DLight1551 commited on
Commit
789c136
1 Parent(s): 946adb8
added_tokens.json ADDED
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1
+ {
2
+ "<|action_end|>": 92547,
3
+ "<|action_start|>": 92546,
4
+ "<|im_end|>": 92545,
5
+ "<|im_start|>": 92544,
6
+ "<|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
5
+ from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
6
+
7
+
8
+ def build_vision_tower():
9
+ vision_tower = '/mnt/petrelfs/dongxiaoyi/gittest/IXC/output/clip_l_560_pro7b'
10
+ return CLIPVisionTower(vision_tower)
11
+
12
+
13
+ def build_vision_projector():
14
+ projector_type = 'mlp2x_gelu'
15
+ mm_hidden_size = 4096
16
+ mid_hidden_size = 4096
17
+ hidden_size = 4096
18
+
19
+ mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
20
+ if mlp_gelu_match:
21
+ mlp_depth = int(mlp_gelu_match.group(1))
22
+ modules = [nn.Linear(mm_hidden_size, mid_hidden_size)]
23
+ for _ in range(1, mlp_depth):
24
+ modules.append(nn.GELU())
25
+ modules.append(nn.Linear(mid_hidden_size, mid_hidden_size))
26
+
27
+ return nn.Sequential(*modules)
28
+
29
+ if projector_type == 'identity':
30
+ return IdentityMap()
31
+
32
+ raise ValueError(f'Unknown projector type: {projector_type}')
33
+
34
+ class IdentityMap(nn.Module):
35
+ def __init__(self):
36
+ super().__init__()
37
+
38
+ def forward(self, x, *args, **kwargs):
39
+ return x
40
+
41
+ @property
42
+ def config(self):
43
+ return {"mm_projector_type": 'identity'}
44
+
45
+
46
+ class CLIPVisionTower(nn.Module):
47
+ def __init__(self, vision_tower):
48
+ super().__init__()
49
+
50
+ self.is_loaded = False
51
+
52
+ self.vision_tower_name = vision_tower
53
+ #self.conv_dim = 8192
54
+ #self.conv = torch.nn.Conv2d(1024, self.conv_dim,3,2,1)
55
+ self.select_layer = -1
56
+ self.select_feature = 'patch'
57
+ self.load_model()
58
+
59
+ def load_model(self):
60
+ self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
61
+ self.vision_tower.requires_grad_(False)
62
+
63
+ self.is_loaded = True
64
+
65
+ def resize_pos(self):
66
+ print ('Dummy Resized')
67
+
68
+ def feature_select(self, image_forward_outs):
69
+ image_features = image_forward_outs.hidden_states[self.select_layer]
70
+ if self.select_feature == 'patch':
71
+ image_features = image_features[:, 1:]
72
+ elif self.select_feature == 'cls_patch':
73
+ image_features = image_features
74
+ else:
75
+ raise ValueError(f'Unexpected select feature: {self.select_feature}')
76
+ return image_features
77
+
78
+ def forward(self, images, glb_GN, sub_GN):
79
+ if not self.is_loaded:
80
+ self.load_model()
81
+ assert type(images) is list
82
+ shapes = []
83
+ input_imgs = []
84
+ for img in images:
85
+ _, C, H, W = img.shape
86
+ shapes.append([H//560, W//560])
87
+ sub_img = img.reshape(1,3,H//560,560,W//560,560).permute(0,2,4,1,3,5).reshape(-1,3,560,560).contiguous()
88
+ glb_img = torch.nn.functional.interpolate(img.float(), size=(560,560), mode='bicubic',).to(sub_img.dtype)
89
+ input_imgs.append(glb_img)
90
+ input_imgs.append(sub_img)
91
+ input_imgs = torch.cat(input_imgs, dim=0)
92
+
93
+ image_forward_outs = self.vision_tower(input_imgs.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
94
+ image_features = self.feature_select(image_forward_outs).to(input_imgs.dtype) ### B*?, N, C
95
+ _, N, C = image_features.shape
96
+ H = int(math.sqrt(N))
97
+ assert N == 40 ** 2
98
+
99
+ output_imgs = []
100
+ output_len = []
101
+ for [h, w] in shapes:
102
+ B_ = h*w
103
+ glb_img = image_features[:1] ### 1, N, C
104
+ 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()
105
+ temp_glb_GN = sub_GN.repeat(1, H//2, 1, 1)
106
+ glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,4*C)
107
+
108
+ sub_img = image_features[1:1+B_] ### ?, N, C
109
+ 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()
110
+ sub_img = sub_img.reshape(1, h, w, 20, 20, -1).permute(0,1,3,2,4,5).reshape(1,h*20,w*20,4*C)
111
+ temp_sub_GN = sub_GN.repeat(1, h*20, 1, 1)
112
+ sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,4*C)
113
+
114
+ output_imgs.append(torch.cat([glb_img, glb_GN, sub_img], dim=1))
115
+ temp_len = int((h*w+1)*400 + 1 + (h+1)*20)
116
+ assert temp_len == output_imgs[-1].shape[1]
117
+ output_len.append(temp_len)
118
+
119
+ image_features = image_features[1+h*w:]
120
+
121
+ output_imgs = torch.cat(output_imgs, dim=1)
122
+
123
+ return output_imgs, output_len
124
+
125
+ @property
126
+ def dummy_feature(self):
127
+ return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
128
+
129
+ @property
130
+ def dtype(self):
131
+ return self.vision_tower.dtype
132
+
133
+ @property
134
+ def device(self):
135
+ return self.vision_tower.device
136
+
137
+ @property
138
+ def config(self):
139
+ if self.is_loaded:
140
+ return self.vision_tower.config
141
+ else:
142
+ return self.cfg_only
143
+
144
+ @property
145
+ def hidden_size(self):
146
+ return self.config.hidden_size
147
+
148
+ @property
149
+ def num_patches(self):
150
+ return (self.config.image_size // self.config.patch_size) ** 2
151
+
152
+ class PLoRA(nn.Linear):
153
+ def __init__(self,
154
+ in_features: int,
155
+ out_features: int,
156
+ bias: bool = True,
157
+ device=None,
158
+ dtype=None,
159
+ lora_r=8,
160
+ lora_alpha=16,
161
+ lora_dropout=0.05,
162
+ lora_len=0,
163
+ **kwargs) -> None:
164
+ super().__init__(in_features, out_features, bias, device, dtype)
165
+ self.lora_r = lora_r
166
+ self.lora_alpha = lora_alpha
167
+ self.lora_len = lora_len
168
+ if lora_dropout > 0.:
169
+ self.lora_dropout = nn.Dropout(p=lora_dropout)
170
+ else:
171
+ self.lora_dropout = lambda x: x
172
+ self.lora_scaling = self.lora_alpha / self.lora_r
173
+
174
+ self.Plora_A = nn.Linear(in_features,
175
+ self.lora_r,
176
+ bias=False,
177
+ device=device,
178
+ dtype=dtype)
179
+ self.Plora_B = nn.Linear(self.lora_r,
180
+ out_features,
181
+ bias=False,
182
+ device=device,
183
+ dtype=dtype)
184
+
185
+ self.lora_sft_A = nn.Linear(in_features,
186
+ 256,
187
+ bias=False,
188
+ device=device,
189
+ dtype=dtype)
190
+ self.lora_sft_B = nn.Linear(256,
191
+ out_features,
192
+ bias=False,
193
+ device=device,
194
+ dtype=dtype)
195
+
196
+ self.lora_dpo_A = nn.Linear(in_features,
197
+ 256,
198
+ bias=False,
199
+ device=device,
200
+ dtype=dtype)
201
+ self.lora_dpo_B = nn.Linear(256,
202
+ out_features,
203
+ bias=False,
204
+ device=device,
205
+ dtype=dtype)
206
+
207
+ self.lora_web_A = nn.Linear(in_features,
208
+ 512,
209
+ bias=False,
210
+ device=device,
211
+ dtype=dtype)
212
+ self.lora_web_B = nn.Linear(512,
213
+ out_features,
214
+ bias=False,
215
+ device=device,
216
+ dtype=dtype)
217
+
218
+ self.reset_parameters()
219
+
220
+ def reset_parameters(self):
221
+ if hasattr(self, 'lora_A'):
222
+ # initialize A the same way as the default for nn.Linear and B to zero
223
+ nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5))
224
+ nn.init.zeros_(self.lora_B.weight)
225
+ #print ("lora weight init {} {}".format(torch.mean(self.lora_A.weight), torch.mean(self.lora_B.weight)))
226
+
227
+ def forward(self, x, im_mask=None, infer_mode='base'):
228
+ B, N, C = x.shape
229
+ im_mask = im_mask.view(-1)
230
+ x = x.reshape(-1, C)
231
+ res = super().forward(x)
232
+ if infer_mode == 'web':
233
+ res += self.lora_web_B(self.lora_web_A(x))
234
+ elif infer_mode == 'write':
235
+ res += self.lora_sft_B(self.lora_sft_A(x))
236
+ res += self.lora_dpo_B(self.lora_dpo_A(x))
237
+ else:
238
+ pass
239
+ if im_mask is not None:
240
+ if torch.sum(im_mask) > 0:
241
+ part_x = x[im_mask]
242
+ res[im_mask] += self.Plora_B(self.Plora_A(
243
+ self.lora_dropout(part_x))) * self.lora_scaling
244
+ else:
245
+ part_x = x[:1]
246
+ res[:1] += self.Plora_B(self.Plora_A(
247
+ self.lora_dropout(part_x))) * 0
248
+
249
+ return res.reshape(B, N, -1)
config.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "/mnt/petrelfs/dongxiaoyi/gittest/temp_model/internlm-xcomposer2d5-7b",
3
+ "architectures": [
4
+ "InternLMXComposer2ForCausalLM"
5
+ ],
6
+ "attn_implementation": "flash_attention_2",
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_internlm_xcomposer2.InternLMXcomposer2Config",
9
+ "AutoModel": "modeling_internlm_xcomposer2.InternLMXComposer2ForCausalLM",
10
+ "AutoModelForCausalLM": "modeling_internlm_xcomposer2.InternLMXComposer2ForCausalLM"
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": 16384,
20
+ "max_position_embeddings": 24576,
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": {
28
+ "type": "dynamic",
29
+ "factor": 2.0
30
+ },
31
+ "rope_theta": 1000000,
32
+ "tie_word_embeddings": false,
33
+ "torch_dtype": "bfloat16",
34
+ "transformers_version": "4.33.1",
35
+ "use_cache": false,
36
+ "vocab_size": 92544
37
+ }
configuration_internlm_xcomposer2.py ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ class InternLMXcomposer2Config(PretrainedConfig):
28
+ r"""
29
+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
30
+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
31
+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
32
+
33
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
34
+ documentation from [`PretrainedConfig`] for more information.
35
+
36
+
37
+ Args:
38
+ vocab_size (`int`, *optional*, defaults to 32000):
39
+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
40
+ `inputs_ids` passed when calling [`InternLM2Model`]
41
+ hidden_size (`int`, *optional*, defaults to 4096):
42
+ Dimension of the hidden representations.
43
+ intermediate_size (`int`, *optional*, defaults to 11008):
44
+ Dimension of the MLP representations.
45
+ num_hidden_layers (`int`, *optional*, defaults to 32):
46
+ Number of hidden layers in the Transformer encoder.
47
+ num_attention_heads (`int`, *optional*, defaults to 32):
48
+ Number of attention heads for each attention layer in the Transformer encoder.
49
+ num_key_value_heads (`int`, *optional*):
50
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
51
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
52
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
53
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
54
+ by meanpooling all the original heads within that group. For more details checkout [this
55
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
56
+ `num_attention_heads`.
57
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
58
+ The non-linear activation function (function or string) in the decoder.
59
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
60
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
61
+ just in case (e.g., 512 or 1024 or 2048).
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
63
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
65
+ The epsilon used by the rms normalization layers.
66
+ use_cache (`bool`, *optional*, defaults to `True`):
67
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
68
+ relevant if `config.is_decoder=True`.
69
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
70
+ Whether to tie weight embeddings
71
+ Example:
72
+
73
+ """
74
+ model_type = "internlm2"
75
+ _auto_class = "AutoConfig"
76
+
77
+ def __init__( # pylint: disable=W0102
78
+ self,
79
+ vocab_size=103168,
80
+ hidden_size=4096,
81
+ intermediate_size=11008,
82
+ num_hidden_layers=32,
83
+ num_attention_heads=32,
84
+ num_key_value_heads=None,
85
+ hidden_act="silu",
86
+ max_position_embeddings=2048,
87
+ initializer_range=0.02,
88
+ rms_norm_eps=1e-6,
89
+ use_cache=True,
90
+ pad_token_id=0,
91
+ bos_token_id=1,
92
+ eos_token_id=2,
93
+ tie_word_embeddings=False,
94
+ bias=True,
95
+ rope_theta=10000,
96
+ rope_scaling=None,
97
+ attn_implementation="eager",
98
+ **kwargs,
99
+ ):
100
+ self.vocab_size = vocab_size
101
+ self.max_position_embeddings = max_position_embeddings
102
+ self.hidden_size = hidden_size
103
+ self.intermediate_size = intermediate_size
104
+ self.num_hidden_layers = num_hidden_layers
105
+ self.num_attention_heads = num_attention_heads
106
+ self.bias = bias
107
+
108
+ if num_key_value_heads is None:
109
+ num_key_value_heads = num_attention_heads
110
+ self.num_key_value_heads = num_key_value_heads
111
+
112
+ self.hidden_act = hidden_act
113
+ self.initializer_range = initializer_range
114
+ self.rms_norm_eps = rms_norm_eps
115
+ self.use_cache = use_cache
116
+ self.rope_theta = rope_theta
117
+ self.rope_scaling = rope_scaling
118
+ self._rope_scaling_validation()
119
+
120
+ self.attn_implementation = attn_implementation
121
+ if self.attn_implementation is None:
122
+ self.attn_implementation = "eager"
123
+ super().__init__(
124
+ pad_token_id=pad_token_id,
125
+ bos_token_id=bos_token_id,
126
+ eos_token_id=eos_token_id,
127
+ tie_word_embeddings=tie_word_embeddings,
128
+ **kwargs,
129
+ )
130
+
131
+ def _rope_scaling_validation(self):
132
+ """
133
+ Validate the `rope_scaling` configuration.
134
+ """
135
+ if self.rope_scaling is None:
136
+ return
137
+
138
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
139
+ raise ValueError(
140
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
141
+ f"got {self.rope_scaling}"
142
+ )
143
+ rope_scaling_type = self.rope_scaling.get("type", None)
144
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
145
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
146
+ raise ValueError(
147
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
148
+ )
149
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
150
+ raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
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": 16384,
6
+ "pad_token_id": 2,
7
+ "transformers_version": "4.33.1",
8
+ "use_cache": false
9
+ }
ixc_utils.py ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import numpy as np
3
+ import torchvision
4
+ from PIL import Image, ImageDraw, ImageFont
5
+ from torchvision.transforms.functional import InterpolationMode
6
+ import torchvision.transforms as transforms
7
+ from decord import VideoReader
8
+
9
+ def padding_336(b, pad=336):
10
+ width, height = b.size
11
+ tar = int(np.ceil(height / pad) * pad)
12
+ top_padding = 0 # int((tar - height)/2)
13
+ bottom_padding = tar - height - top_padding
14
+ left_padding = 0
15
+ right_padding = 0
16
+ b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
17
+
18
+ return b
19
+
20
+ def Image_transform(img, hd_num=25):
21
+ width, height = img.size
22
+ trans = False
23
+ if width < height:
24
+ img = img.transpose(Image.TRANSPOSE)
25
+ trans = True
26
+ width, height = img.size
27
+ ratio = (width/ height)
28
+ scale = 1
29
+ while scale*np.ceil(scale/ratio) <= hd_num:
30
+ scale += 1
31
+ scale -= 1
32
+ scale = min(np.ceil(width / 560), scale)
33
+ new_w = int(scale * 560)
34
+ new_h = int(new_w / ratio)
35
+ #print (scale, f'{height}/{new_h}, {width}/{new_w}')
36
+
37
+ img = transforms.functional.resize(img, [new_h, new_w],)
38
+ img = padding_336(img, 560)
39
+ width, height = img.size
40
+ if trans:
41
+ img = img.transpose(Image.TRANSPOSE)
42
+
43
+ return img
44
+
45
+
46
+ def Video_transform(img, hd_num=25):
47
+ width, height = img.size
48
+ trans = False
49
+ if width < height:
50
+ img = img.transpose(Image.TRANSPOSE)
51
+ trans = True
52
+ width, height = img.size
53
+ ratio = (width/ height)
54
+ scale = 1
55
+ new_h = int(scale * 560)
56
+ new_w = int(new_h * ratio)
57
+ #print (new_h, new_w)
58
+
59
+ img = transforms.functional.resize(img, [new_h, new_w],)
60
+ img = img.transpose(Image.TRANSPOSE)
61
+ img = padding_336(img, 560)
62
+ width, height = img.size
63
+ if not trans:
64
+ img = img.transpose(Image.TRANSPOSE)
65
+
66
+ return img
67
+
68
+ def frame2img(imgs):
69
+ new_imgs = []
70
+ for img in imgs:
71
+ w, h = img.size
72
+ scale = w/h
73
+ if w > h:
74
+ new_w = 560 * 2
75
+ new_h = int(560 * 2 / scale)
76
+ else:
77
+ new_w = int(560 * 2 * scale)
78
+ new_h = 560 * 2
79
+ img = transforms.functional.resize(img, [new_h, new_w],)
80
+ new_imgs.append(img)
81
+ imgs = new_imgs
82
+ new_w = 0
83
+ new_h = 0
84
+ pad = 40
85
+ font = ImageFont.truetype(os.path.join(config._name_or_path, "SimHei.ttf"), pad)
86
+ if w > h:
87
+ for im in imgs:
88
+ w,h = im.size
89
+ new_w = max(new_w, w)
90
+ new_h += h + 10 + pad
91
+ new_img = Image.new('RGB', (new_w, new_h), 'white')
92
+ draw = ImageDraw.Draw(new_img)
93
+ curr_h = 0
94
+ for idx, im in enumerate(imgs):
95
+ w,h = im.size
96
+ new_img.paste(im, (0, pad + curr_h))
97
+ draw.text((0, curr_h ), f'<IMAGE {idx}>', font=font, fill='black')
98
+ if idx + 1 < len(imgs):
99
+ draw.line([(0, pad +curr_h + h +5), (new_w, pad +curr_h + h +5)], fill = 'black', width=2)
100
+ curr_h += h + 10 + pad
101
+ #print (new_w, new_h)
102
+ else:
103
+ for im in imgs:
104
+ w,h = im.size
105
+ new_w += w + 10
106
+ new_h = max(new_h, h)
107
+ new_h += pad
108
+ new_img = Image.new('RGB', (new_w, new_h), 'white')
109
+ draw = ImageDraw.Draw(new_img)
110
+ curr_w = 0
111
+ for idx, im in enumerate(imgs):
112
+ w,h = im.size
113
+ new_img.paste(im, (curr_w, pad))
114
+ draw.text((curr_w, 0), f'<IMAGE {idx}>', font=font, fill='black')
115
+ if idx + 1 < len(imgs):
116
+ draw.line([(curr_w + w + 5, 0), (curr_w + w + 5, new_h)], fill = 'black', width=2)
117
+ curr_w += w + 10
118
+ return new_img
119
+
120
+ def load_video(video_path, num_frm=32, start=None, end=None):
121
+ vid = VideoReader(video_path, num_threads=1)
122
+ fps = vid.get_avg_fps()
123
+ t_stride = int(round(float(fps) / int(1)))
124
+ start_idx = 0 if start is None else start
125
+ end_idx = len(vid) if end is None else end
126
+ all_pos = list(range(start_idx, end_idx, t_stride))
127
+ images = [vid[i].numpy() for i in all_pos]
128
+ if len(images) > num_frm:
129
+ num_frm = min(num_frm, len(images))
130
+ step_size = len(images) / (num_frm + 1)
131
+ indices = [int(i*step_size) for i in range(num_frm)]
132
+ images = [images[i] for i in indices]
133
+ images = [Image.fromarray(arr) for arr in images]
134
+ image = frame2img(images)
135
+ return image
136
+
ixc_utils.py~ ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import numpy as np
3
+ import torchvision
4
+ from PIL import Image, ImageDraw, ImageFont
5
+ from torchvision.transforms.functional import InterpolationMode
6
+ import torchvision.transforms as transforms
7
+ from decord import VideoReader
8
+
9
+ def padding_336(b, pad=336):
10
+ width, height = b.size
11
+ tar = int(np.ceil(height / pad) * pad)
12
+ top_padding = 0 # int((tar - height)/2)
13
+ bottom_padding = tar - height - top_padding
14
+ left_padding = 0
15
+ right_padding = 0
16
+ b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
17
+
18
+ return b
19
+
20
+ def Image_transform(img, hd_num=25):
21
+ width, height = img.size
22
+ trans = False
23
+ if width < height:
24
+ img = img.transpose(Image.TRANSPOSE)
25
+ trans = True
26
+ width, height = img.size
27
+ ratio = (width/ height)
28
+ scale = 1
29
+ while scale*np.ceil(scale/ratio) <= hd_num:
30
+ scale += 1
31
+ scale -= 1
32
+ scale = min(np.ceil(width / 560), scale)
33
+ new_w = int(scale * 560)
34
+ new_h = int(new_w / ratio)
35
+ #print (scale, f'{height}/{new_h}, {width}/{new_w}')
36
+
37
+ img = transforms.functional.resize(img, [new_h, new_w],)
38
+ img = padding_336(img, 560)
39
+ width, height = img.size
40
+ if trans:
41
+ img = img.transpose(Image.TRANSPOSE)
42
+
43
+ return img
44
+
45
+
46
+ def Video_transform(img, hd_num=25):
47
+ width, height = img.size
48
+ trans = False
49
+ if width < height:
50
+ img = img.transpose(Image.TRANSPOSE)
51
+ trans = True
52
+ width, height = img.size
53
+ ratio = (width/ height)
54
+ scale = 1
55
+ new_h = int(scale * 560)
56
+ new_w = int(new_h * ratio)
57
+ #print (new_h, new_w)
58
+
59
+ img = transforms.functional.resize(img, [new_h, new_w],)
60
+ img = img.transpose(Image.TRANSPOSE)
61
+ img = padding_336(img, 560)
62
+ width, height = img.size
63
+ if not trans:
64
+ img = img.transpose(Image.TRANSPOSE)
65
+
66
+ return img
67
+
68
+ def frame2img(imgs):
69
+ new_imgs = []
70
+ for img in imgs:
71
+ w, h = img.size
72
+ scale = w/h
73
+ if w > h:
74
+ new_w = 560 * 2
75
+ new_h = int(560 * 2 / scale)
76
+ else:
77
+ new_w = int(560 * 2 * scale)
78
+ new_h = 560 * 2
79
+ img = transforms.functional.resize(img, [new_h, new_w],)
80
+ new_imgs.append(img)
81
+ imgs = new_imgs
82
+ new_w = 0
83
+ new_h = 0
84
+ pad = 40
85
+ #font = ImageFont.truetype(os.path.join(config._name_or_path, "SimHei.ttf"), pad)
86
+ if w > h:
87
+ for im in imgs:
88
+ w,h = im.size
89
+ new_w = max(new_w, w)
90
+ new_h += h + 10 + pad
91
+ new_img = Image.new('RGB', (new_w, new_h), 'white')
92
+ draw = ImageDraw.Draw(new_img)
93
+ curr_h = 0
94
+ for idx, im in enumerate(imgs):
95
+ w,h = im.size
96
+ new_img.paste(im, (0, pad + curr_h))
97
+ draw.text((0, curr_h ), f'<IMAGE {idx}>', font=font, fill='black')
98
+ if idx + 1 < len(imgs):
99
+ draw.line([(0, pad +curr_h + h +5), (new_w, pad +curr_h + h +5)], fill = 'black', width=2)
100
+ curr_h += h + 10 + pad
101
+ #print (new_w, new_h)
102
+ else:
103
+ for im in imgs:
104
+ w,h = im.size
105
+ new_w += w + 10
106
+ new_h = max(new_h, h)
107
+ new_h += pad
108
+ new_img = Image.new('RGB', (new_w, new_h), 'white')
109
+ draw = ImageDraw.Draw(new_img)
110
+ curr_w = 0
111
+ for idx, im in enumerate(imgs):
112
+ w,h = im.size
113
+ new_img.paste(im, (curr_w, pad))
114
+ draw.text((curr_w, 0), f'<IMAGE {idx}>', font=font, fill='black')
115
+ if idx + 1 < len(imgs):
116
+ draw.line([(curr_w + w + 5, 0), (curr_w + w + 5, new_h)], fill = 'black', width=2)
117
+ curr_w += w + 10
118
+ return new_img
119
+
120
+ def load_video(video_path, num_frm=32, start=None, end=None):
121
+ vid = VideoReader(video_path, num_threads=1)
122
+ fps = vid.get_avg_fps()
123
+ t_stride = int(round(float(fps) / int(1)))
124
+ start_idx = 0 if start is None else start
125
+ end_idx = len(vid) if end is None else end
126
+ all_pos = list(range(start_idx, end_idx, t_stride))
127
+ images = [vid[i].numpy() for i in all_pos]
128
+ if len(images) > num_frm:
129
+ num_frm = min(num_frm, len(images))
130
+ step_size = len(images) / (num_frm + 1)
131
+ indices = [int(i*step_size) for i in range(num_frm)]
132
+ images = [images[i] for i in indices]
133
+ images = [Image.fromarray(arr) for arr in images]
134
+ image = frame2img(images)
135
+ return image
136
+
modeling_internlm2.py ADDED
@@ -0,0 +1,997 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 transformers.activations import ACT2FN
34
+ from transformers.modeling_outputs import (
35
+ BaseModelOutputWithPast,
36
+ CausalLMOutputWithPast,
37
+ SequenceClassifierOutputWithPast,
38
+ )
39
+ from transformers.modeling_utils import PreTrainedModel
40
+ from transformers.utils import (
41
+ add_start_docstrings,
42
+ add_start_docstrings_to_model_forward,
43
+ logging,
44
+ replace_return_docstrings,
45
+ )
46
+
47
+ try:
48
+ from transformers.generation.streamers import BaseStreamer
49
+ except: # noqa # pylint: disable=bare-except
50
+ BaseStreamer = None
51
+
52
+ from .build_mlp import PLoRA
53
+ from .configuration_internlm_xcomposer2 import InternLMXcomposer2Config as InternLM2Config
54
+
55
+ logger = logging.get_logger(__name__)
56
+
57
+ _CONFIG_FOR_DOC = "InternLM2Config"
58
+
59
+ flash_attn_func, flash_attn_varlen_func = None, None
60
+ pad_input, index_first_axis, unpad_input = None, None, None
61
+ def _import_flash_attn():
62
+ global flash_attn_func, flash_attn_varlen_func
63
+ global pad_input, index_first_axis, unpad_input
64
+ try:
65
+ from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
66
+ from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
67
+ flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
68
+ pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
69
+ except ImportError:
70
+ raise ImportError("flash_attn is not installed.")
71
+
72
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
73
+ def _get_unpad_data(attention_mask):
74
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
75
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
76
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
77
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
78
+ return (
79
+ indices,
80
+ cu_seqlens,
81
+ max_seqlen_in_batch,
82
+ )
83
+
84
+
85
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
86
+ def _make_causal_mask(
87
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
88
+ ):
89
+ """
90
+ Make causal mask used for bi-directional self-attention.
91
+ """
92
+ bsz, tgt_len = input_ids_shape
93
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
94
+ mask_cond = torch.arange(mask.size(-1), device=device)
95
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
96
+ mask = mask.to(dtype)
97
+
98
+ if past_key_values_length > 0:
99
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
100
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
101
+
102
+
103
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
104
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
105
+ """
106
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
107
+ """
108
+ bsz, src_len = mask.size()
109
+ tgt_len = tgt_len if tgt_len is not None else src_len
110
+
111
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
112
+
113
+ inverted_mask = 1.0 - expanded_mask
114
+
115
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
116
+
117
+
118
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
119
+ class InternLM2RMSNorm(nn.Module):
120
+ def __init__(self, hidden_size, eps=1e-6):
121
+ """
122
+ InternLM2RMSNorm is equivalent to T5LayerNorm
123
+ """
124
+ super().__init__()
125
+ self.weight = nn.Parameter(torch.ones(hidden_size))
126
+ self.variance_epsilon = eps
127
+
128
+ def forward(self, hidden_states):
129
+ input_dtype = hidden_states.dtype
130
+ hidden_states = hidden_states.to(torch.float32)
131
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
132
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
133
+ return self.weight * hidden_states.to(input_dtype)
134
+
135
+
136
+ # Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
137
+ class InternLM2RotaryEmbedding(nn.Module):
138
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
139
+ super().__init__()
140
+
141
+ self.dim = dim
142
+ self.max_position_embeddings = max_position_embeddings
143
+ self.base = base
144
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
145
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
146
+
147
+ # Build here to make `torch.jit.trace` work.
148
+ self._set_cos_sin_cache(
149
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
150
+ )
151
+
152
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
153
+ self.max_seq_len_cached = seq_len
154
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
155
+
156
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
157
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
158
+ emb = torch.cat((freqs, freqs), dim=-1)
159
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
160
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
161
+
162
+ def forward(self, x, seq_len=None):
163
+ # x: [bs, num_attention_heads, seq_len, head_size]
164
+ if seq_len > self.max_seq_len_cached:
165
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
166
+
167
+ return (
168
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
169
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
170
+ )
171
+
172
+
173
+ # Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
174
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
175
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
176
+
177
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
178
+ self.scaling_factor = scaling_factor
179
+ super().__init__(dim, max_position_embeddings, base, device)
180
+
181
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
182
+ self.max_seq_len_cached = seq_len
183
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
184
+ t = t / self.scaling_factor
185
+
186
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
187
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
188
+ emb = torch.cat((freqs, freqs), dim=-1)
189
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
190
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
191
+
192
+
193
+ # Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
194
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
195
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
196
+ Credits to the Reddit users /u/bloc97 and /u/emozilla.
197
+ """
198
+
199
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
200
+ self.scaling_factor = scaling_factor
201
+ super().__init__(dim, max_position_embeddings, base, device)
202
+
203
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
204
+ self.max_seq_len_cached = seq_len
205
+
206
+ if seq_len > self.max_position_embeddings:
207
+ base = self.base * (
208
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
209
+ ) ** (self.dim / (self.dim - 2))
210
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
211
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
212
+
213
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
214
+
215
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
216
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
217
+ emb = torch.cat((freqs, freqs), dim=-1)
218
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
219
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
220
+
221
+
222
+ # Copied from transformers.model.llama.modeling_llama.rotate_half
223
+ def rotate_half(x):
224
+ """Rotates half the hidden dims of the input."""
225
+ x1 = x[..., : x.shape[-1] // 2]
226
+ x2 = x[..., x.shape[-1] // 2 :]
227
+ return torch.cat((-x2, x1), dim=-1)
228
+
229
+
230
+ # Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
231
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
232
+ """Applies Rotary Position Embedding to the query and key tensors."""
233
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
234
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
235
+ q_embed = (q * cos) + (rotate_half(q) * sin)
236
+ k_embed = (k * cos) + (rotate_half(k) * sin)
237
+ return q_embed, k_embed
238
+
239
+
240
+ class InternLM2MLP(nn.Module):
241
+ def __init__(self, config):
242
+ super().__init__()
243
+ self.config = config
244
+ self.hidden_size = config.hidden_size
245
+ self.intermediate_size = config.intermediate_size
246
+ #self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
247
+ #self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
248
+ #self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
249
+
250
+ self.w1 = PLoRA(self.hidden_size, self.intermediate_size, bias=False,
251
+ lora_r=256, lora_alpha=256, lora_len=1225)
252
+ self.w3 = PLoRA(self.hidden_size, self.intermediate_size, bias=False,
253
+ lora_r=256, lora_alpha=256, lora_len=1225)
254
+ self.w2 = PLoRA(self.intermediate_size, self.hidden_size, bias=False,
255
+ lora_r=256, lora_alpha=256, lora_len=1225)
256
+
257
+ self.act_fn = ACT2FN[config.hidden_act]
258
+
259
+ def forward(self, x, im_mask, infer_mode):
260
+ down_proj = self.w2(self.act_fn(self.w1(x, im_mask, infer_mode)) * self.w3(x, im_mask, infer_mode), im_mask, infer_mode)
261
+
262
+ return down_proj
263
+
264
+
265
+ # Copied from transformers.model.llama.modeling_llama.repeat_kv
266
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
267
+ """
268
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
269
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
270
+ """
271
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
272
+ if n_rep == 1:
273
+ return hidden_states
274
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
275
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
276
+
277
+
278
+ # Modified from transformers.model.llama.modeling_llama.LlamaAttention
279
+ class InternLM2Attention(nn.Module):
280
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
281
+
282
+ def __init__(self, config: InternLM2Config):
283
+ super().__init__()
284
+ self.config = config
285
+ self.hidden_size = config.hidden_size
286
+ self.num_heads = config.num_attention_heads
287
+ self.head_dim = self.hidden_size // self.num_heads
288
+ self.num_key_value_heads = config.num_key_value_heads
289
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
290
+ self.max_position_embeddings = config.max_position_embeddings
291
+ self.is_causal = True
292
+
293
+ if (self.head_dim * self.num_heads) != self.hidden_size:
294
+ raise ValueError(
295
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
296
+ f" and `num_heads`: {self.num_heads})."
297
+ )
298
+
299
+ #self.wqkv = nn.Linear(
300
+ self.wqkv = PLoRA(
301
+ self.hidden_size,
302
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
303
+ bias=config.bias,
304
+ lora_r=256, lora_alpha=256, lora_len=1225
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
+ infer_mode: str = 'base',
353
+ **kwargs,
354
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
355
+ if "padding_mask" in kwargs:
356
+ warnings.warn(
357
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
358
+ "Please make sure use `attention_mask` instead.`"
359
+ )
360
+
361
+ bsz, q_len, _ = hidden_states.size()
362
+
363
+ qkv_states = self.wqkv(hidden_states, im_mask, infer_mode)
364
+
365
+ qkv_states = rearrange(
366
+ qkv_states,
367
+ "b q (h gs d) -> b q h gs d",
368
+ gs=2 + self.num_key_value_groups,
369
+ d=self.head_dim,
370
+ )
371
+
372
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
373
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
374
+ key_states = qkv_states[..., -2, :]
375
+ value_states = qkv_states[..., -1, :]
376
+
377
+ query_states = query_states.transpose(1, 2)
378
+ key_states = key_states.transpose(1, 2)
379
+ value_states = value_states.transpose(1, 2)
380
+
381
+ kv_seq_len = key_states.shape[-2]
382
+ if past_key_value is not None:
383
+ kv_seq_len += past_key_value[0].shape[-2]
384
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
385
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
386
+
387
+ if past_key_value is not None:
388
+ # reuse k, v, self_attention
389
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
390
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
391
+
392
+ past_key_value = (key_states, value_states) if use_cache else None
393
+
394
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
395
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
396
+
397
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
398
+
399
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
400
+ raise ValueError(
401
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
402
+ f" {attn_weights.size()}"
403
+ )
404
+
405
+ if attention_mask is not None:
406
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
407
+ raise ValueError(
408
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
409
+ )
410
+ attn_weights = attn_weights + attention_mask
411
+
412
+ # upcast attention to fp32
413
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
414
+ attn_output = torch.matmul(attn_weights, value_states)
415
+
416
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
417
+ raise ValueError(
418
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
419
+ f" {attn_output.size()}"
420
+ )
421
+
422
+ attn_output = attn_output.transpose(1, 2).contiguous()
423
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
424
+
425
+ attn_output = self.wo(attn_output, im_mask, infer_mode)
426
+
427
+ if not output_attentions:
428
+ attn_weights = None
429
+
430
+ return attn_output, attn_weights, past_key_value
431
+
432
+
433
+ # Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
434
+ class InternLM2FlashAttention2(InternLM2Attention):
435
+ """
436
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
437
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
438
+ flash attention and deal with padding tokens in case the input contains any of them.
439
+ """
440
+
441
+ def forward(
442
+ self,
443
+ hidden_states: torch.Tensor,
444
+ attention_mask: Optional[torch.LongTensor] = None,
445
+ position_ids: Optional[torch.LongTensor] = None,
446
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
447
+ output_attentions: bool = False,
448
+ use_cache: bool = False,
449
+ im_mask: Optional[Tuple[torch.Tensor]] = None,
450
+ infer_mode: str = 'base',
451
+ **kwargs,
452
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
453
+ # InternLM2FlashAttention2 attention does not support output_attentions
454
+ if "padding_mask" in kwargs:
455
+ warnings.warn(
456
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
457
+ "Please make sure use `attention_mask` instead.`"
458
+ )
459
+
460
+ # overwrite attention_mask with padding_mask
461
+ attention_mask = kwargs.pop("padding_mask")
462
+
463
+ output_attentions = False
464
+
465
+ bsz, q_len, _ = hidden_states.size()
466
+
467
+ qkv_states = self.wqkv(hidden_states, im_mask, infer_mode)
468
+
469
+ qkv_states = rearrange(
470
+ qkv_states,
471
+ "b q (h gs d) -> b q h gs d",
472
+ gs=2 + self.num_key_value_groups,
473
+ d=self.head_dim,
474
+ )
475
+
476
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
477
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
478
+ key_states = qkv_states[..., -2, :]
479
+ value_states = qkv_states[..., -1, :]
480
+
481
+ query_states = query_states.transpose(1, 2)
482
+ key_states = key_states.transpose(1, 2)
483
+ value_states = value_states.transpose(1, 2)
484
+
485
+ kv_seq_len = key_states.shape[-2]
486
+ if past_key_value is not None:
487
+ kv_seq_len += past_key_value[0].shape[-2]
488
+
489
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
490
+
491
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
492
+
493
+ if past_key_value is not None:
494
+ # reuse k, v, self_attention
495
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
496
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
497
+
498
+ past_key_value = (key_states, value_states) if use_cache else None
499
+
500
+ query_states = query_states.transpose(1, 2)
501
+ key_states = key_states.transpose(1, 2)
502
+ value_states = value_states.transpose(1, 2)
503
+
504
+ attn_output = self._flash_attention_forward(
505
+ query_states, key_states, value_states, attention_mask, q_len
506
+ )
507
+
508
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
509
+ attn_output = self.wo(attn_output, im_mask, infer_mode)
510
+
511
+ if not output_attentions:
512
+ attn_weights = None
513
+
514
+ return attn_output, attn_weights, past_key_value
515
+
516
+ def _flash_attention_forward(
517
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
518
+ ):
519
+ """
520
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
521
+ first unpad the input, then computes the attention scores and pad the final attention scores.
522
+
523
+ Args:
524
+ query_states (`torch.Tensor`):
525
+ Input query states to be passed to Flash Attention API
526
+ key_states (`torch.Tensor`):
527
+ Input key states to be passed to Flash Attention API
528
+ value_states (`torch.Tensor`):
529
+ Input value states to be passed to Flash Attention API
530
+ attention_mask (`torch.Tensor`):
531
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
532
+ position of padding tokens and 1 for the position of non-padding tokens.
533
+ dropout (`int`, *optional*):
534
+ Attention dropout
535
+ softmax_scale (`float`, *optional*):
536
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
537
+ """
538
+ # Contains at least one padding token in the sequence
539
+ causal = self.is_causal and query_length != 1
540
+ if attention_mask is not None:
541
+ batch_size = query_states.shape[0]
542
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
543
+ query_states, key_states, value_states, attention_mask, query_length
544
+ )
545
+
546
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
547
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
548
+
549
+ attn_output_unpad = flash_attn_varlen_func(
550
+ query_states,
551
+ key_states,
552
+ value_states,
553
+ cu_seqlens_q=cu_seqlens_q,
554
+ cu_seqlens_k=cu_seqlens_k,
555
+ max_seqlen_q=max_seqlen_in_batch_q,
556
+ max_seqlen_k=max_seqlen_in_batch_k,
557
+ dropout_p=dropout,
558
+ softmax_scale=softmax_scale,
559
+ causal=causal,
560
+ )
561
+
562
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
563
+ else:
564
+ attn_output = flash_attn_func(
565
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
566
+ )
567
+
568
+ return attn_output
569
+
570
+ def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
571
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
572
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
573
+
574
+ key_layer = index_first_axis(
575
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
576
+ )
577
+ value_layer = index_first_axis(
578
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
579
+ )
580
+
581
+ if query_length == kv_seq_len:
582
+ query_layer = index_first_axis(
583
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
584
+ )
585
+ cu_seqlens_q = cu_seqlens_k
586
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
587
+ indices_q = indices_k
588
+ elif query_length == 1:
589
+ max_seqlen_in_batch_q = 1
590
+ cu_seqlens_q = torch.arange(
591
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
592
+ ) # There is a memcpy here, that is very bad.
593
+ indices_q = cu_seqlens_q[:-1]
594
+ query_layer = query_layer.squeeze(1)
595
+ else:
596
+ # The -q_len: slice assumes left padding.
597
+ attention_mask = attention_mask[:, -query_length:]
598
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
599
+
600
+ return (
601
+ query_layer,
602
+ key_layer,
603
+ value_layer,
604
+ indices_q.to(torch.int64),
605
+ (cu_seqlens_q, cu_seqlens_k),
606
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
607
+ )
608
+
609
+ INTERNLM2_ATTENTION_CLASSES = {
610
+ "eager": InternLM2Attention,
611
+ "flash_attention_2": InternLM2FlashAttention2,
612
+ }
613
+
614
+ # Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
615
+ class InternLM2DecoderLayer(nn.Module):
616
+ def __init__(self, config: InternLM2Config):
617
+ super().__init__()
618
+ self.hidden_size = config.hidden_size
619
+
620
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
621
+
622
+ self.feed_forward = InternLM2MLP(config)
623
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
624
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
625
+
626
+ def forward(
627
+ self,
628
+ hidden_states: torch.Tensor,
629
+ attention_mask: Optional[torch.Tensor] = None,
630
+ position_ids: Optional[torch.LongTensor] = None,
631
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
632
+ output_attentions: Optional[bool] = False,
633
+ use_cache: Optional[bool] = False,
634
+ im_mask: Optional[Tuple[torch.Tensor]] = None,
635
+ infer_mode: str='base',
636
+ **kwargs,
637
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
638
+ """
639
+ Args:
640
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
641
+ attention_mask (`torch.FloatTensor`, *optional*):
642
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
643
+ query_sequence_length, key_sequence_length)` if default attention is used.
644
+ output_attentions (`bool`, *optional*):
645
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
646
+ returned tensors for more detail.
647
+ use_cache (`bool`, *optional*):
648
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
649
+ (see `past_key_values`).
650
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
651
+ """
652
+ if "padding_mask" in kwargs:
653
+ warnings.warn(
654
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
655
+ "Please make sure use `attention_mask` instead.`"
656
+ )
657
+
658
+ residual = hidden_states
659
+
660
+ hidden_states = self.attention_norm(hidden_states)
661
+
662
+ # Self Attention
663
+ hidden_states, self_attn_weights, present_key_value = self.attention(
664
+ hidden_states=hidden_states,
665
+ attention_mask=attention_mask,
666
+ position_ids=position_ids,
667
+ past_key_value=past_key_value,
668
+ output_attentions=output_attentions,
669
+ use_cache=use_cache,
670
+ im_mask=im_mask,
671
+ infer_mode=infer_mode,
672
+ **kwargs,
673
+ )
674
+ hidden_states = residual + hidden_states
675
+
676
+ # Fully Connected
677
+ residual = hidden_states
678
+ hidden_states = self.ffn_norm(hidden_states)
679
+ hidden_states = self.feed_forward(hidden_states, im_mask, infer_mode)
680
+ hidden_states = residual + hidden_states
681
+
682
+ outputs = (hidden_states,)
683
+
684
+ if output_attentions:
685
+ outputs += (self_attn_weights,)
686
+
687
+ if use_cache:
688
+ outputs += (present_key_value,)
689
+
690
+ return outputs
691
+
692
+
693
+ InternLM2_START_DOCSTRING = r"""
694
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
695
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
696
+ etc.)
697
+
698
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
699
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
700
+ and behavior.
701
+
702
+ Parameters:
703
+ config ([`InternLM2Config`]):
704
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
705
+ load the weights associated with the model, only the configuration. Check out the
706
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
707
+ """
708
+
709
+
710
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
711
+ @add_start_docstrings(
712
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
713
+ InternLM2_START_DOCSTRING,
714
+ )
715
+ class InternLM2PreTrainedModel(PreTrainedModel):
716
+ config_class = InternLM2Config
717
+ base_model_prefix = "model"
718
+ supports_gradient_checkpointing = True
719
+ _no_split_modules = ["InternLM2DecoderLayer"]
720
+ _skip_keys_device_placement = "past_key_values"
721
+
722
+ def _init_weights(self, module):
723
+ std = self.config.initializer_range
724
+ if isinstance(module, nn.Linear):
725
+ module.weight.data.normal_(mean=0.0, std=std)
726
+ if module.bias is not None:
727
+ module.bias.data.zero_()
728
+ elif isinstance(module, nn.Embedding):
729
+ module.weight.data.normal_(mean=0.0, std=std)
730
+ if module.padding_idx is not None:
731
+ module.weight.data[module.padding_idx].zero_()
732
+
733
+
734
+ InternLM2_INPUTS_DOCSTRING = r"""
735
+ Args:
736
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
737
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
738
+ it.
739
+
740
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
741
+ [`PreTrainedTokenizer.__call__`] for details.
742
+
743
+ [What are input IDs?](../glossary#input-ids)
744
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
745
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
746
+
747
+ - 1 for tokens that are **not masked**,
748
+ - 0 for tokens that are **masked**.
749
+
750
+ [What are attention masks?](../glossary#attention-mask)
751
+
752
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
753
+ [`PreTrainedTokenizer.__call__`] for details.
754
+
755
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
756
+ `past_key_values`).
757
+
758
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
759
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
760
+ information on the default strategy.
761
+
762
+ - 1 indicates the head is **not masked**,
763
+ - 0 indicates the head is **masked**.
764
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
765
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
766
+ config.n_positions - 1]`.
767
+
768
+ [What are position IDs?](../glossary#position-ids)
769
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
770
+ when `config.use_cache=True`):
771
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
772
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
773
+ `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
774
+
775
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
776
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
777
+
778
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
779
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
780
+ of shape `(batch_size, sequence_length)`.
781
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
782
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
783
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
784
+ model's internal embedding lookup matrix.
785
+ use_cache (`bool`, *optional*):
786
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
787
+ `past_key_values`).
788
+ output_attentions (`bool`, *optional*):
789
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
790
+ tensors for more detail.
791
+ output_hidden_states (`bool`, *optional*):
792
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
793
+ more detail.
794
+ return_dict (`bool`, *optional*):
795
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
796
+ """
797
+
798
+
799
+ # Modified from transformers.model.llama.modeling_llama.LlamaModel
800
+ @add_start_docstrings(
801
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
802
+ InternLM2_START_DOCSTRING,
803
+ )
804
+ class InternLM2Model(InternLM2PreTrainedModel):
805
+ """
806
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
807
+
808
+ Args:
809
+ config: InternLM2Config
810
+ """
811
+
812
+ _auto_class = "AutoModel"
813
+
814
+ def __init__(self, config: InternLM2Config):
815
+ super().__init__(config)
816
+ self.padding_idx = config.pad_token_id
817
+ self.vocab_size = config.vocab_size
818
+ self.config = config
819
+
820
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
821
+
822
+ self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
823
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
824
+
825
+ self.gradient_checkpointing = False
826
+ # Initialize weights and apply final processing
827
+ self.post_init()
828
+
829
+ def get_input_embeddings(self):
830
+ return self.tok_embeddings
831
+
832
+ def set_input_embeddings(self, value):
833
+ self.tok_embeddings = value
834
+
835
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
836
+ # create causal mask
837
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
838
+ combined_attention_mask = None
839
+ if input_shape[-1] > 1:
840
+ combined_attention_mask = _make_causal_mask(
841
+ input_shape,
842
+ inputs_embeds.dtype,
843
+ device=inputs_embeds.device,
844
+ past_key_values_length=past_key_values_length,
845
+ )
846
+
847
+ if attention_mask is not None:
848
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
849
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
850
+ inputs_embeds.device
851
+ )
852
+ combined_attention_mask = (
853
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
854
+ )
855
+
856
+ return combined_attention_mask
857
+
858
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
859
+ def forward(
860
+ self,
861
+ input_ids: torch.LongTensor = None,
862
+ attention_mask: Optional[torch.Tensor] = None,
863
+ position_ids: Optional[torch.LongTensor] = None,
864
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
865
+ inputs_embeds: Optional[torch.FloatTensor] = None,
866
+ use_cache: Optional[bool] = None,
867
+ output_attentions: Optional[bool] = None,
868
+ output_hidden_states: Optional[bool] = None,
869
+ return_dict: Optional[bool] = None,
870
+ **kwargs
871
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
872
+
873
+ im_mask = kwargs.get('im_mask', None)
874
+ infer_mode = kwargs.get('infer_mode', 'base')
875
+
876
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
877
+ output_hidden_states = (
878
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
879
+ )
880
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
881
+
882
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
883
+
884
+ if self.config.attn_implementation == "flash_attention_2":
885
+ _import_flash_attn()
886
+
887
+ # retrieve input_ids and inputs_embeds
888
+ if input_ids is not None and inputs_embeds is not None:
889
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
890
+ elif input_ids is not None:
891
+ batch_size, seq_length = input_ids.shape[:2]
892
+ elif inputs_embeds is not None:
893
+ batch_size, seq_length = inputs_embeds.shape[:2]
894
+ else:
895
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
896
+
897
+ seq_length_with_past = seq_length
898
+ past_key_values_length = 0
899
+ if past_key_values is not None:
900
+ past_key_values_length = past_key_values[0][0].shape[2]
901
+ seq_length_with_past = seq_length_with_past + past_key_values_length
902
+
903
+ if position_ids is None:
904
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
905
+ position_ids = torch.arange(
906
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
907
+ )
908
+ position_ids = position_ids.unsqueeze(0)
909
+
910
+ if inputs_embeds is None:
911
+ inputs_embeds = self.tok_embeddings(input_ids)
912
+ im_mask = torch.zeros(inputs_embeds.shape[:2]).to(inputs_embeds.device).bool()
913
+
914
+ if self.config.attn_implementation == "flash_attention_2":
915
+ # 2d mask is passed through the layers
916
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
917
+ else:
918
+ if attention_mask is None:
919
+ attention_mask = torch.ones(
920
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
921
+ )
922
+ attention_mask = self._prepare_decoder_attention_mask(
923
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
924
+ )
925
+
926
+ # embed positions
927
+ hidden_states = inputs_embeds
928
+
929
+ if self.gradient_checkpointing and self.training:
930
+ if use_cache:
931
+ logger.warning_once(
932
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
933
+ )
934
+ use_cache = False
935
+
936
+ # decoder layers
937
+ all_hidden_states = () if output_hidden_states else None
938
+ all_self_attns = () if output_attentions else None
939
+ next_decoder_cache = () if use_cache else None
940
+
941
+ for idx, decoder_layer in enumerate(self.layers):
942
+ if output_hidden_states:
943
+ all_hidden_states += (hidden_states,)
944
+
945
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
946
+
947
+ if self.gradient_checkpointing and self.training:
948
+
949
+ def create_custom_forward(module):
950
+ def custom_forward(*inputs):
951
+ # None for past_key_value
952
+ return module(*inputs, output_attentions, None, im_mask, infer_mode)
953
+
954
+ return custom_forward
955
+
956
+ layer_outputs = torch.utils.checkpoint.checkpoint(
957
+ create_custom_forward(decoder_layer),
958
+ hidden_states,
959
+ attention_mask,
960
+ position_ids,
961
+ None,
962
+ )
963
+ else:
964
+ layer_outputs = decoder_layer(
965
+ hidden_states,
966
+ attention_mask=attention_mask,
967
+ position_ids=position_ids,
968
+ past_key_value=past_key_value,
969
+ output_attentions=output_attentions,
970
+ use_cache=use_cache,
971
+ im_mask=im_mask,
972
+ infer_mode=infer_mode,
973
+ )
974
+
975
+ hidden_states = layer_outputs[0]
976
+
977
+ if use_cache:
978
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
979
+
980
+ if output_attentions:
981
+ all_self_attns += (layer_outputs[1],)
982
+
983
+ hidden_states = self.norm(hidden_states)
984
+
985
+ # add hidden states from the last decoder layer
986
+ if output_hidden_states:
987
+ all_hidden_states += (hidden_states,)
988
+
989
+ next_cache = next_decoder_cache if use_cache else None
990
+ if not return_dict:
991
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
992
+ return BaseModelOutputWithPast(
993
+ last_hidden_state=hidden_states,
994
+ past_key_values=next_cache,
995
+ hidden_states=all_hidden_states,
996
+ attentions=all_self_attns,
997
+ )
modeling_internlm_xcomposer2.py ADDED
@@ -0,0 +1,865 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+
17
+ """PyTorch InternLMXComposer2 model."""
18
+ import os
19
+ import re
20
+ import copy
21
+ import queue
22
+ import threading
23
+ from typing import List, Optional, Tuple, Union
24
+
25
+ import torch
26
+ import torch.utils.checkpoint
27
+ from PIL import Image
28
+ import numpy as np
29
+ import random
30
+ from torch import nn
31
+ from torch.nn import CrossEntropyLoss
32
+ from torchvision import transforms
33
+ from torchvision.transforms.functional import InterpolationMode
34
+ from transformers.modeling_outputs import CausalLMOutputWithPast
35
+ from transformers.utils import (add_start_docstrings_to_model_forward,
36
+ replace_return_docstrings)
37
+ from transformers import StoppingCriteria, StoppingCriteriaList
38
+ from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
39
+ try:
40
+ from transformers.generation.streamers import BaseStreamer
41
+ except: # noqa # pylint: disable=bare-except
42
+ BaseStreamer = None
43
+
44
+ import torchvision.transforms as transforms
45
+ from torchvision.transforms.functional import InterpolationMode
46
+
47
+ from .build_mlp import build_vision_projector, build_vision_tower
48
+ from .ixc_utils import Image_transform, Video_transform, load_video
49
+ from .configuration_internlm_xcomposer2 import InternLMXcomposer2Config
50
+ from .modeling_internlm2 import (InternLM2_INPUTS_DOCSTRING, InternLM2Model,
51
+ InternLM2PreTrainedModel)
52
+
53
+ _CONFIG_FOR_DOC = 'InternLMXcomposer2Config'
54
+
55
+ image_extensions = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'}
56
+ video_extensions = {'.mp4', '.avi', '.mkv', '.mov', '.wmv'}
57
+
58
+ class StoppingCriteriaSub(StoppingCriteria):
59
+
60
+ def __init__(self, stops=[], encounters=1):
61
+ super().__init__()
62
+ self.stops = stops
63
+
64
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
65
+ for stop in self.stops:
66
+ if torch.all((stop == input_ids[0][-len(stop):])).item():
67
+ return True
68
+ return False
69
+
70
+
71
+ def get_stopping_criteria(stop_words_ids):
72
+ stop_words_ids = [torch.tensor([i]).cuda() for i in stop_words_ids]
73
+ stopping_criteria = StoppingCriteriaList(
74
+ [StoppingCriteriaSub(stops=stop_words_ids)])
75
+ return stopping_criteria
76
+
77
+ def set_random_seed(seed, set_cudnn=False):
78
+ """Set the random seed for reproducibility.
79
+
80
+ Parameters:
81
+ seed (int): The seed to use for generating random numbers.
82
+ """
83
+ torch.manual_seed(seed)
84
+ if torch.cuda.is_available():
85
+ torch.cuda.manual_seed_all(seed) # For multi-GPU.
86
+ np.random.seed(seed)
87
+ random.seed(seed)
88
+ if set_cudnn and torch.backends.cudnn.is_available():
89
+ torch.backends.cudnn.deterministic = True
90
+ torch.backends.cudnn.benchmark = False
91
+
92
+ class InternLMXComposer2ForCausalLM(InternLM2PreTrainedModel):
93
+ _auto_class = 'AutoModelForCausalLM'
94
+
95
+ _tied_weights_keys = ['output.weight']
96
+
97
+ def __init__(self, config):
98
+ super().__init__(config)
99
+ self.model = InternLM2Model(config)
100
+ self.vocab_size = config.vocab_size
101
+ self.output = nn.Linear(
102
+ config.hidden_size, config.vocab_size, bias=False)
103
+ self.tokenizer = None
104
+ self.hd_num = 25
105
+
106
+ self.max_length = config.max_length
107
+ print(f'Set max length to {self.max_length}')
108
+ # Initialize weights and apply final processing
109
+ self.post_init()
110
+ self.plora_glb_GN = nn.Parameter(torch.zeros([1, 1, 4096]))
111
+ self.plora_sub_GN = nn.Parameter(torch.zeros([1, 1, 1, 4096]))
112
+
113
+ self.vit = build_vision_tower()
114
+ self.vision_proj = build_vision_projector()
115
+
116
+ self.vis_processor = transforms.Compose([
117
+ transforms.ToTensor(),
118
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073),
119
+ (0.26862954, 0.26130258, 0.27577711)),
120
+ ])
121
+
122
+
123
+
124
+
125
+ def _set_gradient_checkpointing(self, module, value=False):
126
+ if isinstance(module, InternLM2Model):
127
+ module.gradient_checkpointing = value
128
+ if value:
129
+ self.vit.vision_tower.vision_model.encoder.gradient_checkpointing = value
130
+
131
+ def get_input_embeddings(self):
132
+ return self.model.tok_embeddings
133
+
134
+ def set_input_embeddings(self, value):
135
+ self.model.tok_embeddings = value
136
+
137
+ def get_output_embeddings(self):
138
+ return self.output
139
+
140
+ def set_output_embeddings(self, new_embeddings):
141
+ self.output = new_embeddings
142
+
143
+ def set_decoder(self, decoder):
144
+ self.model = decoder
145
+
146
+ def get_decoder(self):
147
+ return self.model
148
+
149
+ def encode_text(self, text, add_special_tokens=False):
150
+ token = self.tokenizer(
151
+ text, return_tensors='pt',
152
+ add_special_tokens=add_special_tokens).input_ids.to(self.device)
153
+ embs = self.model.tok_embeddings(token)
154
+ return embs
155
+
156
+ def encode_img(self, image, hd_num=25):
157
+ if image is None:
158
+ return None
159
+ if isinstance(image, str):
160
+ _, ext = os.path.splitext(image)
161
+ if ext.lower() in image_extensions:
162
+ image = Image.open(image)
163
+ image = Image_transform(image, hd_num = hd_num)
164
+ elif ext.lower() in video_extensions:
165
+ image = load_video(image)
166
+ image = Video_transform(image, hd_num = hd_num)
167
+ else:
168
+ print ('Unknow input format', image)
169
+ return None
170
+ image = self.vis_processor(image).unsqueeze(0).to(self.device)
171
+ else:
172
+ assert isinstance(image, torch.Tensor)
173
+
174
+ img_embeds, atts_img, img_target = self.img2emb(image)
175
+ return img_embeds
176
+
177
+ def img2emb(self, image):
178
+ img_embeds, img_split = self.vit([image],
179
+ self.plora_glb_GN, self.plora_sub_GN)
180
+ if len(img_split) > 1:
181
+ print ('Batch Size >1 is not supported.')
182
+ assert 0
183
+ #print (img_embeds.shape)
184
+ img_embeds = self.vision_proj(img_embeds)
185
+ atts_img = torch.ones(
186
+ img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)
187
+
188
+ img_target = torch.ones(
189
+ img_embeds.size()[:2], dtype=torch.long).to(
190
+ img_embeds.device) * -100
191
+
192
+ return img_embeds, atts_img, img_target
193
+
194
+ def prompt_wrap(self, img_embeds, prompt):
195
+ batch_size = img_embeds.shape[0]
196
+ p_before, p_after = prompt.split('<ImageHere>')
197
+ p_before_tokens = self.tokenizer(
198
+ p_before, return_tensors='pt',
199
+ add_special_tokens=True).to(img_embeds.device)
200
+
201
+ p_before_embeds = self.model.tok_embeddings(
202
+ p_before_tokens.input_ids).expand(batch_size, -1, -1)
203
+ wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds], dim=1)
204
+
205
+ wrapped_atts_img = torch.ones(
206
+ wrapped_img_embeds.size()[:-1],
207
+ dtype=torch.long).to(img_embeds.device)
208
+
209
+ wrapped_target = torch.ones(
210
+ batch_size, wrapped_img_embeds.shape[1], dtype=torch.long).to(
211
+ img_embeds.device) * -100
212
+
213
+ return wrapped_img_embeds, wrapped_atts_img, wrapped_target
214
+
215
+ def text2emb(self, text, add_special_tokens=False):
216
+ to_regress_tokens = self.tokenizer(
217
+ text,
218
+ return_tensors='pt',
219
+ padding='longest',
220
+ truncation=True,
221
+ max_length=self.max_length,
222
+ add_special_tokens=add_special_tokens
223
+ ).to(self.device)
224
+
225
+ targets = self.mask_human_targets(to_regress_tokens.input_ids)
226
+ targets = targets.to(self.device)
227
+ return to_regress_tokens, targets
228
+
229
+ def interleav_wrap_chat(self, query, image, history = [], meta_instruction='', max_length=16384, hd_num=24):
230
+ self.max_length = max_length
231
+ prompt = ''
232
+ if meta_instruction:
233
+ prompt += f"""[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n"""
234
+ for record in history:
235
+ prompt += f"""[UNUSED_TOKEN_146]user\n{record[0]}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n{record[1]}[UNUSED_TOKEN_145]\n"""
236
+ prompt += f"""[UNUSED_TOKEN_146]user\n{query}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"""
237
+
238
+ image_nums = len(image)
239
+ if image_nums == 1 and prompt.find('<ImageHere>') == -1:
240
+ #print ('auto append image at the begining')
241
+ prompt = '<ImageHere>' + prompt
242
+
243
+ parts = prompt.split('<ImageHere>')
244
+ wrap_embeds, wrap_im_mask = [], []
245
+ temp_len = 0
246
+ need_bos = True
247
+
248
+ if len(parts) != image_nums + 1:
249
+ #raise ValueError('Invalid <ImageHere> prompt format.')
250
+ print ('Waring! The image number != given position!')
251
+ if image_nums > 1:
252
+ hd_num = 6
253
+ else:
254
+ hu_num = hd_num
255
+ for idx, part in enumerate(parts):
256
+ if need_bos or len(part) > 0:
257
+ part_tokens = self.tokenizer(
258
+ part,
259
+ return_tensors='pt',
260
+ padding='longest',
261
+ add_special_tokens=need_bos).to(self.device)
262
+ if need_bos:
263
+ need_bos = False
264
+
265
+ part_embeds = self.model.tok_embeddings(
266
+ part_tokens.input_ids)
267
+ wrap_embeds.append(part_embeds)
268
+ wrap_im_mask.append(torch.zeros(part_embeds.shape[:2]))
269
+ temp_len += part_embeds.shape[1]
270
+ if idx < image_nums:
271
+ img = self.encode_img(image[idx], hd_num)
272
+ wrap_embeds.append(img)
273
+ wrap_im_mask.append(torch.ones(img.shape[:2]))
274
+ temp_len += img.shape[1]
275
+
276
+ if temp_len > self.max_length:
277
+ break
278
+
279
+ wrap_embeds = torch.cat(wrap_embeds, dim=1)
280
+ wrap_im_mask = torch.cat(wrap_im_mask, dim=1)
281
+ wrap_embeds = wrap_embeds[:, :self.max_length].to(self.device)
282
+ wrap_im_mask = wrap_im_mask[:, :self.max_length].to(self.device).bool()
283
+ inputs = {
284
+ 'inputs_embeds': wrap_embeds
285
+ }
286
+ return inputs, wrap_im_mask, temp_len
287
+
288
+ def interleav_wrap(self, img_list, text_list):
289
+ wrap_embeds_list, wrap_atts_list = [], []
290
+ wrap_target_list, wrap_im_mask_list = [], []
291
+
292
+ for image, text in zip(img_list, text_list):
293
+ img_embeds, atts_img, img_target = self.img2emb(image)
294
+ text = text[0]
295
+ parts = text.split('<ImageHere>')
296
+ wrap_tokens, wrap_embeds, wrap_atts, wrap_im_mask = [], [], [], []
297
+ temp_len = 0
298
+ image_nums, im_len = img_embeds.shape[:2]
299
+ need_bos = True
300
+ for idx, part in enumerate(parts):
301
+ if len(part) > 0:
302
+ part_tokens = self.tokenizer(
303
+ part,
304
+ return_tensors='pt',
305
+ padding='longest',
306
+ add_special_tokens=need_bos).to(self.device)
307
+ if need_bos:
308
+ need_bos = False
309
+ wrap_tokens.append(part_tokens.input_ids)
310
+ part_embeds = self.model.tok_embeddings(
311
+ part_tokens.input_ids)
312
+ wrap_embeds.append(part_embeds)
313
+ wrap_atts.append(part_tokens.attention_mask)
314
+ wrap_im_mask.append(
315
+ torch.zeros(part_embeds.shape[:2]).to(self.device))
316
+
317
+ temp_len += part_embeds.shape[1]
318
+ if idx < image_nums:
319
+ wrap_tokens.append(img_target[idx].unsqueeze(0))
320
+ wrap_embeds.append(img_embeds[idx].unsqueeze(0))
321
+ wrap_atts.append(atts_img[idx].unsqueeze(0))
322
+ wrap_im_mask.append(
323
+ torch.ones_like(atts_img[idx].unsqueeze(0)))
324
+
325
+ temp_len += im_len
326
+ if temp_len > self.max_length:
327
+ break
328
+
329
+ wrap_tokens = torch.cat(wrap_tokens, dim=1)
330
+ wrap_embeds = torch.cat(wrap_embeds, dim=1)
331
+ wrap_atts = torch.cat(wrap_atts, dim=1)
332
+ wrap_im_mask = torch.cat(wrap_im_mask, dim=1)
333
+
334
+ wrap_target = self.mask_human_targets(wrap_tokens).to(self.device)
335
+
336
+ wrap_embeds = wrap_embeds[:, :self.max_length].to(self.device)
337
+ wrap_atts = wrap_atts[:, :self.max_length].to(self.device)
338
+ wrap_target = wrap_target[:, :self.max_length].to(self.device)
339
+ wrap_im_mask = wrap_im_mask[:, :self.max_length].to(self.device)
340
+
341
+ wrap_embeds_list.append(wrap_embeds)
342
+ wrap_atts_list.append(wrap_atts)
343
+ wrap_target_list.append(wrap_target)
344
+ wrap_im_mask_list.append(wrap_im_mask)
345
+
346
+ wrap_embeds = torch.cat(wrap_embeds_list)
347
+ wrap_atts = torch.cat(wrap_atts_list)
348
+ wrap_target = torch.cat(wrap_target_list)
349
+ wrap_im_mask = torch.cat(wrap_im_mask_list)
350
+ return wrap_embeds, wrap_atts, wrap_target, wrap_im_mask
351
+
352
+ def mask_human_targets(self, input_ids, pure=False):
353
+ target_batch = []
354
+ for bs in range(input_ids.shape[0]):
355
+ ids = input_ids[bs]
356
+ targets = copy.deepcopy(ids)
357
+ end_count = 0
358
+ last_eoa = 0
359
+ for i, temp_id in enumerate(ids):
360
+ if temp_id == 92542:
361
+ if end_count % 2 == 0:
362
+ targets[last_eoa:i + 6] = -100
363
+ else:
364
+ last_eoa = i + 1
365
+ end_count += 1
366
+ # # eos and following pad
367
+ elif temp_id == 2:
368
+ # loss on eos, but not on pad
369
+ targets[i + 1:] = -100
370
+ break
371
+ # trunction, end at last question
372
+ if temp_id != 2 and end_count % 2 == 0:
373
+ # mask all after the last answer
374
+ targets[last_eoa + 1:] = -100
375
+ target_batch.append(targets.unsqueeze(0))
376
+ target_batch = torch.cat(target_batch, dim=0)
377
+ return target_batch
378
+
379
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
380
+ @replace_return_docstrings(
381
+ output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
382
+ def forward(self,
383
+ input_ids: torch.LongTensor = None,
384
+ attention_mask: Optional[torch.Tensor] = None,
385
+ position_ids: Optional[torch.LongTensor] = None,
386
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
387
+ inputs_embeds: Optional[torch.FloatTensor] = None,
388
+ labels: Optional[torch.LongTensor] = None,
389
+ use_cache: Optional[bool] = None,
390
+ output_attentions: Optional[bool] = None,
391
+ output_hidden_states: Optional[bool] = None,
392
+ return_dict: Optional[bool] = None,
393
+ **kwargs) -> Union[Tuple, CausalLMOutputWithPast]:
394
+ r"""
395
+ Args:
396
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
397
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
398
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
399
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
400
+ Returns:
401
+ """
402
+
403
+ samples = kwargs.get('samples', None)
404
+ if samples:
405
+ infer_mode = samples.get('infer_mode', 'base')
406
+ if samples['data_type'][0] == 'text':
407
+ has_img = False
408
+ elif samples['data_type'][0] == 'multi':
409
+ has_img = True
410
+ else:
411
+ raise NotImplementedError
412
+
413
+ # encode text
414
+ text = samples['text_input']
415
+ # encode image
416
+ if has_img:
417
+ image = samples['image']
418
+ to_regress_embeds, attention_mask, targets, im_mask = self.interleav_wrap(
419
+ image, text)
420
+ else:
421
+ to_regress_tokens, targets = self.text2emb(
422
+ text, add_special=True)
423
+ to_regress_embeds = self.model.tok_embeddings(
424
+ to_regress_tokens.input_ids)
425
+ attention_mask = to_regress_tokens.attention_mask
426
+ im_mask = torch.zeros(to_regress_embeds.shape[:2]).cuda()
427
+
428
+ inputs_embeds = to_regress_embeds[:, :self.max_length]
429
+ attention_mask = attention_mask[:, :self.max_length]
430
+ targets = targets[:, :self.max_length]
431
+ im_mask = im_mask[:, :self.max_length].bool()
432
+ labels = targets
433
+ else:
434
+ im_mask = kwargs.get('im_mask', None)
435
+ infer_mode = kwargs.get('infer_mode', 'base')
436
+ if im_mask is None and inputs_embeds is not None:
437
+ im_mask = torch.zeros(inputs_embeds.shape[:2]).to(
438
+ inputs_embeds.device)
439
+ im_mask = im_mask.bool()
440
+
441
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
442
+ output_hidden_states = (
443
+ output_hidden_states if output_hidden_states is not None else
444
+ self.config.output_hidden_states)
445
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
446
+
447
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
448
+ outputs = self.model(
449
+ input_ids=input_ids,
450
+ attention_mask=attention_mask,
451
+ position_ids=position_ids,
452
+ past_key_values=past_key_values,
453
+ inputs_embeds=inputs_embeds,
454
+ use_cache=use_cache,
455
+ output_attentions=output_attentions,
456
+ output_hidden_states=output_hidden_states,
457
+ return_dict=return_dict,
458
+ im_mask=im_mask,
459
+ infer_mode=infer_mode,
460
+ )
461
+
462
+ hidden_states = outputs[0]
463
+ logits = self.output(hidden_states)
464
+ logits = logits.float()
465
+
466
+ loss = None
467
+ if labels is not None:
468
+ # Shift so that tokens < n predict n
469
+ shift_logits = logits[..., :-1, :].contiguous()
470
+ shift_labels = labels[..., 1:].contiguous()
471
+ # Flatten the tokens
472
+ loss_fct = CrossEntropyLoss()
473
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
474
+ shift_labels = shift_labels.view(-1)
475
+ # Enable model parallelism
476
+ shift_labels = shift_labels.to(shift_logits.device)
477
+ loss = loss_fct(shift_logits, shift_labels)
478
+
479
+ if not return_dict:
480
+ output = (logits, ) + outputs[1:]
481
+ return (loss, ) + output if loss is not None else output
482
+
483
+ return CausalLMOutputWithPast(
484
+ loss=loss,
485
+ logits=logits,
486
+ past_key_values=outputs.past_key_values,
487
+ hidden_states=outputs.hidden_states,
488
+ attentions=outputs.attentions,
489
+ )
490
+
491
+ def prepare_inputs_for_generation(self,
492
+ input_ids,
493
+ past_key_values=None,
494
+ attention_mask=None,
495
+ inputs_embeds=None,
496
+ im_mask=None,
497
+ infer_mode='base',
498
+ **kwargs):
499
+ if past_key_values is not None:
500
+ past_length = past_key_values[0][0].shape[2]
501
+
502
+ # Some generation methods already pass only the last input ID
503
+ if input_ids.shape[1] > past_length:
504
+ remove_prefix_length = past_length
505
+ else:
506
+ # Default to old behavior: keep only final ID
507
+ remove_prefix_length = input_ids.shape[1] - 1
508
+
509
+ input_ids = input_ids[:, remove_prefix_length:]
510
+
511
+ position_ids = kwargs.get('position_ids', None)
512
+ if attention_mask is not None and position_ids is None:
513
+ # create position_ids on the fly for batch generation
514
+ position_ids = attention_mask.long().cumsum(-1) - 1
515
+ position_ids.masked_fill_(attention_mask == 0, 1)
516
+ if past_key_values:
517
+ position_ids = position_ids[:, -input_ids.shape[1]:]
518
+
519
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
520
+ if inputs_embeds is not None and past_key_values is None:
521
+ model_inputs = {'inputs_embeds': inputs_embeds}
522
+ else:
523
+ model_inputs = {'input_ids': input_ids}
524
+
525
+ im_mask = im_mask
526
+
527
+ model_inputs.update({
528
+ 'position_ids': position_ids,
529
+ 'past_key_values': past_key_values,
530
+ 'use_cache': kwargs.get('use_cache'),
531
+ 'attention_mask': attention_mask,
532
+ 'im_mask': im_mask,
533
+ 'infer_mode': infer_mode,
534
+ })
535
+ return model_inputs
536
+
537
+ @staticmethod
538
+ def _reorder_cache(past_key_values, beam_idx):
539
+ reordered_past = ()
540
+ for layer_past in past_key_values:
541
+ reordered_past += (tuple(
542
+ past_state.index_select(0, beam_idx.to(past_state.device))
543
+ for past_state in layer_past), )
544
+ return reordered_past
545
+
546
+ def build_inputs(self,
547
+ tokenizer,
548
+ query: str,
549
+ history: List[Tuple[str, str]] = [],
550
+ meta_instruction=''):
551
+ prompt = ''
552
+ if meta_instruction:
553
+ prompt += f"""<s>[UNUSED_TOKEN_146]system\n{meta_instruction}[UNUSED_TOKEN_145]\n"""
554
+ else:
555
+ prompt += '<s>'
556
+ for record in history:
557
+ prompt += f"""[UNUSED_TOKEN_146]user\n{record[0]}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n{record[1]}[UNUSED_TOKEN_145]\n"""
558
+ prompt += f"""[UNUSED_TOKEN_146]user\n{query}[UNUSED_TOKEN_145]\n[UNUSED_TOKEN_146]assistant\n"""
559
+ return tokenizer([prompt], return_tensors='pt')
560
+
561
+ @torch.no_grad()
562
+ def chat(
563
+ self,
564
+ tokenizer,
565
+ query: str,
566
+ image: List[Tuple[str, str]] = [],
567
+ hd_num: int = 24,
568
+ history: List[Tuple[str, str]] = [],
569
+ streamer: Optional[BaseStreamer] = None,
570
+ max_new_tokens: int = 1024,
571
+ do_sample: bool = True,
572
+ num_beams: int = 1,
573
+ temperature: float = 1.0,
574
+ top_p: float = 0.8,
575
+ repetition_penalty: float=1.005,
576
+ infer_mode: str = 'base',
577
+ use_meta: bool = False,
578
+ meta_instruction:
579
+ str = 'You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).\n'
580
+ '- InternLM-XComposer (浦语·灵笔) is a multi-modality 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'
581
+ '- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by the user such as English and 中文.\n'
582
+ '- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses effectively based on the provided image.',
583
+ **kwargs,
584
+ ):
585
+
586
+ if not use_meta:
587
+ meta_instruction = ''
588
+ if image is None:
589
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
590
+ im_mask = torch.zeros(inputs['input_ids'].shape[:2]).cuda().bool()
591
+ else:
592
+ inputs, im_mask, _ = self.interleav_wrap_chat(query, image, history=history, meta_instruction=meta_instruction, hd_num=hd_num)
593
+ inputs = {
594
+ k: v.to(self.device)
595
+ for k, v in inputs.items() if torch.is_tensor(v)
596
+ }
597
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
598
+ eos_token_id = [
599
+ tokenizer.eos_token_id,
600
+ tokenizer.convert_tokens_to_ids(['[UNUSED_TOKEN_145]'])[0]
601
+ ]
602
+ outputs = self.generate(
603
+ **inputs,
604
+ streamer=streamer,
605
+ max_new_tokens=max_new_tokens,
606
+ num_beams=num_beams,
607
+ do_sample=do_sample,
608
+ temperature=temperature,
609
+ top_p=top_p,
610
+ eos_token_id=eos_token_id,
611
+ repetition_penalty=repetition_penalty,
612
+ im_mask=im_mask,
613
+ infer_mode=infer_mode,
614
+ **kwargs,
615
+ )
616
+ if image is None:
617
+ outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]):]
618
+ else:
619
+ outputs = outputs[0].cpu().tolist()
620
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
621
+ response = response.split('[UNUSED_TOKEN_145]')[0]
622
+ history = history + [(query, response)]
623
+ return response, history
624
+
625
+ @torch.no_grad()
626
+ def write_artical(
627
+ self,
628
+ inst: str,
629
+ image: List[Tuple[str, str]] = [],
630
+ hd_num: int = 25,
631
+ history: List[Tuple[str, str]] = [],
632
+ streamer: Optional[BaseStreamer] = None,
633
+ max_new_tokens: int = 1024,
634
+ do_sample: bool = True,
635
+ num_beams: int = 1,
636
+ temperature: float = 1.0,
637
+ top_p: float = 0.8,
638
+ repetition_penalty: float=1.005,
639
+ max_length: int=8192,
640
+ seed: int = -1,
641
+ use_meta: bool = False,
642
+ **kwargs,
643
+ ):
644
+ meta_instruction = """You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).
645
+ - InternLM-XComposer (浦语·灵笔) 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.
646
+ - InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by the user such as English and 中文.
647
+ """
648
+ if seed != -1:
649
+ set_seed(seed)
650
+ if len(history):
651
+ print ('Only chat function support multi round now, history will be ignored in the artical mode')
652
+ stop_words_ids = [92542]
653
+ stopping_criteria = get_stopping_criteria(stop_words_ids)
654
+
655
+ if not use_meta:
656
+ meta_instruction = ''
657
+ with torch.no_grad():
658
+ inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image, meta_instruction=meta_instruction, max_length=max_length)
659
+ with torch.autocast(device_type='cuda', dtype=torch.float16):
660
+ with torch.no_grad():
661
+ generate = self.generate(inputs_embeds=inputs['inputs_embeds'],
662
+ do_sample=do_sample,
663
+ num_beams=num_beams,
664
+ temperature=temperature,
665
+ repetition_penalty=repetition_penalty,
666
+ stopping_criteria=stopping_criteria,
667
+ max_new_tokens=max_length - len_input_tokens,
668
+ top_p=0.8,
669
+ top_k=40,
670
+ length_penalty=1.0,
671
+ im_mask=im_mask,
672
+ infer_mode='write'
673
+ )
674
+
675
+ response = generate[0].tolist()
676
+ response = self.tokenizer.decode(response, skip_special_tokens=True)
677
+ # remove eoa
678
+ response = response.replace('[UNUSED_TOKEN_145]', '')
679
+ response = response.replace('[UNUSED_TOKEN_146]', '')
680
+
681
+ return response
682
+
683
+ @torch.no_grad()
684
+ def write_webpage(
685
+ self,
686
+ inst: str,
687
+ image: List[Tuple[str, str]] = [],
688
+ max_new_tokens: int = 4800,
689
+ do_sample: bool = True,
690
+ num_beams: int = 2,
691
+ temperature: float = 1.0,
692
+ repetition_penalty: float=3.0,
693
+ seed: int = -1,
694
+ use_meta: bool = False,
695
+ task: str = 'Instruction-aware Webpage Generation',
696
+ **kwargs,
697
+ ):
698
+
699
+ if seed != -1:
700
+ set_random_seed(seed, set_cudnn=True)
701
+ with torch.no_grad():
702
+ inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image)
703
+
704
+ with torch.autocast(device_type='cuda', dtype=torch.float16):
705
+ with torch.no_grad():
706
+ generate = self.generate(inputs_embeds=inputs['inputs_embeds'],
707
+ do_sample=do_sample,
708
+ temperature=temperature,
709
+ num_beams=num_beams,
710
+ repetition_penalty=repetition_penalty,
711
+ max_new_tokens=max_new_tokens,
712
+ im_mask=im_mask,
713
+ infer_mode='web'
714
+ )
715
+ response = generate[0].tolist()
716
+ response = self.tokenizer.decode(response, skip_special_tokens=True)
717
+ # remove eoa
718
+ response = response.replace('[UNUSED_TOKEN_145]', '')
719
+ out = response.replace('[UNUSED_TOKEN_146]', '')
720
+ image_type = 'random'
721
+ pattern = r'''https://source\.unsplash\.com/random/(\d+)x(\d+)/\?([^'"]+)'''
722
+ if image_type == 'placeholder':
723
+ out = re.sub(pattern, r"https://placehold.co/\1x\2", out)
724
+ elif image_type == 'random':
725
+ out = re.sub(pattern, r"https://picsum.photos/\1/\2", out)
726
+
727
+ with open(task.replace(' ', '_') + ".html", "w") as f:
728
+ f.write(out)
729
+ return out
730
+
731
+ @torch.no_grad()
732
+ def resume_2_webpage(
733
+ self,
734
+ inst: str,
735
+ image: List[Tuple[str, str]] = [],
736
+ max_new_tokens: int = 4800,
737
+ do_sample: bool = True,
738
+ num_beams: int = 2,
739
+ temperature: float = 1.0,
740
+ repetition_penalty: float=3.0,
741
+ seed: int = -1,
742
+ use_meta: bool = False,
743
+ task: str = 'Resume-to-Personal Page',
744
+ **kwargs,
745
+ ):
746
+
747
+ if seed != -1:
748
+ set_random_seed(seed, set_cudnn=True)
749
+ try:
750
+ with open(inst) as fd:
751
+ resume = fd.read()
752
+ except:
753
+ print ('The input should be a resume with markdown format.')
754
+ inst = ' Generate a personal page using the content in the resume:' + resume
755
+ with torch.no_grad():
756
+ inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image)
757
+ with torch.autocast(device_type='cuda', dtype=torch.float16):
758
+ with torch.no_grad():
759
+ generate = self.generate(inputs_embeds=inputs['inputs_embeds'],
760
+ do_sample=do_sample,
761
+ temperature=temperature,
762
+ num_beams=num_beams,
763
+ repetition_penalty=repetition_penalty,
764
+ max_new_tokens=max_new_tokens,
765
+ im_mask=im_mask,
766
+ infer_mode='web'
767
+ )
768
+ response = generate[0].tolist()
769
+ response = self.tokenizer.decode(response, skip_special_tokens=True)
770
+ # remove eoa
771
+ response = response.replace('[UNUSED_TOKEN_145]', '')
772
+ html = response.replace('[UNUSED_TOKEN_146]', '')
773
+
774
+ if seed != -1:
775
+ set_random_seed(seed, set_cudnn=True)
776
+ js_inst = ' Generate JavaScript events for the html code:' + html
777
+ with torch.no_grad():
778
+ inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(js_inst, image)
779
+ with torch.autocast(device_type='cuda', dtype=torch.float16):
780
+ with torch.no_grad():
781
+ generate = self.generate(inputs_embeds=inputs['inputs_embeds'],
782
+ do_sample=do_sample,
783
+ temperature=temperature,
784
+ num_beams=num_beams,
785
+ repetition_penalty=repetition_penalty,
786
+ max_new_tokens=max_new_tokens,
787
+ im_mask=im_mask,
788
+ infer_mode='web'
789
+ )
790
+ response = generate[0].tolist()
791
+ response = self.tokenizer.decode(response, skip_special_tokens=True)
792
+ # remove eoa
793
+ response = response.replace('[UNUSED_TOKEN_145]', '')
794
+ js = response.replace('[UNUSED_TOKEN_146]', '')
795
+
796
+ if re.search(r'</script>', html):
797
+ js = re.findall(r'<script>([\s\S]*?)<\/script>', js)
798
+ html = re.sub(r'(</script>)', f'\n{js}\n' + r'\1', html)
799
+ elif re.search(r'</html>', html):
800
+ html = re.sub(r'(</html>)', f'\n{js}\n' + r'\1', html)
801
+ out = html
802
+
803
+ image_type = 'random'
804
+ pattern = r'''https://source\.unsplash\.com/random/(\d+)x(\d+)/\?([^'"]+)'''
805
+ if image_type == 'placeholder':
806
+ out = re.sub(pattern, r"https://placehold.co/\1x\2", out)
807
+ elif image_type == 'random':
808
+ out = re.sub(pattern, r"https://picsum.photos/\1/\2", out)
809
+
810
+ with open(task.replace(' ', '_') + ".html", "w") as f:
811
+ f.write(out)
812
+ return out
813
+
814
+
815
+ @torch.no_grad()
816
+ def screen_2_webpage(
817
+ self,
818
+ inst: str,
819
+ image: List[Tuple[str, str]] = [],
820
+ max_new_tokens: int = 4800,
821
+ do_sample: bool = True,
822
+ num_beams: int = 2,
823
+ temperature: float = 1.0,
824
+ repetition_penalty: float=3.0,
825
+ seed: int = -1,
826
+ use_meta: bool = False,
827
+ task: str = 'Screenshot-to-Webpage',
828
+ **kwargs,
829
+ ):
830
+
831
+ if seed != -1:
832
+ set_random_seed(seed, set_cudnn=True)
833
+ if len(image) == 0:
834
+ print ('No image is provided, skip')
835
+ return ''
836
+ inst = ' Generate the HTML code of this web image with Tailwind CSS.'
837
+ with torch.no_grad():
838
+ inputs, im_mask, len_input_tokens = self.interleav_wrap_chat(inst, image)
839
+
840
+ with torch.autocast(device_type='cuda'):
841
+ with torch.no_grad():
842
+ generate = self.generate(inputs_embeds=inputs['inputs_embeds'],
843
+ do_sample=do_sample,
844
+ temperature=temperature,
845
+ num_beams=num_beams,
846
+ repetition_penalty=repetition_penalty,
847
+ max_new_tokens=max_new_tokens,
848
+ im_mask=im_mask,
849
+ infer_mode='web'
850
+ )
851
+ response = generate[0].tolist()
852
+ response = self.tokenizer.decode(response, skip_special_tokens=True)
853
+ # remove eoa
854
+ response = response.replace('[UNUSED_TOKEN_145]', '')
855
+ out = response.replace('[UNUSED_TOKEN_146]', '')
856
+ image_type = 'random'
857
+ pattern = r'''https://source\.unsplash\.com/random/(\d+)x(\d+)/\?([^'"]+)'''
858
+ if image_type == 'placeholder':
859
+ out = re.sub(pattern, r"https://placehold.co/\1x\2", out)
860
+ elif image_type == 'random':
861
+ out = re.sub(pattern, r"https://picsum.photos/\1/\2", out)
862
+
863
+ with open(task.replace(' ', '_') + ".html", "w") as f:
864
+ f.write(out)
865
+ return out
pytorch_model-00001-of-00003.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:4b50f5b8df413dade11f624f55ea317bb0158baee38f1ee5f8ed37cbe93f1ba7
3
+ size 9968266170
pytorch_model-00002-of-00003.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:70a747192e2319f481dfb426cda6f99e75e26652442b8369a9de9c72bb3682f4
3
+ size 9999760331
pytorch_model-00003-of-00003.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0880b52a0d27935e83f5b3fef2886d626671adb2e56de9162344c904e368490e
3
+ size 2224146928
pytorch_model.bin.index.json ADDED
The diff for this file is too large to render. See raw diff
 
special_tokens_map.json ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
3
+ size 1477754
tokenizer_config.json ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<unk>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<s>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "92538": {
28
+ "content": "<|plugin|>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "92539": {
36
+ "content": "<|interpreter|>",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ },
43
+ "92540": {
44
+ "content": "<|action_end|>",
45
+ "lstrip": false,
46
+ "normalized": false,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": true
50
+ },
51
+ "92541": {
52
+ "content": "<|action_start|>",
53
+ "lstrip": false,
54
+ "normalized": false,
55
+ "rstrip": false,
56
+ "single_word": false,
57
+ "special": true
58
+ },
59
+ "92542": {
60
+ "content": "<|im_end|>",
61
+ "lstrip": false,
62
+ "normalized": false,
63
+ "rstrip": false,
64
+ "single_word": false,
65
+ "special": true
66
+ },
67
+ "92543": {
68
+ "content": "<|im_start|>",
69
+ "lstrip": false,
70
+ "normalized": false,
71
+ "rstrip": false,
72
+ "single_word": false,
73
+ "special": true
74
+ }
75
+ },
76
+ "additional_special_tokens": [
77
+ "<|im_start|>",
78
+ "<|im_end|>",
79
+ "<|action_start|>",
80
+ "<|action_end|>",
81
+ "<|interpreter|>",
82
+ "<|plugin|>"
83
+ ],
84
+ "auto_map": {
85
+ "AutoTokenizer": [
86
+ "tokenization_internlm2.InternLM2Tokenizer",
87
+ null
88
+ ]
89
+ },
90
+ "bos_token": "<s>",
91
+ "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
+ }