/
test.py
189 lines (156 loc) · 7.91 KB
/
test.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
import torch
import csv
import os
import json
import logging
from fp16 import FP16_Module
import GPUtil
from collections import OrderedDict
from settings import args, MODEL_CLASS, TOKENIZER, SPECIAL_TOKEN_IDS, init_logging
from settings import MEMORY_FACTOR, LEN_FACTOR, TASK_DICT, MODEL_CONFIG, DATA_ATTRS, SPECIAL_TOKENS, CONFIG_CLASS, CONFIG_NAME
from utils import QADataset, top_k_top_p_filtering, create_dataloader, logits_to_tokens, get_model_dir
from utils import sample_sequence, remove_id, get_gen_token, lll_unbound_setting
from metrics import compute_metrics
import random
import numpy as np
logger = logging.getLogger(__name__)
torch.multiprocessing.set_sharing_strategy('file_system')
def test_one_to_one(task_load, task_eval, model, score_dict):
logger.info("start to test { task: %s (load) %s (eval), seq train type: %s }" % (task_load, task_eval, args.seq_train_type))
#print(SPECIAL_TOKEN_IDS)
if args.seq_train_type=='multitask':
test_qadata = QADataset(TASK_DICT[task_eval]["test"] , "test", SPECIAL_TOKEN_IDS[args.tasks[0]]).sort()
else:
test_qadata = QADataset(TASK_DICT[task_eval]["test"] , "test", SPECIAL_TOKEN_IDS[task_load]).sort()
max_a_len = test_qadata.max_a_len
test_dataloader = create_dataloader(test_qadata, "test")
n_examples = len(test_qadata)
logger.info("len of test dataset: {}".format(n_examples))
need_process = OrderedDict()
qa_results = [0 for _ in range(n_examples)]
all_pasts = [[0 for _ in range(n_examples)] for __ in range(MODEL_CONFIG.n_layer)]
max_tot_lens = [0 for _ in range(n_examples)]
cnt = 0
for n_steps, (cqs, len_cqs, _, _, _, _, _, _, _, task_ids, _) in enumerate(test_dataloader):
# assume n_gpus == 1
cqs = cqs[0]
len_cqs = len_cqs[0]
n_inputs = cqs.shape[0]
all_outputs = model(input_ids=cqs.cuda())#,task_ids=task_ids.cuda())
outputs = all_outputs[0]
if args.model_name == "gpt2":
pasts = all_outputs[1]
next_logits = outputs[range(n_inputs), len_cqs-1, :] / args.temperature_qa
next_tokens = logits_to_tokens(next_logits).cpu()
for i in range(n_inputs):
max_tot_lens[cnt] = max_a_len + test_qadata[cnt][1]
qa_results[cnt] = cqs[i][:len_cqs[i]]
if next_tokens[i] != SPECIAL_TOKEN_IDS["eos_token"]:
qa_results[cnt] = torch.cat((cqs[i][:len_cqs[i]], next_tokens[i]))
if len(qa_results[cnt]) not in [max_tot_lens[cnt], args.max_len]:
need_process.update([[cnt, None]])
if args.model_name == "gpt2":
for layer_id in range(MODEL_CONFIG.n_layer):
all_pasts[layer_id][cnt] = pasts[layer_id][:, i, ..., :len_cqs[i], :].type(torch.float32 if args.fp32 else torch.half)
cnt += 1
if len(need_process) > int(12 * args.memory_sizes[0] / cqs.shape[1]): # dynamic threshold to avoid out of memory
sample_sequence(model, need_process, qa_results, all_pasts, max_tot_lens)#,task_ids=task_ids)
sample_sequence(model, need_process, qa_results, all_pasts, max_tot_lens)#,task_ids=task_ids)
if task_eval in ['wikisql','woz.en','multinli.in.out']:
ids = test_qadata.get_indices()
test_qadata.sort_by_index()
qa_results = [x[1] for x in sorted([(i, g) for i, g in zip(ids, qa_results)])]
for i in range(len(test_qadata)):
_, len_cq, _, _, Y, _, _, _,_,_,_,_ = test_qadata[i]
if task_eval in ['wikisql','woz.en']:
Y = test_qadata.answers[i]
else:
Y = list(filter(lambda x: x != -1, Y))[:-1] # remove eos
Y = ' '.join([str(y) for y in Y]).split(str(SPECIAL_TOKEN_IDS["pad_token"]))
Y = [TOKENIZER.decode(list(map(int, y.split()))) for y in Y]
qa_results[i] = [TOKENIZER.decode(qa_results[i].tolist()[len_cq:]), Y]
get_test_score(task_eval, qa_results, score_dict)
model_dir = model.model_dir
ep = model.ep
results_path = os.path.join(model_dir,"qa_{}_{}.csv".format(task_eval,ep+1))
if not args.debug:
with open(results_path, "w",encoding="utf-8") as f:
qa_writer = csv.writer(f,delimiter=',')
qa_writer.writerow(["y","pred"])
for pred, y in qa_results:
if task_eval == 'wikisql':
y = y["answer"]
elif task_eval == 'woz.en':
y = y[1]
qa_writer.writerow([y,pred])
return model, score_dict
def get_test_score(task_eval,qa_results,score_dict):
score = compute_metrics(
qa_results,
bleu='iwslt.en.de' in task_eval or 'multinli.in.out' in task_eval,
dialogue='woz.en' in task_eval,
#rouge='cnn_dailymail' in task_eval,
rouge='cnn_dailymail' in task_eval or 'e2enlg' in task_eval or 'rnnlg.tv' in task_eval or 'rnnlg.rest' in task_eval or 'rnnlg.hotel' in task_eval or 'rnnlg.laptop' in task_eval,
logical_form='wikisql' in task_eval,
corpus_f1='zre' in task_eval
)
score_dict[task_eval] = score
def test_one_to_many(task_load):
score_dicts = []
eps = [args.n_train_epochs[task_load]-1] if args.test_final else [i for i in range(args.n_train_epochs[task_load])]
for ep in eps:#range(args.n_train_epochs[task_load]):
model_dir = get_model_dir([task_load])
model_path = os.path.join(model_dir, 'model-{}'.format(ep+1))
config_path = os.path.join(model_dir,CONFIG_NAME)
if args.seq_train_type=='multitask':
gen_token = get_gen_token(args.tasks[0])
TOKENIZER.add_tokens([gen_token])
SPECIAL_TOKENS[args.tasks[0]] = gen_token
SPECIAL_TOKEN_IDS[args.tasks[0]] = TOKENIZER.convert_tokens_to_ids(gen_token)
else:
gen_token = get_gen_token(task_load)
TOKENIZER.add_tokens([gen_token])
SPECIAL_TOKENS[task_load] = gen_token
SPECIAL_TOKEN_IDS[task_load] = TOKENIZER.convert_tokens_to_ids(gen_token)
model_config = CONFIG_CLASS.from_json_file(config_path)
model_config.use_vae=True
model = MODEL_CLASS(model_config).cuda().eval()
state_dict = torch.load(model_path, map_location='cuda:%s'%args.device_ids[0])
model.load_state_dict(state_dict)
if not args.fp32:
model = FP16_Module(model)
model.ep = ep
model.model_dir = model_dir
logger.info("task: {}, epoch: {}".format(task_load, ep+1))
score_dict = {k:None for k in args.tasks}
with torch.no_grad():
for task_eval in args.tasks:
test_one_to_one(task_load, task_eval, model, score_dict)
logger.info("score: {}".format(score_dict))
score_dicts.append(score_dict)
with open(os.path.join(model_dir, "metrics.json"),"w") as f:
json.dump(score_dicts, f)
if __name__ == '__main__':
if args.n_gpus > 1:
raise NotImplementedError("test can be run with only one gpu currently!")
if args.model_name == "gpt2":
args.fp32 = False # always use fp16 in testing
if not args.debug:
logging.getLogger("pytorch_transformers").setLevel(logging.WARNING)
logging.getLogger("pytorch_transformers.tokenization_utils").setLevel(logging.CRITICAL)
init_logging(os.path.join(args.model_dir_root, 'log_test.txt'))
logger.info('args = {}'.format(args))
if args.seq_train_type == "multitask":
test_one_to_many('_'.join(args.tasks))
#pass
else:
if args.unbound:
TASK_DICT = lll_unbound_setting(split_size=args.unbound, data_type="test",test_target="origin")
for task_load in args.splitted_tasks:
test_one_to_many(task_load)
else:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
for task_load in args.tasks:
test_one_to_many(task_load)