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  1. config.json +47 -0
  2. generation_config.json +6 -0
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  4. modeling_iaa.py +498 -0
  5. modeling_llama_iaa.py +1724 -0
  6. special_tokens_map.json +23 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +2064 -0
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+ }
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+ }
modeling_iaa.py ADDED
@@ -0,0 +1,498 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Optional, Tuple, Union
2
+
3
+ import torch
4
+ import torch.nn as nn
5
+
6
+ from torch.nn import CrossEntropyLoss
7
+
8
+ from transformers import AutoConfig, AutoModelForCausalLM, \
9
+ LlamaConfig
10
+
11
+ from transformers.modeling_outputs import CausalLMOutputWithPast
12
+ from abc import ABC, abstractmethod
13
+ import os
14
+ from .modeling_llama_iaa import LlamaModel, LlamaForCausalLM
15
+ from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
16
+ from functools import partial
17
+ from transformers.configuration_utils import PretrainedConfig
18
+ import re
19
+ from PIL import Image
20
+
21
+ CONTROLLER_HEART_BEAT_EXPIRATION = 30
22
+ WORKER_HEART_BEAT_INTERVAL = 15
23
+
24
+ LOGDIR = "."
25
+
26
+ # Model Constants
27
+ IGNORE_INDEX = -100
28
+ IMAGE_TOKEN_INDEX = -200
29
+ DEFAULT_IMAGE_TOKEN = "<image>"
30
+ DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
31
+ DEFAULT_IM_START_TOKEN = "<im_start>"
32
+ DEFAULT_IM_END_TOKEN = "<im_end>"
33
+
34
+
35
+ import math
36
+ from einops import rearrange
37
+
38
+
39
+
40
+ class CLIPVisionTower(nn.Module):
41
+ def __init__(self, vision_tower, args, delay_load=False):
42
+ super().__init__()
43
+
44
+ self.is_loaded = False
45
+
46
+ self.vision_tower_name = vision_tower
47
+ self.select_layer = args.mm_vision_select_layer
48
+ self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
49
+
50
+ if not delay_load:
51
+ self.load_model()
52
+ else:
53
+ self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
54
+
55
+ def load_model(self):
56
+ self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
57
+ self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
58
+ self.vision_tower.requires_grad_(False)
59
+
60
+ self.is_loaded = True
61
+
62
+ def feature_select(self, image_forward_outs):
63
+ image_features = image_forward_outs.hidden_states[self.select_layer]
64
+ if self.select_feature == 'patch':
65
+ image_features = image_features[:, 1:]
66
+ elif self.select_feature == 'cls_patch':
67
+ image_features = image_features
68
+ else:
69
+ raise ValueError(f'Unexpected select feature: {self.select_feature}')
70
+ return image_features
71
+
72
+ @torch.no_grad()
73
+ def forward(self, images):
74
+ if type(images) is list:
75
+ image_features = []
76
+ for image in images:
77
+ image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
78
+ image_feature = self.feature_select(image_forward_out).to(image.dtype)
79
+ image_features.append(image_feature)
80
+ else:
81
+ image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
82
+ image_features = self.feature_select(image_forward_outs).to(images.dtype)
83
+
84
+ return image_features
85
+
86
+ @property
87
+ def dummy_feature(self):
88
+ return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
89
+
90
+ @property
91
+ def dtype(self):
92
+ return self.vision_tower.dtype
93
+
94
+ @property
95
+ def device(self):
96
+ return self.vision_tower.device
97
+
98
+ @property
99
+ def config(self):
100
+ if self.is_loaded:
101
+ return self.vision_tower.config
102
+ else:
103
+ return self.cfg_only
104
+
105
+ @property
106
+ def hidden_size(self):
107
+ return self.config.hidden_size
108
+
109
+ @property
110
+ def num_patches(self):
111
+ return (self.config.image_size // self.config.patch_size) ** 2
112
+
113
+
114
+ def build_vision_tower(vision_tower_cfg, **kwargs):
115
+ vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
116
+ is_absolute_path_exists = os.path.exists(vision_tower)
117
+
118
+ if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion"):
119
+ return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
120
+
121
+ raise ValueError(f'Unknown vision tower: {vision_tower}')
122
+
123
+ def build_vision_projector(config, delay_load=False, **kwargs):
124
+ projector_type = getattr(config, 'mm_projector_type', 'linear')
125
+
126
+ if projector_type == 'linear':
127
+ return nn.Linear(config.mm_hidden_size, config.hidden_size)
128
+
129
+ mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
130
+ if mlp_gelu_match:
131
+ mlp_depth = int(mlp_gelu_match.group(1))
132
+ modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
133
+ for _ in range(1, mlp_depth):
134
+ modules.append(nn.GELU())
135
+ modules.append(nn.Linear(config.hidden_size, config.hidden_size))
136
+ return nn.Sequential(*modules)
137
+
138
+ raise ValueError(f'Unknown projector type: {projector_type}')
139
+
140
+
141
+ class IAAMetaModel:
142
+
143
+ def __init__(self, config):
144
+ super(IAAMetaModel, self).__init__(config)
145
+ if hasattr(config, "mm_vision_tower"):
146
+ self.vision_tower = build_vision_tower(config, delay_load=True)
147
+ self.mm_projector = build_vision_projector(config)
148
+ self.mm_projector_G = build_vision_projector(config)
149
+
150
+
151
+ def get_vision_tower(self):
152
+ vision_tower = getattr(self, 'vision_tower', None)
153
+ if type(vision_tower) is list:
154
+ vision_tower = vision_tower[0]
155
+ return vision_tower
156
+
157
+
158
+ class IAAMetaForCausalLM(ABC):
159
+
160
+ @abstractmethod
161
+ def get_model(self):
162
+ pass
163
+
164
+ def get_vision_tower(self):
165
+ return self.get_model().get_vision_tower()
166
+
167
+ def encode_images(self, images, task_type):
168
+ image_features = self.get_model().get_vision_tower()(images)
169
+
170
+ if task_type == "MM":
171
+ image_features = self.get_model().mm_projector(image_features)
172
+ else:
173
+ image_features = self.get_model().mm_projector_G(image_features)
174
+
175
+ return image_features
176
+
177
+
178
+ def prepare_inputs_labels_for_multimodal(
179
+ self, input_ids, attention_mask, past_key_values, labels, images, task_type,
180
+ ):
181
+ vision_tower = self.get_vision_tower()
182
+ if vision_tower is None or images is None or input_ids.shape[1] == 1:
183
+ if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
184
+ attention_mask = torch.ones((attention_mask.shape[0], past_key_values[0][-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
185
+ return input_ids, attention_mask, past_key_values, None, labels
186
+
187
+ if type(images) is list or images.ndim == 5:
188
+ image_features = []
189
+ for image in images:
190
+ if image.ndim == 3:
191
+ image_features.append(self.encode_images(image.unsqueeze(0)).squeeze(0))
192
+ elif image.ndim == 4:
193
+ pass
194
+ else:
195
+ image_features = self.encode_images(images, task_type)
196
+
197
+ if task_type == "MM":
198
+ embed_tokens_func = self.get_model().embed_tokens_condition
199
+ elif task_type == "G":
200
+ embed_tokens_func = self.get_model().embed_tokens_condition_grounding
201
+ else:
202
+ embed_tokens_func = self.get_model().embed_tokens
203
+
204
+
205
+ new_input_embeds = []
206
+ new_labels = [] if labels is not None else None
207
+ cur_image_idx = 0
208
+ for batch_idx, cur_input_ids in enumerate(input_ids):
209
+ if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
210
+ # multimodal LLM, but the current sample is not multimodal
211
+ # FIXME: this is a hacky fix, for deepspeed zero3 to work
212
+ half_len = cur_input_ids.shape[0] // 2
213
+ cur_image_features = image_features[cur_image_idx]
214
+ cur_input_embeds_1 = embed_tokens_func(cur_input_ids[:half_len])
215
+ cur_input_embeds_2 = embed_tokens_func(cur_input_ids[half_len:])
216
+ cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
217
+ new_input_embeds.append(cur_input_embeds)
218
+ if labels is not None:
219
+ new_labels.append(labels[batch_idx])
220
+ cur_image_idx += 1
221
+ continue
222
+ image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
223
+ cur_new_input_embeds = []
224
+ if labels is not None:
225
+ cur_labels = labels[batch_idx]
226
+ cur_new_labels = []
227
+ assert cur_labels.shape == cur_input_ids.shape
228
+ while image_token_indices.numel() > 0:
229
+ cur_image_features = image_features[cur_image_idx]
230
+ image_token_start = image_token_indices[0]
231
+ if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
232
+ cur_new_input_embeds.append(embed_tokens_func(cur_input_ids[:image_token_start-1]).detach())
233
+ cur_new_input_embeds.append(embed_tokens_func(cur_input_ids[image_token_start-1:image_token_start]))
234
+ cur_new_input_embeds.append(cur_image_features)
235
+ cur_new_input_embeds.append(embed_tokens_func(cur_input_ids[image_token_start+1:image_token_start+2]))
236
+ if labels is not None:
237
+ cur_new_labels.append(cur_labels[:image_token_start])
238
+ cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
239
+ cur_new_labels.append(cur_labels[image_token_start:image_token_start+1])
240
+ cur_labels = cur_labels[image_token_start+2:]
241
+ else:
242
+ cur_new_input_embeds.append(embed_tokens_func(cur_input_ids[:image_token_start]))
243
+ cur_new_input_embeds.append(cur_image_features)
244
+ if labels is not None:
245
+ cur_new_labels.append(cur_labels[:image_token_start])
246
+ cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
247
+ cur_labels = cur_labels[image_token_start+1:]
248
+ cur_image_idx += 1
249
+ if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
250
+ cur_input_ids = cur_input_ids[image_token_start+2:]
251
+ else:
252
+ cur_input_ids = cur_input_ids[image_token_start+1:]
253
+ image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
254
+ if cur_input_ids.numel() > 0:
255
+ if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
256
+ cur_new_input_embeds.append(embed_tokens_func(cur_input_ids).detach())
257
+ else:
258
+ cur_new_input_embeds.append(embed_tokens_func(cur_input_ids))
259
+ if labels is not None:
260
+ cur_new_labels.append(cur_labels)
261
+ cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
262
+ cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
263
+ new_input_embeds.append(cur_new_input_embeds)
264
+ if labels is not None:
265
+ cur_new_labels = torch.cat(cur_new_labels, dim=0)
266
+ new_labels.append(cur_new_labels)
267
+
268
+ if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
269
+ max_len = max(x.shape[0] for x in new_input_embeds)
270
+
271
+ new_input_embeds_align = []
272
+ for cur_new_embed in new_input_embeds:
273
+ cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
274
+ new_input_embeds_align.append(cur_new_embed)
275
+ new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
276
+
277
+ if labels is not None:
278
+ new_labels_align = []
279
+ _new_labels = new_labels
280
+ for cur_new_label in new_labels:
281
+ cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
282
+ new_labels_align.append(cur_new_label)
283
+ new_labels = torch.stack(new_labels_align, dim=0)
284
+
285
+ if attention_mask is not None:
286
+ new_attention_mask = []
287
+ for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
288
+ new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
289
+ new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
290
+ cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
291
+ new_attention_mask.append(cur_new_attention_mask)
292
+ attention_mask = torch.stack(new_attention_mask, dim=0)
293
+ assert attention_mask.shape == new_labels.shape
294
+ else:
295
+ new_input_embeds = torch.stack(new_input_embeds, dim=0)
296
+ if labels is not None:
297
+ new_labels = torch.stack(new_labels, dim=0)
298
+
299
+ if attention_mask is not None:
300
+ new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device)
301
+ attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
302
+ assert attention_mask.shape == new_input_embeds.shape[:2]
303
+
304
+ return None, attention_mask, past_key_values, new_input_embeds, new_labels
305
+
306
+ class IAAConfig(LlamaConfig):
307
+ model_type = "IAA"
308
+
309
+
310
+ class IAALlamaModel(IAAMetaModel, LlamaModel):
311
+ config_class = IAAConfig
312
+
313
+ def __init__(self, config: LlamaConfig):
314
+ super(IAALlamaModel, self).__init__(config)
315
+
316
+
317
+ class IAALlamaForCausalLM(LlamaForCausalLM, IAAMetaForCausalLM):
318
+ config_class = IAAConfig
319
+
320
+ def __init__(self, config):
321
+ super(LlamaForCausalLM, self).__init__(config)
322
+
323
+ config._attn_implementation = "flash_attention_2"
324
+ self.model = IAALlamaModel(config)
325
+
326
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
327
+
328
+ self.lm_head_condtion = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
329
+
330
+ self.lm_head_condtion_grounding = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
331
+
332
+ self.post_init()
333
+
334
+ def get_model(self):
335
+ return self.model
336
+
337
+
338
+ def forward(
339
+ self,
340
+ input_ids: torch.LongTensor = None,
341
+ attention_mask: Optional[torch.Tensor] = None,
342
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
343
+ inputs_embeds: Optional[torch.FloatTensor] = None,
344
+ labels: Optional[torch.LongTensor] = None,
345
+ use_cache: Optional[bool] = None,
346
+ output_attentions: Optional[bool] = None,
347
+ output_hidden_states: Optional[bool] = None,
348
+ images: Optional[torch.FloatTensor] = None,
349
+ return_dict: Optional[bool] = None,
350
+ task_type = None,
351
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
352
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
353
+ output_hidden_states = (
354
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
355
+ )
356
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
357
+
358
+ input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images, task_type)
359
+
360
+ outputs = self.model(
361
+ input_ids=input_ids,
362
+ attention_mask=attention_mask,
363
+ past_key_values=past_key_values,
364
+ inputs_embeds=inputs_embeds,
365
+ use_cache=use_cache,
366
+ output_attentions=output_attentions,
367
+ output_hidden_states=output_hidden_states,
368
+ return_dict = return_dict,
369
+ task_type=task_type,
370
+ )
371
+
372
+ hidden_states = outputs[0]
373
+
374
+ if task_type == "MM":
375
+ logits = self.lm_head_condtion(hidden_states)
376
+ elif task_type == "G":
377
+ logits = self.lm_head_condtion_grounding(hidden_states)
378
+ else:
379
+ logits = self.lm_head(hidden_states)
380
+
381
+
382
+ loss = None
383
+ assert labels is None
384
+
385
+ if not return_dict:
386
+ output = (logits,) + outputs[1:]
387
+ return (loss,) + output if loss is not None else output
388
+
389
+ return CausalLMOutputWithPast(
390
+ loss=loss,
391
+ logits=logits,
392
+ past_key_values=outputs.past_key_values,
393
+ hidden_states=outputs.hidden_states,
394
+ attentions=outputs.attentions,
395
+ )
396
+
397
+ def prepare_inputs_for_generation(
398
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
399
+ ):
400
+
401
+ # print(attention_mask)
402
+ if past_key_values:
403
+ input_ids = input_ids[:, -1:]
404
+
405
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
406
+ if inputs_embeds is not None and past_key_values is None:
407
+ model_inputs = {"inputs_embeds": inputs_embeds}
408
+ else:
409
+ model_inputs = {"input_ids": input_ids}
410
+
411
+ model_inputs.update(
412
+ {
413
+ "past_key_values": past_key_values,
414
+ "use_cache": kwargs.get("use_cache"),
415
+ "attention_mask": attention_mask,
416
+ "images": kwargs.get("images", None),
417
+ "task_type": kwargs.get("task_type", "textonly"),
418
+ }
419
+ )
420
+ return model_inputs
421
+
422
+
423
+ def build_conversation_input_ids(
424
+ self,
425
+ tokenizer: "PreTrainedTokenizer",
426
+ query: str,
427
+ image = None,
428
+ image_processor=None,
429
+ ):
430
+
431
+ if image:
432
+ input_msg = [
433
+ {
434
+ "role": "system",
435
+ "content": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
436
+ },
437
+ {
438
+ "role": "user",
439
+ "content": "<|reserved_special_token_44|>"+ '\n' + query
440
+ }
441
+ ]
442
+ else:
443
+ input_msg = [
444
+ {
445
+ "role": "system",
446
+ "content": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
447
+ },
448
+ {
449
+ "role": "user",
450
+ "content": query
451
+ }
452
+ ]
453
+
454
+
455
+ input_ids = tokenizer.apply_chat_template(
456
+ input_msg,
457
+ add_generation_prompt=True,
458
+ padding="longest",
459
+ return_tensors="pt",
460
+ )
461
+ input_id_list = input_ids[0].tolist()
462
+
463
+ if image:
464
+ input_id_list[input_id_list.index(128049)]=-200
465
+ image_tensor = self.process_images(image,image_processor).unsqueeze(0)
466
+ else:
467
+ image_tensor = None
468
+
469
+ input_ids = torch.tensor(input_id_list, dtype=input_ids.dtype,device=input_ids.device)
470
+ input_ids = input_ids.unsqueeze(0)
471
+
472
+
473
+ return {
474
+ 'input_ids': input_ids,
475
+ 'image': image_tensor,
476
+ }
477
+
478
+
479
+
480
+ def process_images(self, image, image_processor):
481
+
482
+ def expand2square(pil_img, background_color):
483
+ width, height = pil_img.size
484
+ if width == height:
485
+ return pil_img
486
+ elif width > height:
487
+ result = Image.new(pil_img.mode, (width, width), background_color)
488
+ result.paste(pil_img, (0, (width - height) // 2))
489
+ return result
490
+ else:
491
+ result = Image.new(pil_img.mode, (height, height), background_color)
492
+ result.paste(pil_img, ((height - width) // 2, 0))
493
+ return result
494
+
495
+ image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
496
+ image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
497
+
498
+ return image
modeling_llama_iaa.py ADDED
@@ -0,0 +1,1724 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+
21
+ # coding=utf-8
22
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
23
+ #
24
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
25
+ # and OPT implementations in this library. It has been modified from its
26
+ # original forms to accommodate minor architectural differences compared
27
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
28
+ #
29
+ # Licensed under the Apache License, Version 2.0 (the "License");
30
+ # you may not use this file except in compliance with the License.
31
+ # You may obtain a copy of the License at
32
+ #
33
+ # http://www.apache.org/licenses/LICENSE-2.0
34
+ #
35
+ # Unless required by applicable law or agreed to in writing, software
36
+ # distributed under the License is distributed on an "AS IS" BASIS,
37
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
38
+ # See the License for the specific language governing permissions and
39
+ # limitations under the License.
40
+ """ LLaMA model configuration"""
41
+
42
+ from transformers.configuration_utils import PretrainedConfig
43
+ from transformers.utils import logging
44
+
45
+
46
+ logger = logging.get_logger(__name__)
47
+
48
+ LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
49
+
50
+
51
+ class LlamaConfig(PretrainedConfig):
52
+ r"""
53
+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
54
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
55
+ defaults will yield a similar configuration to that of the LLaMA-7B.
56
+
57
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
58
+ documentation from [`PretrainedConfig`] for more information.
59
+
60
+
61
+ Args:
62
+ vocab_size (`int`, *optional*, defaults to 32000):
63
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
64
+ `inputs_ids` passed when calling [`LlamaModel`]
65
+ hidden_size (`int`, *optional*, defaults to 4096):
66
+ Dimension of the hidden representations.
67
+ intermediate_size (`int`, *optional*, defaults to 11008):
68
+ Dimension of the MLP representations.
69
+ num_hidden_layers (`int`, *optional*, defaults to 32):
70
+ Number of hidden layers in the Transformer decoder.
71
+ num_attention_heads (`int`, *optional*, defaults to 32):
72
+ Number of attention heads for each attention layer in the Transformer decoder.
73
+ num_key_value_heads (`int`, *optional*):
74
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
75
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
76
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
77
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
78
+ by meanpooling all the original heads within that group. For more details checkout [this
79
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
80
+ `num_attention_heads`.
81
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
82
+ The non-linear activation function (function or string) in the decoder.
83
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
84
+ The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
85
+ Llama 2 up to 4096, CodeLlama up to 16384.
86
+ initializer_range (`float`, *optional*, defaults to 0.02):
87
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
88
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
89
+ The epsilon used by the rms normalization layers.
90
+ use_cache (`bool`, *optional*, defaults to `True`):
91
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
92
+ relevant if `config.is_decoder=True`.
93
+ pad_token_id (`int`, *optional*):
94
+ Padding token id.
95
+ bos_token_id (`int`, *optional*, defaults to 1):
96
+ Beginning of stream token id.
97
+ eos_token_id (`int`, *optional*, defaults to 2):
98
+ End of stream token id.
99
+ pretraining_tp (`int`, *optional*, defaults to 1):
100
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
101
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
102
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
103
+ issue](https://github.com/pytorch/pytorch/issues/76232).
104
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
105
+ Whether to tie weight embeddings
106
+ rope_theta (`float`, *optional*, defaults to 10000.0):
107
+ The base period of the RoPE embeddings.
108
+ rope_scaling (`Dict`, *optional*):
109
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
110
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
111
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
112
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
113
+ these scaling strategies behave:
114
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
115
+ experimental feature, subject to breaking API changes in future versions.
116
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
117
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
118
+ attention_dropout (`float`, *optional*, defaults to 0.0):
119
+ The dropout ratio for the attention probabilities.
120
+
121
+ ```python
122
+ >>> from transformers import LlamaModel, LlamaConfig
123
+
124
+ >>> # Initializing a LLaMA llama-7b style configuration
125
+ >>> configuration = LlamaConfig()
126
+
127
+ >>> # Initializing a model from the llama-7b style configuration
128
+ >>> model = LlamaModel(configuration)
129
+
130
+ >>> # Accessing the model configuration
131
+ >>> configuration = model.config
132
+ ```"""
133
+
134
+ model_type = "llama"
135
+ keys_to_ignore_at_inference = ["past_key_values"]
136
+
137
+ def __init__(
138
+ self,
139
+ vocab_size=32000,
140
+ hidden_size=4096,
141
+ intermediate_size=11008,
142
+ num_hidden_layers=32,
143
+ num_attention_heads=32,
144
+ num_key_value_heads=None,
145
+ hidden_act="silu",
146
+ max_position_embeddings=2048,
147
+ initializer_range=0.02,
148
+ rms_norm_eps=1e-6,
149
+ use_cache=True,
150
+ pad_token_id=None,
151
+ bos_token_id=1,
152
+ eos_token_id=2,
153
+ pretraining_tp=1,
154
+ tie_word_embeddings=False,
155
+ rope_theta=10000.0,
156
+ rope_scaling=None,
157
+ attention_bias=False,
158
+ attention_dropout=0.0,
159
+ **kwargs,
160
+ ):
161
+ self.vocab_size = vocab_size
162
+ self.max_position_embeddings = max_position_embeddings
163
+ self.hidden_size = hidden_size
164
+ self.intermediate_size = intermediate_size
165
+ self.num_hidden_layers = num_hidden_layers
166
+ self.num_attention_heads = num_attention_heads
167
+
168
+ # for backward compatibility
169
+ if num_key_value_heads is None:
170
+ num_key_value_heads = num_attention_heads
171
+
172
+ self.num_key_value_heads = num_key_value_heads
173
+ self.hidden_act = hidden_act
174
+ self.initializer_range = initializer_range
175
+ self.rms_norm_eps = rms_norm_eps
176
+ self.pretraining_tp = pretraining_tp
177
+ self.use_cache = use_cache
178
+ self.rope_theta = rope_theta
179
+ self.rope_scaling = rope_scaling
180
+ self._rope_scaling_validation()
181
+ self.attention_bias = attention_bias
182
+ self.attention_dropout = attention_dropout
183
+
184
+ super().__init__(
185
+ pad_token_id=pad_token_id,
186
+ bos_token_id=bos_token_id,
187
+ eos_token_id=eos_token_id,
188
+ tie_word_embeddings=tie_word_embeddings,
189
+ **kwargs,
190
+ )
191
+
192
+ def _rope_scaling_validation(self):
193
+ """
194
+ Validate the `rope_scaling` configuration.
195
+ """
196
+ if self.rope_scaling is None:
197
+ return
198
+
199
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
200
+ raise ValueError(
201
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
202
+ f"got {self.rope_scaling}"
203
+ )
204
+ rope_scaling_type = self.rope_scaling.get("type", None)
205
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
206
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
207
+ raise ValueError(
208
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
209
+ )
210
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
211
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
212
+
213
+
214
+
215
+ """ PyTorch LLaMA model."""
216
+ import math
217
+ import warnings
218
+ from typing import List, Optional, Tuple, Union
219
+
220
+ import torch
221
+ import torch.nn.functional as F
222
+ import torch.utils.checkpoint
223
+ from torch import nn
224
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
225
+
226
+ from transformers.activations import ACT2FN
227
+ from transformers.cache_utils import Cache, DynamicCache
228
+ from transformers.modeling_attn_mask_utils import (
229
+ AttentionMaskConverter,
230
+ _prepare_4d_attention_mask,
231
+ _prepare_4d_causal_attention_mask,
232
+ _prepare_4d_causal_attention_mask_for_sdpa,
233
+ )
234
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
235
+ from transformers.modeling_utils import PreTrainedModel
236
+ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
237
+ from transformers.utils import (
238
+ add_start_docstrings,
239
+ add_start_docstrings_to_model_forward,
240
+ is_flash_attn_2_available,
241
+ is_flash_attn_greater_or_equal_2_10,
242
+ logging,
243
+ replace_return_docstrings,
244
+ )
245
+ from transformers.utils.import_utils import is_torch_fx_available
246
+ # from .configuration_llama import LlamaConfig
247
+
248
+
249
+ if is_flash_attn_2_available():
250
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
251
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
252
+
253
+
254
+ # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
255
+ # It means that the function will not be traced through and simply appear as a node in the graph.
256
+ if is_torch_fx_available():
257
+ if not is_torch_greater_or_equal_than_1_13:
258
+ import torch.fx
259
+
260
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
261
+
262
+
263
+ logger = logging.get_logger(__name__)
264
+
265
+ _CONFIG_FOR_DOC = "LlamaConfig"
266
+
267
+
268
+ def _get_unpad_data(attention_mask):
269
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
270
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
271
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
272
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
273
+ return (
274
+ indices,
275
+ cu_seqlens,
276
+ max_seqlen_in_batch,
277
+ )
278
+
279
+
280
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
281
+ warnings.warn(
282
+ "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
283
+ )
284
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
285
+
286
+
287
+ def _make_causal_mask(
288
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
289
+ ):
290
+ warnings.warn(
291
+ "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
292
+ )
293
+ return AttentionMaskConverter._make_causal_mask(
294
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
295
+ )
296
+
297
+
298
+ class LlamaRMSNorm(nn.Module):
299
+ def __init__(self, hidden_size, eps=1e-6):
300
+ """
301
+ LlamaRMSNorm is equivalent to T5LayerNorm
302
+ """
303
+ super().__init__()
304
+ self.weight = nn.Parameter(torch.ones(hidden_size))
305
+ self.variance_epsilon = eps
306
+
307
+ def forward(self, hidden_states):
308
+ input_dtype = hidden_states.dtype
309
+ hidden_states = hidden_states.to(torch.float32)
310
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
311
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
312
+ return self.weight * hidden_states.to(input_dtype)
313
+
314
+
315
+ ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
316
+
317
+
318
+ class LlamaRotaryEmbedding(nn.Module):
319
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
320
+ super().__init__()
321
+
322
+ self.dim = dim
323
+ self.max_position_embeddings = max_position_embeddings
324
+ self.base = base
325
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
326
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
327
+
328
+ # Build here to make `torch.jit.trace` work.
329
+ self._set_cos_sin_cache(
330
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
331
+ )
332
+
333
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
334
+ self.max_seq_len_cached = seq_len
335
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
336
+
337
+ freqs = torch.outer(t, self.inv_freq)
338
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
339
+ emb = torch.cat((freqs, freqs), dim=-1)
340
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
341
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
342
+
343
+ def forward(self, x, seq_len=None):
344
+ # x: [bs, num_attention_heads, seq_len, head_size]
345
+ if seq_len > self.max_seq_len_cached:
346
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
347
+
348
+ return (
349
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
350
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
351
+ )
352
+
353
+
354
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
355
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
356
+
357
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
358
+ self.scaling_factor = scaling_factor
359
+ super().__init__(dim, max_position_embeddings, base, device)
360
+
361
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
362
+ self.max_seq_len_cached = seq_len
363
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
364
+ t = t / self.scaling_factor
365
+
366
+ freqs = torch.outer(t, self.inv_freq)
367
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
368
+ emb = torch.cat((freqs, freqs), dim=-1)
369
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
370
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
371
+
372
+
373
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
374
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
375
+
376
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
377
+ self.scaling_factor = scaling_factor
378
+ super().__init__(dim, max_position_embeddings, base, device)
379
+
380
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
381
+ self.max_seq_len_cached = seq_len
382
+
383
+ if seq_len > self.max_position_embeddings:
384
+ base = self.base * (
385
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
386
+ ) ** (self.dim / (self.dim - 2))
387
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
388
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
389
+
390
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
391
+
392
+ freqs = torch.outer(t, self.inv_freq)
393
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
394
+ emb = torch.cat((freqs, freqs), dim=-1)
395
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
396
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
397
+
398
+
399
+ def rotate_half(x):
400
+ """Rotates half the hidden dims of the input."""
401
+ x1 = x[..., : x.shape[-1] // 2]
402
+ x2 = x[..., x.shape[-1] // 2 :]
403
+ return torch.cat((-x2, x1), dim=-1)
404
+
405
+
406
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
407
+ """Applies Rotary Position Embedding to the query and key tensors.
408
+
409
+ Args:
410
+ q (`torch.Tensor`): The query tensor.
411
+ k (`torch.Tensor`): The key tensor.
412
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
413
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
414
+ position_ids (`torch.Tensor`):
415
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
416
+ used to pass offsetted position ids when working with a KV-cache.
417
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
418
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
419
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
420
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
421
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
422
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
423
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
424
+ Returns:
425
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
426
+ """
427
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
428
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
429
+ q_embed = (q * cos) + (rotate_half(q) * sin)
430
+ k_embed = (k * cos) + (rotate_half(k) * sin)
431
+ return q_embed, k_embed
432
+
433
+
434
+ class LlamaMLP(nn.Module):
435
+ def __init__(self, config):
436
+ super().__init__()
437
+ self.config = config
438
+ self.hidden_size = config.hidden_size
439
+ self.intermediate_size = config.intermediate_size
440
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
441
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
442
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
443
+ self.act_fn = ACT2FN[config.hidden_act]
444
+
445
+ def forward(self, x):
446
+ if self.config.pretraining_tp > 1:
447
+ slice = self.intermediate_size // self.config.pretraining_tp
448
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
449
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
450
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
451
+
452
+ gate_proj = torch.cat(
453
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
454
+ )
455
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
456
+
457
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
458
+ down_proj = [
459
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
460
+ ]
461
+ down_proj = sum(down_proj)
462
+ else:
463
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
464
+
465
+ return down_proj
466
+
467
+
468
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
469
+ """
470
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
471
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
472
+ """
473
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
474
+ if n_rep == 1:
475
+ return hidden_states
476
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
477
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
478
+
479
+
480
+ class LlamaAttention(nn.Module):
481
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
482
+
483
+ def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
484
+ super().__init__()
485
+ self.config = config
486
+ self.layer_idx = layer_idx
487
+ if layer_idx is None:
488
+ logger.warning_once(
489
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
490
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
491
+ "when creating this class."
492
+ )
493
+
494
+ self.attention_dropout = config.attention_dropout
495
+ self.hidden_size = config.hidden_size
496
+ self.num_heads = config.num_attention_heads
497
+ self.head_dim = self.hidden_size // self.num_heads
498
+ self.num_key_value_heads = config.num_key_value_heads
499
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
500
+ self.max_position_embeddings = config.max_position_embeddings
501
+ self.rope_theta = config.rope_theta
502
+ self.is_causal = True
503
+
504
+ if (self.head_dim * self.num_heads) != self.hidden_size:
505
+ raise ValueError(
506
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
507
+ f" and `num_heads`: {self.num_heads})."
508
+ )
509
+
510
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
511
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
512
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
513
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
514
+ self._init_rope()
515
+
516
+ def _init_rope(self):
517
+ if self.config.rope_scaling is None:
518
+ self.rotary_emb = LlamaRotaryEmbedding(
519
+ self.head_dim,
520
+ max_position_embeddings=self.max_position_embeddings,
521
+ base=self.rope_theta,
522
+ )
523
+ else:
524
+ scaling_type = self.config.rope_scaling["type"]
525
+ scaling_factor = self.config.rope_scaling["factor"]
526
+ if scaling_type == "linear":
527
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
528
+ self.head_dim,
529
+ max_position_embeddings=self.max_position_embeddings,
530
+ scaling_factor=scaling_factor,
531
+ base=self.rope_theta,
532
+ )
533
+ elif scaling_type == "dynamic":
534
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
535
+ self.head_dim,
536
+ max_position_embeddings=self.max_position_embeddings,
537
+ scaling_factor=scaling_factor,
538
+ base=self.rope_theta,
539
+ )
540
+ else:
541
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
542
+
543
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
544
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
545
+
546
+ def forward(
547
+ self,
548
+ hidden_states: torch.Tensor,
549
+ attention_mask: Optional[torch.Tensor] = None,
550
+ position_ids: Optional[torch.LongTensor] = None,
551
+ past_key_value: Optional[Cache] = None,
552
+ output_attentions: bool = False,
553
+ use_cache: bool = False,
554
+ **kwargs,
555
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
556
+ if "padding_mask" in kwargs:
557
+ warnings.warn(
558
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
559
+ )
560
+
561
+ bsz, q_len, _ = hidden_states.size()
562
+
563
+ if self.config.pretraining_tp > 1:
564
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
565
+ query_slices = self.q_proj.weight.split(
566
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
567
+ )
568
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
569
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
570
+
571
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
572
+ query_states = torch.cat(query_states, dim=-1)
573
+
574
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
575
+ key_states = torch.cat(key_states, dim=-1)
576
+
577
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
578
+ value_states = torch.cat(value_states, dim=-1)
579
+
580
+ else:
581
+ query_states = self.q_proj(hidden_states)
582
+ key_states = self.k_proj(hidden_states)
583
+ value_states = self.v_proj(hidden_states)
584
+
585
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
586
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
587
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
588
+
589
+ kv_seq_len = key_states.shape[-2]
590
+ if past_key_value is not None:
591
+ if self.layer_idx is None:
592
+ raise ValueError(
593
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
594
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
595
+ "with a layer index."
596
+ )
597
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
598
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
599
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
600
+
601
+ if past_key_value is not None:
602
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
603
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
604
+
605
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
606
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
607
+
608
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
609
+
610
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
611
+ raise ValueError(
612
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
613
+ f" {attn_weights.size()}"
614
+ )
615
+
616
+ if attention_mask is not None:
617
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
618
+ raise ValueError(
619
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
620
+ )
621
+ attn_weights = attn_weights + attention_mask
622
+
623
+ # upcast attention to fp32
624
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
625
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
626
+ attn_output = torch.matmul(attn_weights, value_states)
627
+
628
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
629
+ raise ValueError(
630
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
631
+ f" {attn_output.size()}"
632
+ )
633
+
634
+ attn_output = attn_output.transpose(1, 2).contiguous()
635
+
636
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
637
+
638
+ if self.config.pretraining_tp > 1:
639
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
640
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
641
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
642
+ else:
643
+ attn_output = self.o_proj(attn_output)
644
+
645
+ if not output_attentions:
646
+ attn_weights = None
647
+
648
+ return attn_output, attn_weights, past_key_value
649
+
650
+
651
+ class LlamaFlashAttention2(LlamaAttention):
652
+ """
653
+ Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
654
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
655
+ flash attention and deal with padding tokens in case the input contains any of them.
656
+ """
657
+
658
+ def __init__(self, *args, **kwargs):
659
+ super().__init__(*args, **kwargs)
660
+
661
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
662
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
663
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
664
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
665
+
666
+ def forward(
667
+ self,
668
+ hidden_states: torch.Tensor,
669
+ attention_mask: Optional[torch.LongTensor] = None,
670
+ position_ids: Optional[torch.LongTensor] = None,
671
+ past_key_value: Optional[Cache] = None,
672
+ output_attentions: bool = False,
673
+ use_cache: bool = False,
674
+ **kwargs,
675
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
676
+ # LlamaFlashAttention2 attention does not support output_attentions
677
+ if "padding_mask" in kwargs:
678
+ warnings.warn(
679
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
680
+ )
681
+
682
+ # overwrite attention_mask with padding_mask
683
+ attention_mask = kwargs.pop("padding_mask")
684
+
685
+ output_attentions = False
686
+
687
+ bsz, q_len, _ = hidden_states.size()
688
+
689
+ query_states = self.q_proj(hidden_states)
690
+ key_states = self.k_proj(hidden_states)
691
+ value_states = self.v_proj(hidden_states)
692
+
693
+ # Flash attention requires the input to have the shape
694
+ # batch_size x seq_length x head_dim x hidden_dim
695
+ # therefore we just need to keep the original shape
696
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
697
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
698
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
699
+
700
+ kv_seq_len = key_states.shape[-2]
701
+ if past_key_value is not None:
702
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
703
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
704
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
705
+
706
+ if past_key_value is not None:
707
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
708
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
709
+
710
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
711
+ # to be able to avoid many of these transpose/reshape/view.
712
+ query_states = query_states.transpose(1, 2)
713
+ key_states = key_states.transpose(1, 2)
714
+ value_states = value_states.transpose(1, 2)
715
+
716
+ dropout_rate = self.attention_dropout if self.training else 0.0
717
+
718
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
719
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
720
+ # cast them back in the correct dtype just to be sure everything works as expected.
721
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
722
+ # in fp32. (LlamaRMSNorm handles it correctly)
723
+
724
+ input_dtype = query_states.dtype
725
+ if input_dtype == torch.float32:
726
+ if torch.is_autocast_enabled():
727
+ target_dtype = torch.get_autocast_gpu_dtype()
728
+ # Handle the case where the model is quantized
729
+ elif hasattr(self.config, "_pre_quantization_dtype"):
730
+ target_dtype = self.config._pre_quantization_dtype
731
+ else:
732
+ target_dtype = self.q_proj.weight.dtype
733
+
734
+ logger.warning_once(
735
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
736
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
737
+ f" {target_dtype}."
738
+ )
739
+
740
+ query_states = query_states.to(target_dtype)
741
+ key_states = key_states.to(target_dtype)
742
+ value_states = value_states.to(target_dtype)
743
+
744
+ attn_output = self._flash_attention_forward(
745
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
746
+ )
747
+
748
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
749
+ attn_output = self.o_proj(attn_output)
750
+
751
+ if not output_attentions:
752
+ attn_weights = None
753
+
754
+ return attn_output, attn_weights, past_key_value
755
+
756
+ def _flash_attention_forward(
757
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
758
+ ):
759
+ """
760
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
761
+ first unpad the input, then computes the attention scores and pad the final attention scores.
762
+
763
+ Args:
764
+ query_states (`torch.Tensor`):
765
+ Input query states to be passed to Flash Attention API
766
+ key_states (`torch.Tensor`):
767
+ Input key states to be passed to Flash Attention API
768
+ value_states (`torch.Tensor`):
769
+ Input value states to be passed to Flash Attention API
770
+ attention_mask (`torch.Tensor`):
771
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
772
+ position of padding tokens and 1 for the position of non-padding tokens.
773
+ dropout (`int`, *optional*):
774
+ Attention dropout
775
+ softmax_scale (`float`, *optional*):
776
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
777
+ """
778
+ if not self._flash_attn_uses_top_left_mask:
779
+ causal = self.is_causal
780
+ else:
781
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
782
+ causal = self.is_causal and query_length != 1
783
+
784
+ # Contains at least one padding token in the sequence
785
+ if attention_mask is not None:
786
+ batch_size = query_states.shape[0]
787
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
788
+ query_states, key_states, value_states, attention_mask, query_length
789
+ )
790
+
791
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
792
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
793
+
794
+ attn_output_unpad = flash_attn_varlen_func(
795
+ query_states,
796
+ key_states,
797
+ value_states,
798
+ cu_seqlens_q=cu_seqlens_q,
799
+ cu_seqlens_k=cu_seqlens_k,
800
+ max_seqlen_q=max_seqlen_in_batch_q,
801
+ max_seqlen_k=max_seqlen_in_batch_k,
802
+ dropout_p=dropout,
803
+ softmax_scale=softmax_scale,
804
+ causal=causal,
805
+ )
806
+
807
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
808
+ else:
809
+ attn_output = flash_attn_func(
810
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
811
+ )
812
+
813
+ return attn_output
814
+
815
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
816
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
817
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
818
+
819
+ key_layer = index_first_axis(
820
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
821
+ )
822
+ value_layer = index_first_axis(
823
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
824
+ )
825
+ if query_length == kv_seq_len:
826
+ query_layer = index_first_axis(
827
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
828
+ )
829
+ cu_seqlens_q = cu_seqlens_k
830
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
831
+ indices_q = indices_k
832
+ elif query_length == 1:
833
+ max_seqlen_in_batch_q = 1
834
+ cu_seqlens_q = torch.arange(
835
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
836
+ ) # There is a memcpy here, that is very bad.
837
+ indices_q = cu_seqlens_q[:-1]
838
+ query_layer = query_layer.squeeze(1)
839
+ else:
840
+ # The -q_len: slice assumes left padding.
841
+ attention_mask = attention_mask[:, -query_length:]
842
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
843
+
844
+ return (
845
+ query_layer,
846
+ key_layer,
847
+ value_layer,
848
+ indices_q,
849
+ (cu_seqlens_q, cu_seqlens_k),
850
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
851
+ )
852
+
853
+
854
+ class LlamaSdpaAttention(LlamaAttention):
855
+ """
856
+ Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
857
+ `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
858
+ SDPA API.
859
+ """
860
+
861
+ # Adapted from LlamaAttention.forward
862
+ def forward(
863
+ self,
864
+ hidden_states: torch.Tensor,
865
+ attention_mask: Optional[torch.Tensor] = None,
866
+ position_ids: Optional[torch.LongTensor] = None,
867
+ past_key_value: Optional[Cache] = None,
868
+ output_attentions: bool = False,
869
+ use_cache: bool = False,
870
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
871
+ if output_attentions:
872
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
873
+ logger.warning_once(
874
+ "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
875
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
876
+ )
877
+ return super().forward(
878
+ hidden_states=hidden_states,
879
+ attention_mask=attention_mask,
880
+ position_ids=position_ids,
881
+ past_key_value=past_key_value,
882
+ output_attentions=output_attentions,
883
+ use_cache=use_cache,
884
+ )
885
+
886
+ bsz, q_len, _ = hidden_states.size()
887
+
888
+ query_states = self.q_proj(hidden_states)
889
+ key_states = self.k_proj(hidden_states)
890
+ value_states = self.v_proj(hidden_states)
891
+
892
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
893
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
894
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
895
+
896
+ kv_seq_len = key_states.shape[-2]
897
+ if past_key_value is not None:
898
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
899
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
900
+
901
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
902
+
903
+ if past_key_value is not None:
904
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
905
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
906
+
907
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
908
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
909
+
910
+ if attention_mask is not None:
911
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
912
+ raise ValueError(
913
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
914
+ )
915
+
916
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
917
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
918
+ if query_states.device.type == "cuda" and attention_mask is not None:
919
+ query_states = query_states.contiguous()
920
+ key_states = key_states.contiguous()
921
+ value_states = value_states.contiguous()
922
+
923
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
924
+ query_states,
925
+ key_states,
926
+ value_states,
927
+ attn_mask=attention_mask,
928
+ dropout_p=self.attention_dropout if self.training else 0.0,
929
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
930
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
931
+ )
932
+
933
+ attn_output = attn_output.transpose(1, 2).contiguous()
934
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
935
+
936
+ attn_output = self.o_proj(attn_output)
937
+
938
+ return attn_output, None, past_key_value
939
+
940
+
941
+ LLAMA_ATTENTION_CLASSES = {
942
+ "eager": LlamaAttention,
943
+ "flash_attention_2": LlamaFlashAttention2,
944
+ "sdpa": LlamaSdpaAttention,
945
+ }
946
+
947
+
948
+ class LlamaDecoderLayer(nn.Module):
949
+ def __init__(self, config: LlamaConfig, layer_idx: int):
950
+ super().__init__()
951
+ self.hidden_size = config.hidden_size
952
+
953
+ self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
954
+
955
+ self.mlp = LlamaMLP(config)
956
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
957
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
958
+
959
+ def forward(
960
+ self,
961
+ hidden_states: torch.Tensor,
962
+ attention_mask: Optional[torch.Tensor] = None,
963
+ position_ids: Optional[torch.LongTensor] = None,
964
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
965
+ output_attentions: Optional[bool] = False,
966
+ use_cache: Optional[bool] = False,
967
+ **kwargs,
968
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
969
+ """
970
+ Args:
971
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
972
+ attention_mask (`torch.FloatTensor`, *optional*):
973
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
974
+ query_sequence_length, key_sequence_length)` if default attention is used.
975
+ output_attentions (`bool`, *optional*):
976
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
977
+ returned tensors for more detail.
978
+ use_cache (`bool`, *optional*):
979
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
980
+ (see `past_key_values`).
981
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
982
+ """
983
+ if "padding_mask" in kwargs:
984
+ warnings.warn(
985
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
986
+ )
987
+
988
+ residual = hidden_states
989
+
990
+ hidden_states = self.input_layernorm(hidden_states)
991
+
992
+ # Self Attention
993
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
994
+ hidden_states=hidden_states,
995
+ attention_mask=attention_mask,
996
+ position_ids=position_ids,
997
+ past_key_value=past_key_value,
998
+ output_attentions=output_attentions,
999
+ use_cache=use_cache,
1000
+ **kwargs,
1001
+ )
1002
+ hidden_states = residual + hidden_states
1003
+
1004
+ # Fully Connected
1005
+ residual = hidden_states
1006
+ hidden_states = self.post_attention_layernorm(hidden_states)
1007
+ hidden_states = self.mlp(hidden_states)
1008
+ hidden_states = residual + hidden_states
1009
+
1010
+ outputs = (hidden_states,)
1011
+
1012
+ if output_attentions:
1013
+ outputs += (self_attn_weights,)
1014
+
1015
+ if use_cache:
1016
+ outputs += (present_key_value,)
1017
+
1018
+ return outputs
1019
+
1020
+
1021
+ LLAMA_START_DOCSTRING = r"""
1022
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
1023
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
1024
+ etc.)
1025
+
1026
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
1027
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
1028
+ and behavior.
1029
+
1030
+ Parameters:
1031
+ config ([`LlamaConfig`]):
1032
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
1033
+ load the weights associated with the model, only the configuration. Check out the
1034
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
1035
+ """
1036
+
1037
+
1038
+ @add_start_docstrings(
1039
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
1040
+ LLAMA_START_DOCSTRING,
1041
+ )
1042
+ class LlamaPreTrainedModel(PreTrainedModel):
1043
+ config_class = LlamaConfig
1044
+ base_model_prefix = "model"
1045
+ supports_gradient_checkpointing = True
1046
+ _no_split_modules = ["LlamaDecoderLayer"]
1047
+ _skip_keys_device_placement = "past_key_values"
1048
+ _supports_flash_attn_2 = True
1049
+ _supports_sdpa = True
1050
+ _supports_cache_class = True
1051
+
1052
+ def _init_weights(self, module):
1053
+ std = self.config.initializer_range
1054
+ if isinstance(module, nn.Linear):
1055
+ module.weight.data.normal_(mean=0.0, std=std)
1056
+ if module.bias is not None:
1057
+ module.bias.data.zero_()
1058
+ elif isinstance(module, nn.Embedding):
1059
+ module.weight.data.normal_(mean=0.0, std=std)
1060
+ if module.padding_idx is not None:
1061
+ module.weight.data[module.padding_idx].zero_()
1062
+
1063
+
1064
+ LLAMA_INPUTS_DOCSTRING = r"""
1065
+ Args:
1066
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
1067
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
1068
+ it.
1069
+
1070
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
1071
+ [`PreTrainedTokenizer.__call__`] for details.
1072
+
1073
+ [What are input IDs?](../glossary#input-ids)
1074
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
1075
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
1076
+
1077
+ - 1 for tokens that are **not masked**,
1078
+ - 0 for tokens that are **masked**.
1079
+
1080
+ [What are attention masks?](../glossary#attention-mask)
1081
+
1082
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
1083
+ [`PreTrainedTokenizer.__call__`] for details.
1084
+
1085
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
1086
+ `past_key_values`).
1087
+
1088
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
1089
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
1090
+ information on the default strategy.
1091
+
1092
+ - 1 indicates the head is **not masked**,
1093
+ - 0 indicates the head is **masked**.
1094
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1095
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
1096
+ config.n_positions - 1]`.
1097
+
1098
+ [What are position IDs?](../glossary#position-ids)
1099
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
1100
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
1101
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
1102
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
1103
+
1104
+ Two formats are allowed:
1105
+ - a [`~cache_utils.Cache`] instance;
1106
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
1107
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
1108
+ cache format.
1109
+
1110
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
1111
+ legacy cache format will be returned.
1112
+
1113
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
1114
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
1115
+ of shape `(batch_size, sequence_length)`.
1116
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
1117
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
1118
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
1119
+ model's internal embedding lookup matrix.
1120
+ use_cache (`bool`, *optional*):
1121
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
1122
+ `past_key_values`).
1123
+ output_attentions (`bool`, *optional*):
1124
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
1125
+ tensors for more detail.
1126
+ output_hidden_states (`bool`, *optional*):
1127
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
1128
+ more detail.
1129
+ return_dict (`bool`, *optional*):
1130
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
1131
+ """
1132
+
1133
+
1134
+ @add_start_docstrings(
1135
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
1136
+ LLAMA_START_DOCSTRING,
1137
+ )
1138
+ class LlamaModel(LlamaPreTrainedModel):
1139
+ """
1140
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
1141
+
1142
+ Args:
1143
+ config: LlamaConfig
1144
+ """
1145
+
1146
+ def __init__(self, config: LlamaConfig):
1147
+ super().__init__(config)
1148
+ self.padding_idx = config.pad_token_id
1149
+ self.vocab_size = config.vocab_size
1150
+
1151
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
1152
+ self.layers = nn.ModuleList(
1153
+ [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
1154
+ )
1155
+ self._use_sdpa = config._attn_implementation == "sdpa"
1156
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
1157
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1158
+
1159
+ self.ehce_layers = [0,1,5,6,10,11,15,16,20,21,25,26,30,31]
1160
+ self.copy_layers = len(self.ehce_layers)
1161
+
1162
+
1163
+ self.embed_tokens_condition = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
1164
+ self.norm_condition = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1165
+
1166
+ self.condition_layers = nn.ModuleList(
1167
+ [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(self.copy_layers)]
1168
+ )
1169
+
1170
+
1171
+ self.embed_tokens_condition_grounding = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
1172
+ self.norm_condition_grounding = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1173
+
1174
+ self.condition_layers_grounding = nn.ModuleList(
1175
+ [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(self.copy_layers)]
1176
+ )
1177
+
1178
+
1179
+ self.gradient_checkpointing = False
1180
+ # Initialize weights and apply final processing
1181
+ self.post_init()
1182
+
1183
+ def get_input_embeddings(self):
1184
+ return self.embed_tokens
1185
+
1186
+ def set_input_embeddings(self, value):
1187
+ self.embed_tokens = value
1188
+
1189
+
1190
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1191
+ def forward(
1192
+ self,
1193
+ input_ids: torch.LongTensor = None,
1194
+ condition_input_ids: torch.LongTensor = None,
1195
+ attention_mask: Optional[torch.Tensor] = None,
1196
+ position_ids: Optional[torch.LongTensor] = None,
1197
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1198
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1199
+ condition_inputs_embeds: Optional[torch.FloatTensor] = None,
1200
+ use_cache: Optional[bool] = None,
1201
+ output_attentions: Optional[bool] = None,
1202
+ output_hidden_states: Optional[bool] = None,
1203
+ return_dict: Optional[bool] = None,
1204
+ task_type = None,
1205
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
1206
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1207
+ output_hidden_states = (
1208
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1209
+ )
1210
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1211
+
1212
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1213
+
1214
+
1215
+
1216
+
1217
+ #----------------------------------------------------------------------------------------------------------------
1218
+ if past_key_values is not None:
1219
+ grounding_condition_past_key_values = past_key_values[2]
1220
+ condition_past_key_values = past_key_values[1]
1221
+ past_key_values = past_key_values[0]
1222
+ else:
1223
+ grounding_condition_past_key_values = None
1224
+ condition_past_key_values = None
1225
+ #----------------------------------------------------------------------------------------------------------------
1226
+
1227
+
1228
+
1229
+ # retrieve input_ids and inputs_embeds
1230
+ if input_ids is not None and inputs_embeds is not None:
1231
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
1232
+ elif input_ids is not None:
1233
+ batch_size, seq_length = input_ids.shape[:2]
1234
+ elif inputs_embeds is not None:
1235
+ batch_size, seq_length = inputs_embeds.shape[:2]
1236
+ else:
1237
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1238
+
1239
+ if self.gradient_checkpointing and self.training:
1240
+ if use_cache:
1241
+ logger.warning_once(
1242
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
1243
+ )
1244
+ use_cache = False
1245
+
1246
+ past_key_values_length = 0
1247
+ if use_cache:
1248
+ use_legacy_cache = not isinstance(past_key_values, Cache)
1249
+ if use_legacy_cache:
1250
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1251
+ #----------------------------------------------------------------------------------------------------------------
1252
+ condition_past_key_values = DynamicCache.from_legacy_cache(condition_past_key_values)
1253
+ grounding_condition_past_key_values = DynamicCache.from_legacy_cache(grounding_condition_past_key_values)
1254
+ #----------------------------------------------------------------------------------------------------------------
1255
+
1256
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
1257
+
1258
+
1259
+
1260
+ if position_ids is None:
1261
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1262
+ position_ids = torch.arange(
1263
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
1264
+ )
1265
+ position_ids = position_ids.unsqueeze(0)
1266
+
1267
+ if inputs_embeds is None:
1268
+ if task_type == "MM":
1269
+ inputs_embeds = self.embed_tokens_condition(input_ids)
1270
+ elif task_type == "G":
1271
+ inputs_embeds = self.embed_tokens_condition_grounding(input_ids)
1272
+ else:
1273
+ inputs_embeds = self.embed_tokens(input_ids)
1274
+
1275
+
1276
+
1277
+ if self._use_flash_attention_2:
1278
+ # 2d mask is passed through the layers
1279
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
1280
+ elif self._use_sdpa and not output_attentions:
1281
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
1282
+ # the manual implementation that requires a 4D causal mask in all cases.
1283
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
1284
+ attention_mask,
1285
+ (batch_size, seq_length),
1286
+ inputs_embeds,
1287
+ past_key_values_length,
1288
+ )
1289
+ else:
1290
+ # 4d mask is passed through the layers
1291
+ attention_mask = _prepare_4d_causal_attention_mask(
1292
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
1293
+ )
1294
+
1295
+ # embed positions
1296
+ hidden_states = inputs_embeds
1297
+
1298
+ # decoder layers
1299
+ all_hidden_states = () if output_hidden_states else None
1300
+ all_self_attns = () if output_attentions else None
1301
+ # all_conidtion_states = () if output_hidden_states else None
1302
+ next_decoder_cache = None
1303
+ next_decoder_cache_condition = None
1304
+ next_decoder_cache_condition_grounding = None
1305
+
1306
+
1307
+
1308
+
1309
+ ehce_layers = self.ehce_layers
1310
+
1311
+ # for decoder_layer in self.layers:
1312
+ for idx, decoder_layer in enumerate(self.layers):
1313
+ if output_hidden_states:
1314
+ all_hidden_states += (hidden_states,)
1315
+
1316
+ if self.gradient_checkpointing and self.training:
1317
+ assert False
1318
+ else:
1319
+
1320
+ layer_outputs = decoder_layer(
1321
+ hidden_states,
1322
+ attention_mask=attention_mask,
1323
+ position_ids=position_ids,
1324
+ past_key_value=past_key_values,
1325
+ output_attentions=output_attentions,
1326
+ use_cache=use_cache,
1327
+ )
1328
+
1329
+ if idx in ehce_layers and task_type in ["MM","G"]:
1330
+
1331
+ cur_index = ehce_layers.index(idx)
1332
+ condition_hidden_states = layer_outputs[0]
1333
+ if task_type=="MM":
1334
+ condition_layer_outputs = self.condition_layers[cur_index](
1335
+ condition_hidden_states,
1336
+ attention_mask=attention_mask,
1337
+ position_ids=position_ids,
1338
+ past_key_value=condition_past_key_values,
1339
+ output_attentions=output_attentions,
1340
+ use_cache=use_cache,
1341
+ )
1342
+ else:
1343
+ condition_layer_outputs = self.condition_layers_grounding[cur_index](
1344
+ condition_hidden_states,
1345
+ attention_mask=attention_mask,
1346
+ position_ids=position_ids,
1347
+ past_key_value=grounding_condition_past_key_values,
1348
+ output_attentions=output_attentions,
1349
+ use_cache=use_cache,
1350
+ )
1351
+
1352
+ #----------------------------------------------------------------------------------------------------------------
1353
+ if task_type in ["MM","G"]:
1354
+ if idx in ehce_layers:
1355
+ hidden_states = condition_layer_outputs[0]
1356
+ else:
1357
+ hidden_states = layer_outputs[0]
1358
+ else:
1359
+ hidden_states = layer_outputs[0]
1360
+ #----------------------------------------------------------------------------------------------------------------
1361
+
1362
+
1363
+ if use_cache:
1364
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1365
+ #----------------------------------------------------------------------------------------------------------------
1366
+ if idx in ehce_layers and task_type=="MM":
1367
+ next_decoder_cache_condition = condition_layer_outputs[2 if output_attentions else 1]
1368
+
1369
+ if idx in ehce_layers and task_type=="G":
1370
+ next_decoder_cache_condition_grounding = condition_layer_outputs[2 if output_attentions else 1]
1371
+ #----------------------------------------------------------------------------------------------------------------
1372
+
1373
+ if output_attentions:
1374
+ all_self_attns += (layer_outputs[1],)
1375
+
1376
+ if task_type=="MM":
1377
+ # hidden_states = condition_hidden_states
1378
+ hidden_states = self.norm_condition(hidden_states)
1379
+ elif task_type=="G":
1380
+ hidden_states = self.norm_condition_grounding(hidden_states)
1381
+ else:
1382
+ hidden_states = self.norm(hidden_states)
1383
+
1384
+ # add hidden states from the last decoder layer
1385
+ if output_hidden_states:
1386
+ all_hidden_states += (hidden_states,)
1387
+
1388
+ next_cache = None
1389
+ next_condition_cache = None
1390
+ next_condition_grounding_cache = None
1391
+
1392
+ cache_all = None
1393
+
1394
+ if use_cache:
1395
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
1396
+
1397
+ if task_type=="MM":
1398
+ next_condition_cache = next_decoder_cache_condition.to_legacy_cache() if use_legacy_cache else next_decoder_cache_condition
1399
+ if task_type=="G":
1400
+ next_condition_grounding_cache = next_decoder_cache_condition_grounding.to_legacy_cache() if use_legacy_cache else next_decoder_cache_condition_grounding
1401
+
1402
+ cache_all = (next_cache,next_condition_cache,next_condition_grounding_cache)
1403
+
1404
+ if not return_dict:
1405
+ return tuple(v for v in [hidden_states, cache_all, all_hidden_states, all_self_attns] if v is not None)
1406
+
1407
+ return BaseModelOutputWithPast(
1408
+ last_hidden_state=hidden_states,
1409
+ past_key_values=cache_all,
1410
+ hidden_states=all_hidden_states,
1411
+ attentions=all_self_attns,
1412
+ )
1413
+
1414
+
1415
+ class LlamaForCausalLM(LlamaPreTrainedModel):
1416
+ _tied_weights_keys = ["lm_head.weight"]
1417
+
1418
+ def __init__(self, config):
1419
+ super().__init__(config)
1420
+ self.model = LlamaModel(config)
1421
+ self.vocab_size = config.vocab_size
1422
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1423
+
1424
+ # Initialize weights and apply final processing
1425
+ self.post_init()
1426
+
1427
+ def get_input_embeddings(self):
1428
+ return self.model.embed_tokens
1429
+
1430
+ def set_input_embeddings(self, value):
1431
+ self.model.embed_tokens = value
1432
+
1433
+ def get_output_embeddings(self):
1434
+ return self.lm_head
1435
+
1436
+ def set_output_embeddings(self, new_embeddings):
1437
+ self.lm_head = new_embeddings
1438
+
1439
+ def set_decoder(self, decoder):
1440
+ self.model = decoder
1441
+
1442
+ def get_decoder(self):
1443
+ return self.model
1444
+
1445
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1446
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1447
+ def forward(
1448
+ self,
1449
+ input_ids: torch.LongTensor = None,
1450
+ attention_mask: Optional[torch.Tensor] = None,
1451
+ position_ids: Optional[torch.LongTensor] = None,
1452
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1453
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1454
+ labels: Optional[torch.LongTensor] = None,
1455
+ use_cache: Optional[bool] = None,
1456
+ output_attentions: Optional[bool] = None,
1457
+ output_hidden_states: Optional[bool] = None,
1458
+ return_dict: Optional[bool] = None,
1459
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1460
+ r"""
1461
+ Args:
1462
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1463
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1464
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1465
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1466
+
1467
+ Returns:
1468
+
1469
+ Example:
1470
+
1471
+ ```python
1472
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
1473
+
1474
+ >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
1475
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
1476
+
1477
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1478
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1479
+
1480
+ >>> # Generate
1481
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1482
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1483
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1484
+ ```"""
1485
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1486
+ output_hidden_states = (
1487
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1488
+ )
1489
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1490
+
1491
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1492
+ outputs = self.model(
1493
+ input_ids=input_ids,
1494
+ attention_mask=attention_mask,
1495
+ position_ids=position_ids,
1496
+ past_key_values=past_key_values,
1497
+ inputs_embeds=inputs_embeds,
1498
+ use_cache=use_cache,
1499
+ output_attentions=output_attentions,
1500
+ output_hidden_states=output_hidden_states,
1501
+ return_dict=return_dict,
1502
+ )
1503
+
1504
+ hidden_states = outputs[0]
1505
+ if self.config.pretraining_tp > 1:
1506
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1507
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
1508
+ logits = torch.cat(logits, dim=-1)
1509
+ else:
1510
+ logits = self.lm_head(hidden_states)
1511
+ logits = logits.float()
1512
+
1513
+ loss = None
1514
+ if labels is not None:
1515
+ # Shift so that tokens < n predict n
1516
+ shift_logits = logits[..., :-1, :].contiguous()
1517
+ shift_labels = labels[..., 1:].contiguous()
1518
+ # Flatten the tokens
1519
+ loss_fct = CrossEntropyLoss()
1520
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1521
+ shift_labels = shift_labels.view(-1)
1522
+ # Enable model parallelism
1523
+ shift_labels = shift_labels.to(shift_logits.device)
1524
+ loss = loss_fct(shift_logits, shift_labels)
1525
+
1526
+ if not return_dict:
1527
+ output = (logits,) + outputs[1:]
1528
+ return (loss,) + output if loss is not None else output
1529
+
1530
+ return CausalLMOutputWithPast(
1531
+ loss=loss,
1532
+ logits=logits,
1533
+ past_key_values=outputs.past_key_values,
1534
+ hidden_states=outputs.hidden_states,
1535
+ attentions=outputs.attentions,
1536
+ )
1537
+
1538
+ def prepare_inputs_for_generation(
1539
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1540
+ ):
1541
+ if past_key_values is not None:
1542
+ if isinstance(past_key_values, Cache):
1543
+ cache_length = past_key_values.get_seq_length()
1544
+ past_length = past_key_values.seen_tokens
1545
+ max_cache_length = past_key_values.get_max_length()
1546
+ else:
1547
+ cache_length = past_length = past_key_values[0][0].shape[2]
1548
+ max_cache_length = None
1549
+
1550
+ # Keep only the unprocessed tokens:
1551
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1552
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
1553
+ # input)
1554
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1555
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1556
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1557
+ # input_ids based on the past_length.
1558
+ elif past_length < input_ids.shape[1]:
1559
+ input_ids = input_ids[:, past_length:]
1560
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1561
+
1562
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1563
+ if (
1564
+ max_cache_length is not None
1565
+ and attention_mask is not None
1566
+ and cache_length + input_ids.shape[1] > max_cache_length
1567
+ ):
1568
+ attention_mask = attention_mask[:, -max_cache_length:]
1569
+
1570
+ position_ids = kwargs.get("position_ids", None)
1571
+ if attention_mask is not None and position_ids is None:
1572
+ # create position_ids on the fly for batch generation
1573
+ position_ids = attention_mask.long().cumsum(-1) - 1
1574
+ position_ids.masked_fill_(attention_mask == 0, 1)
1575
+ if past_key_values:
1576
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1577
+
1578
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1579
+ if inputs_embeds is not None and past_key_values is None:
1580
+ model_inputs = {"inputs_embeds": inputs_embeds}
1581
+ else:
1582
+ model_inputs = {"input_ids": input_ids}
1583
+
1584
+ model_inputs.update(
1585
+ {
1586
+ "position_ids": position_ids,
1587
+ "past_key_values": past_key_values,
1588
+ "use_cache": kwargs.get("use_cache"),
1589
+ "attention_mask": attention_mask,
1590
+ }
1591
+ )
1592
+ return model_inputs
1593
+
1594
+ @staticmethod
1595
+ def _reorder_cache(past_key_values, beam_idx):
1596
+ reordered_past = ()
1597
+ for layer_past in past_key_values:
1598
+ reordered_past += (
1599
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1600
+ )
1601
+ return reordered_past
1602
+
1603
+
1604
+ @add_start_docstrings(
1605
+ """
1606
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
1607
+
1608
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1609
+ (e.g. GPT-2) do.
1610
+
1611
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1612
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1613
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1614
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1615
+ each row of the batch).
1616
+ """,
1617
+ LLAMA_START_DOCSTRING,
1618
+ )
1619
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
1620
+ def __init__(self, config):
1621
+ super().__init__(config)
1622
+ self.num_labels = config.num_labels
1623
+ self.model = LlamaModel(config)
1624
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1625
+
1626
+ # Initialize weights and apply final processing
1627
+ self.post_init()
1628
+
1629
+ def get_input_embeddings(self):
1630
+ return self.model.embed_tokens
1631
+
1632
+ def set_input_embeddings(self, value):
1633
+ self.model.embed_tokens = value
1634
+
1635
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1636
+ def forward(
1637
+ self,
1638
+ input_ids: torch.LongTensor = None,
1639
+ attention_mask: Optional[torch.Tensor] = None,
1640
+ position_ids: Optional[torch.LongTensor] = None,
1641
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1642
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1643
+ labels: Optional[torch.LongTensor] = None,
1644
+ use_cache: Optional[bool] = None,
1645
+ output_attentions: Optional[bool] = None,
1646
+ output_hidden_states: Optional[bool] = None,
1647
+ return_dict: Optional[bool] = None,
1648
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1649
+ r"""
1650
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1651
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1652
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1653
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1654
+ """
1655
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1656
+
1657
+ transformer_outputs = self.model(
1658
+ input_ids,
1659
+ attention_mask=attention_mask,
1660
+ position_ids=position_ids,
1661
+ past_key_values=past_key_values,
1662
+ inputs_embeds=inputs_embeds,
1663
+ use_cache=use_cache,
1664
+ output_attentions=output_attentions,
1665
+ output_hidden_states=output_hidden_states,
1666
+ return_dict=return_dict,
1667
+ )
1668
+ hidden_states = transformer_outputs[0]
1669
+ logits = self.score(hidden_states)
1670
+
1671
+ if input_ids is not None:
1672
+ batch_size = input_ids.shape[0]
1673
+ else:
1674
+ batch_size = inputs_embeds.shape[0]
1675
+
1676
+ if self.config.pad_token_id is None and batch_size != 1:
1677
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1678
+ if self.config.pad_token_id is None:
1679
+ sequence_lengths = -1
1680
+ else:
1681
+ if input_ids is not None:
1682
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
1683
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1684
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
1685
+ sequence_lengths = sequence_lengths.to(logits.device)
1686
+ else:
1687
+ sequence_lengths = -1
1688
+
1689
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1690
+
1691
+ loss = None
1692
+ if labels is not None:
1693
+ labels = labels.to(logits.device)
1694
+ if self.config.problem_type is None:
1695
+ if self.num_labels == 1:
1696
+ self.config.problem_type = "regression"
1697
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1698
+ self.config.problem_type = "single_label_classification"
1699
+ else:
1700
+ self.config.problem_type = "multi_label_classification"
1701
+
1702
+ if self.config.problem_type == "regression":
1703
+ loss_fct = MSELoss()
1704
+ if self.num_labels == 1:
1705
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1706
+ else:
1707
+ loss = loss_fct(pooled_logits, labels)
1708
+ elif self.config.problem_type == "single_label_classification":
1709
+ loss_fct = CrossEntropyLoss()
1710
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1711
+ elif self.config.problem_type == "multi_label_classification":
1712
+ loss_fct = BCEWithLogitsLoss()
1713
+ loss = loss_fct(pooled_logits, labels)
1714
+ if not return_dict:
1715
+ output = (pooled_logits,) + transformer_outputs[1:]
1716
+ return ((loss,) + output) if loss is not None else output
1717
+
1718
+ return SequenceClassifierOutputWithPast(
1719
+ loss=loss,
1720
+ logits=pooled_logits,
1721
+ past_key_values=transformer_outputs.past_key_values,
1722
+ hidden_states=transformer_outputs.hidden_states,
1723
+ attentions=transformer_outputs.attentions,
1724
+ )
special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|begin_of_text|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|end_of_text|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|end_of_text|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
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1788
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1820
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1828
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1830
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1836
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+ },
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+ },
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1892
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1894
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1895
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+ },
1899
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1900
+ "content": "<|reserved_special_token_232|>",
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1902
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1903
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1904
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+ },
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1908
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1910
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1911
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1912
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1913
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1914
+ },
1915
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1916
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1918
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1919
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1924
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1926
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1927
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1929
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1930
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1931
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1932
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1933
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1934
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1935
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1936
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1937
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1938
+ },
1939
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+ "content": "<|reserved_special_token_237|>",
1941
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1942
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1943
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1944
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1945
+ "special": true
1946
+ },
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+ "128243": {
1948
+ "content": "<|reserved_special_token_238|>",
1949
+ "lstrip": false,
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1951
+ "rstrip": false,
1952
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1953
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1954
+ },
1955
+ "128244": {
1956
+ "content": "<|reserved_special_token_239|>",
1957
+ "lstrip": false,
1958
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1959
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1961
+ "special": true
1962
+ },
1963
+ "128245": {
1964
+ "content": "<|reserved_special_token_240|>",
1965
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1966
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1967
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1968
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1969
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1970
+ },
1971
+ "128246": {
1972
+ "content": "<|reserved_special_token_241|>",
1973
+ "lstrip": false,
1974
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1975
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1976
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1977
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1978
+ },
1979
+ "128247": {
1980
+ "content": "<|reserved_special_token_242|>",
1981
+ "lstrip": false,
1982
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1983
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1984
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1985
+ "special": true
1986
+ },
1987
+ "128248": {
1988
+ "content": "<|reserved_special_token_243|>",
1989
+ "lstrip": false,
1990
+ "normalized": false,
1991
+ "rstrip": false,
1992
+ "single_word": false,
1993
+ "special": true
1994
+ },
1995
+ "128249": {
1996
+ "content": "<|reserved_special_token_244|>",
1997
+ "lstrip": false,
1998
+ "normalized": false,
1999
+ "rstrip": false,
2000
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2001
+ "special": true
2002
+ },
2003
+ "128250": {
2004
+ "content": "<|reserved_special_token_245|>",
2005
+ "lstrip": false,
2006
+ "normalized": false,
2007
+ "rstrip": false,
2008
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2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_246|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_247|>",
2021
+ "lstrip": false,
2022
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2023
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2024
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2025
+ "special": true
2026
+ },
2027
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2028
+ "content": "<|reserved_special_token_248|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
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2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_249|>",
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+ "lstrip": false,
2038
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2039
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2040
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2041
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+ },
2043
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2044
+ "content": "<|reserved_special_token_250|>",
2045
+ "lstrip": false,
2046
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2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|end_of_text|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 2048,
2061
+ "pad_token": "<|end_of_text|>",
2062
+ "padding_side": "right",
2063
+ "tokenizer_class": "PreTrainedTokenizerFast"
2064
+ }