def test_tokenizer_no_lower(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, do_lower_case=False)
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was', SPIECE_UNDERLINE + u'b', u'or',
u'n', SPIECE_UNDERLINE + u'in', SPIECE_UNDERLINE + u'',
u'9', u'2', u'0', u'0', u'0', u',', SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u'se', u'.'])
def test_tokenizer_no_lower(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, do_lower_case=False)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"se",
".",
],
)
def xlnetTokenizer(*args, **kwargs):
"""
Instantiate a XLNet sentencepiece tokenizer for XLNet from a pre-trained vocab file.
Peculiarities:
- require Google sentencepiece (https://github.com/google/sentencepiece)
Args:
pretrained_model_name_or_path: Path to pretrained model archive
or one of pre-trained vocab configs below.
* xlnet-large-cased
Keyword args:
special_tokens: Special tokens in vocabulary that are not pretrained
Default: None
max_len: An artificial maximum length to truncate tokenized sequences to;
Effective maximum length is always the minimum of this
value (if specified) and the underlying model's
sequence length.
Default: None
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
>>> text = "Who was Jim Henson ?"
>>> indexed_tokens = tokenizer.encode(tokenized_text)
"""
tokenizer = XLNetTokenizer.from_pretrained(*args, **kwargs)
return tokenizer
def test_full_tokenizer(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize(u'This is a test')
self.assertListEqual(tokens, [u'▁This', u'▁is', u'▁a', u'▁t', u'est'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382])
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [
SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was',
SPIECE_UNDERLINE + u'b', u'or', u'n', SPIECE_UNDERLINE + u'in',
SPIECE_UNDERLINE + u'', u'9', u'2', u'0', u'0', u'0', u',',
SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u's',
u'é', u'.'
])
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [
8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72,
80, 6, 0, 4
])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(back_tokens, [
SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was',
SPIECE_UNDERLINE + u'b', u'or', u'n', SPIECE_UNDERLINE + u'in',
SPIECE_UNDERLINE + u'', u'<unk>', u'2', u'0', u'0', u'0', u',',
SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u's',
u'<unk>', u'.'
])
def test_sequence_builders(self):
tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
encoded_pair = tokenizer.add_special_tokens_sentences_pair(
text, text_2)
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_2 + [4, 3]
def init_params():
processors = {"sentiment_analysis": SentiAnalysisProcessor}
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
if args.model_type == 'bert':
tokenizer = BertTokenizer(vocab_file=args.VOCAB_FILE)
elif args.model_type == 'xlnet':
tokenizer = XLNetTokenizer.from_pretrained(
os.path.join(args.ROOT_DIR, args.xlnet_model),
do_lower_case=args.do_lower_case)
return processor, tokenizer
def test_full_tokenizer(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
with TemporaryDirectory() as tmpdirname:
tokenizer.save_pretrained(tmpdirname)
input_text = u"This is a test"
output_text = u"This is a test"
create_and_check_tokenizer_commons(self, input_text, output_text,
XLNetTokenizer, tmpdirname)
tokens = tokenizer.tokenize(u'This is a test')
self.assertListEqual(tokens,
[u'▁This', u'▁is', u'▁a', u'▁t', u'est'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382])
tokens = tokenizer.tokenize(
u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [
SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was',
SPIECE_UNDERLINE + u'b', u'or', u'n', SPIECE_UNDERLINE + u'in',
SPIECE_UNDERLINE + u'', u'9', u'2', u'0', u'0', u'0', u',',
SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u's',
u'é', u'.'
])
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [
8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46,
72, 80, 6, 0, 4
])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(back_tokens, [
SPIECE_UNDERLINE + u'I', SPIECE_UNDERLINE + u'was',
SPIECE_UNDERLINE + u'b', u'or', u'n', SPIECE_UNDERLINE + u'in',
SPIECE_UNDERLINE + u'', u'<unk>', u'2', u'0', u'0', u'0', u',',
SPIECE_UNDERLINE + u'and', SPIECE_UNDERLINE + u'this',
SPIECE_UNDERLINE + u'is', SPIECE_UNDERLINE + u'f', u'al', u's',
u'<unk>', u'.'
])
def get_tokenizer(self):
return XLNetTokenizer.from_pretrained(self.tmpdirname)
def setUp(self):
super(XLNetTokenizationTest, self).setUp()
# We have a SentencePiece fixture for testing
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return XLNetTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def test_full_tokenizer(self):
tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
with TemporaryDirectory() as tmpdirname:
tokenizer.save_pretrained(tmpdirname)
input_text = "This is a test"
output_text = "This is a test"
create_and_check_tokenizer_commons(self, input_text, output_text,
XLNetTokenizer, tmpdirname)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382])
tokens = tokenizer.tokenize(
"I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66,
46, 72, 80, 6, 0, 4
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
def preprocess():
'''
针对测试集数据预测
:return:
'''
model = load_model(os.path.join(args.ROOT_DIR, args.output_dir),
args.model_type)
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
n_gpu = 1
model.to(device)
processors = {"sentiment_analysis": SentiAnalysisProcessor}
task_name = args.task_name.lower()
processor = processors[task_name]()
examples = processor.get_test_examples(args.data_dir)
tokenizer = XLNetTokenizer.from_pretrained(
os.path.join(args.ROOT_DIR, args.xlnet_model),
do_lower_case=args.do_lower_case)
mode = 'test'
try:
if mode == 'test':
with open(os.path.join(args.data_dir, args.TEST_FEATURE_FILE),
'rb') as f: # TRAIN_FEATURE_FILE
features = pickle.load(f)
except:
features = convert_examples_to_features(
examples,
args.max_seq_length,
args.split_num,
tokenizer,
mode=mode,
cls_token_at_end=bool(args.model_type in ['xlnet']),
# xlnet has a cls token at the end
cls_token=tokenizer.cls_token,
sep_token=tokenizer.sep_token,
cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0,
pad_on_left=bool(
args.model_type in ['xlnet']), # pad on the left for xlnet
pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0)
all_input_ids = torch.tensor(select_field(features, 'input_ids'),
dtype=torch.long)
all_input_mask = torch.tensor(select_field(features, 'input_mask'),
dtype=torch.long)
all_segment_ids = torch.tensor(select_field(features, 'segment_ids'),
dtype=torch.long)
#数据集
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids)
sampler = SequentialSampler(dataset) #作用近似shuffle
dataloader = DataLoader(dataset,
sampler=sampler,
batch_size=args.per_gpu_train_batch_size *
max(1, n_gpu))
model.eval()
y_predicts = []
for input_ids, input_mask, segment_ids in dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
logits = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)[0]
predicts = model.predict(logits)
y_predicts.append(torch.from_numpy(predicts))
eval_predicted = torch.cat(y_predicts, dim=0).cpu().numpy()
df = pd.read_csv(os.path.join(args.data_dir, args.TEST_CORPUS_FILE))
df['labels'] = eval_predicted
df[['id', 'labels']].to_csv('./data/test_final.csv',
sep=',',
encoding='utf_8_sig',
header=True,
index=False)
def convert_split_doc_to_feature(doc,
max_seq_length,
split_num,
cls_token_at_end=False,
pad_on_left=False,
cls_token='[CLS]',
sep_token='[SEP]',
pad_token=0,
sequence_a_segment_id=0,
sequence_b_segment_id=1,
cls_token_segment_id=1,
pad_token_segment_id=0,
mask_padding_with_zero=True):
'''
将测试文本转化为测试特征
:param doc: corpus for test
:return: feature for predicting
'''
tokenizer = XLNetTokenizer.from_pretrained(
os.path.join(args.ROOT_DIR, args.xlnet_model),
do_lower_case=args.do_lower_case)
tokens_a = tokenizer.tokenize(doc)
skip_len = len(tokens_a) / split_num
choices_features = []
for i in range(split_num):
context_tokens_choice = tokens_a[int(i * skip_len):int((i + 1) *
skip_len)]
tokens_b = None
# Account for [CLS] and [SEP] with "- 2"
if len(context_tokens_choice) > max_seq_length - 2:
context_tokens_choice = context_tokens_choice[:(max_seq_length -
2)]
tokens = context_tokens_choice + [sep_token]
segment_ids = [sequence_a_segment_id] * len(tokens)
if tokens_b:
tokens += tokens_b + [sep_token]
segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
if cls_token_at_end:
tokens = tokens + [cls_token]
segment_ids = segment_ids + [cls_token_segment_id]
else:
tokens = [cls_token] + tokens
segment_ids = [cls_token_segment_id] + segment_ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = max_seq_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
input_mask = ([0 if mask_padding_with_zero else 1] *
padding_length) + input_mask
segment_ids = ([pad_token_segment_id] *
padding_length) + segment_ids
else:
input_ids = input_ids + ([pad_token] * padding_length)
input_mask = input_mask + ([0 if mask_padding_with_zero else 1] *
padding_length)
segment_ids = segment_ids + ([pad_token_segment_id] *
padding_length)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
choices_features.append({
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids
})
# logger.info("*** Example ***")
# logger.info("tokens: {}".format(' '.join(tokens).replace('\u2581', '_')))
# logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
# logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
# logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
return choices_features
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--model_type", default=None, type=str, required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--meta_path", default=None, type=str, required=False,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--config_name", default="", type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--cache_dir", default="", type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train", action='store_true',
help="Whether to run training.")
parser.add_argument("--do_test", action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval", action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--evaluate_during_training", action='store_true',
help="Rul evaluation during training at each logging step.")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--split_num", default=3, type=int,
help="text split")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=3.0, type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
parser.add_argument("--eval_steps", default=-1, type=int,
help="")
parser.add_argument("--train_steps", default=-1, type=int,
help="")
parser.add_argument("--report_steps", default=-1, type=int,
help="")
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--lstm_hidden_size", default=300, type=int,
help="")
parser.add_argument("--lstm_layers", default=2, type=int,
help="")
parser.add_argument("--lstm_dropout", default=0.5, type=float,
help="")
parser.add_argument('--logging_steps', type=int, default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--overwrite_output_dir', action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
args = parser.parse_args()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
set_seed(args)
try:
os.makedirs(args.output_dir)
except:
pass
tokenizer = XLNetTokenizer.from_pretrained(args.model_name_or_path, do_lower_case=args.do_lower_case)
config = XLNetConfig.from_pretrained(args.model_name_or_path, num_labels=3)
# Prepare model
model = XLNetForSequenceClassification.from_pretrained(args.model_name_or_path,args,config=config)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
if args.do_train:
# Prepare data loader
train_examples = read_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
train_features = convert_examples_to_features(
train_examples, tokenizer, args.max_seq_length,args.split_num, True)
all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size//args.gradient_accumulation_steps)
num_train_optimization_steps = args.train_steps
# Prepare optimizer
param_optimizer = list(model.named_parameters())
# hack to remove pooler, which is not used
# thus it produce None grad that break apex
param_optimizer = [n for n in param_optimizer]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=5000)
global_step = 0
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
best_acc=0
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
bar = tqdm(range(num_train_optimization_steps),total=num_train_optimization_steps)
train_dataloader=cycle(train_dataloader)
for step in bar:
batch = next(train_dataloader)
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.fp16 and args.loss_scale != 1.0:
loss = loss * args.loss_scale
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
train_loss=round(tr_loss*args.gradient_accumulation_steps/(nb_tr_steps+1),4)
bar.set_description("loss {}".format(train_loss))
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
if (nb_tr_steps + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if (step + 1) %(args.eval_steps*args.gradient_accumulation_steps)==0:
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
logger.info("***** Report result *****")
logger.info(" %s = %s", 'global_step', str(global_step))
logger.info(" %s = %s", 'train loss', str(train_loss))
if args.do_eval and (step + 1) %(args.eval_steps*args.gradient_accumulation_steps)==0:
for file in ['dev.csv']:
inference_labels=[]
gold_labels=[]
inference_logits=[]
eval_examples = read_examples(os.path.join(args.data_dir, file), is_training = True)
eval_features = convert_examples_to_features(eval_examples, tokenizer, args.max_seq_length,args.split_num,False)
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss= model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
logits = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
inference_labels.append(np.argmax(logits, axis=1))
gold_labels.append(label_ids)
inference_logits.append(logits)
eval_loss += tmp_eval_loss.mean().item()
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
gold_labels=np.concatenate(gold_labels,0)
inference_logits=np.concatenate(inference_logits,0)
model.train()
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = accuracy(inference_logits, gold_labels)
result = {'eval_loss': eval_loss,
'eval_F1': eval_accuracy,
'global_step': global_step,
'loss': train_loss}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a") as writer:
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write('*'*80)
writer.write('\n')
if eval_accuracy>best_acc and 'dev' in file:
print("="*80)
print("Best F1",eval_accuracy)
print("Saving Model......")
best_acc=eval_accuracy
# Save a trained model
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
print("="*80)
else:
print("="*80)
if args.do_test:
del model
gc.collect()
args.do_train=False
model = XLNetForSequenceClassification.from_pretrained(os.path.join(args.output_dir, "pytorch_model.bin"),args,config=config)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
for file,flag in [('dev.csv','dev'),('test.csv','test')]:
inference_labels=[]
gold_labels=[]
eval_examples = read_examples(os.path.join(args.data_dir, file), is_training = False)
eval_features = convert_examples_to_features(eval_examples, tokenizer, args.max_seq_length,args.split_num,False)
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_label)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask).detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
inference_labels.append(logits)
gold_labels.append(label_ids)
gold_labels=np.concatenate(gold_labels,0)
logits=np.concatenate(inference_labels,0)
print(flag, accuracy(logits, gold_labels))
if flag=='test':
df=pd.read_csv(os.path.join(args.data_dir, file))
df['label_0']=logits[:,0]
df['label_1']=logits[:,1]
df['label_2']=logits[:,2]
df[['id','label_0','label_1','label_2']].to_csv(os.path.join(args.output_dir, "sub.csv"),index=False)