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Browse files- __init__.py +0 -0
- added_tokens.json +295 -0
- config.json +77 -0
- configuration_neo_chat.py +77 -0
- configuration_neo_vit.py +52 -0
- conversation.py +394 -0
- merges.txt +0 -0
- model-00001-of-00014.safetensors +3 -0
- model-00002-of-00014.safetensors +3 -0
- model-00003-of-00014.safetensors +3 -0
- model-00004-of-00014.safetensors +3 -0
- model-00005-of-00014.safetensors +3 -0
- model-00006-of-00014.safetensors +3 -0
- model-00007-of-00014.safetensors +3 -0
- model-00008-of-00014.safetensors +3 -0
- model-00009-of-00014.safetensors +3 -0
- model-00010-of-00014.safetensors +3 -0
- model-00011-of-00014.safetensors +3 -0
- model-00012-of-00014.safetensors +3 -0
- model-00013-of-00014.safetensors +3 -0
- model-00014-of-00014.safetensors +3 -0
- model.safetensors.index.json +692 -0
- modeling_neo_chat.py +363 -0
- modeling_neo_vit.py +235 -0
- modeling_qwen3.py +594 -0
- special_tokens_map.json +1900 -0
- tokenizer_config.json +2643 -0
- vocab.json +0 -0
__init__.py
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added_tokens.json
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{
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| 188 |
+
"<FAKE_PAD_32>": 151714,
|
| 189 |
+
"<FAKE_PAD_33>": 151715,
|
| 190 |
+
"<FAKE_PAD_34>": 151716,
|
| 191 |
+
"<FAKE_PAD_35>": 151717,
|
| 192 |
+
"<FAKE_PAD_36>": 151718,
|
| 193 |
+
"<FAKE_PAD_37>": 151719,
|
| 194 |
+
"<FAKE_PAD_38>": 151720,
|
| 195 |
+
"<FAKE_PAD_39>": 151721,
|
| 196 |
+
"<FAKE_PAD_3>": 151685,
|
| 197 |
+
"<FAKE_PAD_40>": 151722,
|
| 198 |
+
"<FAKE_PAD_41>": 151723,
|
| 199 |
+
"<FAKE_PAD_42>": 151724,
|
| 200 |
+
"<FAKE_PAD_43>": 151725,
|
| 201 |
+
"<FAKE_PAD_44>": 151726,
|
| 202 |
+
"<FAKE_PAD_45>": 151727,
|
| 203 |
+
"<FAKE_PAD_46>": 151728,
|
| 204 |
+
"<FAKE_PAD_47>": 151729,
|
| 205 |
+
"<FAKE_PAD_48>": 151730,
|
| 206 |
+
"<FAKE_PAD_49>": 151731,
|
| 207 |
+
"<FAKE_PAD_4>": 151686,
|
| 208 |
+
"<FAKE_PAD_50>": 151732,
|
| 209 |
+
"<FAKE_PAD_51>": 151733,
|
| 210 |
+
"<FAKE_PAD_52>": 151734,
|
| 211 |
+
"<FAKE_PAD_53>": 151735,
|
| 212 |
+
"<FAKE_PAD_54>": 151736,
|
| 213 |
+
"<FAKE_PAD_55>": 151737,
|
| 214 |
+
"<FAKE_PAD_56>": 151738,
|
| 215 |
+
"<FAKE_PAD_57>": 151739,
|
| 216 |
+
"<FAKE_PAD_58>": 151740,
|
| 217 |
+
"<FAKE_PAD_59>": 151741,
|
| 218 |
+
"<FAKE_PAD_5>": 151687,
|
| 219 |
+
"<FAKE_PAD_60>": 151742,
|
| 220 |
+
"<FAKE_PAD_61>": 151743,
|
| 221 |
+
"<FAKE_PAD_62>": 151744,
|
| 222 |
+
"<FAKE_PAD_63>": 151745,
|
| 223 |
+
"<FAKE_PAD_64>": 151746,
|
| 224 |
+
"<FAKE_PAD_65>": 151747,
|
| 225 |
+
"<FAKE_PAD_66>": 151748,
|
| 226 |
+
"<FAKE_PAD_67>": 151749,
|
| 227 |
+
"<FAKE_PAD_68>": 151750,
|
| 228 |
+
"<FAKE_PAD_69>": 151751,
|
| 229 |
+
"<FAKE_PAD_6>": 151688,
|
| 230 |
+
"<FAKE_PAD_70>": 151752,
|
| 231 |
+
"<FAKE_PAD_71>": 151753,
|
| 232 |
+
"<FAKE_PAD_72>": 151754,
|
| 233 |
+
"<FAKE_PAD_73>": 151755,
|
| 234 |
+
"<FAKE_PAD_74>": 151756,
|
| 235 |
+
"<FAKE_PAD_75>": 151757,
|
| 236 |
+
"<FAKE_PAD_76>": 151758,
|
| 237 |
+
"<FAKE_PAD_77>": 151759,
|
| 238 |
+
"<FAKE_PAD_78>": 151760,
|
| 239 |
+
"<FAKE_PAD_79>": 151761,
|
| 240 |
+
"<FAKE_PAD_7>": 151689,
|
| 241 |
+
"<FAKE_PAD_80>": 151762,
|
| 242 |
+
"<FAKE_PAD_81>": 151763,
|
| 243 |
+
"<FAKE_PAD_82>": 151764,
|
| 244 |
+
"<FAKE_PAD_83>": 151765,
|
| 245 |
+
"<FAKE_PAD_84>": 151766,
|
| 246 |
+
"<FAKE_PAD_85>": 151767,
|
| 247 |
+
"<FAKE_PAD_86>": 151768,
|
| 248 |
+
"<FAKE_PAD_87>": 151769,
|
| 249 |
+
"<FAKE_PAD_88>": 151770,
|
| 250 |
+
"<FAKE_PAD_89>": 151771,
|
| 251 |
+
"<FAKE_PAD_8>": 151690,
|
| 252 |
+
"<FAKE_PAD_90>": 151772,
|
| 253 |
+
"<FAKE_PAD_91>": 151773,
|
| 254 |
+
"<FAKE_PAD_92>": 151774,
|
| 255 |
+
"<FAKE_PAD_93>": 151775,
|
| 256 |
+
"<FAKE_PAD_94>": 151776,
|
| 257 |
+
"<FAKE_PAD_95>": 151777,
|
| 258 |
+
"<FAKE_PAD_96>": 151778,
|
| 259 |
+
"<FAKE_PAD_97>": 151779,
|
| 260 |
+
"<FAKE_PAD_98>": 151780,
|
| 261 |
+
"<FAKE_PAD_99>": 151781,
|
| 262 |
+
"<FAKE_PAD_9>": 151691,
|
| 263 |
+
"<IMG_CONTEXT>": 151669,
|
| 264 |
+
"<box>": 151676,
|
| 265 |
+
"<img>": 151670,
|
| 266 |
+
"<quad>": 151672,
|
| 267 |
+
"<ref>": 151674,
|
| 268 |
+
"<think>": 151667,
|
| 269 |
+
"<tool_call>": 151657,
|
| 270 |
+
"<tool_response>": 151665,
|
| 271 |
+
"<|action_end|>": 151679,
|
| 272 |
+
"<|action_start|>": 151678,
|
| 273 |
+
"<|box_end|>": 151649,
|
| 274 |
+
"<|box_start|>": 151648,
|
| 275 |
+
"<|endoftext|>": 151643,
|
| 276 |
+
"<|file_sep|>": 151664,
|
| 277 |
+
"<|fim_middle|>": 151660,
|
| 278 |
+
"<|fim_pad|>": 151662,
|
| 279 |
+
"<|fim_prefix|>": 151659,
|
| 280 |
+
"<|fim_suffix|>": 151661,
|
| 281 |
+
"<|im_end|>": 151645,
|
| 282 |
+
"<|im_start|>": 151644,
|
| 283 |
+
"<|image_pad|>": 151655,
|
| 284 |
+
"<|interpreter|>": 151681,
|
| 285 |
+
"<|object_ref_end|>": 151647,
|
| 286 |
+
"<|object_ref_start|>": 151646,
|
| 287 |
+
"<|plugin|>": 151680,
|
| 288 |
+
"<|quad_end|>": 151651,
|
| 289 |
+
"<|quad_start|>": 151650,
|
| 290 |
+
"<|repo_name|>": 151663,
|
| 291 |
+
"<|video_pad|>": 151656,
|
| 292 |
+
"<|vision_end|>": 151653,
|
| 293 |
+
"<|vision_pad|>": 151654,
|
| 294 |
+
"<|vision_start|>": 151652
|
| 295 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,77 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"NEOChatModel"
|
| 4 |
+
],
|
| 5 |
+
"auto_map": {
|
| 6 |
+
"AutoConfig": "configuration_neo_chat.NEOChatConfig",
|
| 7 |
+
"AutoModel": "modeling_neo_chat.NEOChatModel",
|
| 8 |
+
"AutoModelForCausalLM": "modeling_neo_chat.NEOChatModel"
|
| 9 |
+
},
|
| 10 |
+
"downsample_ratio": 0.5,
|
| 11 |
+
"eos_token_id": 151645,
|
| 12 |
+
"llm_config": {
|
| 13 |
+
"_name_or_path": null,
|
| 14 |
+
"architectures": [
|
| 15 |
+
"Qwen3ForCausalLM"
|
| 16 |
+
],
|
| 17 |
+
"attention_bias": false,
|
| 18 |
+
"attention_dropout": 0.0,
|
| 19 |
+
"bos_token_id": 151643,
|
| 20 |
+
"eos_token_id": 151645,
|
| 21 |
+
"head_dim": 128,
|
| 22 |
+
"hidden_act": "silu",
|
| 23 |
+
"hidden_size": 2048,
|
| 24 |
+
"intermediate_size": 6144,
|
| 25 |
+
"max_position_embeddings": 40960,
|
| 26 |
+
"max_position_embeddings_hw": 10000,
|
| 27 |
+
"max_window_layers": 40,
|
| 28 |
+
"model_type": "qwen3",
|
| 29 |
+
"num_attention_heads": 16,
|
| 30 |
+
"num_hidden_layers": 40,
|
| 31 |
+
"num_key_value_heads": 8,
|
| 32 |
+
"rms_norm_eps": 1e-06,
|
| 33 |
+
"rope_scaling": null,
|
| 34 |
+
"rope_theta": 1000000.0,
|
| 35 |
+
"rope_theta_hw": 10000.0,
|
| 36 |
+
"sliding_window": null,
|
| 37 |
+
"torch_dtype": "bfloat16",
|
| 38 |
+
"use_cache": false,
|
| 39 |
+
"use_deepep": false,
|
| 40 |
+
"use_sliding_window": false,
|
| 41 |
+
"vocab_size": 151936,
|
| 42 |
+
"pure_llm": false
|
| 43 |
+
},
|
| 44 |
+
"model_type": "neo_chat",
|
| 45 |
+
"pad_token_id": 151643,
|
| 46 |
+
"template": "neo1_0",
|
| 47 |
+
"tie_word_embeddings": false,
|
| 48 |
+
"torch_dtype": "bfloat16",
|
| 49 |
+
"transformers_version": "4.37.2",
|
| 50 |
+
"use_backbone_lora": 0,
|
| 51 |
+
"use_llm_lora": 0,
|
| 52 |
+
"min_pixels": 65536,
|
| 53 |
+
"max_pixels": 4194304,
|
| 54 |
+
"patch_size": 16,
|
| 55 |
+
"vision_config": {
|
| 56 |
+
"architectures": [
|
| 57 |
+
"NEOVisionModel"
|
| 58 |
+
],
|
| 59 |
+
"attention_dropout": 0.0,
|
| 60 |
+
"auto_map": {
|
| 61 |
+
"AutoConfig": "configuration_neo_vit.NEOVisionConfig",
|
| 62 |
+
"AutoModel": "modeling_neo_vit.NEOVisionModel"
|
| 63 |
+
},
|
| 64 |
+
"llm_hidden_size": 2048,
|
| 65 |
+
"downsample_ratio": 0.5,
|
| 66 |
+
"hidden_size": 1024,
|
| 67 |
+
"model_type": "neo_vision",
|
| 68 |
+
"rope_theta_vision": 10000.0,
|
| 69 |
+
"max_position_embeddings_vision": 10000,
|
| 70 |
+
"num_channels": 3,
|
| 71 |
+
"patch_size": 16,
|
| 72 |
+
"torch_dtype": "bfloat16",
|
| 73 |
+
"transformers_version": "4.37.2",
|
| 74 |
+
"min_pixels": 65536,
|
| 75 |
+
"max_pixels": 4194304
|
| 76 |
+
}
|
| 77 |
+
}
|
configuration_neo_chat.py
ADDED
|
@@ -0,0 +1,77 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import copy
|
| 2 |
+
|
| 3 |
+
from transformers import Qwen3Config
|
| 4 |
+
from transformers.utils import logging
|
| 5 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 6 |
+
|
| 7 |
+
from .configuration_neo_vit import NEOVisionConfig
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
logger = logging.get_logger(__name__)
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
class NEOLLMConfig(Qwen3Config):
|
| 14 |
+
def __init__(self, rope_theta_hw=10000.0, max_position_embeddings_hw=10000, **kwargs):
|
| 15 |
+
super().__init__(**kwargs)
|
| 16 |
+
self.rope_theta_hw = rope_theta_hw
|
| 17 |
+
self.max_position_embeddings_hw = max_position_embeddings_hw
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
class NEOChatConfig(PretrainedConfig):
|
| 21 |
+
model_type = 'neo_chat'
|
| 22 |
+
is_composition = True
|
| 23 |
+
|
| 24 |
+
def __init__(
|
| 25 |
+
self,
|
| 26 |
+
vision_config=None,
|
| 27 |
+
llm_config=None,
|
| 28 |
+
use_backbone_lora=0,
|
| 29 |
+
use_llm_lora=0,
|
| 30 |
+
downsample_ratio=0.5,
|
| 31 |
+
template=None,
|
| 32 |
+
**kwargs,
|
| 33 |
+
):
|
| 34 |
+
super().__init__(**kwargs)
|
| 35 |
+
|
| 36 |
+
if vision_config is None:
|
| 37 |
+
vision_config = {'architectures': ['NEOVisionModel']}
|
| 38 |
+
logger.info('vision_config is None. Initializing the NEOVisionConfig with default values.')
|
| 39 |
+
|
| 40 |
+
if llm_config is None:
|
| 41 |
+
llm_config = {'architectures': ['Qwen3ForCausalLM']}
|
| 42 |
+
logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
|
| 43 |
+
assert 'architectures' in llm_config, "Should specify architecture in llm_config"
|
| 44 |
+
|
| 45 |
+
if isinstance(vision_config, dict):
|
| 46 |
+
self.vision_config = NEOVisionConfig(**vision_config)
|
| 47 |
+
else:
|
| 48 |
+
self.vision_config = vision_config
|
| 49 |
+
|
| 50 |
+
if isinstance(llm_config, dict):
|
| 51 |
+
self.llm_config = NEOLLMConfig(**llm_config)
|
| 52 |
+
else:
|
| 53 |
+
self.llm_config = llm_config
|
| 54 |
+
|
| 55 |
+
self.use_backbone_lora = use_backbone_lora
|
| 56 |
+
self.use_llm_lora = use_llm_lora
|
| 57 |
+
self.downsample_ratio = downsample_ratio
|
| 58 |
+
self.template = template
|
| 59 |
+
self.tie_word_embeddings = self.llm_config.tie_word_embeddings
|
| 60 |
+
|
| 61 |
+
def to_dict(self):
|
| 62 |
+
"""
|
| 63 |
+
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
|
| 64 |
+
|
| 65 |
+
Returns:
|
| 66 |
+
`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
|
| 67 |
+
"""
|
| 68 |
+
output = copy.deepcopy(self.__dict__)
|
| 69 |
+
output['vision_config'] = self.vision_config.to_dict()
|
| 70 |
+
output['llm_config'] = self.llm_config.to_dict()
|
| 71 |
+
output['model_type'] = self.__class__.model_type
|
| 72 |
+
output['use_backbone_lora'] = self.use_backbone_lora
|
| 73 |
+
output['use_llm_lora'] = self.use_llm_lora
|
| 74 |
+
output['downsample_ratio'] = self.downsample_ratio
|
| 75 |
+
output['template'] = self.template
|
| 76 |
+
|
| 77 |
+
return output
|
configuration_neo_vit.py
ADDED
|
@@ -0,0 +1,52 @@
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from typing import Union
|
| 3 |
+
|
| 4 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 5 |
+
from transformers.utils import logging
|
| 6 |
+
|
| 7 |
+
logger = logging.get_logger(__name__)
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
class NEOVisionConfig(PretrainedConfig):
|
| 11 |
+
|
| 12 |
+
model_type = 'neo_vision'
|
| 13 |
+
|
| 14 |
+
def __init__(
|
| 15 |
+
self,
|
| 16 |
+
num_channels=3,
|
| 17 |
+
patch_size=16,
|
| 18 |
+
hidden_size=1024,
|
| 19 |
+
llm_hidden_size=2048,
|
| 20 |
+
downsample_ratio=0.5,
|
| 21 |
+
rope_theta_vision=10000.0,
|
| 22 |
+
max_position_embeddings_vision=10000,
|
| 23 |
+
min_pixels=65536,
|
| 24 |
+
max_pixels=4194304,
|
| 25 |
+
**kwargs,
|
| 26 |
+
):
|
| 27 |
+
super().__init__(**kwargs)
|
| 28 |
+
|
| 29 |
+
self.hidden_size = hidden_size
|
| 30 |
+
self.llm_hidden_size = llm_hidden_size,
|
| 31 |
+
self.downsample_ratio = downsample_ratio,
|
| 32 |
+
self.rope_theta_vision = rope_theta_vision
|
| 33 |
+
self.max_position_embeddings_vision = max_position_embeddings_vision
|
| 34 |
+
self.num_channels = num_channels
|
| 35 |
+
self.patch_size = patch_size
|
| 36 |
+
self.min_pixels = min_pixels
|
| 37 |
+
self.max_pixels = max_pixels
|
| 38 |
+
|
| 39 |
+
@classmethod
|
| 40 |
+
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
|
| 41 |
+
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
| 42 |
+
|
| 43 |
+
if 'vision_config' in config_dict:
|
| 44 |
+
config_dict = config_dict['vision_config']
|
| 45 |
+
|
| 46 |
+
if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
|
| 47 |
+
logger.warning(
|
| 48 |
+
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
| 49 |
+
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
|
| 50 |
+
)
|
| 51 |
+
|
| 52 |
+
return cls.from_dict(config_dict, **kwargs)
|
conversation.py
ADDED
|
@@ -0,0 +1,394 @@
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|
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|
|
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|
|
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|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
|
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|
|
|
|
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|
|
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|
|
|
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|
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|
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|
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|
|
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|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
Conversation prompt templates.
|
| 3 |
+
|
| 4 |
+
We kindly request that you import fastchat instead of copying this file if you wish to use it.
|
| 5 |
+
If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
|
| 6 |
+
|
| 7 |
+
Modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
|
| 8 |
+
"""
|
| 9 |
+
|
| 10 |
+
import dataclasses
|
| 11 |
+
from enum import IntEnum, auto
|
| 12 |
+
from typing import Dict, List, Tuple, Union
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
class SeparatorStyle(IntEnum):
|
| 16 |
+
"""Separator styles."""
|
| 17 |
+
|
| 18 |
+
ADD_COLON_SINGLE = auto()
|
| 19 |
+
ADD_COLON_TWO = auto()
|
| 20 |
+
ADD_COLON_SPACE_SINGLE = auto()
|
| 21 |
+
NO_COLON_SINGLE = auto()
|
| 22 |
+
NO_COLON_TWO = auto()
|
| 23 |
+
ADD_NEW_LINE_SINGLE = auto()
|
| 24 |
+
LLAMA2 = auto()
|
| 25 |
+
CHATGLM = auto()
|
| 26 |
+
CHATML = auto()
|
| 27 |
+
CHATINTERN = auto()
|
| 28 |
+
DOLLY = auto()
|
| 29 |
+
RWKV = auto()
|
| 30 |
+
PHOENIX = auto()
|
| 31 |
+
ROBIN = auto()
|
| 32 |
+
FALCON_CHAT = auto()
|
| 33 |
+
CHATGLM3 = auto()
|
| 34 |
+
INTERNVL_ZH = auto()
|
| 35 |
+
MPT = auto()
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
@dataclasses.dataclass
|
| 39 |
+
class Conversation:
|
| 40 |
+
"""A class that manages prompt templates and keeps all conversation history."""
|
| 41 |
+
|
| 42 |
+
# The name of this template
|
| 43 |
+
name: str
|
| 44 |
+
# The template of the system prompt
|
| 45 |
+
system_template: str = '{system_message}'
|
| 46 |
+
# The system message
|
| 47 |
+
system_message: str = ''
|
| 48 |
+
# The names of two roles
|
| 49 |
+
roles: Tuple[str] = ('USER', 'ASSISTANT')
|
| 50 |
+
# All messages. Each item is (role, message).
|
| 51 |
+
messages: List[List[str]] = ()
|
| 52 |
+
# The number of few shot examples
|
| 53 |
+
offset: int = 0
|
| 54 |
+
# The separator style and configurations
|
| 55 |
+
sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
|
| 56 |
+
sep: str = '\n'
|
| 57 |
+
sep2: str = None
|
| 58 |
+
# Stop criteria (the default one is EOS token)
|
| 59 |
+
stop_str: Union[str, List[str]] = None
|
| 60 |
+
# Stops generation if meeting any token in this list
|
| 61 |
+
stop_token_ids: List[int] = None
|
| 62 |
+
|
| 63 |
+
def get_prompt(self) -> str:
|
| 64 |
+
"""Get the prompt for generation."""
|
| 65 |
+
if self.system_message is not None and self.system_message != '':
|
| 66 |
+
system_prompt = self.system_template.format(system_message=self.system_message)
|
| 67 |
+
else:
|
| 68 |
+
system_prompt = ''
|
| 69 |
+
|
| 70 |
+
if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
|
| 71 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 72 |
+
for role, message in self.messages:
|
| 73 |
+
if message:
|
| 74 |
+
ret += role + ': ' + message + self.sep
|
| 75 |
+
else:
|
| 76 |
+
ret += role + ':'
|
| 77 |
+
return ret
|
| 78 |
+
elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
|
| 79 |
+
seps = [self.sep, self.sep2]
|
| 80 |
+
ret = '' if system_prompt == '' else system_prompt + seps[0]
|
| 81 |
+
for i, (role, message) in enumerate(self.messages):
|
| 82 |
+
if message:
|
| 83 |
+
ret += role + ': ' + message + seps[i % 2]
|
| 84 |
+
else:
|
| 85 |
+
ret += role + ':'
|
| 86 |
+
return ret
|
| 87 |
+
elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
|
| 88 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 89 |
+
for role, message in self.messages:
|
| 90 |
+
if message:
|
| 91 |
+
ret += role + ': ' + message + self.sep
|
| 92 |
+
else:
|
| 93 |
+
ret += role + ': ' # must be end with a space
|
| 94 |
+
return ret
|
| 95 |
+
elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
|
| 96 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 97 |
+
for role, message in self.messages:
|
| 98 |
+
if message:
|
| 99 |
+
ret += role + '\n' + message + self.sep
|
| 100 |
+
else:
|
| 101 |
+
ret += role + '\n'
|
| 102 |
+
return ret
|
| 103 |
+
elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
|
| 104 |
+
ret = system_prompt
|
| 105 |
+
for role, message in self.messages:
|
| 106 |
+
if message:
|
| 107 |
+
ret += role + message + self.sep
|
| 108 |
+
else:
|
| 109 |
+
ret += role
|
| 110 |
+
return ret
|
| 111 |
+
elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
|
| 112 |
+
seps = [self.sep, self.sep2]
|
| 113 |
+
ret = system_prompt
|
| 114 |
+
for i, (role, message) in enumerate(self.messages):
|
| 115 |
+
if message:
|
| 116 |
+
ret += role + message + seps[i % 2]
|
| 117 |
+
else:
|
| 118 |
+
ret += role
|
| 119 |
+
return ret
|
| 120 |
+
elif self.sep_style == SeparatorStyle.RWKV:
|
| 121 |
+
ret = system_prompt
|
| 122 |
+
for i, (role, message) in enumerate(self.messages):
|
| 123 |
+
if message:
|
| 124 |
+
ret += (
|
| 125 |
+
role
|
| 126 |
+
+ ': '
|
| 127 |
+
+ message.replace('\r\n', '\n').replace('\n\n', '\n')
|
| 128 |
+
)
|
| 129 |
+
ret += '\n\n'
|
| 130 |
+
else:
|
| 131 |
+
ret += role + ':'
|
| 132 |
+
return ret
|
| 133 |
+
elif self.sep_style == SeparatorStyle.LLAMA2:
|
| 134 |
+
seps = [self.sep, self.sep2]
|
| 135 |
+
ret = system_prompt if system_prompt != '' else '[INST] '
|
| 136 |
+
for i, (role, message) in enumerate(self.messages):
|
| 137 |
+
tag = self.roles[i % 2]
|
| 138 |
+
if message:
|
| 139 |
+
if i == 0:
|
| 140 |
+
ret += message + ' '
|
| 141 |
+
else:
|
| 142 |
+
ret += tag + ' ' + message + seps[i % 2]
|
| 143 |
+
else:
|
| 144 |
+
ret += tag
|
| 145 |
+
return ret
|
| 146 |
+
elif self.sep_style == SeparatorStyle.CHATGLM:
|
| 147 |
+
# source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
|
| 148 |
+
# source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
|
| 149 |
+
round_add_n = 1 if self.name == 'chatglm2' else 0
|
| 150 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 151 |
+
|
| 152 |
+
for i, (role, message) in enumerate(self.messages):
|
| 153 |
+
if i % 2 == 0:
|
| 154 |
+
ret += f'[Round {i//2 + round_add_n}]{self.sep}'
|
| 155 |
+
|
| 156 |
+
if message:
|
| 157 |
+
ret += f'{role}：{message}{self.sep}'
|
| 158 |
+
else:
|
| 159 |
+
ret += f'{role}：'
|
| 160 |
+
return ret
|
| 161 |
+
elif self.sep_style == SeparatorStyle.CHATML:
|
| 162 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
|
| 163 |
+
for role, message in self.messages:
|
| 164 |
+
if message:
|
| 165 |
+
ret += role + '\n' + message + self.sep + '\n'
|
| 166 |
+
else:
|
| 167 |
+
ret += role + '\n'
|
| 168 |
+
return ret
|
| 169 |
+
elif self.sep_style == SeparatorStyle.CHATGLM3:
|
| 170 |
+
ret = system_prompt
|
| 171 |
+
for role, message in self.messages:
|
| 172 |
+
if message:
|
| 173 |
+
ret += role + '\n' + ' ' + message
|
| 174 |
+
else:
|
| 175 |
+
ret += role
|
| 176 |
+
return ret
|
| 177 |
+
elif self.sep_style == SeparatorStyle.CHATINTERN:
|
| 178 |
+
# source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
|
| 179 |
+
seps = [self.sep, self.sep2]
|
| 180 |
+
ret = system_prompt
|
| 181 |
+
for i, (role, message) in enumerate(self.messages):
|
| 182 |
+
# if i % 2 == 0:
|
| 183 |
+
# ret += "<s>"
|
| 184 |
+
if message:
|
| 185 |
+
ret += role + ':' + message + seps[i % 2] + '\n'
|
| 186 |
+
else:
|
| 187 |
+
ret += role + ':'
|
| 188 |
+
return ret
|
| 189 |
+
elif self.sep_style == SeparatorStyle.DOLLY:
|
| 190 |
+
seps = [self.sep, self.sep2]
|
| 191 |
+
ret = system_prompt
|
| 192 |
+
for i, (role, message) in enumerate(self.messages):
|
| 193 |
+
if message:
|
| 194 |
+
ret += role + ':\n' + message + seps[i % 2]
|
| 195 |
+
if i % 2 == 1:
|
| 196 |
+
ret += '\n\n'
|
| 197 |
+
else:
|
| 198 |
+
ret += role + ':\n'
|
| 199 |
+
return ret
|
| 200 |
+
elif self.sep_style == SeparatorStyle.PHOENIX:
|
| 201 |
+
ret = system_prompt
|
| 202 |
+
for role, message in self.messages:
|
| 203 |
+
if message:
|
| 204 |
+
ret += role + ': ' + '<s>' + message + '</s>'
|
| 205 |
+
else:
|
| 206 |
+
ret += role + ': ' + '<s>'
|
| 207 |
+
return ret
|
| 208 |
+
elif self.sep_style == SeparatorStyle.ROBIN:
|
| 209 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 210 |
+
for role, message in self.messages:
|
| 211 |
+
if message:
|
| 212 |
+
ret += role + ':\n' + message + self.sep
|
| 213 |
+
else:
|
| 214 |
+
ret += role + ':\n'
|
| 215 |
+
return ret
|
| 216 |
+
elif self.sep_style == SeparatorStyle.FALCON_CHAT:
|
| 217 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 218 |
+
for role, message in self.messages:
|
| 219 |
+
if message:
|
| 220 |
+
ret += role + ': ' + message + self.sep
|
| 221 |
+
else:
|
| 222 |
+
ret += role + ':'
|
| 223 |
+
|
| 224 |
+
return ret
|
| 225 |
+
elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
|
| 226 |
+
seps = [self.sep, self.sep2]
|
| 227 |
+
ret = '' if system_prompt == '' else self.system_message + seps[0]
|
| 228 |
+
for i, (role, message) in enumerate(self.messages):
|
| 229 |
+
if message:
|
| 230 |
+
ret += role + ': ' + message + seps[i % 2]
|
| 231 |
+
else:
|
| 232 |
+
ret += role + ':'
|
| 233 |
+
return ret
|
| 234 |
+
elif self.sep_style == SeparatorStyle.MPT:
|
| 235 |
+
ret = '' if system_prompt == '' else system_prompt + self.sep
|
| 236 |
+
for role, message in self.messages:
|
| 237 |
+
if message:
|
| 238 |
+
if type(message) is tuple:
|
| 239 |
+
message, _, _ = message
|
| 240 |
+
ret += role + message + self.sep
|
| 241 |
+
else:
|
| 242 |
+
ret += role
|
| 243 |
+
return ret
|
| 244 |
+
else:
|
| 245 |
+
raise ValueError(f'Invalid style: {self.sep_style}')
|
| 246 |
+
|
| 247 |
+
def set_system_message(self, system_message: str):
|
| 248 |
+
"""Set the system message."""
|
| 249 |
+
self.system_message = system_message
|
| 250 |
+
|
| 251 |
+
def append_message(self, role: str, message: str):
|
| 252 |
+
"""Append a new message."""
|
| 253 |
+
self.messages.append([role, message])
|
| 254 |
+
|
| 255 |
+
def update_last_message(self, message: str):
|
| 256 |
+
"""Update the last output.
|
| 257 |
+
|
| 258 |
+
The last message is typically set to be None when constructing the prompt,
|
| 259 |
+
so we need to update it in-place after getting the response from a model.
|
| 260 |
+
"""
|
| 261 |
+
self.messages[-1][1] = message
|
| 262 |
+
|
| 263 |
+
def to_gradio_chatbot(self):
|
| 264 |
+
"""Convert the conversation to gradio chatbot format."""
|
| 265 |
+
ret = []
|
| 266 |
+
for i, (role, msg) in enumerate(self.messages[self.offset :]):
|
| 267 |
+
if i % 2 == 0:
|
| 268 |
+
ret.append([msg, None])
|
| 269 |
+
else:
|
| 270 |
+
ret[-1][-1] = msg
|
| 271 |
+
return ret
|
| 272 |
+
|
| 273 |
+
def to_openai_api_messages(self):
|
| 274 |
+
"""Convert the conversation to OpenAI chat completion format."""
|
| 275 |
+
ret = [{'role': 'system', 'content': self.system_message}]
|
| 276 |
+
|
| 277 |
+
for i, (_, msg) in enumerate(self.messages[self.offset :]):
|
| 278 |
+
if i % 2 == 0:
|
| 279 |
+
ret.append({'role': 'user', 'content': msg})
|
| 280 |
+
else:
|
| 281 |
+
if msg is not None:
|
| 282 |
+
ret.append({'role': 'assistant', 'content': msg})
|
| 283 |
+
return ret
|
| 284 |
+
|
| 285 |
+
def copy(self):
|
| 286 |
+
return Conversation(
|
| 287 |
+
name=self.name,
|
| 288 |
+
system_template=self.system_template,
|
| 289 |
+
system_message=self.system_message,
|
| 290 |
+
roles=self.roles,
|
| 291 |
+
messages=[[x, y] for x, y in self.messages],
|
| 292 |
+
offset=self.offset,
|
| 293 |
+
sep_style=self.sep_style,
|
| 294 |
+
sep=self.sep,
|
| 295 |
+
sep2=self.sep2,
|
| 296 |
+
stop_str=self.stop_str,
|
| 297 |
+
stop_token_ids=self.stop_token_ids,
|
| 298 |
+
)
|
| 299 |
+
|
| 300 |
+
def dict(self):
|
| 301 |
+
return {
|
| 302 |
+
'template_name': self.name,
|
| 303 |
+
'system_message': self.system_message,
|
| 304 |
+
'roles': self.roles,
|
| 305 |
+
'messages': self.messages,
|
| 306 |
+
'offset': self.offset,
|
| 307 |
+
}
|
| 308 |
+
|
| 309 |
+
|
| 310 |
+
# A global registry for all conversation templates
|
| 311 |
+
conv_templates: Dict[str, Conversation] = {}
|
| 312 |
+
|
| 313 |
+
|
| 314 |
+
def register_conv_template(template: Conversation, override: bool = False):
|
| 315 |
+
"""Register a new conversation template."""
|
| 316 |
+
if not override:
|
| 317 |
+
assert (
|
| 318 |
+
template.name not in conv_templates
|
| 319 |
+
), f'{template.name} has been registered.'
|
| 320 |
+
|
| 321 |
+
conv_templates[template.name] = template
|
| 322 |
+
|
| 323 |
+
|
| 324 |
+
def get_conv_template(name: str) -> Conversation:
|
| 325 |
+
"""Get a conversation template."""
|
| 326 |
+
return conv_templates[name].copy()
|
| 327 |
+
|
| 328 |
+
|
| 329 |
+
# Both Hermes-2 and neo1_0-chat are chatml-format conversation templates. The difference
|
| 330 |
+
# is that during training, the preprocessing function for the Hermes-2 template doesn't add
|
| 331 |
+
# <s> at the beginning of the tokenized sequence, while the neo1_0-chat template does.
|
| 332 |
+
# Therefore, they are completely equivalent during inference.
|
| 333 |
+
|
| 334 |
+
register_conv_template(
|
| 335 |
+
Conversation(
|
| 336 |
+
name='Hermes-2',
|
| 337 |
+
system_template='<|im_start|>system\n{system_message}',
|
| 338 |
+
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
| 339 |
+
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
| 340 |
+
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
| 341 |
+
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
| 342 |
+
sep_style=SeparatorStyle.MPT,
|
| 343 |
+
sep='<|im_end|>',
|
| 344 |
+
stop_str='<|endoftext|>',
|
| 345 |
+
)
|
| 346 |
+
)
|
| 347 |
+
|
| 348 |
+
register_conv_template(
|
| 349 |
+
Conversation(
|
| 350 |
+
name='internlm2-chat',
|
| 351 |
+
system_template='<|im_start|>system\n{system_message}',
|
| 352 |
+
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
| 353 |
+
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
| 354 |
+
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
| 355 |
+
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
| 356 |
+
sep_style=SeparatorStyle.MPT,
|
| 357 |
+
sep='<|im_end|>',
|
| 358 |
+
)
|
| 359 |
+
)
|
| 360 |
+
|
| 361 |
+
register_conv_template(
|
| 362 |
+
Conversation(
|
| 363 |
+
name='phi3-chat',
|
| 364 |
+
system_template='<|system|>\n{system_message}',
|
| 365 |
+
# note: The new system prompt was not used here to avoid changes in benchmark performance.
|
| 366 |
+
# system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
| 367 |
+
system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
|
| 368 |
+
roles=('<|user|>\n', '<|assistant|>\n'),
|
| 369 |
+
sep_style=SeparatorStyle.MPT,
|
| 370 |
+
sep='<|end|>',
|
| 371 |
+
)
|
| 372 |
+
)
|
| 373 |
+
|
| 374 |
+
register_conv_template(
|
| 375 |
+
Conversation(
|
| 376 |
+
name='internvl2_5',
|
| 377 |
+
system_template='<|im_start|>system\n{system_message}',
|
| 378 |
+
system_message='你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
|
| 379 |
+
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
| 380 |
+
sep_style=SeparatorStyle.MPT,
|
| 381 |
+
sep='<|im_end|>\n',
|
| 382 |
+
)
|
| 383 |
+
)
|
| 384 |
+
|
| 385 |
+
register_conv_template(
|
| 386 |
+
Conversation(
|
| 387 |
+
name='neo1_0',
|
| 388 |
+
system_template='<|im_start|>system\n{system_message}',
|
| 389 |
+
system_message='',
|
| 390 |
+
roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
|
| 391 |
+
sep_style=SeparatorStyle.MPT,
|
| 392 |
+
sep='<|im_end|>\n',
|
| 393 |
+
)
|
| 394 |
+
)
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model-00001-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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size 18356696
|
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ADDED
|
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|
|
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|
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|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:9bbcbf73561f6bc5d0a17ea6a2081feed2d1304e87602d8c502d9a5c4bd85576
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size 16
|
model-00003-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
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|
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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ADDED
|
@@ -0,0 +1,3 @@
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|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:9bbcbf73561f6bc5d0a17ea6a2081feed2d1304e87602d8c502d9a5c4bd85576
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size 16
|
model-00005-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
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|
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|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:4566caa48d3a46eb41adcf288ac94ffd716bafa8ea36a28908e2ea3935bdf1da
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| 3 |
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size 1697694656
|
model-00006-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:3a4ca56192c7a276a8bfaad165bc5990d9386521c8613116952fda53dddb4d21
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| 3 |
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size 453030344
|
model-00007-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:f61e1bf44aac52048c6f0f1691e622c906bb5d563d2840b9015d45eae8291a7f
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| 3 |
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size 453030376
|
model-00008-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:1e80edc472dd605ef2f3aab4935558a669b3e10c09c90ef4bd4383a18d56e212
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| 3 |
+
size 453030416
|
model-00009-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:5d660acce00de8a566dd806aca967d109c102e3fcde2aad30f0403edd8fe52f8
|
| 3 |
+
size 453030416
|
model-00010-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:e133cfff4f32ddc1a117f936eede8e45f472e2193472441b000780689aeb738a
|
| 3 |
+
size 453030416
|
model-00011-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:a6c5eb134857565cf77af8bb9a209e2635cfe4cd1208ffc15010a5c8c297c7ab
|
| 3 |
+
size 453030416
|
model-00012-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:8a5984c954de70e810f52246556b6f684b8c2e636a9d29326990c79c6d70e1b6
|
| 3 |
+
size 453030416
|
model-00013-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:a5d232aa0e41a8f6035f75995c06caeff29a550426b6478a219f6d327a3e5a80
|
| 3 |
+
size 453030416
|
model-00014-of-00014.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:26209192c436ed5a5bdfc349acce83a50bd850cb70f072b8b34926f11fca8ef7
|
| 3 |
+
size 453030416
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,692 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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"language_model.model.layers.39.self_attn.o_proj.weight": "model-00014-of-00014.safetensors",
|
| 686 |
+
"language_model.model.layers.39.input_layernorm.weight": "model-00014-of-00014.safetensors",
|
| 687 |
+
"language_model.model.layers.39.post_attention_layernorm.weight": "model-00014-of-00014.safetensors",
|
| 688 |
+
"language_model.model.layers.39.mlp.gate_proj.weight": "model-00014-of-00014.safetensors",
|
| 689 |
+
"language_model.model.layers.39.mlp.up_proj.weight": "model-00014-of-00014.safetensors",
|
| 690 |
+
"language_model.model.layers.39.mlp.down_proj.weight": "model-00014-of-00014.safetensors"
|
| 691 |
+
}
|
| 692 |
+
}
|
modeling_neo_chat.py
ADDED
|
@@ -0,0 +1,363 @@
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|
|
|
|
|
|
| 1 |
+
from typing import List, Optional, Tuple, Union
|
| 2 |
+
|
| 3 |
+
import torch.utils.checkpoint
|
| 4 |
+
import transformers
|
| 5 |
+
from torch.nn import CrossEntropyLoss
|
| 6 |
+
from transformers import GenerationConfig
|
| 7 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
| 8 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 9 |
+
from transformers.utils import logging
|
| 10 |
+
|
| 11 |
+
from .configuration_neo_chat import NEOChatConfig
|
| 12 |
+
from .conversation import get_conv_template
|
| 13 |
+
from .modeling_neo_vit import NEOVisionModel
|
| 14 |
+
from .modeling_qwen3 import Qwen3ForCausalLM
|
| 15 |
+
|
| 16 |
+
logger = logging.get_logger(__name__)
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def version_cmp(v1, v2, op='eq'):
|
| 20 |
+
import operator
|
| 21 |
+
|
| 22 |
+
from packaging import version
|
| 23 |
+
op_func = getattr(operator, op)
|
| 24 |
+
return op_func(version.parse(v1), version.parse(v2))
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
def build_abs_positions_from_grid_hw(grid_hw: torch.Tensor, device=None):
|
| 28 |
+
"""
|
| 29 |
+
Compute patch coordinates (x, y)
|
| 30 |
+
|
| 31 |
+
Args:
|
| 32 |
+
grid_hw: (B, 2) tensor representing (H, W) per image
|
| 33 |
+
"""
|
| 34 |
+
device = grid_hw.device
|
| 35 |
+
B = grid_hw.shape[0]
|
| 36 |
+
|
| 37 |
+
# Get the number of patches per image
|
| 38 |
+
H = grid_hw[:, 0]
|
| 39 |
+
W = grid_hw[:, 1]
|
| 40 |
+
N = H * W
|
| 41 |
+
N_total = N.sum()
|
| 42 |
+
|
| 43 |
+
# Create the batch index for each patch (B x patch count)
|
| 44 |
+
patch_to_sample = torch.repeat_interleave(torch.arange(B, device=device), N) # (N_total,)
|
| 45 |
+
|
| 46 |
+
# Generate intra-image patch index (row-major order)
|
| 47 |
+
patch_id_within_image = torch.arange(N_total, device=device)
|
| 48 |
+
patch_id_within_image = patch_id_within_image - torch.cumsum(
|
| 49 |
+
torch.cat([torch.tensor([0], device=device), N[:-1]]), dim=0
|
| 50 |
+
)[patch_to_sample]
|
| 51 |
+
|
| 52 |
+
# Get H/W for each patch according to its image
|
| 53 |
+
W_per_patch = W[patch_to_sample]
|
| 54 |
+
abs_x = patch_id_within_image % W_per_patch
|
| 55 |
+
abs_y = patch_id_within_image // W_per_patch
|
| 56 |
+
|
| 57 |
+
return abs_x, abs_y
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
class NEOChatModel(PreTrainedModel):
|
| 61 |
+
config_class = NEOChatConfig
|
| 62 |
+
main_input_name = 'pixel_values'
|
| 63 |
+
base_model_prefix = 'language_model'
|
| 64 |
+
_supports_flash_attn_2 = True
|
| 65 |
+
supports_gradient_checkpointing = True
|
| 66 |
+
_no_split_modules = [
|
| 67 |
+
"NEOVisionModel",
|
| 68 |
+
"Qwen3DecoderLayer",
|
| 69 |
+
]
|
| 70 |
+
|
| 71 |
+
# support transformers 4.51.+
|
| 72 |
+
_tp_plan = ''
|
| 73 |
+
|
| 74 |
+
def __init__(self, config: NEOChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
|
| 75 |
+
super().__init__(config)
|
| 76 |
+
|
| 77 |
+
assert version_cmp(transformers.__version__, '4.37.0', 'ge')
|
| 78 |
+
patch_size = config.vision_config.patch_size
|
| 79 |
+
self.patch_size = patch_size
|
| 80 |
+
self.template = config.template
|
| 81 |
+
self.downsample_ratio = config.downsample_ratio
|
| 82 |
+
config.llm_config._attn_implementation = 'eager'
|
| 83 |
+
|
| 84 |
+
if vision_model is not None:
|
| 85 |
+
self.vision_model = vision_model
|
| 86 |
+
else:
|
| 87 |
+
self.vision_model = NEOVisionModel(config.vision_config)
|
| 88 |
+
if language_model is not None:
|
| 89 |
+
self.language_model = language_model
|
| 90 |
+
else:
|
| 91 |
+
self.language_model = Qwen3ForCausalLM(config.llm_config)
|
| 92 |
+
|
| 93 |
+
self.img_context_token_id = None
|
| 94 |
+
self.img_start_token_id = None
|
| 95 |
+
self.conv_template = get_conv_template(self.template)
|
| 96 |
+
self.system_message = self.conv_template.system_message
|
| 97 |
+
|
| 98 |
+
def forward(
|
| 99 |
+
self,
|
| 100 |
+
pixel_values: torch.FloatTensor,
|
| 101 |
+
input_ids: torch.LongTensor = None,
|
| 102 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 103 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 104 |
+
image_flags: Optional[torch.LongTensor] = None,
|
| 105 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 106 |
+
labels: Optional[torch.LongTensor] = None,
|
| 107 |
+
use_cache: Optional[bool] = None,
|
| 108 |
+
output_attentions: Optional[bool] = None,
|
| 109 |
+
output_hidden_states: Optional[bool] = None,
|
| 110 |
+
return_dict: Optional[bool] = None,
|
| 111 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 112 |
+
raise NotImplementedError('forward')
|
| 113 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 114 |
+
|
| 115 |
+
image_flags = image_flags.squeeze(-1)
|
| 116 |
+
input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
|
| 117 |
+
|
| 118 |
+
vit_embeds = self.extract_feature(pixel_values)
|
| 119 |
+
vit_embeds = vit_embeds[image_flags == 1]
|
| 120 |
+
|
| 121 |
+
B, N, C = input_embeds.shape
|
| 122 |
+
input_embeds = input_embeds.reshape(B * N, C)
|
| 123 |
+
|
| 124 |
+
# if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
|
| 125 |
+
# print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
|
| 126 |
+
|
| 127 |
+
input_ids = input_ids.reshape(B * N)
|
| 128 |
+
selected = (input_ids == self.img_context_token_id)
|
| 129 |
+
try:
|
| 130 |
+
input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
|
| 131 |
+
except Exception as e:
|
| 132 |
+
vit_embeds = vit_embeds.reshape(-1, C)
|
| 133 |
+
print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
|
| 134 |
+
f'vit_embeds.shape={vit_embeds.shape}')
|
| 135 |
+
n_token = min(selected.sum(), vit_embeds.size(0))
|
| 136 |
+
input_embeds[selected][:n_token] = input_embeds[selected][:n_token] * 0.0 + vit_embeds[:n_token]
|
| 137 |
+
|
| 138 |
+
input_embeds = input_embeds.reshape(B, N, C)
|
| 139 |
+
|
| 140 |
+
outputs = self.language_model(
|
| 141 |
+
inputs_embeds=input_embeds,
|
| 142 |
+
attention_mask=attention_mask,
|
| 143 |
+
position_ids=position_ids,
|
| 144 |
+
past_key_values=past_key_values,
|
| 145 |
+
use_cache=use_cache,
|
| 146 |
+
output_attentions=output_attentions,
|
| 147 |
+
output_hidden_states=output_hidden_states,
|
| 148 |
+
return_dict=return_dict,
|
| 149 |
+
)
|
| 150 |
+
logits = outputs.logits
|
| 151 |
+
|
| 152 |
+
loss = None
|
| 153 |
+
if labels is not None:
|
| 154 |
+
# Shift so that tokens < n predict n
|
| 155 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 156 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 157 |
+
# Flatten the tokens
|
| 158 |
+
loss_fct = CrossEntropyLoss()
|
| 159 |
+
shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
|
| 160 |
+
shift_labels = shift_labels.view(-1)
|
| 161 |
+
# Enable model parallelism
|
| 162 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 163 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 164 |
+
|
| 165 |
+
if not return_dict:
|
| 166 |
+
output = (logits,) + outputs[1:]
|
| 167 |
+
return (loss,) + output if loss is not None else output
|
| 168 |
+
|
| 169 |
+
return CausalLMOutputWithPast(
|
| 170 |
+
loss=loss,
|
| 171 |
+
logits=logits,
|
| 172 |
+
past_key_values=outputs.past_key_values,
|
| 173 |
+
hidden_states=outputs.hidden_states,
|
| 174 |
+
attentions=outputs.attentions,
|
| 175 |
+
)
|
| 176 |
+
|
| 177 |
+
def extract_feature(self, pixel_values, grid_hw=None):
|
| 178 |
+
|
| 179 |
+
return self.vision_model(pixel_values=pixel_values,
|
| 180 |
+
output_hidden_states=False,
|
| 181 |
+
return_dict=True,
|
| 182 |
+
grid_hw=grid_hw).last_hidden_state
|
| 183 |
+
|
| 184 |
+
def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
|
| 185 |
+
history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
|
| 186 |
+
IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
|
| 187 |
+
raise NotImplementedError('batch_chat')
|
| 188 |
+
if history is not None or return_history:
|
| 189 |
+
print('Now multi-turn chat is not supported in batch_chat.')
|
| 190 |
+
raise NotImplementedError
|
| 191 |
+
|
| 192 |
+
if image_counts is not None:
|
| 193 |
+
num_patches_list = image_counts
|
| 194 |
+
print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
|
| 195 |
+
|
| 196 |
+
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
| 197 |
+
self.img_context_token_id = img_context_token_id
|
| 198 |
+
|
| 199 |
+
if verbose and pixel_values is not None:
|
| 200 |
+
image_bs = pixel_values.shape[0]
|
| 201 |
+
print(f'dynamic ViT batch size: {image_bs}')
|
| 202 |
+
|
| 203 |
+
queries = []
|
| 204 |
+
for idx, num_patches in enumerate(num_patches_list):
|
| 205 |
+
question = questions[idx]
|
| 206 |
+
if pixel_values is not None and '<image>' not in question:
|
| 207 |
+
question = '<image>\n' + question
|
| 208 |
+
template = get_conv_template(self.template)
|
| 209 |
+
template.system_message = self.system_message
|
| 210 |
+
template.append_message(template.roles[0], question)
|
| 211 |
+
template.append_message(template.roles[1], None)
|
| 212 |
+
query = template.get_prompt()
|
| 213 |
+
|
| 214 |
+
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN + IMG_END_TOKEN
|
| 215 |
+
query = query.replace('<image>', image_tokens, 1)
|
| 216 |
+
queries.append(query)
|
| 217 |
+
|
| 218 |
+
tokenizer.padding_side = 'left'
|
| 219 |
+
model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
|
| 220 |
+
input_ids = model_inputs['input_ids'].to(self.device)
|
| 221 |
+
attention_mask = model_inputs['attention_mask'].to(self.device)
|
| 222 |
+
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
|
| 223 |
+
generation_config['eos_token_id'] = eos_token_id
|
| 224 |
+
generation_output = self.generate(
|
| 225 |
+
pixel_values=pixel_values,
|
| 226 |
+
input_ids=input_ids,
|
| 227 |
+
attention_mask=attention_mask,
|
| 228 |
+
**generation_config
|
| 229 |
+
)
|
| 230 |
+
responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
|
| 231 |
+
responses = [response.split(template.sep.strip())[0].strip() for response in responses]
|
| 232 |
+
return responses
|
| 233 |
+
|
| 234 |
+
def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False, grid_hw=None,
|
| 235 |
+
IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False):
|
| 236 |
+
|
| 237 |
+
if history is None and pixel_values is not None and '<image>' not in question:
|
| 238 |
+
question = '<image>\n' + question
|
| 239 |
+
|
| 240 |
+
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
|
| 241 |
+
self.img_context_token_id = img_context_token_id
|
| 242 |
+
self.img_start_token_id = tokenizer.convert_tokens_to_ids(IMG_START_TOKEN)
|
| 243 |
+
|
| 244 |
+
template = get_conv_template(self.template)
|
| 245 |
+
template.system_message = self.system_message
|
| 246 |
+
eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
|
| 247 |
+
|
| 248 |
+
history = [] if history is None else history
|
| 249 |
+
for (old_question, old_answer) in history:
|
| 250 |
+
template.append_message(template.roles[0], old_question)
|
| 251 |
+
template.append_message(template.roles[1], old_answer)
|
| 252 |
+
template.append_message(template.roles[0], question)
|
| 253 |
+
template.append_message(template.roles[1], None)
|
| 254 |
+
query = template.get_prompt()
|
| 255 |
+
|
| 256 |
+
if verbose and pixel_values is not None:
|
| 257 |
+
print(f'dynamic image size: {grid_hw * self.patch_size}')
|
| 258 |
+
|
| 259 |
+
for i in range(grid_hw.shape[0]):
|
| 260 |
+
num_patch_token = int(grid_hw[i, 0] * grid_hw[i, 1] * self.downsample_ratio**2)
|
| 261 |
+
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * num_patch_token + IMG_END_TOKEN
|
| 262 |
+
query = query.replace('<image>', image_tokens, 1)
|
| 263 |
+
|
| 264 |
+
model_inputs = tokenizer(query, return_tensors='pt')
|
| 265 |
+
input_ids = model_inputs['input_ids'].to(self.device)
|
| 266 |
+
attention_mask = model_inputs['attention_mask'].to(self.device)
|
| 267 |
+
generation_config['eos_token_id'] = eos_token_id
|
| 268 |
+
generation_output = self.generate(
|
| 269 |
+
pixel_values=pixel_values,
|
| 270 |
+
input_ids=input_ids,
|
| 271 |
+
grid_hw=grid_hw,
|
| 272 |
+
attention_mask=attention_mask,
|
| 273 |
+
**generation_config
|
| 274 |
+
)
|
| 275 |
+
response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
|
| 276 |
+
response = response.split(template.sep.strip())[0].strip()
|
| 277 |
+
history.append((question, response))
|
| 278 |
+
if return_history:
|
| 279 |
+
return response, history
|
| 280 |
+
else:
|
| 281 |
+
query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
|
| 282 |
+
query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
|
| 283 |
+
if verbose:
|
| 284 |
+
print(query_to_print, response)
|
| 285 |
+
return response
|
| 286 |
+
|
| 287 |
+
@torch.no_grad()
|
| 288 |
+
def generate(
|
| 289 |
+
self,
|
| 290 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
| 291 |
+
input_ids: Optional[torch.FloatTensor] = None,
|
| 292 |
+
grid_hw: Optional[torch.LongTensor] = None,
|
| 293 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
| 294 |
+
visual_features: Optional[torch.FloatTensor] = None,
|
| 295 |
+
generation_config: Optional[GenerationConfig] = None,
|
| 296 |
+
output_hidden_states: Optional[bool] = None,
|
| 297 |
+
**generate_kwargs,
|
| 298 |
+
) -> torch.LongTensor:
|
| 299 |
+
assert input_ids.shape[0] == 1
|
| 300 |
+
assert self.img_context_token_id is not None
|
| 301 |
+
indexes = self.get_thw_indexes(input_ids[0], grid_hw)
|
| 302 |
+
if pixel_values is not None:
|
| 303 |
+
if visual_features is not None:
|
| 304 |
+
vit_embeds = visual_features
|
| 305 |
+
else:
|
| 306 |
+
vit_embeds = self.extract_feature(pixel_values, grid_hw=grid_hw)
|
| 307 |
+
|
| 308 |
+
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
| 309 |
+
B, N, C = input_embeds.shape
|
| 310 |
+
input_embeds = input_embeds.reshape(B * N, C)
|
| 311 |
+
|
| 312 |
+
input_ids = input_ids.reshape(B * N)
|
| 313 |
+
selected = (input_ids == self.img_context_token_id)
|
| 314 |
+
assert selected.sum() != 0
|
| 315 |
+
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
|
| 316 |
+
|
| 317 |
+
input_embeds = input_embeds.reshape(B, N, C)
|
| 318 |
+
else:
|
| 319 |
+
input_embeds = self.language_model.get_input_embeddings()(input_ids)
|
| 320 |
+
|
| 321 |
+
outputs = self.language_model.generate(
|
| 322 |
+
inputs_embeds=input_embeds,
|
| 323 |
+
indexes=indexes,
|
| 324 |
+
attention_mask=attention_mask,
|
| 325 |
+
generation_config=generation_config,
|
| 326 |
+
output_hidden_states=output_hidden_states,
|
| 327 |
+
use_cache=True,
|
| 328 |
+
**generate_kwargs,
|
| 329 |
+
)
|
| 330 |
+
|
| 331 |
+
return outputs
|
| 332 |
+
|
| 333 |
+
@property
|
| 334 |
+
def lm_head(self):
|
| 335 |
+
return self.language_model.get_output_embeddings()
|
| 336 |
+
|
| 337 |
+
def get_output_embeddings(self):
|
| 338 |
+
return self.language_model.get_output_embeddings()
|
| 339 |
+
|
| 340 |
+
def get_input_embeddings(self):
|
| 341 |
+
return self.language_model.get_input_embeddings()
|
| 342 |
+
|
| 343 |
+
def set_input_embeddings(self, value):
|
| 344 |
+
return self.language_model.set_input_embeddings(value)
|
| 345 |
+
|
| 346 |
+
def set_output_embeddings(self, value):
|
| 347 |
+
return self.language_model.set_output_embeddings(value)
|
| 348 |
+
|
| 349 |
+
def get_thw_indexes(self, input_ids, grid_hw):
|
| 350 |
+
img_start_shift = torch.cat([torch.zeros(1, dtype=torch.long).to(input_ids.device),
|
| 351 |
+
(input_ids == self.img_start_token_id).long()], dim=0)[:-1]
|
| 352 |
+
not_img_token = (input_ids != self.img_context_token_id).long()
|
| 353 |
+
t_indexes = ((img_start_shift + not_img_token).cumsum(0) - 1)
|
| 354 |
+
h_indexes = torch.zeros_like(t_indexes).to(t_indexes.device)
|
| 355 |
+
w_indexes = torch.zeros_like(t_indexes).to(t_indexes.device)
|
| 356 |
+
|
| 357 |
+
selected = (input_ids == self.img_context_token_id)
|
| 358 |
+
if selected.long().sum() > 0:
|
| 359 |
+
abs_pos_w, abs_pos_h = build_abs_positions_from_grid_hw(
|
| 360 |
+
grid_hw // int(1 / self.downsample_ratio), device=t_indexes.device)
|
| 361 |
+
h_indexes[selected] = abs_pos_h.to(t_indexes.device, t_indexes.dtype)
|
| 362 |
+
w_indexes[selected] = abs_pos_w.to(t_indexes.device, t_indexes.dtype)
|
| 363 |
+
return torch.stack([t_indexes, h_indexes, w_indexes], dim=0)
|
modeling_neo_vit.py
ADDED
|
@@ -0,0 +1,235 @@
|
|
|
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|
|
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|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from typing import Optional, Tuple, Union
|
| 2 |
+
|
| 3 |
+
import torch
|
| 4 |
+
import torch.utils.checkpoint
|
| 5 |
+
from torch import nn
|
| 6 |
+
from transformers.modeling_outputs import BaseModelOutputWithPooling
|
| 7 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 8 |
+
|
| 9 |
+
from .configuration_neo_vit import NEOVisionConfig
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def precompute_rope_freqs_sincos(
|
| 13 |
+
dim: int, max_position: int, base: float = 10000.0, device=None
|
| 14 |
+
):
|
| 15 |
+
"""预计算 RoPE 的 cos 和 sin 值 (1D)。"""
|
| 16 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device).float() / dim))
|
| 17 |
+
t = torch.arange(max_position, device=device).type_as(inv_freq)
|
| 18 |
+
freqs = torch.outer(t, inv_freq)
|
| 19 |
+
return torch.cos(freqs), torch.sin(freqs)
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
def build_abs_positions_from_grid_hw(grid_hw: torch.Tensor, device=None):
|
| 23 |
+
"""
|
| 24 |
+
Compute patch coordinates (x, y)
|
| 25 |
+
|
| 26 |
+
Args:
|
| 27 |
+
grid_hw: (B, 2) tensor representing (H, W) per image
|
| 28 |
+
"""
|
| 29 |
+
device = grid_hw.device
|
| 30 |
+
B = grid_hw.shape[0]
|
| 31 |
+
|
| 32 |
+
# Get the number of patches per image
|
| 33 |
+
H = grid_hw[:, 0]
|
| 34 |
+
W = grid_hw[:, 1]
|
| 35 |
+
N = H * W
|
| 36 |
+
N_total = N.sum()
|
| 37 |
+
|
| 38 |
+
# Create the batch index for each patch (B x patch count)
|
| 39 |
+
patch_to_sample = torch.repeat_interleave(torch.arange(B, device=device), N) # (N_total,)
|
| 40 |
+
|
| 41 |
+
# Generate intra-image patch index (row-major order)
|
| 42 |
+
patch_id_within_image = torch.arange(N_total, device=device)
|
| 43 |
+
patch_id_within_image = patch_id_within_image - torch.cumsum(
|
| 44 |
+
torch.cat([torch.tensor([0], device=device), N[:-1]]), dim=0
|
| 45 |
+
)[patch_to_sample]
|
| 46 |
+
|
| 47 |
+
# Get H/W for each patch according to its image
|
| 48 |
+
W_per_patch = W[patch_to_sample]
|
| 49 |
+
abs_x = patch_id_within_image % W_per_patch
|
| 50 |
+
abs_y = patch_id_within_image // W_per_patch
|
| 51 |
+
|
| 52 |
+
return abs_x, abs_y
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
def apply_rotary_emb_1d(
|
| 56 |
+
x: torch.Tensor,
|
| 57 |
+
cos_cached: torch.Tensor,
|
| 58 |
+
sin_cached: torch.Tensor,
|
| 59 |
+
positions: torch.Tensor,
|
| 60 |
+
):
|
| 61 |
+
"""对输入张量的一部分应用1D RoPE。"""
|
| 62 |
+
# x: (..., seq_len, dim_part)
|
| 63 |
+
# positions: (..., seq_len)
|
| 64 |
+
# cos_cached: (max_pos, dim_part / 2)
|
| 65 |
+
|
| 66 |
+
cos = cos_cached[positions] # Shape: (positions.shape, dim_part / 2)
|
| 67 |
+
sin = sin_cached[positions] # Shape: (positions.shape, dim_part / 2)
|
| 68 |
+
|
| 69 |
+
x1 = x[..., 0::2]
|
| 70 |
+
x2 = x[..., 1::2]
|
| 71 |
+
|
| 72 |
+
rotated_x1 = x1 * cos - x2 * sin
|
| 73 |
+
rotated_x2 = x1 * sin + x2 * cos
|
| 74 |
+
|
| 75 |
+
x_rotated = torch.empty_like(x)
|
| 76 |
+
x_rotated[..., 0::2] = rotated_x1
|
| 77 |
+
x_rotated[..., 1::2] = rotated_x2
|
| 78 |
+
return x_rotated
|
| 79 |
+
|
| 80 |
+
|
| 81 |
+
def apply_2d_rotary_pos_emb(
|
| 82 |
+
x: torch.Tensor,
|
| 83 |
+
cos_cached_x: torch.Tensor,
|
| 84 |
+
sin_cached_x: torch.Tensor,
|
| 85 |
+
cos_cached_y: torch.Tensor,
|
| 86 |
+
sin_cached_y: torch.Tensor,
|
| 87 |
+
abs_positions_x: torch.Tensor,
|
| 88 |
+
abs_positions_y: torch.Tensor
|
| 89 |
+
):
|
| 90 |
+
"""应用2D RoPE到输入张量x。"""
|
| 91 |
+
dim = x.shape[-1]
|
| 92 |
+
dim_half = dim // 2
|
| 93 |
+
|
| 94 |
+
# 假设我们将embedding的前半部分用于一个方向的RoPE，后半部分用于另一个方向
|
| 95 |
+
# 例如，前一半给X坐标，后一半给Y坐标 (或者反过来，但要保持一致)
|
| 96 |
+
x_part_1 = x[..., :dim_half]
|
| 97 |
+
x_part_2 = x[..., dim_half:]
|
| 98 |
+
|
| 99 |
+
# 将与 abs_positions_x 相关的旋转应用于 x_part_1
|
| 100 |
+
rotated_part_1 = apply_rotary_emb_1d(
|
| 101 |
+
x_part_1, cos_cached_x, sin_cached_x, abs_positions_x
|
| 102 |
+
)
|
| 103 |
+
# 将与 abs_positions_y 相关的旋转应用于 x_part_2
|
| 104 |
+
rotated_part_2 = apply_rotary_emb_1d(
|
| 105 |
+
x_part_2, cos_cached_y, sin_cached_y, abs_positions_y
|
| 106 |
+
)
|
| 107 |
+
|
| 108 |
+
# 将它们重新拼接起来。确保顺序与你分割时一致。
|
| 109 |
+
return torch.cat((rotated_part_1, rotated_part_2), dim=-1)
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
class NEOVisionEmbeddings(nn.Module):
|
| 113 |
+
"""
|
| 114 |
+
Embedding Module for Vision.
|
| 115 |
+
"""
|
| 116 |
+
|
| 117 |
+
def __init__(self, config: NEOVisionConfig):
|
| 118 |
+
super().__init__()
|
| 119 |
+
self.config = config
|
| 120 |
+
self.embed_dim = config.hidden_size
|
| 121 |
+
self.llm_embed_dim = config.llm_hidden_size[0]
|
| 122 |
+
self.downsample_factor = int(1 / config.downsample_ratio[0])
|
| 123 |
+
self.patch_size = config.patch_size
|
| 124 |
+
|
| 125 |
+
self.patch_embedding = nn.Conv2d(
|
| 126 |
+
in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
|
| 127 |
+
)
|
| 128 |
+
self.dense_embedding = nn.Conv2d(
|
| 129 |
+
in_channels=self.embed_dim, out_channels=self.llm_embed_dim, kernel_size=self.downsample_factor, stride=self.downsample_factor
|
| 130 |
+
)
|
| 131 |
+
self.gelu = nn.GELU()
|
| 132 |
+
|
| 133 |
+
self.rope_dim_part = self.embed_dim // 2
|
| 134 |
+
cos_x, sin_x = precompute_rope_freqs_sincos(
|
| 135 |
+
self.rope_dim_part, config.max_position_embeddings_vision, base=config.rope_theta_vision, device=None
|
| 136 |
+
)
|
| 137 |
+
cos_y, sin_y = precompute_rope_freqs_sincos(
|
| 138 |
+
self.rope_dim_part, config.max_position_embeddings_vision, base=config.rope_theta_vision, device=None
|
| 139 |
+
)
|
| 140 |
+
|
| 141 |
+
self.register_buffer("cos_cached_x", cos_x, persistent=False)
|
| 142 |
+
self.register_buffer("sin_cached_x", sin_x, persistent=False)
|
| 143 |
+
self.register_buffer("cos_cached_y", cos_y, persistent=False)
|
| 144 |
+
self.register_buffer("sin_cached_y", sin_y, persistent=False)
|
| 145 |
+
|
| 146 |
+
def _apply_2d_rotary_pos_emb(self, patch_embeds, grid_hw):
|
| 147 |
+
"""
|
| 148 |
+
Apply 2D Rotary Position Embedding to the patch embeddings.
|
| 149 |
+
"""
|
| 150 |
+
abs_pos_x, abs_pos_y = build_abs_positions_from_grid_hw(grid_hw, device=patch_embeds.device)
|
| 151 |
+
embeddings = apply_2d_rotary_pos_emb(
|
| 152 |
+
patch_embeds.to(torch.float32), # RoPE calculations are often more stable in float32
|
| 153 |
+
self.cos_cached_x, self.sin_cached_x,
|
| 154 |
+
self.cos_cached_y, self.sin_cached_y,
|
| 155 |
+
abs_pos_x,
|
| 156 |
+
abs_pos_y
|
| 157 |
+
).to(self.patch_embedding.weight.dtype)
|
| 158 |
+
return embeddings
|
| 159 |
+
|
| 160 |
+
def forward(self, pixel_values: torch.FloatTensor, grid_hw=None) -> torch.Tensor:
|
| 161 |
+
|
| 162 |
+
pixel_values = pixel_values.view( #
|
| 163 |
+
-1,
|
| 164 |
+
3,
|
| 165 |
+
self.patch_size,
|
| 166 |
+
self.patch_size,
|
| 167 |
+
) # [28072, 768] -> [28072, 3, 16, 16]
|
| 168 |
+
patch_embeds = self.gelu(self.patch_embedding(pixel_values)).view(-1, self.embed_dim)
|
| 169 |
+
self.cos_cached_x = self.cos_cached_x.to(patch_embeds.device)
|
| 170 |
+
self.sin_cached_x = self.sin_cached_x.to(patch_embeds.device)
|
| 171 |
+
self.cos_cached_y = self.cos_cached_y.to(patch_embeds.device)
|
| 172 |
+
self.sin_cached_y = self.sin_cached_y.to(patch_embeds.device)
|
| 173 |
+
patch_embeds = self._apply_2d_rotary_pos_emb(patch_embeds, grid_hw) # [28072, 1024]
|
| 174 |
+
assert (grid_hw[:,0] * grid_hw[:,1]).sum() == patch_embeds.shape[0]
|
| 175 |
+
|
| 176 |
+
patches_list = []
|
| 177 |
+
cur_position = 0
|
| 178 |
+
for i in range(grid_hw.shape[0]):
|
| 179 |
+
h, w = grid_hw[i]
|
| 180 |
+
patches_per_img = patch_embeds[cur_position : cur_position + h * w].view(h, w, -1).unsqueeze(0)
|
| 181 |
+
patches_per_img = self.dense_embedding(patches_per_img.permute(0, 3, 1, 2))
|
| 182 |
+
patches_per_img = patches_per_img.permute(0, 2, 3, 1)
|
| 183 |
+
patches_list.append(patches_per_img.view(-1, patches_per_img.shape[-1]))
|
| 184 |
+
cur_position += h * w
|
| 185 |
+
|
| 186 |
+
embeddings = torch.cat(patches_list, dim=0) # (N_total // downsample_factor**2, C)
|
| 187 |
+
|
| 188 |
+
assert cur_position == patch_embeds.shape[0]
|
| 189 |
+
assert embeddings.shape[0] == int(patch_embeds.shape[0] / self.downsample_factor**2)
|
| 190 |
+
|
| 191 |
+
return embeddings
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
class NEOVisionModel(PreTrainedModel):
|
| 195 |
+
main_input_name = 'pixel_values'
|
| 196 |
+
_supports_flash_attn_2 = True
|
| 197 |
+
supports_gradient_checkpointing = True
|
| 198 |
+
config_class = NEOVisionConfig
|
| 199 |
+
# support transformers 4.51.+
|
| 200 |
+
_tp_plan = ''
|
| 201 |
+
|
| 202 |
+
def __init__(self, config: NEOVisionConfig):
|
| 203 |
+
super().__init__(config)
|
| 204 |
+
self.config = config
|
| 205 |
+
|
| 206 |
+
self.embeddings = NEOVisionEmbeddings(config)
|
| 207 |
+
|
| 208 |
+
def forward(
|
| 209 |
+
self,
|
| 210 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
| 211 |
+
output_hidden_states: Optional[bool] = None,
|
| 212 |
+
return_dict: Optional[bool] = None,
|
| 213 |
+
pixel_embeds: Optional[torch.FloatTensor] = None,
|
| 214 |
+
grid_hw: Optional[torch.Tensor] = None
|
| 215 |
+
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
| 216 |
+
output_hidden_states = (
|
| 217 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 218 |
+
)
|
| 219 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 220 |
+
|
| 221 |
+
if pixel_values is None and pixel_embeds is None:
|
| 222 |
+
raise ValueError('You have to specify pixel_values or pixel_embeds')
|
| 223 |
+
|
| 224 |
+
if pixel_embeds is not None:
|
| 225 |
+
hidden_states = pixel_embeds
|
| 226 |
+
else:
|
| 227 |
+
assert pixel_values.dim() == 2, f"pixel_values must be 2D for native resolution, got: {pixel_values.dim()}"
|
| 228 |
+
hidden_states = self.embeddings(pixel_values, grid_hw=grid_hw)
|
| 229 |
+
|
| 230 |
+
return BaseModelOutputWithPooling(
|
| 231 |
+
last_hidden_state=hidden_states,
|
| 232 |
+
pooler_output=None,
|
| 233 |
+
hidden_states=None,
|
| 234 |
+
attentions=None,
|
| 235 |
+
)
|
modeling_qwen3.py
ADDED
|
@@ -0,0 +1,594 @@
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|
| 1 |
+
from typing import Callable, Optional, Union
|
| 2 |
+
|
| 3 |
+
import torch
|
| 4 |
+
from torch import nn
|
| 5 |
+
|
| 6 |
+
import copy
|
| 7 |
+
from transformers.activations import ACT2FN
|
| 8 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 9 |
+
from transformers.generation import GenerationMixin
|
| 10 |
+
from transformers.integrations import use_kernel_forward_from_hub
|
| 11 |
+
from transformers.masking_utils import create_causal_mask
|
| 12 |
+
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
| 13 |
+
from transformers.modeling_layers import (
|
| 14 |
+
GenericForQuestionAnswering,
|
| 15 |
+
GenericForSequenceClassification,
|
| 16 |
+
GenericForTokenClassification,
|
| 17 |
+
GradientCheckpointingLayer,
|
| 18 |
+
)
|
| 19 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
| 20 |
+
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
| 21 |
+
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
| 22 |
+
from transformers.processing_utils import Unpack
|
| 23 |
+
from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
|
| 24 |
+
from transformers.utils.deprecation import deprecate_kwarg
|
| 25 |
+
from transformers.utils.generic import check_model_inputs
|
| 26 |
+
from transformers import Qwen3Config
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
def create_block_causal_mask(index: torch.Tensor):
|
| 30 |
+
"""
|
| 31 |
+
index: (L)
|
| 32 |
+
return: (1, 1, L, L) block-wise causal attention mask
|
| 33 |
+
"""
|
| 34 |
+
L = index.size(0)
|
| 35 |
+
idx_i = index.unsqueeze(1).expand(L, L)
|
| 36 |
+
idx_j = index.unsqueeze(0).expand(L, L)
|
| 37 |
+
|
| 38 |
+
arange = torch.arange(L, device=index.device)
|
| 39 |
+
mask = (idx_j == idx_i) | (arange.unsqueeze(0) <= arange.unsqueeze(1))
|
| 40 |
+
|
| 41 |
+
return torch.where(mask[None, None, :, :] > 0, torch.tensor(0.0), torch.tensor(float('-inf')))
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
def visualize_mask(mask: torch.Tensor, i: int = 0, j: int = 12):
|
| 45 |
+
"""
|
| 46 |
+
mask: (1,1, L, L)
|
| 47 |
+
"""
|
| 48 |
+
submask = torch.where(mask[0, 0, :, :] == 0, torch.tensor(1.0), torch.tensor(0.0))
|
| 49 |
+
submask = mask[i:j, i:j].int().cpu().numpy()
|
| 50 |
+
for row in submask:
|
| 51 |
+
print(" ".join(map(str, row)))
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
@use_kernel_forward_from_hub("RMSNorm")
|
| 55 |
+
class Qwen3RMSNorm(nn.Module):
|
| 56 |
+
def __init__(self, hidden_size, eps: float = 1e-6) -> None:
|
| 57 |
+
"""
|
| 58 |
+
Qwen3RMSNorm is equivalent to T5LayerNorm
|
| 59 |
+
"""
|
| 60 |
+
super().__init__()
|
| 61 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
| 62 |
+
self.variance_epsilon = eps
|
| 63 |
+
|
| 64 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
| 65 |
+
input_dtype = hidden_states.dtype
|
| 66 |
+
hidden_states = hidden_states.to(torch.float32)
|
| 67 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
| 68 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
| 69 |
+
return self.weight * hidden_states.to(input_dtype)
|
| 70 |
+
|
| 71 |
+
def extra_repr(self):
|
| 72 |
+
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
class Qwen3MLP(nn.Module):
|
| 76 |
+
def __init__(self, config):
|
| 77 |
+
super().__init__()
|
| 78 |
+
self.config = config
|
| 79 |
+
self.hidden_size = config.hidden_size
|
| 80 |
+
self.intermediate_size = config.intermediate_size
|
| 81 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 82 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
| 83 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
| 84 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
| 85 |
+
|
| 86 |
+
def forward(self, x):
|
| 87 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
| 88 |
+
return down_proj
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
def rotate_half(x):
|
| 92 |
+
"""Rotates half the hidden dims of the input."""
|
| 93 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 94 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 95 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 96 |
+
|
| 97 |
+
|
| 98 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
| 99 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
| 100 |
+
|
| 101 |
+
Args:
|
| 102 |
+
q (`torch.Tensor`): The query tensor.
|
| 103 |
+
k (`torch.Tensor`): The key tensor.
|
| 104 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
| 105 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
| 106 |
+
position_ids (`torch.Tensor`, *optional*):
|
| 107 |
+
Deprecated and unused.
|
| 108 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
| 109 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
| 110 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
| 111 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
| 112 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
| 113 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
| 114 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
| 115 |
+
Returns:
|
| 116 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
| 117 |
+
"""
|
| 118 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
| 119 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
| 120 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 121 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 122 |
+
return q_embed, k_embed
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 126 |
+
"""
|
| 127 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 128 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 129 |
+
"""
|
| 130 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 131 |
+
if n_rep == 1:
|
| 132 |
+
return hidden_states
|
| 133 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
| 134 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 135 |
+
|
| 136 |
+
|
| 137 |
+
def eager_attention_forward(
|
| 138 |
+
module: nn.Module,
|
| 139 |
+
query: torch.Tensor,
|
| 140 |
+
key: torch.Tensor,
|
| 141 |
+
value: torch.Tensor,
|
| 142 |
+
attention_mask: Optional[torch.Tensor],
|
| 143 |
+
scaling: float,
|
| 144 |
+
dropout: float = 0.0,
|
| 145 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 146 |
+
):
|
| 147 |
+
key_states = repeat_kv(key, module.num_key_value_groups)
|
| 148 |
+
value_states = repeat_kv(value, module.num_key_value_groups)
|
| 149 |
+
|
| 150 |
+
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
| 151 |
+
if attention_mask is not None:
|
| 152 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
| 153 |
+
attn_weights = attn_weights + causal_mask
|
| 154 |
+
|
| 155 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
| 156 |
+
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
| 157 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 158 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 159 |
+
|
| 160 |
+
return attn_output, attn_weights
|
| 161 |
+
|
| 162 |
+
|
| 163 |
+
class Qwen3RotaryEmbedding(nn.Module):
|
| 164 |
+
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
| 165 |
+
|
| 166 |
+
def __init__(self, config: Qwen3Config, device=None):
|
| 167 |
+
super().__init__()
|
| 168 |
+
# BC: "rope_type" was originally "type"
|
| 169 |
+
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
| 170 |
+
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
| 171 |
+
else:
|
| 172 |
+
self.rope_type = "default"
|
| 173 |
+
self.max_seq_len_cached = config.max_position_embeddings
|
| 174 |
+
self.original_max_seq_len = config.max_position_embeddings
|
| 175 |
+
|
| 176 |
+
self.config = config
|
| 177 |
+
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
| 178 |
+
|
| 179 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
| 180 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 181 |
+
self.original_inv_freq = self.inv_freq
|
| 182 |
+
|
| 183 |
+
@torch.no_grad()
|
| 184 |
+
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
| 185 |
+
def forward(self, x, position_ids):
|
| 186 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
| 187 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
| 188 |
+
|
| 189 |
+
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
| 190 |
+
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
| 191 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
| 192 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 193 |
+
cos = emb.cos() * self.attention_scaling
|
| 194 |
+
sin = emb.sin() * self.attention_scaling
|
| 195 |
+
|
| 196 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
| 197 |
+
|
| 198 |
+
|
| 199 |
+
class Qwen3Attention(nn.Module):
|
| 200 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 201 |
+
|
| 202 |
+
def __init__(self, config: Qwen3Config, layer_idx: int):
|
| 203 |
+
super().__init__()
|
| 204 |
+
self.config = config
|
| 205 |
+
self.layer_idx = layer_idx
|
| 206 |
+
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
| 207 |
+
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
| 208 |
+
self.scaling = self.head_dim**-0.5
|
| 209 |
+
self.attention_dropout = config.attention_dropout
|
| 210 |
+
self.is_causal = True
|
| 211 |
+
|
| 212 |
+
self.q_proj = nn.Linear(
|
| 213 |
+
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
|
| 214 |
+
)
|
| 215 |
+
self.q_proj_hw = nn.Linear(
|
| 216 |
+
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
|
| 217 |
+
)
|
| 218 |
+
|
| 219 |
+
self.k_proj = nn.Linear(
|
| 220 |
+
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
| 221 |
+
)
|
| 222 |
+
self.k_proj_hw = nn.Linear(
|
| 223 |
+
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
| 224 |
+
)
|
| 225 |
+
|
| 226 |
+
self.v_proj = nn.Linear(
|
| 227 |
+
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
| 228 |
+
)
|
| 229 |
+
self.o_proj = nn.Linear(
|
| 230 |
+
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
|
| 231 |
+
)
|
| 232 |
+
|
| 233 |
+
self.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps) # unlike olmo, only on the head dim!
|
| 234 |
+
self.q_norm_h = Qwen3RMSNorm(self.head_dim // 2, eps=config.rms_norm_eps)
|
| 235 |
+
self.q_norm_w = Qwen3RMSNorm(self.head_dim // 2, eps=config.rms_norm_eps)
|
| 236 |
+
|
| 237 |
+
self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps) # thus post q_norm does not need reshape
|
| 238 |
+
self.k_norm_h = Qwen3RMSNorm(self.head_dim // 2, eps=config.rms_norm_eps) # thus post q_norm does not need reshape
|
| 239 |
+
self.k_norm_w = Qwen3RMSNorm(self.head_dim // 2, eps=config.rms_norm_eps) # thus post q_norm does not need reshape
|
| 240 |
+
|
| 241 |
+
self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
|
| 242 |
+
|
| 243 |
+
self.rotary_emb = Qwen3RotaryEmbedding(config=config)
|
| 244 |
+
|
| 245 |
+
hw_config = copy.deepcopy(config)
|
| 246 |
+
hw_config.head_dim = config.head_dim // 2
|
| 247 |
+
hw_config.rope_theta = config.rope_theta_hw
|
| 248 |
+
hw_config.max_position_embeddings = config.max_position_embeddings_hw
|
| 249 |
+
self.rotary_emb_hw = Qwen3RotaryEmbedding(config=hw_config)
|
| 250 |
+
|
| 251 |
+
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
| 252 |
+
def forward(
|
| 253 |
+
self,
|
| 254 |
+
hidden_states: torch.Tensor,
|
| 255 |
+
indexes: Optional[torch.LongTensor],
|
| 256 |
+
attention_mask: Optional[torch.Tensor],
|
| 257 |
+
past_key_values: Optional[Cache] = None,
|
| 258 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 259 |
+
**kwargs: Unpack[FlashAttentionKwargs],
|
| 260 |
+
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
|
| 261 |
+
|
| 262 |
+
assert self.config._attn_implementation == "eager"
|
| 263 |
+
input_shape = hidden_states.shape[:-1]
|
| 264 |
+
hidden_shape = (*input_shape, -1, self.head_dim)
|
| 265 |
+
|
| 266 |
+
query_states_t = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
|
| 267 |
+
query_states_h, query_states_w = self.q_proj_hw(hidden_states).view(hidden_shape).transpose(1, 2).chunk(2, dim=-1)
|
| 268 |
+
query_states_h, query_states_w = self.q_norm_h(query_states_h), self.q_norm_w(query_states_w)
|
| 269 |
+
|
| 270 |
+
key_states_t = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
|
| 271 |
+
key_states_h, key_states_w = self.k_proj_hw(hidden_states).view(hidden_shape).transpose(1, 2).chunk(2, dim=-1)
|
| 272 |
+
key_states_h, key_states_w = self.k_norm_h(key_states_h), self.k_norm_w(key_states_w)
|
| 273 |
+
|
| 274 |
+
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
| 275 |
+
|
| 276 |
+
cos_t, sin_t = self.rotary_emb(hidden_states, indexes[0].unsqueeze(0))
|
| 277 |
+
query_states_t, key_states_t = apply_rotary_pos_emb(query_states_t, key_states_t, cos_t, sin_t)
|
| 278 |
+
|
| 279 |
+
cos_h, sin_h = self.rotary_emb_hw(hidden_states, indexes[1].unsqueeze(0))
|
| 280 |
+
query_states_h, key_states_h = apply_rotary_pos_emb(query_states_h, key_states_h, cos_h, sin_h)
|
| 281 |
+
|
| 282 |
+
cos_w, sin_w = self.rotary_emb_hw(hidden_states, indexes[2].unsqueeze(0))
|
| 283 |
+
query_states_w, key_states_w = apply_rotary_pos_emb(query_states_w, key_states_w, cos_w, sin_w)
|
| 284 |
+
|
| 285 |
+
query_states = torch.cat([query_states_t, query_states_h, query_states_w], dim=-1)
|
| 286 |
+
key_states = torch.cat([key_states_t, key_states_h, key_states_w], dim=-1)
|
| 287 |
+
|
| 288 |
+
|
| 289 |
+
if past_key_values is not None:
|
| 290 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
| 291 |
+
# cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
| 292 |
+
# key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 293 |
+
|
| 294 |
+
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs=None)
|
| 295 |
+
|
| 296 |
+
attention_interface: Callable = eager_attention_forward
|
| 297 |
+
if self.config._attn_implementation != "eager":
|
| 298 |
+
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
| 299 |
+
|
| 300 |
+
attn_output, attn_weights = attention_interface(
|
| 301 |
+
self,
|
| 302 |
+
query_states,
|
| 303 |
+
key_states,
|
| 304 |
+
value_states,
|
| 305 |
+
attention_mask,
|
| 306 |
+
dropout=0.0 if not self.training else self.attention_dropout,
|
| 307 |
+
scaling=self.scaling,
|
| 308 |
+
sliding_window=self.sliding_window, # diff with Llama
|
| 309 |
+
**kwargs,
|
| 310 |
+
)
|
| 311 |
+
|
| 312 |
+
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
| 313 |
+
attn_output = self.o_proj(attn_output)
|
| 314 |
+
return attn_output, attn_weights
|
| 315 |
+
|
| 316 |
+
|
| 317 |
+
class Qwen3DecoderLayer(GradientCheckpointingLayer):
|
| 318 |
+
def __init__(self, config: Qwen3Config, layer_idx: int):
|
| 319 |
+
super().__init__()
|
| 320 |
+
self.hidden_size = config.hidden_size
|
| 321 |
+
|
| 322 |
+
self.self_attn = Qwen3Attention(config=config, layer_idx=layer_idx)
|
| 323 |
+
|
| 324 |
+
self.mlp = Qwen3MLP(config)
|
| 325 |
+
self.input_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 326 |
+
self.post_attention_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 327 |
+
self.attention_type = config.layer_types[layer_idx]
|
| 328 |
+
|
| 329 |
+
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
| 330 |
+
def forward(
|
| 331 |
+
self,
|
| 332 |
+
hidden_states: torch.Tensor,
|
| 333 |
+
indexes: Optional[torch.LongTensor] = None,
|
| 334 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 335 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 336 |
+
past_key_values: Optional[Cache] = None,
|
| 337 |
+
use_cache: Optional[bool] = False,
|
| 338 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 339 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 340 |
+
) -> torch.Tensor:
|
| 341 |
+
residual = hidden_states
|
| 342 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 343 |
+
# Self Attention
|
| 344 |
+
hidden_states, _ = self.self_attn(
|
| 345 |
+
hidden_states=hidden_states,
|
| 346 |
+
indexes=indexes,
|
| 347 |
+
attention_mask=attention_mask,
|
| 348 |
+
position_ids=position_ids,
|
| 349 |
+
past_key_values=past_key_values,
|
| 350 |
+
use_cache=use_cache,
|
| 351 |
+
cache_position=cache_position,
|
| 352 |
+
**kwargs,
|
| 353 |
+
)
|
| 354 |
+
hidden_states = residual + hidden_states
|
| 355 |
+
|
| 356 |
+
# Fully Connected
|
| 357 |
+
residual = hidden_states
|
| 358 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
| 359 |
+
hidden_states = self.mlp(hidden_states)
|
| 360 |
+
hidden_states = residual + hidden_states
|
| 361 |
+
return hidden_states
|
| 362 |
+
|
| 363 |
+
|
| 364 |
+
@auto_docstring
|
| 365 |
+
class Qwen3PreTrainedModel(PreTrainedModel):
|
| 366 |
+
config: Qwen3Config
|
| 367 |
+
base_model_prefix = "model"
|
| 368 |
+
supports_gradient_checkpointing = True
|
| 369 |
+
_no_split_modules = ["Qwen3DecoderLayer"]
|
| 370 |
+
_skip_keys_device_placement = ["past_key_values"]
|
| 371 |
+
_supports_flash_attn = True
|
| 372 |
+
_supports_sdpa = True
|
| 373 |
+
_supports_flex_attn = True
|
| 374 |
+
|
| 375 |
+
_can_compile_fullgraph = True
|
| 376 |
+
_supports_attention_backend = True
|
| 377 |
+
_can_record_outputs = {
|
| 378 |
+
"hidden_states": Qwen3DecoderLayer,
|
| 379 |
+
"attentions": Qwen3Attention,
|
| 380 |
+
}
|
| 381 |
+
|
| 382 |
+
|
| 383 |
+
@auto_docstring
|
| 384 |
+
class Qwen3Model(Qwen3PreTrainedModel):
|
| 385 |
+
def __init__(self, config: Qwen3Config):
|
| 386 |
+
super().__init__(config)
|
| 387 |
+
self.padding_idx = config.pad_token_id
|
| 388 |
+
self.vocab_size = config.vocab_size
|
| 389 |
+
|
| 390 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
| 391 |
+
self.layers = nn.ModuleList(
|
| 392 |
+
[Qwen3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
| 393 |
+
)
|
| 394 |
+
self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 395 |
+
|
| 396 |
+
self.gradient_checkpointing = False
|
| 397 |
+
self.has_sliding_layers = "sliding_attention" in self.config.layer_types
|
| 398 |
+
self.current_index = -1
|
| 399 |
+
|
| 400 |
+
# Initialize weights and apply final processing
|
| 401 |
+
self.post_init()
|
| 402 |
+
|
| 403 |
+
@check_model_inputs
|
| 404 |
+
@auto_docstring
|
| 405 |
+
def forward(
|
| 406 |
+
self,
|
| 407 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 408 |
+
indexes: Optional[torch.LongTensor] = None,
|
| 409 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 410 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 411 |
+
past_key_values: Optional[Cache] = None,
|
| 412 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 413 |
+
use_cache: Optional[bool] = None,
|
| 414 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 415 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 416 |
+
) -> BaseModelOutputWithPast:
|
| 417 |
+
|
| 418 |
+
assert position_ids is not None
|
| 419 |
+
assert cache_position is not None
|
| 420 |
+
assert past_key_values is not None
|
| 421 |
+
|
| 422 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
| 423 |
+
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
| 424 |
+
|
| 425 |
+
if inputs_embeds is None:
|
| 426 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 427 |
+
|
| 428 |
+
if use_cache and past_key_values is None:
|
| 429 |
+
past_key_values = DynamicCache(config=self.config)
|
| 430 |
+
|
| 431 |
+
if cache_position is None:
|
| 432 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 433 |
+
cache_position = torch.arange(
|
| 434 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
| 435 |
+
)
|
| 436 |
+
|
| 437 |
+
if position_ids is None:
|
| 438 |
+
position_ids = cache_position.unsqueeze(0)
|
| 439 |
+
|
| 440 |
+
# It may already have been prepared by e.g. `generate`
|
| 441 |
+
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
| 442 |
+
# Prepare mask arguments
|
| 443 |
+
if input_ids is not None:
|
| 444 |
+
mask_kwargs = {
|
| 445 |
+
"config": self.config,
|
| 446 |
+
"input_embeds": inputs_embeds,
|
| 447 |
+
"attention_mask": attention_mask,
|
| 448 |
+
"cache_position": cache_position,
|
| 449 |
+
"past_key_values": past_key_values,
|
| 450 |
+
"position_ids": position_ids,
|
| 451 |
+
}
|
| 452 |
+
# Create the masks
|
| 453 |
+
causal_mask_mapping = {
|
| 454 |
+
"full_attention": create_causal_mask(**mask_kwargs),
|
| 455 |
+
}
|
| 456 |
+
self.current_index += 1
|
| 457 |
+
indexes = torch.LongTensor([[self.current_index], [0], [0]]).to(input_ids.device)
|
| 458 |
+
else:
|
| 459 |
+
causal_mask_mapping = {
|
| 460 |
+
"full_attention": create_block_causal_mask(indexes[0]),
|
| 461 |
+
}
|
| 462 |
+
self.current_index = indexes[0].max()
|
| 463 |
+
else:
|
| 464 |
+
raise NotImplementedError('not isinstance(causal_mask_mapping := attention_mask, dict)')
|
| 465 |
+
|
| 466 |
+
# The sliding window alternating layers are not always activated depending on the config
|
| 467 |
+
# if self.has_sliding_layers:
|
| 468 |
+
# causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
|
| 469 |
+
|
| 470 |
+
hidden_states = inputs_embeds
|
| 471 |
+
|
| 472 |
+
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
| 473 |
+
hidden_states = decoder_layer(
|
| 474 |
+
hidden_states,
|
| 475 |
+
indexes=indexes,
|
| 476 |
+
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
| 477 |
+
position_ids=position_ids,
|
| 478 |
+
past_key_values=past_key_values,
|
| 479 |
+
use_cache=use_cache,
|
| 480 |
+
cache_position=cache_position,
|
| 481 |
+
**kwargs,
|
| 482 |
+
)
|
| 483 |
+
|
| 484 |
+
hidden_states = self.norm(hidden_states)
|
| 485 |
+
return BaseModelOutputWithPast(
|
| 486 |
+
last_hidden_state=hidden_states,
|
| 487 |
+
past_key_values=past_key_values if use_cache else None,
|
| 488 |
+
)
|
| 489 |
+
|
| 490 |
+
|
| 491 |
+
@auto_docstring
|
| 492 |
+
class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin):
|
| 493 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 494 |
+
_tp_plan = {"lm_head": "colwise_rep"}
|
| 495 |
+
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
| 496 |
+
|
| 497 |
+
def __init__(self, config):
|
| 498 |
+
super().__init__(config)
|
| 499 |
+
self.model = Qwen3Model(config)
|
| 500 |
+
self.vocab_size = config.vocab_size
|
| 501 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 502 |
+
|
| 503 |
+
# Initialize weights and apply final processing
|
| 504 |
+
self.post_init()
|
| 505 |
+
|
| 506 |
+
@can_return_tuple
|
| 507 |
+
@auto_docstring
|
| 508 |
+
def forward(
|
| 509 |
+
self,
|
| 510 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 511 |
+
indexes: Optional[torch.LongTensor] = None,
|
| 512 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 513 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 514 |
+
past_key_values: Optional[Cache] = None,
|
| 515 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 516 |
+
labels: Optional[torch.LongTensor] = None,
|
| 517 |
+
use_cache: Optional[bool] = None,
|
| 518 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 519 |
+
logits_to_keep: Union[int, torch.Tensor] = 0,
|
| 520 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 521 |
+
) -> CausalLMOutputWithPast:
|
| 522 |
+
r"""
|
| 523 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 524 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 525 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 526 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 527 |
+
|
| 528 |
+
Example:
|
| 529 |
+
|
| 530 |
+
```python
|
| 531 |
+
>>> from transformers import AutoTokenizer, Qwen3ForCausalLM
|
| 532 |
+
|
| 533 |
+
>>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
|
| 534 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
|
| 535 |
+
|
| 536 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
| 537 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
| 538 |
+
|
| 539 |
+
>>> # Generate
|
| 540 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
| 541 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
| 542 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
| 543 |
+
```"""
|
| 544 |
+
|
| 545 |
+
outputs: BaseModelOutputWithPast = self.model(
|
| 546 |
+
input_ids=input_ids,
|
| 547 |
+
indexes=indexes,
|
| 548 |
+
attention_mask=attention_mask,
|
| 549 |
+
position_ids=position_ids,
|
| 550 |
+
past_key_values=past_key_values,
|
| 551 |
+
inputs_embeds=inputs_embeds,
|
| 552 |
+
use_cache=use_cache,
|
| 553 |
+
cache_position=cache_position,
|
| 554 |
+
**kwargs,
|
| 555 |
+
)
|
| 556 |
+
|
| 557 |
+
hidden_states = outputs.last_hidden_state
|
| 558 |
+
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
| 559 |
+
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
| 560 |
+
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
| 561 |
+
|
| 562 |
+
loss = None
|
| 563 |
+
if labels is not None:
|
| 564 |
+
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
| 565 |
+
|
| 566 |
+
return CausalLMOutputWithPast(
|
| 567 |
+
loss=loss,
|
| 568 |
+
logits=logits,
|
| 569 |
+
past_key_values=outputs.past_key_values,
|
| 570 |
+
hidden_states=outputs.hidden_states,
|
| 571 |
+
attentions=outputs.attentions,
|
| 572 |
+
)
|
| 573 |
+
|
| 574 |
+
|
| 575 |
+
class Qwen3ForSequenceClassification(GenericForSequenceClassification, Qwen3PreTrainedModel):
|
| 576 |
+
pass
|
| 577 |
+
|
| 578 |
+
|
| 579 |
+
class Qwen3ForTokenClassification(GenericForTokenClassification, Qwen3PreTrainedModel):
|
| 580 |
+
pass
|
| 581 |
+
|
| 582 |
+
|
| 583 |
+
class Qwen3ForQuestionAnswering(GenericForQuestionAnswering, Qwen3PreTrainedModel):
|
| 584 |
+
base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model`
|
| 585 |
+
|
| 586 |
+
|
| 587 |
+
__all__ = [
|
| 588 |
+
"Qwen3ForCausalLM",
|
| 589 |
+
"Qwen3ForQuestionAnswering",
|
| 590 |
+
"Qwen3PreTrainedModel",
|
| 591 |
+
"Qwen3Model",
|
| 592 |
+
"Qwen3ForSequenceClassification",
|
| 593 |
+
"Qwen3ForTokenClassification",
|
| 594 |
+
]
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,1900 @@
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
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|
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| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
"<|im_end|>",
|
| 5 |
+
"<|object_ref_start|>",
|
| 6 |
+
"<|object_ref_end|>",
|
| 7 |
+
"<|box_start|>",
|
| 8 |
+
"<|box_end|>",
|
| 9 |
+
"<|quad_start|>",
|
| 10 |
+
"<|quad_end|>",
|
| 11 |
+
"<|vision_start|>",
|
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| 1743 |
+
"single_word": false
|
| 1744 |
+
},
|
| 1745 |
+
{
|
| 1746 |
+
"content": "<FAKE_PAD_234>",
|
| 1747 |
+
"lstrip": false,
|
| 1748 |
+
"normalized": false,
|
| 1749 |
+
"rstrip": false,
|
| 1750 |
+
"single_word": false
|
| 1751 |
+
},
|
| 1752 |
+
{
|
| 1753 |
+
"content": "<FAKE_PAD_235>",
|
| 1754 |
+
"lstrip": false,
|
| 1755 |
+
"normalized": false,
|
| 1756 |
+
"rstrip": false,
|
| 1757 |
+
"single_word": false
|
| 1758 |
+
},
|
| 1759 |
+
{
|
| 1760 |
+
"content": "<FAKE_PAD_236>",
|
| 1761 |
+
"lstrip": false,
|
| 1762 |
+
"normalized": false,
|
| 1763 |
+
"rstrip": false,
|
| 1764 |
+
"single_word": false
|
| 1765 |
+
},
|
| 1766 |
+
{
|
| 1767 |
+
"content": "<FAKE_PAD_237>",
|
| 1768 |
+
"lstrip": false,
|
| 1769 |
+
"normalized": false,
|
| 1770 |
+
"rstrip": false,
|
| 1771 |
+
"single_word": false
|
| 1772 |
+
},
|
| 1773 |
+
{
|
| 1774 |
+
"content": "<FAKE_PAD_238>",
|
| 1775 |
+
"lstrip": false,
|
| 1776 |
+
"normalized": false,
|
| 1777 |
+
"rstrip": false,
|
| 1778 |
+
"single_word": false
|
| 1779 |
+
},
|
| 1780 |
+
{
|
| 1781 |
+
"content": "<FAKE_PAD_239>",
|
| 1782 |
+
"lstrip": false,
|
| 1783 |
+
"normalized": false,
|
| 1784 |
+
"rstrip": false,
|
| 1785 |
+
"single_word": false
|
| 1786 |
+
},
|
| 1787 |
+
{
|
| 1788 |
+
"content": "<FAKE_PAD_240>",
|
| 1789 |
+
"lstrip": false,
|
| 1790 |
+
"normalized": false,
|
| 1791 |
+
"rstrip": false,
|
| 1792 |
+
"single_word": false
|
| 1793 |
+
},
|
| 1794 |
+
{
|
| 1795 |
+
"content": "<FAKE_PAD_241>",
|
| 1796 |
+
"lstrip": false,
|
| 1797 |
+
"normalized": false,
|
| 1798 |
+
"rstrip": false,
|
| 1799 |
+
"single_word": false
|
| 1800 |
+
},
|
| 1801 |
+
{
|
| 1802 |
+
"content": "<FAKE_PAD_242>",
|
| 1803 |
+
"lstrip": false,
|
| 1804 |
+
"normalized": false,
|
| 1805 |
+
"rstrip": false,
|
| 1806 |
+
"single_word": false
|
| 1807 |
+
},
|
| 1808 |
+
{
|
| 1809 |
+
"content": "<FAKE_PAD_243>",
|
| 1810 |
+
"lstrip": false,
|
| 1811 |
+
"normalized": false,
|
| 1812 |
+
"rstrip": false,
|
| 1813 |
+
"single_word": false
|
| 1814 |
+
},
|
| 1815 |
+
{
|
| 1816 |
+
"content": "<FAKE_PAD_244>",
|
| 1817 |
+
"lstrip": false,
|
| 1818 |
+
"normalized": false,
|
| 1819 |
+
"rstrip": false,
|
| 1820 |
+
"single_word": false
|
| 1821 |
+
},
|
| 1822 |
+
{
|
| 1823 |
+
"content": "<FAKE_PAD_245>",
|
| 1824 |
+
"lstrip": false,
|
| 1825 |
+
"normalized": false,
|
| 1826 |
+
"rstrip": false,
|
| 1827 |
+
"single_word": false
|
| 1828 |
+
},
|
| 1829 |
+
{
|
| 1830 |
+
"content": "<FAKE_PAD_246>",
|
| 1831 |
+
"lstrip": false,
|
| 1832 |
+
"normalized": false,
|
| 1833 |
+
"rstrip": false,
|
| 1834 |
+
"single_word": false
|
| 1835 |
+
},
|
| 1836 |
+
{
|
| 1837 |
+
"content": "<FAKE_PAD_247>",
|
| 1838 |
+
"lstrip": false,
|
| 1839 |
+
"normalized": false,
|
| 1840 |
+
"rstrip": false,
|
| 1841 |
+
"single_word": false
|
| 1842 |
+
},
|
| 1843 |
+
{
|
| 1844 |
+
"content": "<FAKE_PAD_248>",
|
| 1845 |
+
"lstrip": false,
|
| 1846 |
+
"normalized": false,
|
| 1847 |
+
"rstrip": false,
|
| 1848 |
+
"single_word": false
|
| 1849 |
+
},
|
| 1850 |
+
{
|
| 1851 |
+
"content": "<FAKE_PAD_249>",
|
| 1852 |
+
"lstrip": false,
|
| 1853 |
+
"normalized": false,
|
| 1854 |
+
"rstrip": false,
|
| 1855 |
+
"single_word": false
|
| 1856 |
+
},
|
| 1857 |
+
{
|
| 1858 |
+
"content": "<FAKE_PAD_250>",
|
| 1859 |
+
"lstrip": false,
|
| 1860 |
+
"normalized": false,
|
| 1861 |
+
"rstrip": false,
|
| 1862 |
+
"single_word": false
|
| 1863 |
+
},
|
| 1864 |
+
{
|
| 1865 |
+
"content": "<FAKE_PAD_251>",
|
| 1866 |
+
"lstrip": false,
|
| 1867 |
+
"normalized": false,
|
| 1868 |
+
"rstrip": false,
|
| 1869 |
+
"single_word": false
|
| 1870 |
+
},
|
| 1871 |
+
{
|
| 1872 |
+
"content": "<FAKE_PAD_252>",
|
| 1873 |
+
"lstrip": false,
|
| 1874 |
+
"normalized": false,
|
| 1875 |
+
"rstrip": false,
|
| 1876 |
+
"single_word": false
|
| 1877 |
+
},
|
| 1878 |
+
{
|
| 1879 |
+
"content": "<FAKE_PAD_253>",
|
| 1880 |
+
"lstrip": false,
|
| 1881 |
+
"normalized": false,
|
| 1882 |
+
"rstrip": false,
|
| 1883 |
+
"single_word": false
|
| 1884 |
+
}
|
| 1885 |
+
],
|
| 1886 |
+
"eos_token": {
|
| 1887 |
+
"content": "<|im_end|>",
|
| 1888 |
+
"lstrip": false,
|
| 1889 |
+
"normalized": false,
|
| 1890 |
+
"rstrip": false,
|
| 1891 |
+
"single_word": false
|
| 1892 |
+
},
|
| 1893 |
+
"pad_token": {
|
| 1894 |
+
"content": "<|endoftext|>",
|
| 1895 |
+
"lstrip": false,
|
| 1896 |
+
"normalized": false,
|
| 1897 |
+
"rstrip": false,
|
| 1898 |
+
"single_word": false
|
| 1899 |
+
}
|
| 1900 |
+
}
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2643 @@
|
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| 1 |
+
{
|
| 2 |
+
"add_bos_token": false,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": false,
|
| 5 |
+
"added_tokens_decoder": {
|
| 6 |
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"151643": {
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| 7 |
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"content": "<|endoftext|>",
|
| 8 |
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"lstrip": false,
|
| 9 |
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|
| 10 |
+
"rstrip": false,
|
| 11 |
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"single_word": false,
|
| 12 |
+
"special": true
|
| 13 |
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},
|
| 14 |
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|
| 15 |
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"content": "<|im_start|>",
|
| 16 |
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"lstrip": false,
|
| 17 |
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"normalized": false,
|
| 18 |
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|
| 19 |
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| 20 |
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"special": true
|
| 21 |
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| 22 |
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| 23 |
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"content": "<|im_end|>",
|
| 24 |
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| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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"special": true
|
| 29 |
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|
| 30 |
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|
| 31 |
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"content": "<|object_ref_start|>",
|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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| 38 |
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|
| 39 |
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"content": "<|object_ref_end|>",
|
| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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|
| 45 |
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| 46 |
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|
| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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|
| 53 |
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| 54 |
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|
| 55 |
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"content": "<|box_end|>",
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 |
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|
| 152 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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| 159 |
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| 160 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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|
| 199 |
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| 200 |
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| 201 |
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| 202 |
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| 203 |
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| 204 |
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| 205 |
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| 206 |
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| 207 |
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| 208 |
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| 209 |
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| 210 |
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| 211 |
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| 212 |
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| 213 |
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| 214 |
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| 215 |
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|
| 216 |
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| 217 |
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| 218 |
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| 219 |
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| 220 |
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| 221 |
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| 222 |
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| 223 |
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| 224 |
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| 225 |
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| 226 |
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| 231 |
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| 232 |
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| 279 |
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| 318 |
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| 1695 |
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| 1800 |
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| 1829 |
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| 1830 |
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| 1831 |
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| 1832 |
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| 1837 |
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| 1838 |
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| 1839 |
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| 1840 |
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| 1845 |
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| 1847 |
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| 1848 |
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| 1849 |
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| 1850 |
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| 1851 |
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| 1852 |
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| 1853 |
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| 1854 |
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| 1855 |
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| 1856 |
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| 1857 |
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| 1859 |
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| 1860 |
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| 1861 |
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| 1862 |
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| 1863 |
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| 1864 |
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| 1865 |
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| 1866 |
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| 1867 |
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| 1868 |
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| 1869 |
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| 1870 |
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| 1871 |
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| 1872 |
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| 1875 |
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| 1876 |
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| 1877 |
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| 1878 |
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| 1879 |
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|
| 1880 |
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| 1881 |
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| 1882 |
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| 1883 |
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| 1884 |
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| 1885 |
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| 1886 |
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| 1887 |
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| 1888 |
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| 1889 |
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| 1890 |
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| 1891 |
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| 1892 |
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| 1893 |
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| 1894 |
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| 1895 |
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| 1896 |
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| 1897 |
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| 1898 |
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| 1899 |
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| 1900 |
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| 1901 |
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| 1902 |
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| 1903 |
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| 1904 |
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| 1905 |
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| 1907 |
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| 1908 |
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| 1909 |
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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 |
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| 1915 |
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| 1916 |
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| 1917 |
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| 1918 |
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| 1919 |
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| 1920 |
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| 1921 |
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| 1922 |
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| 1923 |
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| 1924 |
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| 1925 |
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| 1926 |
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| 1927 |
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|
| 1928 |
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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 |
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|
| 1939 |
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| 1940 |
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| 1941 |
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|
| 1942 |
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|
| 1943 |
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|
| 1944 |
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| 1945 |
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| 1946 |
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| 1947 |
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| 1948 |
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|
| 1949 |
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|
| 1950 |
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|
| 1951 |
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|
| 1952 |
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| 1953 |
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| 1954 |
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| 1955 |
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| 1956 |
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| 1957 |
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| 1958 |
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|
| 1959 |
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|
| 1960 |
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| 1961 |
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| 1962 |
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| 1963 |
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| 1964 |
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| 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 |
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| 1971 |
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| 1972 |
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| 1973 |
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| 1974 |
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| 1975 |
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| 1976 |
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| 1977 |
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| 1978 |
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| 1979 |
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| 1980 |
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| 1981 |
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| 1982 |
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| 1983 |
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| 1984 |
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| 1985 |
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| 1986 |
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"rstrip": false,
|
| 1987 |
+
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|
| 1988 |
+
"special": true
|
| 1989 |
+
},
|
| 1990 |
+
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|
| 1991 |
+
"content": "<FAKE_PAD_209>",
|
| 1992 |
+
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|
| 1993 |
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|
| 1994 |
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|
| 1995 |
+
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|
| 1996 |
+
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|
| 1997 |
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},
|
| 1998 |
+
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|
| 1999 |
+
"content": "<FAKE_PAD_210>",
|
| 2000 |
+
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|
| 2001 |
+
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|
| 2002 |
+
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|
| 2003 |
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|
| 2004 |
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|
| 2005 |
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},
|
| 2006 |
+
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|
| 2007 |
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|
| 2008 |
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|
| 2009 |
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|
| 2010 |
+
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|
| 2011 |
+
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|
| 2012 |
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|
| 2013 |
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},
|
| 2014 |
+
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|
| 2015 |
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|
| 2016 |
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|
| 2017 |
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|
| 2018 |
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|
| 2019 |
+
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|
| 2020 |
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|
| 2021 |
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|
| 2022 |
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|
| 2023 |
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|
| 2024 |
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|
| 2025 |
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|
| 2026 |
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|
| 2027 |
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|
| 2028 |
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|
| 2029 |
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|
| 2030 |
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|
| 2031 |
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|
| 2032 |
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| 2033 |
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| 2034 |
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| 2035 |
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| 2036 |
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| 2037 |
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| 2038 |
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|
| 2039 |
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|
| 2040 |
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| 2041 |
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| 2042 |
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| 2043 |
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| 2044 |
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| 2045 |
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| 2046 |
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| 2047 |
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| 2048 |
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| 2049 |
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| 2050 |
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| 2051 |
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| 2052 |
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|
| 2053 |
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| 2054 |
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| 2055 |
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| 2056 |
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| 2057 |
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| 2058 |
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| 2059 |
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| 2060 |
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| 2061 |
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| 2062 |
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|
| 2063 |
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| 2064 |
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| 2065 |
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| 2066 |
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| 2067 |
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| 2068 |
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| 2069 |
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| 2070 |
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| 2071 |
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| 2072 |
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| 2073 |
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| 2074 |
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| 2075 |
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| 2076 |
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|
| 2077 |
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| 2078 |
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| 2079 |
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| 2080 |
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| 2081 |
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| 2082 |
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| 2083 |
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| 2084 |
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| 2085 |
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| 2086 |
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| 2087 |
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| 2088 |
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| 2089 |
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| 2090 |
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| 2091 |
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| 2092 |
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|
| 2093 |
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|
| 2094 |
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|
| 2095 |
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| 2096 |
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| 2097 |
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| 2098 |
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| 2099 |
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| 2100 |
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|
| 2101 |
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|
| 2102 |
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|
| 2103 |
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|
| 2104 |
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| 2105 |
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| 2106 |
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| 2107 |
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| 2108 |
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| 2109 |
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| 2110 |
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| 2111 |
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| 2112 |
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| 2113 |
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| 2114 |
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| 2115 |
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| 2116 |
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| 2117 |
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| 2118 |
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| 2119 |
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| 2120 |
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|
| 2121 |
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| 2122 |
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| 2123 |
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| 2124 |
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|
| 2125 |
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| 2126 |
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| 2127 |
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|
| 2128 |
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|
| 2129 |
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| 2130 |
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|
| 2131 |
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|
| 2132 |
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|
| 2133 |
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| 2134 |
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| 2135 |
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| 2136 |
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| 2137 |
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|
| 2138 |
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|
| 2139 |
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| 2140 |
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|
| 2141 |
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|
| 2142 |
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| 2143 |
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| 2144 |
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|
| 2145 |
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| 2146 |
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| 2147 |
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| 2148 |
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| 2149 |
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| 2150 |
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| 2151 |
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| 2152 |
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| 2153 |
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| 2155 |
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| 2156 |
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| 2157 |
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| 2158 |
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| 2159 |
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| 2160 |
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| 2161 |
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| 2166 |
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| 2167 |
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| 2168 |
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| 2169 |
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| 2170 |
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| 2172 |
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| 2173 |
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| 2174 |
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| 2175 |
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| 2176 |
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| 2177 |
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| 2180 |
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| 2182 |
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| 2183 |
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| 2184 |
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| 2185 |
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| 2188 |
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| 2189 |
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| 2191 |
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| 2192 |
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| 2193 |
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| 2194 |
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| 2195 |
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| 2196 |
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| 2197 |
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| 2198 |
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| 2199 |
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| 2200 |
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| 2201 |
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| 2202 |
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| 2203 |
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| 2204 |
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| 2205 |
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| 2206 |
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| 2207 |
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| 2208 |
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| 2209 |
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| 2210 |
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| 2211 |
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| 2212 |
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| 2213 |
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| 2214 |
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| 2215 |
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| 2216 |
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| 2217 |
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| 2218 |
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| 2219 |
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| 2220 |
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| 2221 |
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| 2222 |
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| 2223 |
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| 2224 |
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| 2225 |
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| 2226 |
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| 2227 |
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| 2228 |
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| 2229 |
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| 2230 |
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| 2231 |
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| 2232 |
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| 2233 |
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| 2237 |
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| 2239 |
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| 2240 |
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| 2241 |
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| 2245 |
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| 2246 |
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| 2247 |
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| 2248 |
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| 2249 |
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| 2253 |
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| 2254 |
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| 2255 |
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| 2257 |
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| 2260 |
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| 2261 |
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| 2262 |
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| 2263 |
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| 2264 |
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| 2265 |
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| 2267 |
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| 2269 |
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| 2270 |
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| 2271 |
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| 2272 |
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| 2273 |
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| 2275 |
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| 2276 |
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| 2277 |
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| 2278 |
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| 2279 |
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| 2280 |
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| 2281 |
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| 2283 |
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| 2284 |
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| 2285 |
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| 2286 |
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| 2287 |
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| 2288 |
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| 2289 |
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| 2290 |
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| 2291 |
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| 2292 |
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| 2293 |
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| 2294 |
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| 2295 |
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| 2299 |
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| 2300 |
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| 2301 |
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| 2302 |
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| 2303 |
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| 2304 |
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| 2305 |
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| 2306 |
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| 2307 |
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| 2308 |
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| 2309 |
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| 2310 |
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| 2311 |
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| 2312 |
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| 2313 |
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| 2314 |
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| 2315 |
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| 2316 |
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| 2317 |
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| 2318 |
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| 2319 |
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| 2320 |
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| 2321 |
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| 2325 |
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| 2327 |
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| 2328 |
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| 2330 |
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| 2332 |
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| 2333 |
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| 2335 |
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| 2336 |
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| 2340 |
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| 2341 |
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| 2342 |
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| 2343 |
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| 2349 |
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| 2350 |
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| 2351 |
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| 2352 |
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| 2353 |
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| 2354 |
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| 2355 |
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| 2356 |
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| 2357 |
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| 2358 |
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| 2359 |
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| 2360 |
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| 2361 |
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| 2362 |
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| 2363 |
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| 2364 |
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| 2365 |
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"<IMG_CONTEXT>",
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| 2366 |
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|
| 2367 |
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"</img>",
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| 2368 |
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| 2369 |
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"</quad>",
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| 2370 |
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|
| 2371 |
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"</ref>",
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| 2372 |
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| 2373 |
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"</box>",
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| 2374 |
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| 2375 |
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|
| 2376 |
+
"<|plugin|>",
|
| 2377 |
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"<|interpreter|>",
|
| 2378 |
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|
| 2379 |
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"<FAKE_PAD_1>",
|
| 2380 |
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|
| 2381 |
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"<FAKE_PAD_3>",
|
| 2382 |
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"<FAKE_PAD_4>",
|
| 2383 |
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"<FAKE_PAD_5>",
|
| 2384 |
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"<FAKE_PAD_6>",
|
| 2385 |
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|
| 2386 |
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"<FAKE_PAD_8>",
|
| 2387 |
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"<FAKE_PAD_9>",
|
| 2388 |
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|
| 2389 |
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"<FAKE_PAD_11>",
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| 2390 |
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|
| 2391 |
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|
| 2392 |
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| 2393 |
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|
| 2394 |
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"<FAKE_PAD_16>",
|
| 2395 |
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"<FAKE_PAD_17>",
|
| 2396 |
+
"<FAKE_PAD_18>",
|
| 2397 |
+
"<FAKE_PAD_19>",
|
| 2398 |
+
"<FAKE_PAD_20>",
|
| 2399 |
+
"<FAKE_PAD_21>",
|
| 2400 |
+
"<FAKE_PAD_22>",
|
| 2401 |
+
"<FAKE_PAD_23>",
|
| 2402 |
+
"<FAKE_PAD_24>",
|
| 2403 |
+
"<FAKE_PAD_25>",
|
| 2404 |
+
"<FAKE_PAD_26>",
|
| 2405 |
+
"<FAKE_PAD_27>",
|
| 2406 |
+
"<FAKE_PAD_28>",
|
| 2407 |
+
"<FAKE_PAD_29>",
|
| 2408 |
+
"<FAKE_PAD_30>",
|
| 2409 |
+
"<FAKE_PAD_31>",
|
| 2410 |
+
"<FAKE_PAD_32>",
|
| 2411 |
+
"<FAKE_PAD_33>",
|
| 2412 |
+
"<FAKE_PAD_34>",
|
| 2413 |
+
"<FAKE_PAD_35>",
|
| 2414 |
+
"<FAKE_PAD_36>",
|
| 2415 |
+
"<FAKE_PAD_37>",
|
| 2416 |
+
"<FAKE_PAD_38>",
|
| 2417 |
+
"<FAKE_PAD_39>",
|
| 2418 |
+
"<FAKE_PAD_40>",
|
| 2419 |
+
"<FAKE_PAD_41>",
|
| 2420 |
+
"<FAKE_PAD_42>",
|
| 2421 |
+
"<FAKE_PAD_43>",
|
| 2422 |
+
"<FAKE_PAD_44>",
|
| 2423 |
+
"<FAKE_PAD_45>",
|
| 2424 |
+
"<FAKE_PAD_46>",
|
| 2425 |
+
"<FAKE_PAD_47>",
|
| 2426 |
+
"<FAKE_PAD_48>",
|
| 2427 |
+
"<FAKE_PAD_49>",
|
| 2428 |
+
"<FAKE_PAD_50>",
|
| 2429 |
+
"<FAKE_PAD_51>",
|
| 2430 |
+
"<FAKE_PAD_52>",
|
| 2431 |
+
"<FAKE_PAD_53>",
|
| 2432 |
+
"<FAKE_PAD_54>",
|
| 2433 |
+
"<FAKE_PAD_55>",
|
| 2434 |
+
"<FAKE_PAD_56>",
|
| 2435 |
+
"<FAKE_PAD_57>",
|
| 2436 |
+
"<FAKE_PAD_58>",
|
| 2437 |
+
"<FAKE_PAD_59>",
|
| 2438 |
+
"<FAKE_PAD_60>",
|
| 2439 |
+
"<FAKE_PAD_61>",
|
| 2440 |
+
"<FAKE_PAD_62>",
|
| 2441 |
+
"<FAKE_PAD_63>",
|
| 2442 |
+
"<FAKE_PAD_64>",
|
| 2443 |
+
"<FAKE_PAD_65>",
|
| 2444 |
+
"<FAKE_PAD_66>",
|
| 2445 |
+
"<FAKE_PAD_67>",
|
| 2446 |
+
"<FAKE_PAD_68>",
|
| 2447 |
+
"<FAKE_PAD_69>",
|
| 2448 |
+
"<FAKE_PAD_70>",
|
| 2449 |
+
"<FAKE_PAD_71>",
|
| 2450 |
+
"<FAKE_PAD_72>",
|
| 2451 |
+
"<FAKE_PAD_73>",
|
| 2452 |
+
"<FAKE_PAD_74>",
|
| 2453 |
+
"<FAKE_PAD_75>",
|
| 2454 |
+
"<FAKE_PAD_76>",
|
| 2455 |
+
"<FAKE_PAD_77>",
|
| 2456 |
+
"<FAKE_PAD_78>",
|
| 2457 |
+
"<FAKE_PAD_79>",
|
| 2458 |
+
"<FAKE_PAD_80>",
|
| 2459 |
+
"<FAKE_PAD_81>",
|
| 2460 |
+
"<FAKE_PAD_82>",
|
| 2461 |
+
"<FAKE_PAD_83>",
|
| 2462 |
+
"<FAKE_PAD_84>",
|
| 2463 |
+
"<FAKE_PAD_85>",
|
| 2464 |
+
"<FAKE_PAD_86>",
|
| 2465 |
+
"<FAKE_PAD_87>",
|
| 2466 |
+
"<FAKE_PAD_88>",
|
| 2467 |
+
"<FAKE_PAD_89>",
|
| 2468 |
+
"<FAKE_PAD_90>",
|
| 2469 |
+
"<FAKE_PAD_91>",
|
| 2470 |
+
"<FAKE_PAD_92>",
|
| 2471 |
+
"<FAKE_PAD_93>",
|
| 2472 |
+
"<FAKE_PAD_94>",
|
| 2473 |
+
"<FAKE_PAD_95>",
|
| 2474 |
+
"<FAKE_PAD_96>",
|
| 2475 |
+
"<FAKE_PAD_97>",
|
| 2476 |
+
"<FAKE_PAD_98>",
|
| 2477 |
+
"<FAKE_PAD_99>",
|
| 2478 |
+
"<FAKE_PAD_100>",
|
| 2479 |
+
"<FAKE_PAD_101>",
|
| 2480 |
+
"<FAKE_PAD_102>",
|
| 2481 |
+
"<FAKE_PAD_103>",
|
| 2482 |
+
"<FAKE_PAD_104>",
|
| 2483 |
+
"<FAKE_PAD_105>",
|
| 2484 |
+
"<FAKE_PAD_106>",
|
| 2485 |
+
"<FAKE_PAD_107>",
|
| 2486 |
+
"<FAKE_PAD_108>",
|
| 2487 |
+
"<FAKE_PAD_109>",
|
| 2488 |
+
"<FAKE_PAD_110>",
|
| 2489 |
+
"<FAKE_PAD_111>",
|
| 2490 |
+
"<FAKE_PAD_112>",
|
| 2491 |
+
"<FAKE_PAD_113>",
|
| 2492 |
+
"<FAKE_PAD_114>",
|
| 2493 |
+
"<FAKE_PAD_115>",
|
| 2494 |
+
"<FAKE_PAD_116>",
|
| 2495 |
+
"<FAKE_PAD_117>",
|
| 2496 |
+
"<FAKE_PAD_118>",
|
| 2497 |
+
"<FAKE_PAD_119>",
|
| 2498 |
+
"<FAKE_PAD_120>",
|
| 2499 |
+
"<FAKE_PAD_121>",
|
| 2500 |
+
"<FAKE_PAD_122>",
|
| 2501 |
+
"<FAKE_PAD_123>",
|
| 2502 |
+
"<FAKE_PAD_124>",
|
| 2503 |
+
"<FAKE_PAD_125>",
|
| 2504 |
+
"<FAKE_PAD_126>",
|
| 2505 |
+
"<FAKE_PAD_127>",
|
| 2506 |
+
"<FAKE_PAD_128>",
|
| 2507 |
+
"<FAKE_PAD_129>",
|
| 2508 |
+
"<FAKE_PAD_130>",
|
| 2509 |
+
"<FAKE_PAD_131>",
|
| 2510 |
+
"<FAKE_PAD_132>",
|
| 2511 |
+
"<FAKE_PAD_133>",
|
| 2512 |
+
"<FAKE_PAD_134>",
|
| 2513 |
+
"<FAKE_PAD_135>",
|
| 2514 |
+
"<FAKE_PAD_136>",
|
| 2515 |
+
"<FAKE_PAD_137>",
|
| 2516 |
+
"<FAKE_PAD_138>",
|
| 2517 |
+
"<FAKE_PAD_139>",
|
| 2518 |
+
"<FAKE_PAD_140>",
|
| 2519 |
+
"<FAKE_PAD_141>",
|
| 2520 |
+
"<FAKE_PAD_142>",
|
| 2521 |
+
"<FAKE_PAD_143>",
|
| 2522 |
+
"<FAKE_PAD_144>",
|
| 2523 |
+
"<FAKE_PAD_145>",
|
| 2524 |
+
"<FAKE_PAD_146>",
|
| 2525 |
+
"<FAKE_PAD_147>",
|
| 2526 |
+
"<FAKE_PAD_148>",
|
| 2527 |
+
"<FAKE_PAD_149>",
|
| 2528 |
+
"<FAKE_PAD_150>",
|
| 2529 |
+
"<FAKE_PAD_151>",
|
| 2530 |
+
"<FAKE_PAD_152>",
|
| 2531 |
+
"<FAKE_PAD_153>",
|
| 2532 |
+
"<FAKE_PAD_154>",
|
| 2533 |
+
"<FAKE_PAD_155>",
|
| 2534 |
+
"<FAKE_PAD_156>",
|
| 2535 |
+
"<FAKE_PAD_157>",
|
| 2536 |
+
"<FAKE_PAD_158>",
|
| 2537 |
+
"<FAKE_PAD_159>",
|
| 2538 |
+
"<FAKE_PAD_160>",
|
| 2539 |
+
"<FAKE_PAD_161>",
|
| 2540 |
+
"<FAKE_PAD_162>",
|
| 2541 |
+
"<FAKE_PAD_163>",
|
| 2542 |
+
"<FAKE_PAD_164>",
|
| 2543 |
+
"<FAKE_PAD_165>",
|
| 2544 |
+
"<FAKE_PAD_166>",
|
| 2545 |
+
"<FAKE_PAD_167>",
|
| 2546 |
+
"<FAKE_PAD_168>",
|
| 2547 |
+
"<FAKE_PAD_169>",
|
| 2548 |
+
"<FAKE_PAD_170>",
|
| 2549 |
+
"<FAKE_PAD_171>",
|
| 2550 |
+
"<FAKE_PAD_172>",
|
| 2551 |
+
"<FAKE_PAD_173>",
|
| 2552 |
+
"<FAKE_PAD_174>",
|
| 2553 |
+
"<FAKE_PAD_175>",
|
| 2554 |
+
"<FAKE_PAD_176>",
|
| 2555 |
+
"<FAKE_PAD_177>",
|
| 2556 |
+
"<FAKE_PAD_178>",
|
| 2557 |
+
"<FAKE_PAD_179>",
|
| 2558 |
+
"<FAKE_PAD_180>",
|
| 2559 |
+
"<FAKE_PAD_181>",
|
| 2560 |
+
"<FAKE_PAD_182>",
|
| 2561 |
+
"<FAKE_PAD_183>",
|
| 2562 |
+
"<FAKE_PAD_184>",
|
| 2563 |
+
"<FAKE_PAD_185>",
|
| 2564 |
+
"<FAKE_PAD_186>",
|
| 2565 |
+
"<FAKE_PAD_187>",
|
| 2566 |
+
"<FAKE_PAD_188>",
|
| 2567 |
+
"<FAKE_PAD_189>",
|
| 2568 |
+
"<FAKE_PAD_190>",
|
| 2569 |
+
"<FAKE_PAD_191>",
|
| 2570 |
+
"<FAKE_PAD_192>",
|
| 2571 |
+
"<FAKE_PAD_193>",
|
| 2572 |
+
"<FAKE_PAD_194>",
|
| 2573 |
+
"<FAKE_PAD_195>",
|
| 2574 |
+
"<FAKE_PAD_196>",
|
| 2575 |
+
"<FAKE_PAD_197>",
|
| 2576 |
+
"<FAKE_PAD_198>",
|
| 2577 |
+
"<FAKE_PAD_199>",
|
| 2578 |
+
"<FAKE_PAD_200>",
|
| 2579 |
+
"<FAKE_PAD_201>",
|
| 2580 |
+
"<FAKE_PAD_202>",
|
| 2581 |
+
"<FAKE_PAD_203>",
|
| 2582 |
+
"<FAKE_PAD_204>",
|
| 2583 |
+
"<FAKE_PAD_205>",
|
| 2584 |
+
"<FAKE_PAD_206>",
|
| 2585 |
+
"<FAKE_PAD_207>",
|
| 2586 |
+
"<FAKE_PAD_208>",
|
| 2587 |
+
"<FAKE_PAD_209>",
|
| 2588 |
+
"<FAKE_PAD_210>",
|
| 2589 |
+
"<FAKE_PAD_211>",
|
| 2590 |
+
"<FAKE_PAD_212>",
|
| 2591 |
+
"<FAKE_PAD_213>",
|
| 2592 |
+
"<FAKE_PAD_214>",
|
| 2593 |
+
"<FAKE_PAD_215>",
|
| 2594 |
+
"<FAKE_PAD_216>",
|
| 2595 |
+
"<FAKE_PAD_217>",
|
| 2596 |
+
"<FAKE_PAD_218>",
|
| 2597 |
+
"<FAKE_PAD_219>",
|
| 2598 |
+
"<FAKE_PAD_220>",
|
| 2599 |
+
"<FAKE_PAD_221>",
|
| 2600 |
+
"<FAKE_PAD_222>",
|
| 2601 |
+
"<FAKE_PAD_223>",
|
| 2602 |
+
"<FAKE_PAD_224>",
|
| 2603 |
+
"<FAKE_PAD_225>",
|
| 2604 |
+
"<FAKE_PAD_226>",
|
| 2605 |
+
"<FAKE_PAD_227>",
|
| 2606 |
+
"<FAKE_PAD_228>",
|
| 2607 |
+
"<FAKE_PAD_229>",
|
| 2608 |
+
"<FAKE_PAD_230>",
|
| 2609 |
+
"<FAKE_PAD_231>",
|
| 2610 |
+
"<FAKE_PAD_232>",
|
| 2611 |
+
"<FAKE_PAD_233>",
|
| 2612 |
+
"<FAKE_PAD_234>",
|
| 2613 |
+
"<FAKE_PAD_235>",
|
| 2614 |
+
"<FAKE_PAD_236>",
|
| 2615 |
+
"<FAKE_PAD_237>",
|
| 2616 |
+
"<FAKE_PAD_238>",
|
| 2617 |
+
"<FAKE_PAD_239>",
|
| 2618 |
+
"<FAKE_PAD_240>",
|
| 2619 |
+
"<FAKE_PAD_241>",
|
| 2620 |
+
"<FAKE_PAD_242>",
|
| 2621 |
+
"<FAKE_PAD_243>",
|
| 2622 |
+
"<FAKE_PAD_244>",
|
| 2623 |
+
"<FAKE_PAD_245>",
|
| 2624 |
+
"<FAKE_PAD_246>",
|
| 2625 |
+
"<FAKE_PAD_247>",
|
| 2626 |
+
"<FAKE_PAD_248>",
|
| 2627 |
+
"<FAKE_PAD_249>",
|
| 2628 |
+
"<FAKE_PAD_250>",
|
| 2629 |
+
"<FAKE_PAD_251>",
|
| 2630 |
+
"<FAKE_PAD_252>",
|
| 2631 |
+
"<FAKE_PAD_253>"
|
| 2632 |
+
],
|
| 2633 |
+
"bos_token": null,
|
| 2634 |
+
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
| 2635 |
+
"clean_up_tokenization_spaces": false,
|
| 2636 |
+
"eos_token": "<|im_end|>",
|
| 2637 |
+
"errors": "replace",
|
| 2638 |
+
"model_max_length": 12288,
|
| 2639 |
+
"pad_token": "<|endoftext|>",
|
| 2640 |
+
"split_special_tokens": false,
|
| 2641 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 2642 |
+
"unk_token": null
|
| 2643 |
+
}
|
vocab.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|