File size: 16,997 Bytes
96b6673 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 |
import torch
from citekit.prompt.prompt import Prompt
import re
from citekit.utils.utils import one_paragraph, first_sentence, make_as
import random
import os
class Module:
module_count = 1
def __init__(self,prompt_maker: Prompt = None, pipeline = None, self_prompt = {}, iterative = False, merge = False, max_turn =6, output_as = None, parallel = False) -> None:
self.self_prompt = self_prompt
self.use_head_prompt = True
self.connect_to(pipeline)
self.prompt_maker = prompt_maker
self.last_message = ''
self.destinations = []
self.conditions = {}
self.head_key = None
self.parallel = parallel
self.iterative = iterative
self.merge = merge
self.head_process = one_paragraph
self.max_turn = max_turn
self.multi_process = False
self.output_cond = {} # {cond : {'post_processing':post, 'end':end}}
self.count = Module.module_count
Module.module_count += 1
self.if_add_output_to_head = False
self.turns = 0
self.end = False
def __str__(self) -> str:
if self.model_type:
return f'{self.model_type}-[{self.count}]'
else:
return f'Unknown-type module-[{self.count}]'
def get_json_config(self, config):
print('get_json_config:',config)
avaliable_mapping = {
'max turn': 'max_turn',
'prompt': 'prompt',
'destination': 'destination',
'global prompt': 'head_key',
}
if config == 'prompt':
prompt_info = {
'template': self.prompt_maker.template,
'components': self.prompt_maker.components
}
self_info = self.self_prompt
return {
'prompt_info': prompt_info,
'self_info': self_info
}
elif config == 'destination':
return {
'destination': str(self.destinations[0])
}
elif config in ['max turn','global prompt']:
config = avaliable_mapping[config]
print('getting the config:',config)
return getattr(self, config)
else:
raise NotImplementedError(f'get_json_config for {config} is not implemented')
def get_destinations(self):
return self.destinations
def update(self, config, update_info):
if config == 'prompt':
template = update_info['template']
components = update_info['components']
self_prompt = update_info['self_prompt']
import copy
# avoid changing the original prompt_maker
self.prompt_maker = copy.deepcopy(self.prompt_maker)
self.prompt_maker.update(template=template, components=components)
self.self_prompt = self_prompt
elif config == 'destination':
print('update destination:',update_info[0], 'post_processing:',update_info[1])
if update_info[1] == 'None':
self.set_target(update_info[0])
else:
self.set_target(update_info[0], post_processing=make_as(update_info[1]))
elif config == 'delete_destination':
for i, d in enumerate(self.destinations):
if str(d) == str(update_info):
self.destinations.remove(d)
del self.conditions[d]
break
elif config == 'header':
self.add_to_head(update_info, sub = True)
elif config == 'max turn':
self.max_turn = update_info
else:
raise NotImplementedError(f'update for {config} is not implemented')
def end_multi(self):
return
def set_use_head_prompt(self,use):
assert isinstance(use,bool)
self.use_head_prompt = use
def reset(self):
self.end = False
self.turns = 0
def change_to_multi_process(self,bool_value):
if bool_value:
self.last_message = []
else:
self.last_message = ''
self.multi_process = bool_value
@property
def get_use_head_prompt(self):
return self.use_head_prompt
def generate(self, head_prompt: dict = {}, dynamic_prompt: dict = {}):
raise NotImplementedError
def send(self):
for destination in self.destinations:
cond = self.conditions[destination]['condition']
if cond(self):
return destination
return None
def set_target(self,destination, condition = lambda self: True, post_processing = lambda x:x) -> None:
self.conditions[destination] = {'condition': condition, 'post_processing' : post_processing}
self.destinations = [destination] + self.destinations
destination.connect_to(self.pipeline)
def clear_destination(self):
self.destinations = []
self.conditions = {}
def add_output_to_head(self, outputs):
if self.if_add_output_to_head:
if not self.head_sub:
if self.head_key not in self.pipeline.head.keys():
self.pipeline.head.update({self.head_key: self.head_process(outputs)})
else:
self.pipeline.head[self.head_key] += '\n'
self.pipeline.head[self.head_key] += self.head_process(outputs)
else:
self.pipeline.head[self.head_key] = self.head_process(outputs)
def connect_to(self, pipeline = None) -> None:
self.pipeline = pipeline
if pipeline:
pipeline.module.append(self)
def output(self):
outed = False
for cond, post_and_end in self.output_cond.items():
if cond(self):
if not outed:
if not self.merge:
self.pipeline.output.append(post_and_end['post_processing'](self.last_message))
else:
self.pipeline.output.append(post_and_end['post_processing'](''.join(self.last_message)))
outed = True
if post_and_end['end']:
self.end = True
def set_output(self, cond = lambda self: True, post_processing = lambda x:x, end = True):
self.output_cond[cond] = {'post_processing': post_processing, 'end' : end}
def get_first_module(self):
return self
def add_to_head(self, datakey, sub = False, process = None):
self.if_add_output_to_head = True
self.head_key = datakey
self.head_sub = sub
if process:
self.head_process = process
def load_model(model_name_or_path,dtype = torch.float16):
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
torch_dtype=dtype,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model.eval()
return model, tokenizer
class LLM(Module):
model_type = 'Generator'
def __init__(self, model = None, prompt_maker: Prompt =None, pipeline = None, post_processing = None, self_prompt = {}, device = 'cpu',temperature = 0.5 ,stop = None, max_turn = 6, share_model_with = None, iterative = False, auto_cite = False, output = None,merge = False, noisy = True, parallel = False, output_as ='Answer', auto_cite_from = 'docs') -> None:
super().__init__(prompt_maker,pipeline,self_prompt, iterative, merge, parallel = parallel)
self.max_turn = max_turn
if post_processing:
self.post_processing = post_processing
else:
self.post_processing = lambda x: {output_as:x}
if model:
self.model_name = model
self.stop = stop
self.multi_process = False
self.noisy = noisy
self.head_process = one_paragraph
self.auto_cite = auto_cite
if auto_cite:
self.cite_from = auto_cite_from
if model:
if 'gpt' not in model.lower():
if not share_model_with:
print('loading model...')
self.model, self.tokenizer = self.load_model(model)
else:
print('sharing model...')
self.model, self.tokenizer = share_model_with.model, share_model_with.tokenizer
self.temperature = temperature
self.device = device
else:
self.openai_key = os.getenv('OPENAI_API_KEY')
self.output_cond = {} # {cond : {'post_processing':post, 'end':end}}
self.if_add_output_to_head = False
self.token_used = 0
def reset(self):
self.end = False
self.turns = 0
self.token_used = 0
def __str__(self) -> str:
if self.model_name:
return f'{self.model_name}-[{self.count}]'
else:
return 'unknown model'
def __repr__(self) -> str:
return (f'{self.prompt_maker}\n|\n|\nV\n{self}\n|\n|\nV\n'+ '/'.join([str(des) for des in self.destinations]+['output']))
def load_model(self, model_name_or_path,dtype = torch.float16):
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
torch_dtype=dtype,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model.eval()
return model, tokenizer
def set_cite(self,key):
self.cite_from = key
self.auto_cite = True
def generate_content(self, prompt):
if 'gpt' in self.model_name.lower():
import openai
openai.api_key = self.openai_key
prompt = [
{'role': 'system',
'content': "You are a good helper who follow the instructions"},
{'role': 'user', 'content': prompt}
]
response = openai.ChatCompletion.create(
model=self.model_name,
messages=prompt,
max_tokens=500,
stop = self.stop
)
self.token_used += response['usage']['completion_tokens'] + response['usage']['prompt_tokens']
return response['choices'][0]['message']['content']
else:
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
stop = [] if self.stop is None else self.stop
outputs = self.model.generate(
**inputs,
do_sample = True,
max_new_tokens = 200,
temperature = self.temperature
)
self.token_used += len(outputs[0])
outputs = self.tokenizer.decode(outputs[0][inputs['input_ids'].size(1):], skip_special_tokens=True)
return one_paragraph(outputs)
print(outputs)
def generate(self, head_prompt: dict = {}, dynamic_prompt: dict = {}):
if self.use_head_prompt:
#print(head_prompt,self.self_prompt,dynamic_prompt)
prompt = self.prompt_maker(head_prompt,self.self_prompt,dynamic_prompt)
else:
prompt = self.prompt_maker(self.self_prompt,dynamic_prompt)
if self.noisy:
print(f'prompt to {str(self)}:\n',prompt,'\n\n')
self.turns += 1
outputs = self.generate_content(prompt)
#print('DEBUG:',outputs)
if self.noisy:
print('OUTPUT:')
print(outputs)
if self.auto_cite:
outputs = self.cite_from_prompt({**head_prompt,**self.self_prompt,**dynamic_prompt},outputs)
if self.multi_process:
self.last_message.append(outputs)
else:
self.last_message = outputs
self.add_output_to_head(outputs)
destination = self.send()
if self.turns > self.max_turn:
self.end = True
if destination in self.conditions:
return self.conditions[destination]['post_processing'](outputs)
else:
return self.post_processing(outputs)
def add_output_to_head(self, outputs):
if self.if_add_output_to_head:
if not self.head_sub:
if self.head_key not in self.pipeline.head.keys():
self.pipeline.head.update({self.head_key: self.head_process(outputs)})
else:
self.pipeline.head[self.head_key] += '\n'
self.pipeline.head[self.head_key] += self.head_process(outputs)
else:
self.pipeline.head[self.head_key] = self.head_process(outputs)
def output(self):
outed = False
for cond, post_and_end in self.output_cond.items():
if cond(self):
if not outed:
if not self.merge and not self.iterative:
self.pipeline.output.append(post_and_end['post_processing'](self.last_message))
else:
self.pipeline.output.append(post_and_end['post_processing'](' '.join(self.last_message)))
outed = True
if post_and_end['end']:
self.end = True
def set_output(self, cond = lambda self: True, post_processing = lambda x:x, end = True):
self.output_cond[cond] = {'post_processing': post_processing, 'end' : end}
def cite_from_prompt(self,prompt_dict,input):
input = first_sentence(input)
cite_docs = prompt_dict[self.cite_from]
refs = re.findall(r'\[\d+\]', cite_docs)
pattern = r'([.!?])\s*$'
if refs:
cite = ''.join(refs)
else:
cite = ''
output = re.sub(pattern, rf' {cite}\1 ', input)
if cite not in output:
output += cite
return output
def add_to_head(self, datakey, sub = False, process = None):
self.if_add_output_to_head = True
self.head_key = datakey
self.head_sub = sub
if process:
self.head_process = process
class TestLLM(LLM):
def __init__(self, model='gpt-4', prompt_maker: Prompt = None, pipeline=None, post_processing=lambda x: x, self_prompt={}, device='cpu', temperature=0.5, stop=None, max_turn=6,share_model_with = None, iterative= False, ans = None) -> None:
super().__init__(model,prompt_maker,pipeline,self_prompt=self_prompt,share_model_with=share_model_with,iterative=iterative)
self.max_turn = max_turn
self.post_processing = post_processing
self.model_name = model
self.last_message = ''
self.stop = stop
self.output_cond = {} # {cond : {'post_processing':post, 'end':end}}
self.if_add_output_to_head = False
self.token_used = 0
self.ans = 'Strain[1], turns:, heat[2][4]. Sent2[5]. Sent3.\n\n rdd' if not ans else ans
def generate_content(self, prompt):
return self.ans
class AutoAISLLM(LLM):
def __init__(self, model=None, prompt_maker: Prompt = None, pipeline=None, post_processing=None, self_prompt={}, device='cpu', temperature=0.5, stop=None, max_turn=6, share_model_with=None, iterative=False, auto_cite=False, output=None, merge=False, noisy=False, output_as='Answer') -> None:
super().__init__(model, prompt_maker, pipeline, post_processing, self_prompt, device, temperature, stop, max_turn, share_model_with, iterative, auto_cite, output, merge, noisy, output_as)
self.prompt_maker = Prompt('<INST><premise><claim>\n Answer: ',components={
'INST':'{INST}\n\n',
'premise':'Premise: {premise}\n\n',
'claim':'Claim: {claim}\n',
})
self.self_prompt={'INST': 'In this task, you will be presented a premise and a claim. If the premise entails the claim, output "1", otherwise output "1". Your answer should only contains one number without any other letters and punctuations.'}
def generate(self, premise, claim):
dict_answer = super().generate({'premise':premise,'claim':claim})
return dict_answer.get('Answer')
if __name__ == '__main__':
prompt = Prompt(template='<INST><Question><Docs><feedback><Answer>',components={'INST':'{INST}\n\n',
'Question':'Question:{Question}\n\n',
'Docs':'{Docs}\n',
'feedback':'Here is the feed back of your last response:{feedback}\n',
'Answer':'Here is answer and you have to give feedback:{Answer}'})
m = LLM('gpt') |