoint-3600",
3
+ "architectures": [
4
+ "InternLM2ForCausalLM"
5
+ ],
6
+ "attn_implementation": "flash_attention_2",
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_internlm2.InternLM2Config",
9
+ "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
10
+ "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
11
+ },
12
+ "bias": false,
13
+ "bos_token_id": 1,
14
+ "eos_token_id": 2,
15
+ "hidden_act": "silu",
16
+ "hidden_size": 4096,
17
+ "initializer_range": 0.02,
18
+ "intermediate_size": 14336,
19
+ "max_length": 2600,
20
+ "max_position_embeddings": 32768,
21
+ "model_type": "internlm2",
22
+ "num_attention_heads": 32,
23
+ "num_hidden_layers": 32,
24
+ "num_key_value_heads": 8,
25
+ "pad_token_id": 2,
26
+ "rms_norm_eps": 1e-05,
27
+ "rope_scaling": null,
28
+ "rope_theta": 1000000,
29
+ "tie_word_embeddings": false,
30
+ "torch_dtype": "bfloat16",
31
+ "transformers_version": "4.33.1",
32
+ "use_cache": false,
33
+ "vocab_size": 92544
34
+ }
configuration_internlm2.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/configuration_llama.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+ """ InternLM2 model configuration"""
18
+
19
+ from transformers.configuration_utils import PretrainedConfig
20
+ from transformers.utils import logging
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
25
+
26
+
27
+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
28
+ class InternLM2Config(PretrainedConfig):
29
+ r"""
30
+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
31
+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
32
+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
33
+
34
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
35
+ documentation from [`PretrainedConfig`] for more information.
36
+
37
+
38
+ Args:
39
+ vocab_size (`int`, *optional*, defaults to 32000):
40
+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
41
+ `inputs_ids` passed when calling [`InternLM2Model`]
42
+ hidden_size (`int`, *optional*, defaults to 4096):
43
+ Dimension of the hidden representations.
44
+ intermediate_size (`int`, *optional*, defaults to 11008):
45
+ Dimension of the MLP representations.
46
+ num_hidden_layers (`int`, *optional*, defaults to 32):
47
+ Number of hidden layers in the Transformer encoder.
48
+ num_attention_heads (`int`, *optional*, defaults to 32):
49
+ Number of attention heads for each attention layer in the Transformer encoder.
50
+ num_key_value_heads (`int`, *optional*):
51
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
52
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
53
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
54
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
55
+ by meanpooling all the original heads within that group. For more details checkout [this
56
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
57
+ `num_attention_heads`.
58
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
59
+ The non-linear activation function (function or string) in the decoder.
60
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
61
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
62
+ just in case (e.g., 512 or 1024 or 2048).
63
+ initializer_range (`float`, *optional*, defaults to 0.02):
64
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
65
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
66
+ The epsilon used by the rms normalization layers.
67
+ use_cache (`bool`, *optional*, defaults to `True`):
68
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
69
+ relevant if `config.is_decoder=True`.
70
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
71
+ Whether to tie weight embeddings
72
+ Example:
73
+
74
+ """
75
+ model_type = "internlm2"
76
+ _auto_class = "AutoConfig"
77
+
78
+ def __init__( # pylint: disable=W0102
79
+ self,
80
+ vocab_size=103168,
81
+ hidden_size=4096,
82
+ intermediate_size=11008,
83
+ num_hidden_layers=32,
84
+ num_attention_heads=32,
85
+ num_key_value_heads=None,
86
+ hidden_act="silu",
87
+ max_position_embeddings=2048,
88
+ initializer_range=0.02,
89
+ rms_norm_eps=1e-6,
90
+ use_cache=True,
91
+ pad_token_id=0,
92
+ bos_token_id=1,
93
+ eos_token_id=2,
94
+ tie_word_embeddings=False,
95
+ bias=True,
96
+ rope_theta=10000,
97
+ rope_scaling=None,
98
+ attn_implementation="eager",
99
+ **kwargs,
100
+ ):
101
+ self.vocab_size = vocab_size
102
+ self.max_position_embeddings = max_position_embeddings
103
+ self.hidden_size = hidden_size
104
+ self.intermediate_size = intermediate_size
105
+ self.num_hidden_layers = num_hidden_layers
106
+ self.num_attention_heads = num_attention_heads
107
+ self.bias = bias
108
+
109
+ if num_key_value_heads is None:
110
+ num_key_value_heads = num_attention_heads
111
+ self.num_key_value_heads = num_key_value_heads
112
+
113
+ self.hidden_act = hidden_act
114
+ self.initializer_range = initializer_range
115
+ self.rms_norm_eps = rms_norm_eps
116
+ self.use_cache = use_cache
117
+ self.rope_theta = rope_theta
118
+ self.rope_scaling = rope_scaling
119
+ self._rope_scaling_validation()
120
+
121
+ self.attn_implementation = attn_implementation
122
+ if self.attn_implementation is None:
123
+ self.attn_implementation = "eager"
124
+ super().__init__(
125
+ pad_token_id=pad_token_id,
126
+ bos_token_id=bos_token_id,
127
+ eos_token_id=eos_token_id,
128
+ tie_word_embeddings=tie_word_embeddings,
129
+ **kwargs,
130
+ )
131
+
132
+ def _rope_scaling_validation(self):
133
+ """
134
+ Validate the `rope_scaling` configuration.
135
+ """
136
+ if self.rope_scaling is None:
137
+ return
138
+
139
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
140
+ raise ValueError(
141
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
142
+ f"got {self.rope_scaling}"
143
+ )
144
+ rope_scaling_type = self.rope_scaling.get("type", None)
145
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
146
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
147
+ raise ValueError(
148
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
149
+ )
150
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
151
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
generation_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "max_length": 4480,
6
+ "pad_token_id": 2,
7
+ "transformers_version": "4.33.1",
8
+ "use_cache": false
9
+ }
modeling_internlm2.py ADDED
@@ -0,0 +1,1535 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ import copy
22
+ import numpy as np
23
+ from typing import List, Optional, Tuple, Union
24
+ from torchvision import transforms
25
+ from torchvision.transforms.functional import InterpolationMode
26
+ from PIL import Image
27
+
28
+ import torch
29
+ import torch.nn.functional as F
30
+ import torch.utils.checkpoint
31
+ from einops import rearrange
32
+ from torch import nn
33
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
34
+ from transformers.activations import ACT2FN
35
+ from transformers.modeling_outputs import (
36
+ BaseModelOutputWithPast,
37
+ CausalLMOutputWithPast,
38
+ SequenceClassifierOutputWithPast,
39
+ )
40
+ from transformers.modeling_utils import PreTrainedModel
41
+ from transformers.utils import (
42
+ add_start_docstrings,
43
+ add_start_docstrings_to_model_forward,
44
+ logging,
45
+ replace_return_docstrings,
46
+ )
47
+
48
+ try:
49
+ from transformers.generation.streamers import BaseStreamer
50
+ except: # noqa # pylint: disable=bare-except
51
+ BaseStreamer = None
52
+
53
+ from .configuration_internlm2 import InternLM2Config
54
+ from .build_mlp import build_vision_tower, build_vision_projector, PLoRA
55
+
56
+ logger = logging.get_logger(__name__)
57
+
58
+ _CONFIG_FOR_DOC = "InternLM2Config"
59
+
60
+ flash_attn_func, flash_attn_varlen_func = None, None
61
+ pad_input, index_first_axis, unpad_input = None, None, None
62
+ def _import_flash_attn():
63
+ global flash_attn_func, flash_attn_varlen_func
64
+ global pad_input, index_first_axis, unpad_input
65
+ try:
66
+ from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
67
+ from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
68
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
69
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
70
+ except ImportError:
71
+ raise ImportError("flash_attn is not installed.")
72
+
73
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
74
+ def _get_unpad_data(attention_mask):
75
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
76
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
77
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
78
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
79
+ return (
80
+ indices,
81
+ cu_seqlens,
82
+ max_seqlen_in_batch,
83
+ )
84
+
85
+
86
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
87
+ def _make_causal_mask(
88
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
89
+ ):
90
+ """
91
+ Make causal mask used for bi-directional self-attention.
92
+ """
93
+ bsz, tgt_len = input_ids_shape
94
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
95
+ mask_cond = torch.arange(mask.size(-1), device=device)
96
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
97
+ mask = mask.to(dtype)
98
+
99
+ if past_key_values_length > 0:
100
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
101
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
102
+
103
+
104
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
105
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
106
+ """
107
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
108
+ """
109
+ bsz, src_len = mask.size()
110
+ tgt_len = tgt_len if tgt_len is not None else src_len
111
+
112
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
113
+
114
+ inverted_mask = 1.0 - expanded_mask
115
+
116
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
117
+
118
+
119
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
120
+ class InternLM2RMSNorm(nn.Module):
121
+ def __init__(self, hidden_size, eps=1e-6):
122
+ """
123
+ InternLM2RMSNorm is equivalent to T5LayerNorm
124
+ """
125
+ super().__init__()
126
+ self.weight = nn.Parameter(torch.ones(hidden_size))
127
+ self.variance_epsilon = eps
128
+
129
+ def forward(self, hidden_states):
130
+ input_dtype = hidden_states.dtype
131
+ hidden_states = hidden_states.to(torch.float32)
132
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
133
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
134
+ return self.weight * hidden_states.to(input_dtype)
135
+
136
+
137
+ # Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
138
+ class InternLM2RotaryEmbedding(nn.Module):
139
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
140
+ super().__init__()
141
+
142
+ self.dim = dim
143
+ self.max_position_embeddings = max_position_embeddings
144
+ self.base = base
145
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
146
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
147
+
148
+ # Build here to make `torch.jit.trace` work.
149
+ self._set_cos_sin_cache(
150
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
151
+ )
152
+
153
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
154
+ self.max_seq_len_cached = seq_len
155
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
156
+
157
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
158
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
159
+ emb = torch.cat((freqs, freqs), dim=-1)
160
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
161
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
162
+
163
+ def forward(self, x, seq_len=None):
164
+ # x: [bs, num_attention_heads, seq_len, head_size]
165
+ if seq_len > self.max_seq_len_cached:
166
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
167
+
168
+ return (
169
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
170
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
171
+ )
172
+
173
+
174
+ # Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
175
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
176
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
177
+
178
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
179
+ self.scaling_factor = scaling_factor
180
+ super().__init__(dim, max_position_embeddings, base, device)
181
+
182
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
183
+ self.max_seq_len_cached = seq_len
184
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
185
+ t = t / self.scaling_factor
186
+
187
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
188
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
189
+ emb = torch.cat((freqs, freqs), dim=-1)
190
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
191
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
192
+
193
+
194
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
195
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
196
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
197
+ Credits to the Reddit users /u/bloc97 and /u/emozilla.
198
+ """
199
+
200
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
201
+ self.scaling_factor = scaling_factor
202
+ super().__init__(dim, max_position_embeddings, base, device)
203
+
204
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
205
+ self.max_seq_len_cached = seq_len
206
+
207
+ if seq_len > self.max_position_embeddings:
208
+ base = self.base * (
209
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
210
+ ) ** (self.dim / (self.dim - 2))
211
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
212
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
213
+
214
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
215
+
216
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
217
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
218
+ emb = torch.cat((freqs, freqs), dim=-1)
219
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
220
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
221
+
222
+
223
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
224
+ def rotate_half(x):
225
+ """Rotates half the hidden dims of the input."""
226
+ x1 = x[..., : x.shape[-1] // 2]
227
+ x2 = x[..., x.shape[-1] // 2 :]
228
+ return torch.cat((-x2, x1), dim=-1)
229
+
230
+
231
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
232
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
233
+ """Applies Rotary Position Embedding to the query and key tensors."""
234
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
235
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
236
+ q_embed = (q * cos) + (rotate_half(q) * sin)
237
+ k_embed = (k * cos) + (rotate_half(k) * sin)
238
+ return q_embed, k_embed
239
+
240
+
241
+ class InternLM2MLP(nn.Module):
242
+ def __init__(self, config):
243
+ super().__init__()
244
+ self.config = config
245
+ self.hidden_size = config.hidden_size
246
+ self.intermediate_size = config.intermediate_size
247
+ #self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
248
+ #self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
249
+ #self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
250
+
251
+ self.w1 = PLoRA(self.hidden_size, self.intermediate_size, bias=False,
252
+ lora_r=256, lora_alpha=256, lora_len=1225)
253
+ self.w3 = PLoRA(self.hidden_size, self.intermediate_size, bias=False,
254
+ lora_r=256, lora_alpha=256, lora_len=1225)
255
+ self.w2 = PLoRA(self.intermediate_size, self.hidden_size, bias=False,
256
+ lora_r=256, lora_alpha=256, lora_len=1225)
257
+
258
+ self.act_fn = ACT2FN[config.hidden_act]
259
+
260
+ def forward(self, x, im_mask):
261
+ down_proj = self.w2(self.act_fn(self.w1(x, im_mask)) * self.w3(x, im_mask), im_mask)
262
+
263
+ return down_proj
264
+
265
+
266
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
267
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
268
+ """
269
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
270
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
271
+ """
272
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
273
+ if n_rep == 1:
274
+ return hidden_states
275
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
276
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
277
+
278
+
279
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
280
+ class InternLM2Attention(nn.Module):
281
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
282
+
283
+ def __init__(self, config: InternLM2Config):
284
+ super().__init__()
285
+ self.config = config
286
+ self.hidden_size = config.hidden_size
287
+ self.num_heads = config.num_attention_heads
288
+ self.head_dim = self.hidden_size // self.num_heads
289
+ self.num_key_value_heads = config.num_key_value_heads
290
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
291
+ self.max_position_embeddings = config.max_position_embeddings
292
+ self.is_causal = True
293
+
294
+ if (self.head_dim * self.num_heads) != self.hidden_size:
295
+ raise ValueError(
296
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
297
+ f" and `num_heads`: {self.num_heads})."
298
+ )
299
+
300
+ #self.wqkv = nn.Linear(
301
+ self.wqkv = PLoRA(
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.wo = PLoRA(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias,
309
+ lora_r=256, lora_alpha=256, lora_len=1225)
310
+ self._init_rope()
311
+
312
+ def _init_rope(self):
313
+ if self.config.rope_scaling is None:
314
+ self.rotary_emb = InternLM2RotaryEmbedding(
315
+ self.head_dim,
316
+ max_position_embeddings=self.max_position_embeddings,
317
+ base=self.config.rope_theta,
318
+ )
319
+ else:
320
+ scaling_type = self.config.rope_scaling["type"]
321
+ scaling_factor = self.config.rope_scaling["factor"]
322
+ if scaling_type == "dynamic":
323
+ self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
324
+ self.head_dim,
325
+ max_position_embeddings=self.max_position_embeddings,
326
+ base=self.config.rope_theta,
327
+ scaling_factor=scaling_factor,
328
+ )
329
+ elif scaling_type == "linear":
330
+ self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
331
+ self.head_dim,
332
+ max_position_embeddings=self.max_position_embeddings,
333
+ base=self.config.rope_theta,
334
+ scaling_factor=scaling_factor,
335
+ )
336
+ else:
337
+ raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
338
+ return self.rotary_emb
339
+
340
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
341
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
342
+
343
+ def forward(
344
+ self,
345
+ hidden_states: torch.Tensor,
346
+ attention_mask: Optional[torch.Tensor] = None,
347
+ position_ids: Optional[torch.LongTensor] = None,
348
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
349
+ output_attentions: bool = False,
350
+ use_cache: bool = False,
351
+ im_mask: Optional[Tuple[torch.Tensor]] = None,
352
+ **kwargs,
353
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
354
+ if "padding_mask" in kwargs:
355
+ warnings.warn(
356
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
357
+ "Please make sure use `attention_mask` instead.`"
358
+ )
359
+
360
+ bsz, q_len, _ = hidden_states.size()
361
+
362
+ qkv_states = self.wqkv(hidden_states, im_mask)
363
+
364
+ qkv_states = rearrange(
365
+ qkv_states,
366
+ "b q (h gs d) -> b q h gs d",
367
+ gs=2 + self.num_key_value_groups,
368
+ d=self.head_dim,
369
+ )
370
+
371
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
372
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
373
+ key_states = qkv_states[..., -2, :]
374
+ value_states = qkv_states[..., -1, :]
375
+
376
+ query_states = query_states.transpose(1, 2)
377
+ key_states = key_states.transpose(1, 2)
378
+ value_states = value_states.transpose(1, 2)
379
+
380
+ kv_seq_len = key_states.shape[-2]
381
+ if past_key_value is not None:
382
+ kv_seq_len += past_key_value[0].shape[-2]
383
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
384
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
385
+
386
+ if past_key_value is not None:
387
+ # reuse k, v, self_attention
388
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
389
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
390
+
391
+ past_key_value = (key_states, value_states) if use_cache else None
392
+
393
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
394
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
395
+
396
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
397
+
398
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
399
+ raise ValueError(
400
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
401
+ f" {attn_weights.size()}"
402
+ )
403
+
404
+ if attention_mask is not None:
405
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
406
+ raise ValueError(
407
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
408
+ )
409
+ attn_weights = attn_weights + attention_mask
410
+
411
+ # upcast attention to fp32
412
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
413
+ attn_output = torch.matmul(attn_weights, value_states)
414
+
415
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
416
+ raise ValueError(
417
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
418
+ f" {attn_output.size()}"
419
+ )
420
+
421
+ attn_output = attn_output.transpose(1, 2).contiguous()
422
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
423
+
424
+ attn_output = self.wo(attn_output, im_mask)
425
+
426
+ if not output_attentions:
427
+ attn_weights = None
428
+
429
+ return attn_output, attn_weights, past_key_value
430
+
431
+
432
+ # Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
433
+ class InternLM2FlashAttention2(InternLM2Attention):
434
+ """
435
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
436
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
437
+ flash attention and deal with padding tokens in case the input contains any of them.
438
+ """
439
+
440
+ def forward(
441
+ self,
442
+ hidden_states: torch.Tensor,
443
+ attention_mask: Optional[torch.LongTensor] = None,
444
+ position_ids: Optional[torch.LongTensor] = None,
445
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
446
+ output_attentions: bool = False,
447
+ use_cache: bool = False,
448
+ im_mask: Optional[Tuple[torch.Tensor]] = None,
449
+ **kwargs,
450
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
451
+ # InternLM2FlashAttention2 attention does not support output_attentions
452
+ if "padding_mask" in kwargs:
453
+ warnings.warn(
454
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
455
+ "Please make sure use `attention_mask` instead.`"
456
+ )
457
+
458
+ # overwrite attention_mask with padding_mask
459
+ attention_mask = kwargs.pop("padding_mask")
460
+
461
+ output_attentions = False
462
+
463
+ bsz, q_len, _ = hidden_states.size()
464
+
465
+ qkv_states = self.wqkv(hidden_states, im_mask)
466
+
467
+ qkv_states = rearrange(
468
+ qkv_states,
469
+ "b q (h gs d) -> b q h gs d",
470
+ gs=2 + self.num_key_value_groups,
471
+ d=self.head_dim,
472
+ )
473
+
474
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
475
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
476
+ key_states = qkv_states[..., -2, :]
477
+ value_states = qkv_states[..., -1, :]
478
+
479
+ query_states = query_states.transpose(1, 2)
480
+ key_states = key_states.transpose(1, 2)
481
+ value_states = value_states.transpose(1, 2)
482
+
483
+ kv_seq_len = key_states.shape[-2]
484
+ if past_key_value is not None:
485
+ kv_seq_len += past_key_value[0].shape[-2]
486
+
487
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
488
+
489
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
490
+
491
+ if past_key_value is not None:
492
+ # reuse k, v, self_attention
493
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
494
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
495
+
496
+ past_key_value = (key_states, value_states) if use_cache else None
497
+
498
+ query_states = query_states.transpose(1, 2)
499
+ key_states = key_states.transpose(1, 2)
500
+ value_states = value_states.transpose(1, 2)
501
+
502
+ attn_output = self._flash_attention_forward(
503
+ query_states, key_states, value_states, attention_mask, q_len
504
+ )
505
+
506
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
507
+ attn_output = self.wo(attn_output, im_mask)
508
+
509
+ if not output_attentions:
510
+ attn_weights = None
511
+
512
+ return attn_output, attn_weights, past_key_value
513
+
514
+ def _flash_attention_forward(
515
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
516
+ ):
517
+ """
518
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
519
+ first unpad the input, then computes the attention scores and pad the final attention scores.
520
+
521
+ Args:
522
+ query_states (`torch.Tensor`):
523
+ Input query states to be passed to Flash Attention API
524
+ key_states (`torch.Tensor`):
525
+ Input key states to be passed to Flash Attention API
526
+ value_states (`torch.Tensor`):
527
+ Input value states to be passed to Flash Attention API
528
+ attention_mask (`torch.Tensor`):
529
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
530
+ position of padding tokens and 1 for the position of non-padding tokens.
531
+ dropout (`int`, *optional*):
532
+ Attention dropout
533
+ softmax_scale (`float`, *optional*):
534
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
535
+ """
536
+ # Contains at least one padding token in the sequence
537
+ causal = self.is_causal and query_length != 1
538
+ if attention_mask is not None:
539
+ batch_size = query_states.shape[0]
540
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
541
+ query_states, key_states, value_states, attention_mask, query_length
542
+ )
543
+
544
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
545
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
546
+
547
+ attn_output_unpad = flash_attn_varlen_func(
548
+ query_states,
549
+ key_states,
550
+ value_states,
551
+ cu_seqlens_q=cu_seqlens_q,
552
+ cu_seqlens_k=cu_seqlens_k,
553
+ max_seqlen_q=max_seqlen_in_batch_q,
554
+ max_seqlen_k=max_seqlen_in_batch_k,
555
+ dropout_p=dropout,
556
+ softmax_scale=softmax_scale,
557
+ causal=causal,
558
+ )
559
+
560
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
561
+ else:
562
+ attn_output = flash_attn_func(
563
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
564
+ )
565
+
566
+ return attn_output
567
+
568
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
569
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
570
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
571
+
572
+ key_layer = index_first_axis(
573
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
574
+ )
575
+ value_layer = index_first_axis(
576
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
577
+ )
578
+
579
+ if query_length == kv_seq_len:
580
+ query_layer = index_first_axis(
581
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
582
+ )
583
+ cu_seqlens_q = cu_seqlens_k
584
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
585
+ indices_q = indices_k
586
+ elif query_length == 1:
587
+ max_seqlen_in_batch_q = 1
588
+ cu_seqlens_q = torch.arange(
589
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
590
+ ) # There is a memcpy here, that is very bad.
591
+ indices_q = cu_seqlens_q[:-1]
592
+ query_layer = query_layer.squeeze(1)
593
+ else:
594
+ # The -q_len: slice assumes left padding.
595
+ attention_mask = attention_mask[:, -query_length:]
596
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
597
+
598
+ return (
599
+ query_layer,
600
+ key_layer,
601
+ value_layer,
602
+ indices_q.to(torch.int64),
603
+ (cu_seqlens_q, cu_seqlens_k),
604
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
605
+ )
606
+
607
+ INTERNLM2_ATTENTION_CLASSES = {
608
+ "eager": InternLM2Attention,
609
+ "flash_attention_2": InternLM2FlashAttention2,
610
+ }
611
+
612
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
613
+ class InternLM2DecoderLayer(nn.Module):
614
+ def __init__(self, config: InternLM2Config):
615
+ super().__init__()
616
+ self.hidden_size = config.hidden_size
617
+
618
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
619
+
620
+ self.feed_forward = InternLM2MLP(config)
621
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
622
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
623
+
624
+ def forward(
625
+ self,
626
+ hidden_states: torch.Tensor,
627
+ attention_mask: Optional[torch.Tensor] = None,
628
+ position_ids: Optional[torch.LongTensor] = None,
629
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
630
+ output_attentions: Optional[bool] = False,
631
+ use_cache: Optional[bool] = False,
632
+ im_mask: Optional[Tuple[torch.Tensor]] = None,
633
+ **kwargs,
634
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
635
+ """
636
+ Args:
637
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
638
+ attention_mask (`torch.FloatTensor`, *optional*):
639
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
640
+ query_sequence_length, key_sequence_length)` if default attention is used.
641
+ output_attentions (`bool`, *optional*):
642
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
643
+ returned tensors for more detail.
644
+ use_cache (`bool`, *optional*):
645
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
646
+ (see `past_key_values`).
647
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
648
+ """
649
+ if "padding_mask" in kwargs:
650
+ warnings.warn(
651
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
652
+ "Please make sure use `attention_mask` instead.`"
653
+ )
654
+
655
+ residual = hidden_states
656
+
657
+ hidden_states = self.attention_norm(hidden_states)
658
+
659
+ # Self Attention
660
+ hidden_states, self_attn_weights, present_key_value = self.attention(
661
+ hidden_states=hidden_states,
662
+ attention_mask=attention_mask,
663
+ position_ids=position_ids,
664
+ past_key_value=past_key_value,
665
+ output_attentions=output_attentions,
666
+ use_cache=use_cache,
667
+ im_mask=im_mask,
668
+ **kwargs,
669
+ )
670
+ hidden_states = residual + hidden_states
671
+
672
+ # Fully Connected
673
+ residual = hidden_states
674
+ hidden_states = self.ffn_norm(hidden_states)
675
+ hidden_states = self.feed_forward(hidden_states, im_mask)
676
+ hidden_states = residual + hidden_states
677
+
678
+ outputs = (hidden_states,)
679
+
680
+ if output_attentions:
681
+ outputs += (self_attn_weights,)
682
+
683
+ if use_cache:
684
+ outputs += (present_key_value,)
685
+
686
+ return outputs
687
+
688
+
689
+ InternLM2_START_DOCSTRING = r"""
690
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
691
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
692
+ etc.)
693
+
694
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
695
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
696
+ and behavior.
697
+
698
+ Parameters:
699
+ config ([`InternLM2Config`]):
700
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
701
+ load the weights associated with the model, only the configuration. Check out the
702
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
703
+ """
704
+
705
+
706
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
707
+ @add_start_docstrings(
708
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
709
+ InternLM2_START_DOCSTRING,
710
+ )
711
+ class InternLM2PreTrainedModel(PreTrainedModel):
712
+ config_class = InternLM2Config
713
+ base_model_prefix = "model"
714
+ supports_gradient_checkpointing = True
715
+ _no_split_modules = ["InternLM2DecoderLayer"]
716
+ _skip_keys_device_placement = "past_key_values"
717
+
718
+ def _init_weights(self, module):
719
+ std = self.config.initializer_range
720
+ if isinstance(module, nn.Linear):
721
+ module.weight.data.normal_(mean=0.0, std=std)
722
+ if module.bias is not None:
723
+ module.bias.data.zero_()
724
+ elif isinstance(module, nn.Embedding):
725
+ module.weight.data.normal_(mean=0.0, std=std)
726
+ if module.padding_idx is not None:
727
+ module.weight.data[module.padding_idx].zero_()
728
+
729
+
730
+ InternLM2_INPUTS_DOCSTRING = r"""
731
+ Args:
732
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
733
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
734
+ it.
735
+
736
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
737
+ [`PreTrainedTokenizer.__call__`] for details.
738
+
739
+ [What are input IDs?](../glossary#input-ids)
740
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
741
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
742
+
743
+ - 1 for tokens that are **not masked**,
744
+ - 0 for tokens that are **masked**.
745
+
746
+ [What are attention masks?](../glossary#attention-mask)
747
+
748
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
749
+ [`PreTrainedTokenizer.__call__`] for details.
750
+
751
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
752
+ `past_key_values`).
753
+
754
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
755
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
756
+ information on the default strategy.
757
+
758
+ - 1 indicates the head is **not masked**,
759
+ - 0 indicates the head is **masked**.
760
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
761
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
762
+ config.n_positions - 1]`.
763
+
764
+ [What are position IDs?](../glossary#position-ids)
765
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
766
+ when `config.use_cache=True`):
767
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
768
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
769
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
770
+
771
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
772
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
773
+
774
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
775
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
776
+ of shape `(batch_size, sequence_length)`.
777
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
778
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
779
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
780
+ model's internal embedding lookup matrix.
781
+ use_cache (`bool`, *optional*):
782
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
783
+ `past_key_values`).
784
+ output_attentions (`bool`, *optional*):
785
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
786
+ tensors for more detail.
787
+ output_hidden_states (`bool`, *optional*):
788
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
789
+ more detail.
790
+ return_dict (`bool`, *optional*):
791
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
792
+ """
793
+
794
+
795
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
796
+ @add_start_docstrings(
797
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
798
+ InternLM2_START_DOCSTRING,
799
+ )
800
+ class InternLM2Model(InternLM2PreTrainedModel):
801
+ """
802
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
803
+
804
+ Args:
805
+ config: InternLM2Config
806
+ """
807
+
808
+ _auto_class = "AutoModel"
809
+
810
+ def __init__(self, config: InternLM2Config):
811
+ super().__init__(config)
812
+ self.padding_idx = config.pad_token_id
813
+ self.vocab_size = config.vocab_size
814
+ self.config = config
815
+
816
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
817
+
818
+ self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
819
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
820
+
821
+ self.gradient_checkpointing = False
822
+ # Initialize weights and apply final processing
823
+ self.post_init()
824
+
825
+ def get_input_embeddings(self):
826
+ return self.tok_embeddings
827
+
828
+ def set_input_embeddings(self, value):
829
+ self.tok_embeddings = value
830
+
831
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
832
+ # create causal mask
833
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
834
+ combined_attention_mask = None
835
+ if input_shape[-1] > 1:
836
+ combined_attention_mask = _make_causal_mask(
837
+ input_shape,
838
+ inputs_embeds.dtype,
839
+ device=inputs_embeds.device,
840
+ past_key_values_length=past_key_values_length,
841
+ )
842
+
843
+ if attention_mask is not None:
844
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
845
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
846
+ inputs_embeds.device
847
+ )
848
+ combined_attention_mask = (
849
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
850
+ )
851
+
852
+ return combined_attention_mask
853
+
854
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
855
+ def forward(
856
+ self,
857
+ input_ids: torch.LongTensor = None,
858
+ attention_mask: Optional[torch.Tensor] = None,
859
+ position_ids: Optional[torch.LongTensor] = None,
860
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
861
+ inputs_embeds: Optional[torch.FloatTensor] = None,
862
+ use_cache: Optional[bool] = None,
863
+ output_attentions: Optional[bool] = None,
864
+ output_hidden_states: Optional[bool] = None,
865
+ return_dict: Optional[bool] = None,
866
+ **kwargs
867
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
868
+
869
+ im_mask = kwargs.get('im_mask', None)
870
+
871
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
872
+ output_hidden_states = (
873
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
874
+ )
875
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
876
+
877
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
878
+
879
+ if self.config.attn_implementation == "flash_attention_2":
880
+ _import_flash_attn()
881
+
882
+ # retrieve input_ids and inputs_embeds
883
+ if input_ids is not None and inputs_embeds is not None:
884
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
885
+ elif input_ids is not None:
886
+ batch_size, seq_length = input_ids.shape[:2]
887
+ elif inputs_embeds is not None:
888
+ batch_size, seq_length = inputs_embeds.shape[:2]
889
+ else:
890
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
891
+
892
+ seq_length_with_past = seq_length
893
+ past_key_values_length = 0
894
+ if past_key_values is not None:
895
+ past_key_values_length = past_key_values[0][0].shape[2]
896
+ seq_length_with_past = seq_length_with_past + past_key_values_length
897
+
898
+ if position_ids is None:
899
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
900
+ position_ids = torch.arange(
901
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
902
+ )
903
+ position_ids = position_ids.unsqueeze(0)
904
+
905
+ if inputs_embeds is None:
906
+ inputs_embeds = self.tok_embeddings(input_ids)
907
+ im_mask = torch.zeros(inputs_embeds.shape[:2]).to(inputs_embeds.device).bool()
908
+
909
+ if self.config.attn_implementation == "flash_attention_2":
910
+ # 2d mask is passed through the layers
911
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
912
+ else:
913
+ if attention_mask is None:
914
+ attention_mask = torch.ones(
915
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
916
+ )
917
+ attention_mask = self._prepare_decoder_attention_mask(
918
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
919
+ )
920
+
921
+ # embed positions
922
+ hidden_states = inputs_embeds
923
+
924
+ if self.gradient_checkpointing and self.training:
925
+ if use_cache:
926
+ logger.warning_once(
927
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
928
+ )
929
+ use_cache = False
930
+
931
+ # decoder layers
932
+ all_hidden_states = () if output_hidden_states else None
933
+ all_self_attns = () if output_attentions else None
934
+ next_decoder_cache = () if use_cache else None
935
+
936
+ for idx, decoder_layer in enumerate(self.layers):
937
+ if output_hidden_states:
938
+ all_hidden_states += (hidden_states,)
939
+
940
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
941
+
942
+ if self.gradient_checkpointing and self.training:
943
+
944
+ def create_custom_forward(module):
945
+ def custom_forward(*inputs):
946
+ # None for past_key_value
947
+ return module(*inputs, output_attentions, None, im_mask)
948
+
949
+ return custom_forward
950
+
951
+ layer_outputs = torch.utils.checkpoint.checkpoint(
952
+ create_custom_forward(decoder_layer),
953
+ hidden_states,
954
+ attention_mask,
955
+ position_ids,
956
+ None,
957
+ )
958
+ else:
959
+ layer_outputs = decoder_layer(
960
+ hidden_states,
961
+ attention_mask=attention_mask,
962
+ position_ids=position_ids,
963
+ past_key_value=past_key_value,
964
+ output_attentions=output_attentions,
965
+ use_cache=use_cache,
966
+ im_mask=im_mask,
967
+ )
968
+
969
+ hidden_states = layer_outputs[0]
970
+
971
+ if use_cache:
972
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
973
+
974
+ if output_attentions:
975
+ all_self_attns += (layer_outputs[1],)
976
+
977
+ hidden_states = self.norm(hidden_states)
978
+
979
+ # add hidden states from the last decoder layer
980
+ if output_hidden_states:
981
+ all_hidden_states += (hidden_states,)
982
+
983
+ next_cache = next_decoder_cache if use_cache else None
984
+ if not return_dict:
985
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
986
+ return BaseModelOutputWithPast(
987
+ last_hidden_state=hidden_states,
988
+ past_key_values=next_cache,
989
+ hidden_states=all_hidden_states,
990
+ attentions=all_self_attns,
991
+ )
992
+
993
+
994
+ # Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
995
+ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
996
+ _auto_class = "AutoModelForCausalLM"
997
+
998
+ _tied_weights_keys = ["output.weight"]
999
+
1000
+ def __init__(self, config):
1001
+ super().__init__(config)
1002
+ self.model = InternLM2Model(config)
1003
+ self.vocab_size = config.vocab_size
1004
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1005
+ self.debug_flag = 1
1006
+ self.mask_flag = 1
1007
+ self.tokenizer = None
1008
+
1009
+ self.max_length = config.max_length
1010
+ print (f'Set max length to {self.max_length}')
1011
+ self.debug_flag = 1
1012
+ # Initialize weights and apply final processing
1013
+ self.post_init()
1014
+ self.plora_glb_GN = nn.Parameter(torch.zeros([1, 1, 4096]))
1015
+ self.plora_sub_GN = nn.Parameter(torch.zeros([1, 1, 1, 4096]))
1016
+
1017
+ self.vit = build_vision_tower()
1018
+ self.vision_proj = build_vision_projector()
1019
+ self.im_size = 490
1020
+ self.vis_processor = transforms.Compose([
1021
+ transforms.ToTensor(),
1022
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073),
1023
+ (0.26862954, 0.26130258, 0.27577711)),
1024
+ ])
1025
+
1026
+ def _set_gradient_checkpointing(self, module, value=False):
1027
+ if isinstance(module, InternLM2Model):
1028
+ module.gradient_checkpointing = value
1029
+ if value:
1030
+ self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value
1031
+
1032
+ def get_input_embeddings(self):
1033
+ return self.model.tok_embeddings
1034
+
1035
+ def set_input_embeddings(self, value):
1036
+ self.model.tok_embeddings = value
1037
+
1038
+ def get_output_embeddings(self):
1039
+ return self.output
1040
+
1041
+ def set_output_embeddings(self, new_embeddings):
1042
+ self.output = new_embeddings
1043
+
1044
+ def set_decoder(self, decoder):
1045
+ self.model = decoder
1046
+
1047
+ def get_decoder(self):
1048
+ return self.model
1049
+ def encode_text(self, t, add_special_tokens=False):
1050
+ t = t.replace('<|User|>:', '[UNUSED_TOKEN_146]user\n')
1051
+ t = t.replace('<|Bot|>:', '[UNUSED_TOKEN_146]assistant\n')
1052
+ t = t.replace('<TOKENS_UNUSED_0>', '[UNUSED_TOKEN_145]')
1053
+ t = t.replace('<TOKENS_UNUSED_1>', '[UNUSED_TOKEN_145]')
1054
+ t = t.replace('[UNUSED_TOKEN_0]', '[UNUSED_TOKEN_145]')
1055
+ t = t.replace('[UNUSED_TOKEN_1]', '[UNUSED_TOKEN_145]')
1056
+
1057
+ text = t
1058
+ token = self.tokenizer(text,
1059
+ return_tensors='pt',
1060
+ add_special_tokens=add_special_tokens).input_ids.to(self.device)
1061
+ embs = self.model.tok_embeddings(token)
1062
+ return embs
1063
+
1064
+ def encode_img(self, image):
1065
+ if image is None:
1066
+ return None
1067
+ if isinstance(image, str):
1068
+ image = Image.open(image).convert("RGB")
1069
+ image = self.vis_processor(image).unsqueeze(0).to(self.device)
1070
+ else:
1071
+ assert isinstance(image, torch.Tensor)
1072
+
1073
+ img_embeds, _ = self.img2emb([image])
1074
+ return img_embeds
1075
+
1076
+
1077
+
1078
+ def img2emb(self, image):
1079
+ img_embeds, img_split = self.vit(image,
1080
+ self.plora_glb_GN, self.plora_sub_GN)
1081
+ img_embeds = self.vision_proj(img_embeds)
1082
+
1083
+ return img_embeds, img_split
1084
+
1085
+ def prompt_wrap(self, img_embeds, prompt):
1086
+ batch_size = img_embeds.shape[0]
1087
+ p_before, p_after = prompt.split('<ImageHere>')
1088
+ p_before_tokens = self.tokenizer(
1089
+ p_before, return_tensors="pt", add_special_tokens=True).to(img_embeds.device)
1090
+
1091
+ p_before_embeds = self.model.tok_embeddings(p_before_tokens.input_ids).expand(batch_size, -1, -1)
1092
+ wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds], dim=1)
1093
+
1094
+ wrapped_atts_img = torch.ones(wrapped_img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)
1095
+
1096
+ wrapped_target = torch.ones(batch_size, wrapped_img_embeds.shape[1], dtype=torch.long).to(img_embeds.device) * -100
1097
+
1098
+
1099
+ return wrapped_img_embeds, wrapped_atts_img, wrapped_target
1100
+
1101
+ def text2emb(self, text, add_special=False):
1102
+ new_text = []
1103
+ for t in text:
1104
+ t = t.replace('<|User|>:', '[UNUSED_TOKEN_146]user\n')
1105
+ t = t.replace('<|Bot|>:', '[UNUSED_TOKEN_146]assistant\n')
1106
+ t = t.replace('<TOKENS_UNUSED_0>', '[UNUSED_TOKEN_145]')
1107
+ t = t.replace('<TOKENS_UNUSED_1>', '[UNUSED_TOKEN_145]')
1108
+ new_text.append(t)
1109
+ text = new_text
1110
+ to_regress_tokens = self.tokenizer(
1111
+ text,
1112
+ return_tensors="pt",
1113
+ padding="longest",
1114
+ truncation=True,
1115
+ add_special_tokens=add_special
1116
+ ).to(self.device)
1117
+
1118
+ targets = self.mask_human_targets(to_regress_tokens.input_ids)
1119
+ targets = targets.to(self.device)
1120
+
1121
+ return to_regress_tokens, targets
1122
+
1123
+ def mask_human_targets(self, input_ids, pure=False):
1124
+ target_batch = []
1125
+ for bs in range(input_ids.shape[0]):
1126
+ cur_idx = 0
1127
+ ids = input_ids[bs]
1128
+ targets = copy.deepcopy(ids)
1129
+ end_count = 0
1130
+ last_eoa = 0
1131
+ for i, temp_id in enumerate(ids):
1132
+ if temp_id == 92542:
1133
+ if end_count % 2 == 0:
1134
+ targets[last_eoa: i+6] = -100
1135
+ else:
1136
+ last_eoa = i + 1
1137
+ end_count += 1
1138
+ elif temp_id == 2: ### eos and following pad
1139
+ targets[i+1:] = -100 #### loss on eos, but not on pad
1140
+ break
1141
+ if temp_id != 2 and end_count % 2 == 0: ### trunction, end at last question
1142
+ targets[last_eoa+1:] = -100 #### mask all after the last answer
1143
+
1144
+ target_batch.append(targets.unsqueeze(0))
1145
+ if self.debug_flag and 0:
1146
+ print ('#### Warining! System meta is not support now')
1147
+ targets_vis = targets.clone()
1148
+ targets_vis[targets_vis==-100] = 92399
1149
+ targets_vis_tokens = ''.join(self.tokenizer.convert_ids_to_tokens(targets_vis)).replace('[UNUSED_TOKEN_2]', " ")
1150
+ print(''.join(self.tokenizer.convert_ids_to_tokens(ids)))
1151
+ print('-----------')
1152
+ print([targets_vis_tokens])
1153
+ print('-----------------------------')
1154
+
1155
+ target_batch = torch.cat(target_batch, dim=0)
1156
+ return target_batch
1157
+
1158
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1159
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1160
+ def forward(
1161
+ self,
1162
+ input_ids: torch.LongTensor = None,
1163
+ attention_mask: Optional[torch.Tensor] = None,
1164
+ position_ids: Optional[torch.LongTensor] = None,
1165
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1166
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1167
+ labels: Optional[torch.LongTensor] = None,
1168
+ use_cache: Optional[bool] = None,
1169
+ output_attentions: Optional[bool] = None,
1170
+ output_hidden_states: Optional[bool] = None,
1171
+ return_dict: Optional[bool] = None,
1172
+ **kwargs
1173
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1174
+ r"""
1175
+ Args:
1176
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1177
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1178
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1179
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1180
+
1181
+ Returns:
1182
+
1183
+ Example:
1184
+
1185
+ ```python
1186
+ >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
1187
+
1188
+ >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1189
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1190
+
1191
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1192
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1193
+
1194
+ >>> # Generate
1195
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1196
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1197
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1198
+ ```"""
1199
+ samples = kwargs.get('samples', None)
1200
+ if samples:
1201
+ if self.debug_flag:
1202
+ self.debug_flag += 1
1203
+ if self.debug_flag > 5:
1204
+ self.debug_flag = 0
1205
+
1206
+ has_img = 'image' in samples.keys()
1207
+
1208
+ ### encode text
1209
+ sp_token = samples["sp_token"]
1210
+
1211
+ text = samples['text_input'][0].split(sp_token)
1212
+ text = ['<|User|>:' + t for t in text]
1213
+ to_regress_tokens, targets = self.text2emb(text, add_special = True)
1214
+
1215
+ to_regress_embeds = self.model.tok_embeddings(to_regress_tokens.input_ids)
1216
+ attention_mask = to_regress_tokens.attention_mask
1217
+
1218
+ if has_img:
1219
+ ### encode image
1220
+ image = samples["image"][0]
1221
+ bs = to_regress_embeds.shape[0]
1222
+ ### combine text and image
1223
+ if samples['data_type'][0] != 'nlp':
1224
+ temp_max_len = int(samples.get('max_length', [self.max_length])[0])
1225
+ assert type(image) is list and len(image) == bs
1226
+ img_embeds, img_split = self.img2emb(image)
1227
+ temp_max_len = np.max(img_split) + 320
1228
+ final_input = []
1229
+ final_atts = []
1230
+ final_tars = []
1231
+ final_masks = []
1232
+ pad = torch.ones([1, 1]) * self.tokenizer.pad_token_id
1233
+ pad = pad.long().to(to_regress_embeds.device)
1234
+ pad_emb = self.model.tok_embeddings(pad)
1235
+
1236
+ for idx, sp in enumerate(img_split):
1237
+ st = int(np.sum(img_split[:idx]))
1238
+ temp_img = img_embeds[:, st:st+sp]
1239
+ temp_img_atts = torch.ones(temp_img.size()[:-1], dtype=torch.long).to(temp_img.device)
1240
+ temp_img_tar = torch.ones(temp_img.size()[:2], dtype=torch.long).to(temp_img.device) * -100
1241
+
1242
+ temp_input = torch.cat([to_regress_embeds[idx:idx+1,:1], temp_img, to_regress_embeds[idx:idx+1,1:]], dim=1)
1243
+ temp_atts = torch.cat([attention_mask[idx:idx+1,:1], temp_img_atts, attention_mask[idx:idx+1,1:]], dim=1)
1244
+ temp_tars = torch.cat([targets[idx:idx+1,:1], temp_img_tar, targets[idx:idx+1,1:]], dim=1)
1245
+
1246
+ temp_len = temp_input.shape[1]
1247
+ if temp_len >= temp_max_len:
1248
+ final_input.append(temp_input[:, :temp_max_len])
1249
+ final_atts.append(temp_atts[:, :temp_max_len])
1250
+ final_tars.append(temp_tars[:, :temp_max_len])
1251
+ else:
1252
+ final_input.append(torch.cat([temp_input, pad_emb.repeat(1, temp_max_len-temp_len, 1)], dim=1))
1253
+ final_atts.append(torch.cat([temp_atts, torch.zeros(1, temp_max_len-temp_len).to(temp_atts.dtype).to(temp_atts.device)], dim=1))
1254
+ final_tars.append(torch.cat([temp_tars, (torch.ones(1, temp_max_len-temp_len)*-100).to(temp_tars.dtype).to(temp_tars.device)], dim=1))
1255
+
1256
+ im_mask = torch.zeros(temp_max_len).cuda()
1257
+ im_mask[1:1+sp] = 1
1258
+ final_masks.append(im_mask)
1259
+
1260
+ inputs_embeds = torch.cat(final_input, dim=0)
1261
+ attention_mask = torch.cat(final_atts, dim=0)
1262
+ targets = torch.cat(final_tars, dim=0)
1263
+ im_mask = torch.cat(final_masks, dim=0).bool() ### B*N
1264
+
1265
+ else:
1266
+ img_embeds, img_split = self.img2emb([torch.zeros(1,3,336,336).to(to_regress_embeds.device).to(to_regress_embeds.dtype)])
1267
+ to_regress_embeds += img_embeds.sum() * 0
1268
+ im_mask = torch.zeros(to_regress_embeds.shape[:2]).cuda()
1269
+ temp_max_len = 8192
1270
+ inputs_embeds = to_regress_embeds[:2, :temp_max_len]
1271
+ attention_mask = attention_mask[:2, :temp_max_len]
1272
+ targets = targets[:2, :temp_max_len]
1273
+ im_mask = im_mask[:2, :temp_max_len].bool().view(-1)
1274
+
1275
+
1276
+
1277
+ labels = targets
1278
+ if self.debug_flag:
1279
+ print (targets.shape, inputs_embeds.shape, attention_mask.shape)
1280
+ le = len(samples['text_input'])
1281
+ data_type = samples['data_type'][0]
1282
+ print (f'DataType: {data_type}. Has Image: {has_img}. Current max length: {temp_max_len}, BatchSize is {le}')
1283
+ if has_img:
1284
+ print (img_embeds.shape, img_split)
1285
+
1286
+ else:
1287
+ self.debug_flag = 0
1288
+ im_mask = kwargs.get('im_mask', None)
1289
+ if im_mask is None and inputs_embeds is not None:
1290
+ im_mask = torch.zeros(inputs_embeds.shape[:2]).to(inputs_embeds.device)
1291
+ if self.mask_flag:
1292
+ print ('Warning! image mask will be 0')
1293
+ self.mask_flag = 0
1294
+ im_mask = im_mask.bool()
1295
+ im_mask = im_mask.view(-1)
1296
+
1297
+
1298
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1299
+ output_hidden_states = (
1300
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1301
+ )
1302
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1303
+
1304
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1305
+ outputs = self.model(
1306
+ input_ids=input_ids,
1307
+ attention_mask=attention_mask,
1308
+ position_ids=position_ids,
1309
+ past_key_values=past_key_values,
1310
+ inputs_embeds=inputs_embeds,
1311
+ use_cache=use_cache,
1312
+ output_attentions=output_attentions,
1313
+ output_hidden_states=output_hidden_states,
1314
+ return_dict=return_dict,
1315
+ im_mask = im_mask,
1316
+ )
1317
+
1318
+ hidden_states = outputs[0]
1319
+ logits = self.output(hidden_states)
1320
+ logits = logits.float()
1321
+
1322
+ loss = None
1323
+ if labels is not None:
1324
+ # Shift so that tokens < n predict n
1325
+ shift_logits = logits[..., :-1, :].contiguous()
1326
+ shift_labels = labels[..., 1:].contiguous()
1327
+ # Flatten the tokens
1328
+ loss_fct = CrossEntropyLoss(reduce=False)
1329
+ B, N = shift_logits.shape[:2]
1330
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1331
+ shift_labels = shift_labels.view(-1)
1332
+ mask = shift_labels >= 0
1333
+ # Enable model parallelism
1334
+ shift_labels = shift_labels.to(shift_logits.device)
1335
+ loss = loss_fct(shift_logits, shift_labels)
1336
+ loss = (loss.view(B,N).sum(dim=1) / mask.view(B,N).sum(dim=1)).mean()
1337
+
1338
+ if not return_dict:
1339
+ output = (logits,) + outputs[1:]
1340
+ return (loss,) + output if loss is not None else output
1341
+
1342
+ return CausalLMOutputWithPast(
1343
+ loss=loss,
1344
+ logits=logits,
1345
+ past_key_values=outputs.past_key_values,
1346
+ hidden_states=outputs.hidden_states,
1347
+ attentions=outputs.attentions,
1348
+ )
1349
+
1350
+ def prepare_inputs_for_generation(
1351
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, im_mask=None, **kwargs
1352
+ ):
1353
+ if past_key_values is not None:
1354
+ past_length = past_key_values[0][0].shape[2]
1355
+
1356
+ # Some generation methods already pass only the last input ID
1357
+ if input_ids.shape[1] > past_length:
1358
+ remove_prefix_length = past_length
1359
+ else:
1360
+ # Default to old behavior: keep only final ID
1361
+ remove_prefix_length = input_ids.shape[1] - 1
1362
+
1363
+ input_ids = input_ids[:, remove_prefix_length:]
1364
+
1365
+ position_ids = kwargs.get("position_ids", None)
1366
+ if attention_mask is not None and position_ids is None:
1367
+ # create position_ids on the fly for batch generation
1368
+ position_ids = attention_mask.long().cumsum(-1) - 1
1369
+ position_ids.masked_fill_(attention_mask == 0, 1)
1370
+ if past_key_values:
1371
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1372
+
1373
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1374
+ if inputs_embeds is not None and past_key_values is None:
1375
+ model_inputs = {"inputs_embeds": inputs_embeds}
1376
+ else:
1377
+ model_inputs = {"input_ids": input_ids}
1378
+
1379
+ im_mask = im_mask
1380
+
1381
+ model_inputs.update(
1382
+ {
1383
+ "position_ids": position_ids,
1384
+ "past_key_values": past_key_values,
1385
+ "use_cache": kwargs.get("use_cache"),
1386
+ "attention_mask": attention_mask,
1387
+ "im_mask": im_mask,
1388
+ }
1389
+ )
1390
+ return model_inputs
1391
+
1392
+ @staticmethod
1393
+ def _reorder_cache(past_key_values, beam_idx):
1394
+ reordered_past = ()
1395
+ for layer_past in past_key_values:
1396
+ reordered_past += (
1397
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1398
+ )
1399
+ return reordered_past
1400
+
1401
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=""):
1402
+ if tokenizer.add_bos_token:
1403
+ prompt = ""
1404
+ else:
1405
+ prompt = tokenizer.bos_token
1406
+ if meta_instruction:
1407
+ prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
1408
+ for record in history:
1409
+ prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
1410
+ prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
1411
+ return tokenizer([prompt], return_tensors="pt")
1412
+
1413
+ @torch.no_grad()
1414
+ def chat(
1415
+ self,
1416
+ tokenizer,
1417
+ query: str,
1418
+ history: List[Tuple[str, str]] = [],
1419
+ streamer: Optional[BaseStreamer] = None,
1420
+ max_new_tokens: int = 1024,
1421
+ do_sample: bool = True,
1422
+ temperature: float = 0.8,
1423
+ top_p: float = 0.8,
1424
+ meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
1425
+ "- 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"
1426
+ "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
1427
+ **kwargs,
1428
+ ):
1429
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1430
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1431
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1432
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
1433
+ outputs = self.generate(
1434
+ **inputs,
1435
+ streamer=streamer,
1436
+ max_new_tokens=max_new_tokens,
1437
+ do_sample=do_sample,
1438
+ temperature=temperature,
1439
+ top_p=top_p,
1440
+ eos_token_id=eos_token_id,
1441
+ **kwargs,
1442
+ )
1443
+ outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
1444
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1445
+ response = response.split("<|im_end|>")[0]
1446
+ history = history + [(query, response)]
1447
+ return response, history
1448
+
1449
+ @torch.no_grad()
1450
+ def stream_chat(
1451
+ self,
1452
+ tokenizer,
1453
+ query: str,
1454
+ history: List[Tuple[str, str]] = [],
1455
+ max_new_tokens: int = 1024,
1456
+ do_sample: bool = True,
1457
+ temperature: float = 0.8,
1458
+ top_p: float = 0.8,
1459
+ **kwargs,
1460
+ ):
1461
+ """
1462
+ Return a generator in format: (response, history)
1463
+ Eg.
1464
+ ('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')])
1465
+ ('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?')])
1466
+ """
1467
+ if BaseStreamer is None:
1468
+ raise ModuleNotFoundError(
1469
+ "The version of `transformers` is too low. Please make sure "
1470
+ "that you have installed `transformers>=4.28.0`."
1471
+ )
1472
+
1473
+ response_queue = queue.Queue(maxsize=20)
1474
+
1475
+ class ChatStreamer(BaseStreamer):
1476
+ def __init__(self, tokenizer) -> None:
1477
+ super().__init__()
1478
+ self.tokenizer = tokenizer
1479
+ self.queue = response_queue
1480
+ self.query = query
1481
+ self.history = history
1482
+ self.response = ""
1483
+ self.cache = []
1484
+ self.received_inputs = False
1485
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1486
+
1487
+ def put(self, value):
1488
+ if len(value.shape) > 1 and value.shape[0] > 1:
1489
+ raise ValueError("ChatStreamer only supports batch size 1")
1490
+ elif len(value.shape) > 1:
1491
+ value = value[0]
1492
+
1493
+ if not self.received_inputs:
1494
+ # The first received value is input_ids, ignore here
1495
+ self.received_inputs = True
1496
+ return
1497
+
1498
+ self.cache.extend(value.tolist())
1499
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1500
+ if token.strip() != "<|im_end|>":
1501
+ self.response = self.response + token
1502
+ history = self.history + [(self.query, self.response)]
1503
+ self.queue.put((self.response, history))
1504
+ self.cache = []
1505
+ else:
1506
+ self.end()
1507
+
1508
+ def end(self):
1509
+ self.queue.put(None)
1510
+
1511
+ def stream_producer():
1512
+ return self.chat(
1513
+ tokenizer=tokenizer,
1514
+ query=query,
1515
+ streamer=ChatStreamer(tokenizer=tokenizer),
1516
+ history=history,
1517
+ max_new_tokens=max_new_tokens,
1518
+ do_sample=do_sample,
1519
+ temperature=temperature,
1520
+ top_p=top_p,
1521
+ **kwargs,
1522
+ )
1523
+
1524
+ def consumer():
1525
+ producer = threading.Thread(target=stream_producer)
1526
+ producer.start()
1527
+ while True:
1528
+ res = response_queue.get()
1529
+ if res is None:
1530
+ return
1531
+ yield res
1532
+
1533
+ return consumer()
1534
+
1535
+
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3747c5f5259484383043a7227c95d1b7dfb3ac1a53e84ed34ea845ad9bfbfb85
3
+ size 17195150492
special_tokens_map.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>",
5
+ "<|action_start|>",
6
+ "<|action_end|>",
7
+ "<|interpreter|>",
8
+ "<|plugin|>"
9
+ ],
10
+ "bos_token": {
11
+ "content": "<s>",
12
+ "lstrip": false,
13
+ "normalized": false,
14
+ "rstrip": false,
15
+ "single_word": false
16
+ },
17
+ "eos_token": {
18
+ "content": "</s>",
19
+ "lstrip": false,
20
+ "normalized": false,
21
+ "rstrip": false,
22
+ "single_word": false
23
+ },
24
+ "pad_token": {
25
+ "content": "</s>",
26
+ "lstrip": false,
27
+ "normalized": false,
28
+ "rstrip": false,
29
+ "single_word": false
30
+ },
31
+ "unk_token": {
32
+ "content": "<unk>",
33
+ "lstrip": false,
34
+ "normalized": false,
35
+ "rstrip": false,
36
+ "single_word": false
37
+ }
38
+ }
tokenization_internlm2.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+
18
+ """Tokenization classes for InternLM."""
19
+ import os
20
+ from shutil import copyfile
21
+ from typing import Any, Dict, List, Optional, Tuple
22
+
23
+ import sentencepiece as spm
24
+ from transformers.tokenization_utils import PreTrainedTokenizer
25
+ from transformers.utils import logging
26
+
27
+ logger = logging.get_logger(__name__)
28
+
29
+ VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
30
+
31
+ PRETRAINED_VOCAB_FILES_MAP = {}
32
+
33
+
34
+ # Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
35
+ class InternLM2Tokenizer(PreTrainedTokenizer):
36
+ """
37
+ Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
38
+
39
+ Args:
40
+ vocab_file (`str`):
41
+ Path to the vocabulary file.
42
+ """
43
+
44
+ vocab_files_names = VOCAB_FILES_NAMES
45
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
46
+ model_input_names = ["input_ids", "attention_mask"]
47
+ _auto_class = "AutoTokenizer"
48
+
49
+ def __init__(
50
+ self,
51
+ vocab_file,
52
+ unk_token="<unk>",
53
+ bos_token="<s>",
54
+ eos_token="</s>",
55
+ pad_token="</s>",
56
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
57
+ add_bos_token=True,
58
+ add_eos_token=False,
59
+ decode_with_prefix_space=False,
60
+ clean_up_tokenization_spaces=False,
61
+ **kwargs,
62
+ ):
63
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
64
+ self.vocab_file = vocab_file
65
+ self.add_bos_token = add_bos_token
66
+ self.add_eos_token = add_eos_token
67
+ self.decode_with_prefix_space = decode_with_prefix_space
68
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
69
+ self.sp_model.Load(vocab_file)
70
+ self._no_prefix_space_tokens = None
71
+ super().__init__(
72
+ bos_token=bos_token,
73
+ eos_token=eos_token,
74
+ unk_token=unk_token,
75
+ pad_token=pad_token,
76
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
77
+ **kwargs,
78
+ )
79
+
80
+ @property
81
+ def no_prefix_space_tokens(self):
82
+ if self._no_prefix_space_tokens is None:
83
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
84
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
85
+ return self._no_prefix_space_tokens
86
+
87
+ @property
88
+ def vocab_size(self):
89
+ """Returns vocab size"""
90
+ return self.sp_model.get_piece_size()
91
+
92
+ @property
93
+ def bos_token_id(self) -> Optional[int]:
94
+ return self.sp_model.bos_id()
95
+
96
+ @property
97
+ def eos_token_id(self) -> Optional[int]:
98
+ return self.sp_model.eos_id()
99
+
100
+ def get_vocab(self):
101
+ """Returns vocab as a dict"""
102
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
103
+ vocab.update(self.added_tokens_encoder)
104
+ return vocab
105
+
106
+ def _tokenize(self, text):
107
+ """Returns a tokenized string."""
108
+ return self.sp_model.encode(text, out_type=str)
109
+
110
+ def _convert_token_to_id(self, token):
111
+ """Converts a token (str) in an id using the vocab."""
112
+ return self.sp_model.piece_to_id(token)
113
+
114
+ def _convert_id_to_token(self, index):
115
+ """Converts an index (integer) in a token (str) using the vocab."""
116
+ token = self.sp_model.IdToPiece(index)
117
+ return token
118
+
119
+ def _maybe_add_prefix_space(self, tokens, decoded):
120
+ if tokens and tokens[0] not in self.no_prefix_space_tokens:
121
+ return " " + decoded
122
+ else:
123
+ return decoded
124
+
125
+ def convert_tokens_to_string(self, tokens):
126
+ """Converts a sequence of tokens (string) in a single string."""
127
+ current_sub_tokens = []
128
+ out_string = ""
129
+ prev_is_special = False
130
+ for token in tokens:
131
+ # make sure that special tokens are not decoded using sentencepiece model
132
+ if token in self.all_special_tokens:
133
+ if not prev_is_special:
134
+ out_string += " "
135
+ out_string += self.sp_model.decode(current_sub_tokens) + token
136
+ prev_is_special = True
137
+ current_sub_tokens = []
138
+ else:
139
+ current_sub_tokens.append(token)
140
+ prev_is_special = False
141
+ out_string += self.sp_model.decode(current_sub_tokens)
142
+ out_string = self.clean_up_tokenization(out_string)
143
+ out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
144
+ return out_string[1:]
145
+
146
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
147
+ """
148
+ Save the vocabulary and special tokens file to a directory.
149
+
150
+ Args:
151
+ save_directory (`str`):
152
+ The directory in which to save the vocabulary.
153
+
154
+ Returns:
155
+ `Tuple(str)`: Paths to the files saved.
156
+ """
157
+ if not os.path.isdir(save_directory):
158
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
159
+ return
160
+ out_vocab_file = os.path.join(
161
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
162
+ )
163
+
164
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
165
+ copyfile(self.vocab_file, out_vocab_file)
166
+ elif not os.path.isfile(self.vocab_file):
167
+ with open(out_vocab_file, "wb") as fi:
168
+ content_spiece_model = self.sp_model.serialized_model_proto()
169
+ fi.write(content_spiece_model)
170
+
171
+ return (out_vocab_file,)
172
+
173
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
174
+ if self.add_bos_token:
175
+ bos_token_ids = [self.bos_token_id]
176
+ else:
177
+ bos_token_ids = []
178
+
179
+ output = bos_token_ids + token_ids_0
180
+
181
+ if token_ids_1 is not None:
182
+ output = output + token_ids_1
183
+
184
+ if self.add_eos_token:
185
+ output = output + [self.eos_token_id]
186
+
187
+ return output
188
+
189
+ def get_special_tokens_mask(
190
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
191
+ ) -> List[int]:
192
+ """
193
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
194
+ special tokens using the tokenizer `prepare_for_model` method.
195
+
196
+ Args:
197
+ token_ids_0 (`List[int]`):
198
+ List of IDs.
199
+ token_ids_1 (`List[int]`, *optional*):
200
+ Optional second list of IDs for sequence pairs.
201
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
202
+ Whether or not the token list is already formatted with special tokens for the model.
203
+
204
+ Returns:
205
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
206
+ """
207
+ if already_has_special_tokens:
208
+ return super().get_special_tokens_mask(
209
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
210
+ )
211
+
212
+ if token_ids_1 is None:
213
+ return [1] + ([0] * len(token_ids_0)) + [1]
214
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
215
+
216
+ def create_token_type_ids_from_sequences(
217
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
218
+ ) -> List[int]:
219
+ """
220
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
221
+ use of token type ids, therefore a list of zeros is returned.
222
+
223
+ Args:
224
+ token_ids_0 (`List[int]`):
225
+ List of IDs.
226
+ token_ids_1 (`List[int]`, *optional*):
227
+ Optional second list of IDs for sequence pairs.
228
+
229
+ Returns:
230
+ `List[int]`: List of zeros.
231
+ """
232
+ eos = [self.eos_token_id]
233
+
234
+ if token_ids_1 is None:
235
+ return len(token_ids_0 + eos) * [0]
236
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
tokenizer.model ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
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+ size 1477754
tokenizer_config.json ADDED
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+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "<unk>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92538": {
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+ "content": "<|plugin|>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92539": {
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+ "content": "<|interpreter|>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92540": {
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+ "content": "<|action_end|>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92541": {
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+ "content": "<|action_start|>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92542": {
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+ "content": "<|im_end|>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92543": {
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+ "content": "<|im_start|>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "additional_special_tokens": [
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+ "<|im_start|>",
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+ "<|im_end|>",
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+ "<|action_start|>",
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+ "<|action_end|>",
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+ "<|interpreter|>",
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+ "<|plugin|>"
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+ ],
84
+ "auto_map": {
85
+ "AutoTokenizer": [
86
+ "tokenization_internlm2.InternLM2Tokenizer",
87
+ null
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+ ]
89
+ },
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+ "bos_token": "<s>",
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+ "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
92
+ "clean_up_tokenization_spaces": false,
93
+ "eos_token": "</s>",
94
+ "model_max_length": 1000000000000000019884624838656,
95
+ "pad_token": "</s>",
96
+ "padding_side": "right",
97
+ "tokenizer_class": "InternLM2Tokenizer",
98
+ "unk_token": "<unk>"
99
+ }
trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
training_args.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e5ac2d90ae21b4945a6cd2411062eb4728e8367809b922ea889c53ab0871b1df
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+ size 6011