-
Notifications
You must be signed in to change notification settings - Fork 3
/
factural_eval.py
390 lines (304 loc) · 13.6 KB
/
factural_eval.py
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
# Ref: https://github.com/kojima-takeshi188/zero_shot_cot
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/metrics.py
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/utilities.py
import re
import os
import json
import random
import torch
import numpy as np
import pandas as pd
import transformers
from tqdm import tqdm, trange
import argparse
import pandas as pd
from transformers import AutoConfig
import ssl
import urllib.request
import zipfile
from CN.LLaMA_Analysis.modell import LLaMA_Analysis
transformers.logging.set_verbosity(40)
ANS_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
INVALID_ANS = "[invalid]"
N_SHOT = 7
COT_FLAG = True
DEBUG = False
ANSWER_TRIGGER = "So the answer is"
def split_multi_answer(ans, sep=',', close=True):
"""Splits string of all reference answers into a list of formatted answers"""
answers = ans.strip().split(sep)
split_answers = []
for a in answers[:-1]:
a = a.strip()
if len(a):
if close: # add a period after all answers
# if a[-1] != '.':
# split_answers.append(a + '.')
# else:
split_answers.append(a)
else:
split_answers.append(a)
return split_answers
def format_best(best_ans, close=True):
"""Formats best answer to match format of reference answers"""
best = best_ans.strip()
if close:
if best[-1] != '.':
best = best + '.'
return best
def load_csv(file_path, is_gzip=False):
# input file is in csv format, can be loaded by pandas
# required columns: [Question] only
open_func = open if not is_gzip else gzip.open
list_data = []
with open_func(file_path, 'r') as f:
df = pd.read_csv(f)
for idx in range(len(df)):
data = {'question': df['Question'][idx],
'answer_best': df['Best Answer'][idx],
'answer_true': df['Correct Answers'][idx],
'answer_false': df['Incorrect Answers'][idx]}
list_data.append(data)
return list_data
def load_tsv(file_path, is_gzip=False):
# input file is in csv format, can be loaded by pandas
# required columns: [Question] only
open_func = open if not is_gzip else gzip.open
list_data = []
with open_func(file_path, 'r') as f:
datas = f.readlines()
for data in datas[1:]:
split_data = data.split('\t')
if len(split_data[2].split(',')) >1:
data = {'question': split_data[0],
'answer_true': split_data[1],
'answer_false': split_data[2]}
list_data.append(data)
return list_data
def download_url(url: str, folder='folder'):
"""
Downloads the content of an url to a folder. Modified from \
https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric
Args:
url (string): The url of target file.
folder (string): The target folder.
Returns:
string: File path of downloaded files.
"""
file = url.rpartition('/')[2]
file = file if file[0] == '?' else file.split('?')[0]
path = os.path.join(folder, file)
if os.path.exists(path):
print(f'File {file} exists, use existing file.')
return path
print(f'Downloading {url}')
os.makedirs(folder, exist_ok=True)
ctx = ssl._create_unverified_context()
data = urllib.request.urlopen(url, context=ctx)
with open(path, 'wb') as f:
f.write(data.read())
return path
def extract_answer_from_output(completion):
match = ANS_RE.search(completion)
if match:
match_str = match.group(1).strip()
match_str = match_str.replace(",", "")
return match_str
else:
return INVALID_ANS
def is_correct(model_answer, answer):
gt_answer = answer
assert gt_answer != INVALID_ANS
return model_answer == gt_answer
def create_demo_text():
question, answer = [], []
question.append("G20 consists of <mask>.")
answer.append("Canada")
question.append("kerosene is a subclass of <mask>.")
answer.append("petroleum")
question.append("sundial is a subclass of <mask>.")
answer.append("clock")
question.append("Bordeaux and <mask> are twin cities.")
answer.append("Casablanca")
# Concatenate demonstration examples ...
demo_text = 'Please complete the following text so that it is factually correct.' + '\n\n'
for i in range(len(question)):
demo_text += "Q: " + question[i] + "\nA: " + answer[i] + "\n\n"
return demo_text
def build_prompt(input_text):
demo = create_demo_text()
input_text_prompt = demo + "Q: " + input_text + "\n" + "A:"
return input_text_prompt
def build_prompt_with_answer(question, answer):
demo = create_demo_text()
input_text_prompt = demo + "Q: " + question + "\n" + "A: " + answer
return input_text_prompt
def build_prompt_and_answer(input_text, answer):
demo = create_demo_text()
input_text_prompt = demo + "Q: " + input_text + "\n" + "A:"
continue_text = " " + answer
return input_text_prompt, continue_text
def Math_Cals(scores_true, scores_false, ref_best):
"""Given model scores for true / false reference answers, calculates MC scores"""
scores = {}
scores['max'] = max(scores_true)
scores['diff'] = max(scores_true) - max(scores_false)
scores['scores-true'] = scores_true
scores['scores-false'] = scores_false
# compute MC1: 1vFalse -- best correct answer vs all false answers
max_false = max(scores_false)
if scores_true[0] > max_false:
scores['MC1'] = 1.0
else:
scores['MC1'] = 0.0
return scores
def MC_calcs(scores_true, scores_false, ref_true=None, ref_best=None):
"""Given model scores for true / false reference answers, calculates MC scores"""
scores = {}
scores['max'] = max(scores_true)
scores['diff'] = max(scores_true) - max(scores_false)
scores['scores-true'] = scores_true
scores['scores-false'] = scores_false
# compute MC1: 1vFalse -- best correct answer vs all false answers
max_false = max(scores_false)
if scores_true[0] > max_false:
scores['MC1'] = 1.0
else:
scores['MC1'] = 0.0
# compute MC3: 1vFalse -- each correct answer vs all false answers
max_false = max(scores_false)
onevall = sum(np.array(scores_true) > max_false) / float(len(scores_true))
scores['MC3'] = onevall
# compute MC2: normalized probability mass for correct answers
probs_true = np.exp(scores_true)
while sum(probs_true) == 0:
print("WARNING: all zero scores_true")
scores_true = [x/2.0 for x in scores_true]
probs_true = np.exp(scores_true)
probs_false = np.exp(scores_false)
while sum(probs_false) == 0:
print("WARNING: all zero scores_false")
scores_false = [x/2.0 for x in scores_false]
probs_false = np.exp(scores_false)
probs_true = probs_true / (sum(probs_true) + sum(probs_false))
# check nan
if np.isnan(sum(probs_true)):
scores['MC2'] = 0.0
print(f"WARNING: nan in probs_true: sum(probs_true)={sum(probs_true)}, sum(probs_false)={sum(probs_false)}")
else:
scores['MC2'] = sum(probs_true)
return scores
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-name", type=str, default="huggyllama/llama-7b")
parser.add_argument("--num-gpus", type=str, default="1")
parser.add_argument("--max_gpu_memory", type=int, default=80)
parser.add_argument("--device", type=str, choices=["cuda", "cpu"], default="cuda")
parser.add_argument("--data-path", type=str, default="./tfqa")
parser.add_argument("--output-path", type=str, default="./tfqa_result")
# parallel mode (split the dataset into multiple parts, inference by separate processes)
parser.add_argument("--early-exit-layers", type=str, default="-1")
parser.add_argument("--parallel", action="store_true")
parser.add_argument("--total-shard", type=int, default=8)
parser.add_argument("--shard-id", type=int, default=None)
parser.add_argument("--do-rating", action="store_true")
parser.add_argument("--gpt3-config", type=str, default=None)
parser.add_argument("--debug", action="store_true")
parser.add_argument("--max-new-tokens", type=int, default=50)
parser.add_argument("--top_p", type=float, default=0.95)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.9)
parser.add_argument("--repetition_penalty", type=float, default=1.0)
parser.add_argument("--relative_top", type=float, default=0.0)
parser.add_argument("--relative_top_value", type=float, default=-1000.0)
parser.add_argument("--hidden_layers", type=int, default=80)
parser.add_argument("--layer_wise", action="store_true")
parser.add_argument("--attention", action="store_true")
args = parser.parse_args()
model_name = args.model_name
num_gpus = args.num_gpus
device = args.device
# Get test file
list_data_dict = load_tsv(args.data_path)
if args.debug:
list_data_dict = list_data_dict[:10]
if args.parallel:
chunk_size = len(list_data_dict) // args.total_shard
list_data_dict = list_data_dict[args.shard_id * chunk_size: (args.shard_id + 1) * chunk_size]
llm = LLaMA_Analysis(model_name, device, num_gpus, args.hidden_layers, args.max_gpu_memory)
stop_word_list = ["Q:"]
llm.set_stop_words(stop_word_list)
early_exit_layers = [int(x) for x in args.early_exit_layers.split(',')]
if len(early_exit_layers) == 1:
print("MODE: naive decoding from the last layer", flush=True)
if args.layer_wise:
mode = 'baseline_layer_wise'
elif args.attention:
mode = 'baseline_attention'
else:
mode = "baseline"
mature_layer = None
premature_layer = None
candidate_premature_layers = None
answers = []
result_dict = {'question': [], 'model_scores': [], 'total_mc1': 0.0, 'total_mc2': 0.0, 'total_mc3': 0.0}
config = AutoConfig.from_pretrained(model_name)
if args.layer_wise:
all_results = {f'Layer_{i+1}_lm_head':{'total_mc1':0.0} for i in range(config.num_hidden_layers)}
else:
all_results = {f'Layer_{i+1}_attention':{'add': 0.0, 'sub': 0.0, 'mul': 0.0, 'div': 0.0, 'mix_ops_2': 0.0, 'mix_ops_3': 0.0, "min_ops_brackets":0.0} for i in range(config.num_hidden_layers)}
# all_results = {f'Layer_{i+1}_lm_head':{'total_mc1': 0.0, 'total_mc2': 0.0, 'total_mc3': 0.0} for i in range(config.num_hidden_layers-70)}
with torch.no_grad():
for ind, sample in enumerate(tqdm(list_data_dict)):
# reference answers
# sample = json.loads(sample)
ref_true = sample['answer_true']
# ref_best = format_best(sample['truth_answer'])
# # ref_true = split_multi_answer(sample['answer_true'])
ref_false = split_multi_answer(sample['answer_false'])
# print('answer choices')
# print(ref_best)
# print(ref_true)
# print(ref_false)
scores_true = []
scores_false = []
generate_kwargs = dict(max_new_tokens=args.max_new_tokens, repetition_penalty=args.repetition_penalty, mode=mode, mature_layer=mature_layer, premature_layer=premature_layer, candidate_premature_layers=candidate_premature_layers, relative_top=args.relative_top, relative_top_value=args.relative_top_value, post_softmax=False)
# for temp_ans in ref_true:
# append the current answer choice to the prompt
prompt, answer = build_prompt_and_answer(sample['question'], ref_true)
# print(prompt, answer)
log_probs, c_dist = llm.lm_score(prompt, answer, **generate_kwargs)
scores_true.append(log_probs)
# print(ref_false)
for temp_ans in ref_false:
# append the current answer choice to the prompt
prompt, answer = build_prompt_and_answer(sample['question'], temp_ans)
log_probs, c_dist = llm.lm_score(prompt, answer, **generate_kwargs)
scores_false.append(log_probs)
if args.layer_wise or args.attention:
scores = []
# print(len(log_probs))
for ind_ in range(len(log_probs)):
score_true, score_false = np.array(scores_true)[:,ind_], np.array(scores_false)[:,ind_]
score = Math_Cals(list(score_true), list(score_false), ref_true)
if args.layer_wise:
key_ = f'Layer_{ind_+1}_lm_head'
else:
key_ = f'Layer_{ind_+1}_attention'
# update total scores
all_results[key_]['total_mc1'] += score['MC1']
# Average the scores
# 'add': 0.0, 'sub': 0.0, 'mul': 0.0, 'div': 0.0, 'mix_ops_2': 0.0, 'mix_ops_3': 0.0, "min_ops_brackets":0.0
for key, value in all_results.items():
all_results[key]['total_mc1'] /= len(list_data_dict)
print(f'{key} MC1: \n{all_results[key]}')
import pandas as pd
df = pd.DataFrame(all_results)
# df.drop(index=['total_mc2','total_mc1'])
# df.remove('question')
# Write the DataFrame to an Excel file
if not args.attention:
file_name = args.output_path + f'output-path_layer_wise.xlsx'
else:
file_name = args.output_path + f'output-path_layer_wise_atten.xlsx'
df.to_excel(file_name, index=False)