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dev.py
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dev.py
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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
logger = logging.getLogger(__name__)
import pdb
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))
if hasattr(args, 'extra_e2e'):
if args.extra_e2e and task_eval=='e2enlg':
print("USE extra_e2e!", flush=True)
TASK_DICT[task_eval]["test"] = TASK_DICT[task_eval]["test"].replace('test', 'extra')
test_qadata = QADataset(TASK_DICT[task_eval]["test"] if not args.test_training_set else TASK_DICT[task_eval]["train"] , "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, _, _, _, _, _, _) 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())
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)
sample_sequence(model, need_process, qa_results, all_pasts, max_tot_lens)
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, _, _, hashcode, _ = test_qadata[i]
if task_eval in ['wikisql','woz.en']:
Y = test_qadata.answers[i] if not args.test_training_set else test_qadata.answers[0]
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]
if not args.test_training_set:
qa_results[i] = [TOKENIZER.decode(qa_results[i].tolist()[len_cq:]), Y]
else:
qa_results[i] = [TOKENIZER.decode(qa_results[i].tolist()[len_cq:]), Y, hashcode]
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.test_training_set else "qa_trainset_{}_{}.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=',')
if not args.test_training_set:
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])
else:
qa_writer.writerow(["y","pred", "hashcode"])
for pred, y, x in qa_results:
if task_eval == 'wikisql':
y = y["answer"]
elif task_eval == 'woz.en':
y = y[1]
qa_writer.writerow([y,pred,x])
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, #or '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,
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 = []
for ep in range(args.n_train_epochs[task_load]-1, args.n_train_epochs[task_load]) if not args.test_all else 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)
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 = MODEL_CLASS(model_config).cuda().eval()
state_dict = torch.load(model_path, map_location='cuda: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 in ["multitask", "multilm"]:
test_one_to_many('_'.join(args.tasks))
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:
for task_load in args.tasks:
test_one_to_many(task_load)