def __init__(self, args):
torch.manual_seed(args.seed)
self.args = args
# Tokenizer, Generator, Discriminator
if args.load_epoch > -1: # NOTE: 0-indexed. Load from trained
gen_path, dis_path = get_gan_path(self.args.model_out,
self.args.load_epoch)
else:
gen_path, dis_path = args.bert_model, args.bert_model
self.tokenizer = BertTokenizer.from_pretrained(
gen_path) # TODO requires_grad = False?
self.generator = BertForMaskedLM.from_pretrained(gen_path)
self.discriminator = BertForSequenceClassification.from_pretrained(
dis_path, num_labels=self.args.num_labels)
# Optimizer
self.optimizerG = self._get_optimizer_(self.generator)
self.optimizerD = self._get_optimizer_(self.discriminator)
# DataLoader
self.msk_data = load_data(args.data_in, args.maxlen, args.batch_size,
self.tokenizer, args.seed, 'masked')
self.org_data = load_data(args.data_in, args.maxlen, args.batch_size,
self.tokenizer, args.seed, 'original')
self.mask_id = self.tokenizer.convert_tokens_to_ids(['[MASK]'])[0]
self.device = torch.device("cuda:0" if args.cuda else "cpu")
self.generator.to(self.device)
self.discriminator.to(self.device)
def main():
batch_size = 16
max_seq_len = 128
model_dir = 'fine_tuned--bert-base-uncased--SEQ_LEN=128--BATCH_SIZE=32--HEAD=1'
output_filename = os.path.join(
model_dir, "fine-tuned-sent-classifer-test-results.csv")
test_sets_dir = "dataset\custom_test_set"
test_files = [filename for filename in os.listdir(test_sets_dir)]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = BertTokenizer.from_pretrained(model_dir)
model = BertForSequenceClassification.from_pretrained(model_dir)
model.to(device)
criterion = Softmax()
accuracies = {}
for filename in test_files:
print("Testing on dataset: {}".format(filename))
file_path = os.path.join(test_sets_dir, filename)
test_dataset = Dataset(file_path, tokenizer, max_seq_len)
test_dataloader = data.DataLoader(test_dataset, batch_size=batch_size)
accuracy = 0
for batch in test_dataloader:
with torch.no_grad():
batch = (t.to(device) for t in batch)
input_ids, input_mask, segment_ids, labels = batch
outputs = model(input_ids, input_mask, segment_ids)
logits = outputs[0]
_, predictions = criterion(logits).max(-1)
results = predictions == labels
accuracy += results.sum().item()
accuracy = accuracy / len(test_dataset)
print("Model achieved {}'%' accuracy".format(accuracy))
dataset_name = filename.split('.')[0]
accuracies[dataset_name] = accuracy
with open(output_filename, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=accuracies.keys())
writer.writeheader()
writer.writerow(accuracies)
def bertForSequenceClassification(*args, **kwargs):
"""
BertForSequenceClassification is a fine-tuning model that includes
BertModel and a sequence-level (sequence or pair of sequences) classifier
on top of the BertModel. Note that the classification head is only initialized
and has to be trained.
The sequence-level classifier is a linear layer that takes as input the
last hidden state of the first character in the input sequence
(see Figures 3a and 3b in the BERT paper).
Args:
num_labels: the number (>=2) of classes for the classifier.
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
# Load bertForSequenceClassification
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2)
>>> model.eval()
# Predict the sequence classification logits
>>> with torch.no_grad():
seq_classif_logits = model(tokens_tensor, segments_tensors)
# Or get the sequence classification loss
>>> labels = torch.tensor([1])
>>> seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
"""
model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
return model
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("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
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("--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 WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
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("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=64,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=256,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
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('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
# if args.server_ip and args.server_port:
# # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
# import ptvsd
# print("Waiting for debugger attach")
# ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
# ptvsd.wait_for_attach()
processors = {
# "cola": ColaProcessor,
# "mnli": MnliProcessor,
# "mnli-mm": MnliMismatchedProcessor,
# "mrpc": MrpcProcessor,
# "sst-2": Sst2Processor,
# "sts-b": StsbProcessor,
# "qqp": QqpProcessor,
# "qnli": QnliProcessor,
"rte": RteProcessor
# "wnli": WnliProcessor,
}
output_modes = {
# "cola": "classification",
# "mnli": "classification",
# "mrpc": "classification",
# "sst-2": "classification",
# "sts-b": "regression",
# "qqp": "classification",
# "qnli": "classification",
"rte": "classification"
# "wnli": "classification",
}
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:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels() #[0,1]
num_labels = len(label_list)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples, seen_types = processor.get_examples_Wikipedia_train('/export/home/Dataset/wikipedia/parsed_output/tokenized_wiki/tokenized_wiki2categories.txt', 100000) #train_pu_half_v1.txt
# seen_classes=[0,2,4,6,8]
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_TRANSFORMERS_CACHE), 'distributed_{}'.format(args.local_rank))
# model = BertForSequenceClassification.from_pretrained(args.bert_model,
# cache_dir=cache_dir,
# num_labels=num_labels)
# tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
pretrain_model_dir = 'bert-base-uncased' #FineTuneOnCombined'# FineTuneOnMNLI
model = BertForSequenceClassification.from_pretrained(pretrain_model_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(pretrain_model_dir, do_lower_case=args.do_lower_case)
if args.fp16:
model.half()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
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': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
else:
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
max_test_unseen_acc = 0.0
max_dev_unseen_acc = 0.0
max_dev_seen_acc = 0.0
max_overall_acc = 0.0
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
'''load dev set'''
eval_examples, eval_label_list, eval_hypo_seen_str_indicator, eval_hypo_2_type_index = processor.get_examples_situation_test('/export/home/Dataset/LORELEI/zero-shot-split/dev.txt', seen_types)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
eval_all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
eval_all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
eval_all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
eval_all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(eval_all_input_ids, eval_all_input_mask, eval_all_segment_ids, eval_all_label_ids)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
'''load test set'''
test_examples, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index = processor.get_examples_situation_test('/export/home/Dataset/LORELEI/zero-shot-split/test.txt', seen_types)
test_features = convert_examples_to_features(
test_examples, label_list, args.max_seq_length, tokenizer, output_mode)
test_all_input_ids = torch.tensor([f.input_ids for f in test_features], dtype=torch.long)
test_all_input_mask = torch.tensor([f.input_mask for f in test_features], dtype=torch.long)
test_all_segment_ids = torch.tensor([f.segment_ids for f in test_features], dtype=torch.long)
test_all_label_ids = torch.tensor([f.label_id for f in test_features], dtype=torch.long)
test_data = TensorDataset(test_all_input_ids, test_all_input_mask, test_all_segment_ids, test_all_label_ids)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=args.eval_batch_size)
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)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
# print('train all_label_ids:', all_label_ids)
# exit(0)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
iter_co = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
model.train()
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
logits = model(input_ids, segment_ids, input_mask, labels=None)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits[0].view(-1, num_labels), label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
optimizer.step()
optimizer.zero_grad()
global_step += 1
iter_co+=1
if iter_co %200==0:
'''
start evaluate on dev set after this epoch
'''
model.eval()
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0
nb_eval_steps = 0
preds = []
print('Evaluating...')
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, segment_ids, input_mask, labels=None)
logits = logits[0]
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(preds[0], logits.detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
'''
preds: size*2 (entail, not_entail)
wenpeng added a softxmax so that each row is a prob vec
'''
pred_probs = softmax(preds,axis=1)[:,0]
pred_binary_labels_harsh = []
pred_binary_labels_loose = []
for i in range(preds.shape[0]):
if preds[i][0]>preds[i][1]+0.1:
pred_binary_labels_harsh.append(0)
else:
pred_binary_labels_harsh.append(1)
if preds[i][0]>preds[i][1]:
pred_binary_labels_loose.append(0)
else:
pred_binary_labels_loose.append(1)
seen_acc, unseen_acc = evaluate_situation_zeroshot_TwpPhasePred(pred_probs, pred_binary_labels_harsh, pred_binary_labels_loose, eval_label_list, eval_hypo_seen_str_indicator, eval_hypo_2_type_index, seen_types)
# result = compute_metrics('F1', preds, all_label_ids.numpy())
loss = tr_loss/nb_tr_steps if args.do_train else None
# test_acc = mean_f1#result.get("f1")
if unseen_acc > max_dev_unseen_acc:
max_dev_unseen_acc = unseen_acc
print('\ndev seen_f1 & unseen_f1:', seen_acc,unseen_acc, ' max_dev_unseen_f1:', max_dev_unseen_acc, '\n')
# if seen_acc+unseen_acc > max_overall_acc:
# max_overall_acc = seen_acc + unseen_acc
# if seen_acc > max_dev_seen_acc:
# max_dev_seen_acc = seen_acc
'''
start evaluate on test set after this epoch
'''
model.eval()
logger.info("***** Running testing *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
test_loss = 0
nb_test_steps = 0
preds = []
print('Testing...')
for input_ids, input_mask, segment_ids, label_ids in test_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, segment_ids, input_mask, labels=None)
logits = logits[0]
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(preds[0], logits.detach().cpu().numpy(), axis=0)
# eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
pred_probs = softmax(preds,axis=1)[:,0]
pred_binary_labels_harsh = []
pred_binary_labels_loose = []
for i in range(preds.shape[0]):
if preds[i][0]>preds[i][1]+0.1:
pred_binary_labels_harsh.append(0)
else:
pred_binary_labels_harsh.append(1)
if preds[i][0]>preds[i][1]:
pred_binary_labels_loose.append(0)
else:
pred_binary_labels_loose.append(1)
seen_acc, unseen_acc = evaluate_situation_zeroshot_TwpPhasePred(pred_probs, pred_binary_labels_harsh, pred_binary_labels_loose, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index, seen_types)
# result = compute_metrics('F1', preds, all_label_ids.numpy())
# loss = tr_loss/nb_tr_steps if args.do_train else None
# test_acc = mean_f1#result.get("f1")
if unseen_acc > max_test_unseen_acc:
max_test_unseen_acc = unseen_acc
print('\n\n\t test seen_f1 & unseen_f1:', seen_acc,unseen_acc, ' max_test_unseen_f1:', max_test_unseen_acc, '\n')
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("--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("--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("--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("--split_num", default=3, type=int, help="text split")
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 = BertTokenizer.from_pretrained(args.model_name_or_path,
do_lower_case=args.do_lower_case)
config = BertConfig.from_pretrained(args.model_name_or_path, num_labels=3)
# Prepare model
model = BertForSequenceClassification.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=args.train_steps)
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 = BertForSequenceClassification.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)
def train(self):
model = BertForSequenceClassification.from_pretrained(
self.args.model_name_or_path, self.args, config=self.config)
model.to(self.device)
logger.info('准备数据')
data = DATABDCI(
debug=False,
data_dir='/home/lsy2018/文本匹配/DATA/DATA_BDCI/',
data_process_output='/home/lsy2018/文本匹配/DATA/DATA_BDCI/data_1014/')
train_examples = data.read_examples(
os.path.join(self.data_process_output, 'train.csv'))
train_features = data.convert_examples_to_features(
train_examples, self.tokenizer, self.max_seq_length)
all_input_ids = torch.tensor(data.select_field(train_features,
'input_ids'),
dtype=torch.long)
all_input_mask = torch.tensor(data.select_field(
train_features, 'input_mask'),
dtype=torch.long)
all_segment_ids = torch.tensor(data.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)
# 这步干嘛的？
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=self.batch_size //
self.gradient_accumulation_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':
self.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=self.learning_rate,
eps=self.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=self.warmup_steps,
t_total=self.train_steps)
best_acc = 0
global_step = 0
model.train()
train_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
bar = tqdm(range(self.train_steps), total=self.train_steps)
train_dataloader = cycle(train_dataloader)
for step in bar:
batch = next(train_dataloader)
batch = tuple(t.to(self.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)
train_loss += loss.item()
train_loss = round(
train_loss * self.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
loss.backward()
if (nb_tr_steps + 1) % self.gradient_accumulation_steps == 0:
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if self.do_eval and (step + 1) % (
self.eval_steps * self.gradient_accumulation_steps) == 0:
inference_labels = []
scores = []
gold_labels = []
inference_logits = []
eval_examples = data.read_examples(
os.path.join(self.data_process_output, 'dev.csv'))
eval_features = data.convert_examples_to_features(
eval_examples, self.tokenizer, self.max_seq_length)
ID1 = [x.sentence_ID1 for x in eval_examples]
ID2 = [x.sentence_ID2 for x in eval_examples]
all_input_ids = torch.tensor(data.select_field(
eval_features, 'input_ids'),
dtype=torch.long)
all_input_mask = torch.tensor(data.select_field(
eval_features, 'input_mask'),
dtype=torch.long)
all_segment_ids = torch.tensor(data.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", self.batch_size)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=self.batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
count = 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
# ID1_list_eachbatch = ID1[count*args.eval_batch_size:(count+1)*args.eval_batch_size]
# ID2_list_eachbatch = ID2[count * args.eval_batch_size:(count + 1) * args.eval_batch_size]
input_ids = input_ids.to(self.device)
input_mask = input_mask.to(self.device)
segment_ids = segment_ids.to(self.device)
label_ids = label_ids.to(self.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))
# scores.append(logits)
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)
# scores = np.concatenate(scores, 0)
model.train()
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = accuracy(inference_logits, gold_labels)
# eval_mrr = compute_MRR(scores, gold_labels, ID1, ID2)
result = {
'eval_loss': eval_loss,
'eval_F1': eval_accuracy,
'global_step': global_step,
# 'mrr':eval_mrr,
'loss': train_loss
}
output_eval_file = os.path.join(self.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:
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(
self.output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(),
output_model_file)
print("=" * 80)
else:
print("=" * 80)
def main():
parser = argparse.ArgumentParser()
## Required parameters(即required=True的参数必须在命令上出现)
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"数据集路径. 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="模型类型(这里为bert). Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()))
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help=
"下载好的预训练模型. 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=
"模型预测和断点文件的存放路径. The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help=
"预训练的配置名字或路径. Pretrained config name or path if not the same as model_name"
)
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help=
"预训练分词器名字或路径. Pretrained tokenizer name or path if not the same as model_name"
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"从亚马逊s3下载的预训练模型存放路径. Where do you want to store the pre-trained models downloaded from s3"
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"最长序列长度. 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="是否训练. Whether to run training.")
parser.add_argument("--do_test",
action='store_true',
help="是否测试. Whether to run testing.")
parser.add_argument("--predict_eval",
action='store_true',
help="是否预测验证集. Whether to predict eval set.")
parser.add_argument("--do_eval",
action='store_true',
help="是否验证. Whether to run eval on the dev set.")
parser.add_argument(
"--evaluate_during_training",
action='store_true',
help="是否训练中跑验证. Run evaluation during training at each logging step.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="是否用小写模型. Set this flag if you are using an uncased model.")
parser.add_argument(
"--per_gpu_train_batch_size",
default=8,
type=int,
help="训练时每个GPU/CPU上的batch size. Batch size per GPU/CPU for training.")
parser.add_argument(
"--per_gpu_eval_batch_size",
default=8,
type=int,
help="验证时每个GPU/CPU上的batch size. Batch size per GPU/CPU for evaluation."
)
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"反向传播前梯度累计的次数. Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="Adam的初始学习率. The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="权重衰减系数. Weight deay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Adam的Epsilon系数. Epsilon for Adam optimizer.")
parser.add_argument(
"--max_grad_norm",
default=1.0,
type=float,
help=
" 如果所有参数的gradient组成的向量的L2 norm大于max norm，那么需要根据L2 norm/max_norm进行缩放。从而使得L2 norm小于预设的clip_norm. Max gradient norm."
)
parser.add_argument(
"--num_train_epochs",
default=3.0,
type=float,
help="训练epoch数. 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("--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("--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="线性warmup的steps. Linear warmup over warmup_steps.")
parser.add_argument("--split_num",
default=3,
type=int,
help="测试集划分. text split")
parser.add_argument('--logging_steps',
type=int,
default=50,
help="日志更新steps. Log every X updates steps.")
parser.add_argument(
'--save_steps',
type=int,
default=50,
help="断点文件保存steps. Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action='store_true',
help=
"评估所有的断点. 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="不用cuda. Avoid using CUDA when available")
parser.add_argument(
'--overwrite_output_dir',
action='store_true',
help="重写输出路径. Overwrite the content of the output directory")
parser.add_argument(
'--overwrite_cache',
action='store_true',
help="重写训练和评估的缓存. Overwrite the cached training and evaluation sets")
parser.add_argument('--seed',
type=int,
default=42,
help="初始化用的随机种子. random seed for initialization")
parser.add_argument(
'--fp16',
action='store_true',
help=
"是否用16位混合精度. 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=
"fp16的优化level. 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="为了分布式训练. For distributed training: local_rank")
parser.add_argument('--server_ip',
type=str,
default='',
help="远程debug用的ip. For distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="远程debug用的端口. For distant debugging.")
parser.add_argument("--freeze",
default=0,
type=int,
required=False,
help="冻结BERT. freeze bert.")
parser.add_argument("--not_do_eval_steps",
default=0.35,
type=float,
help="not_do_eval_steps.")
args = parser.parse_args()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
# 如果无指定GPU或允许使用CUDA，就使用当前所有GPU
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
# 指定使用哪个GPU（local_rank代表当前程序进程使用的GPU标号）
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 初始化日志
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 设置种子数
set_seed(args)
# 创建存放路径
try:
os.makedirs(args.output_dir)
except:
pass
# 载入预训练好的BERT分词器
tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path,
do_lower_case=args.do_lower_case)
# 载入预设好的BERT配置文件
config = BertConfig.from_pretrained(args.model_name_or_path, num_labels=2)
# Prepare model 载入并配置好基于BERT的序列分类模型
model = BertForSequenceClassification.from_pretrained(
args.model_name_or_path, args, config=config)
# 开启FP16
if args.fp16:
model.half()
model.to(device)
# 如果是指定了单个GPU，用DistributedDataParallel进行GPU训练
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)
# 如果有多个GPU，就直接用torch.nn.DataParallel，会自动调用当前可用的多个GPU
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# 总batch size = GPU数量 * 每个GPU上的mbatch size
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 导入数据并准备符合格式的输入
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)
# 如果无指定GPU就随机采样，如果指定了GPU就分布式采样
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
# 准备dataloader
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size //
args.gradient_accumulation_steps)
# 训练steps
num_train_optimization_steps = args.train_steps
# Prepare optimizer 准备优化器
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_dacay内的参数不参与权重衰减
# BN是固定C,[B,H,W]进行归一化处理（处理为均值0,方差1的正太分布上），适用于CNN
# LN是固定N,[C,H,W]进行归一化处理，适用于RNN（BN适用于固定深度的前向神经网络，而RNN因输入序列长度不一致而深度不固定，因此BN不合适，而LN不依赖于batch的大小和输入sequence的深度，因此可以用于batchsize为1和RNN中对边长的输入sequence的normalize操作）
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
}]
# 配置优化器和warmup机制
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=args.train_steps //
args.gradient_accumulation_steps)
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
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) # 循环遍历
# 先做一个eval
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 准备验证集的dataloader
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# 开启预测模式（不用dropout和BN）
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:
# 将数据放在GPU上
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
# 禁止进行梯度更新
with torch.no_grad():
tmp_eval_loss, logits = 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 # 计算验证集的预测损失
eval_accuracy = accuracy(inference_logits,
gold_labels) # 计算验证集的预测准确性
result = {
'eval_loss': eval_loss,
'eval_F1': eval_accuracy,
'global_step': global_step
}
# 将验证集的预测评价写入到evel_results.txt中
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')
# 如果当前训练的模型表现最佳，则保存该模型
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)
model.train()
# 分batch循环迭代训练模型
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)
nb_tr_examples += input_ids.size(0)
del input_ids, input_mask, segment_ids, 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_steps += 1
# 用FP16去做反向传播
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
# 梯度累计后进行更新
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
optimizer.step() # 梯度更新
scheduler.step() # 梯度更新
optimizer.zero_grad() # 清空现有梯度，避免累计
global_step += 1
# 每隔args.eval_steps*args.gradient_accumulation_steps，打印训练过程中的结果
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))
# 每隔args.eval_steps*args.gradient_accumulation_steps，预测验证集并评估结果
if args.do_eval and step > num_train_optimization_steps * args.not_do_eval_steps 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, logits = 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)
# 预测测试集
if args.do_test:
del model
gc.collect() # 清理内存
args.do_train = False # 停止训练
# 载入训练好的的最佳模型文件
model = BertForSequenceClassification.from_pretrained(os.path.join(
args.output_dir, "pytorch_model.bin"),
args,
config=config)
if args.fp16:
# nn.Module中的half()方法将模型中的float32转化为float16
model.half()
model.to(device) # 将模型放在GPU上
# 设置GPU训练方式
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)
# 预测验证集和测试集
for file, flag in [('dev.csv', 'dev'), ('CSC_test.csv', 'CSC_test'),
('NS_test.csv', 'NS_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))
# 保存预测结果文件
if flag == 'CSC_test':
df = pd.read_csv(os.path.join(args.data_dir, file))
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
df[['qid', 'label_0',
'label_1']].to_csv(os.path.join(args.output_dir,
"sub_CSC.csv"),
index=False)
if flag == 'NS_test':
df = pd.read_csv(os.path.join(args.data_dir, file))
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
df[['qid', 'label_0',
'label_1']].to_csv(os.path.join(args.output_dir,
"sub_NS.csv"),
index=False)
if flag == 'dev':
df = pd.read_csv(os.path.join(args.data_dir, file))
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
df[['label_0',
'label_1']].to_csv(os.path.join(args.output_dir,
"sub_dev.csv"),
index=False)
# 只预测验证集
if args.predict_eval:
del model
gc.collect()
args.do_train = False
model = BertForSequenceClassification.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')]:
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 == 'dev':
df = pd.read_csv(os.path.join(args.data_dir, file))
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
df[['label_0',
'label_1']].to_csv(os.path.join(args.output_dir,
"sub_dev.csv"),
index=False)
def train(**kwargs):
# kwargs.update({'model': 'CNN'})
opt.parse(kwargs)
if (opt.use_gpu):
torch.cuda.set_device(opt.gpu_id)
if opt.encoder == 'BERT':
encoder_model = BertForSequenceClassification.from_pretrained(
"./downloaded_weights/downloaded_bert_base_uncased",
num_labels=opt.rel_num)
# print(encoder_model)
opt.encoder_out_dimension = opt.rel_num
else:
encoder_model = getattr(encoder_models, opt.encoder)(opt)
opt.encoder_out_dimension = encoder_model.out_dimension
selector_model = getattr(selector_models, opt.selector)(opt)
# encoder_model = torch.nn.DataParallel(encoder_model, device_ids=[3,6])
if (opt.use_gpu):
encoder_model = encoder_model.cuda()
selector_model = selector_model.cuda()
# Loading data
DataModel = getattr(dataset, opt.data + 'Data')
train_data = DataModel(opt.data_root,
train=True,
use_bert=opt.use_bert_tokenizer)
train_data_loader = DataLoader(train_data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
collate_fn=collate_fn)
print('train data: {}'.format(len(train_data)))
test_data = DataModel(opt.data_root,
train=False,
use_bert=opt.use_bert_tokenizer)
test_data_loader = DataLoader(test_data,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
collate_fn=collate_fn)
print('test data: {}'.format(len(test_data)))
criterion = nn.CrossEntropyLoss()
if opt.encoder == 'BERT':
optimizer = AdamW(
[{
'params': encoder_model.parameters()
}, {
'params': selector_model.parameters()
}],
lr=opt.lr,
correct_bias=True
) # To reproduce BertAdam specific behavior set correct_bias=False
else:
optimizer = optim.Adadelta([{
'params': encoder_model.parameters()
}, {
'params': selector_model.parameters()
}],
lr=opt.lr,
rho=1.0,
eps=1e-6,
weight_decay=opt.weight_decay)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=2,
t_total=3) # PyTorch scheduler
### and used like this:
# for batch in train_data:
# loss = model(batch)
# loss.backward()
# torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
# optimizer.zero_grad()
# if opt.encoder == "BERT" and False:
# optimizer = optim.SGD([
# {'params': selector_model.parameters()}
# ], lr=opt.lr)
# else:
optimizer = optim.SGD([{
'params': encoder_model.parameters()
}, {
'params': selector_model.parameters()
}],
lr=opt.lr)
max_pre = 0.0
max_rec = 0.0
for epoch in range(opt.num_epochs):
# if opt.encoder == "BERT":
encoder_model.train()
selector_model.train()
print("*" * 50)
print("Epoch {}".format(epoch))
total_loss = 0
max_insNum = 0
for batch_num, (data, label_set) in enumerate(train_data_loader):
# if (batch_num>2000):
# break
# label_set is the label of each bag (there may be no more than 4 labels, but we only wants the first)
labels = []
outs = torch.empty([0, 53])
empty = True # if all labels of bags in one batch are zeros, then it's empty, continue to avoid error
for l in label_set:
if (l[0] != 0):
labels.append(l[0])
empty = False
if empty:
continue
# labels = [l[0] for l in label_set]
# Each time enters {batch_size} bags
# Each time I want one bag!!
# The model need to give me a representation of an instance!!!
if opt.use_gpu:
labels = torch.LongTensor(labels).cuda()
outs = outs.cuda()
else:
labels = torch.LongTensor(labels)
optimizer.zero_grad()
train_cor = 0
for idx, bag in enumerate(data):
insNum = bag[1]
# if insNum > max_insNum:
# max_insNum = insNum
# print(max_insNum)
label = label_set[idx][0] # Label of the current bag
if (label_set[idx][0] == 0):
continue
ins_outs = torch.empty(0)
instances = bag[2]
pf_list = []
mask_list = []
if opt.encoder != 'BERT':
pf_list = bag[3]
mask_list = bag[5]
# pf_list = bag[3]
ins_out = torch.empty(0)
encoder_model.batch_size = insNum
if opt.use_gpu:
instances = torch.LongTensor(instances).cuda()
if opt.encoder == 'BERT':
# with torch.no_grad():
# print(instances.size(0))
if insNum > opt.max_sentence_in_bag:
ins_outs = encoder_model(
instances[:opt.max_sentence_in_bag])[0]
else:
ins_outs = encoder_model(instances)[0]
# ins_outs = ins_outs[0]
# print(ins_outs[0].size())
else:
for idx, instance in enumerate(instances):
if opt.use_gpu:
pfs = torch.LongTensor(pf_list[idx]).cuda()
masks = torch.LongTensor(mask_list[idx]).cuda()
else:
pfs = torch.LongTensor(pf_list[idx])
masks = torch.LongTensor(mask_list[idx])
if opt.encoder == 'PCNN':
ins_out = encoder_model(instance, pfs, masks)
else:
ins_out = encoder_model(instance, pfs)
if (opt.use_gpu):
ins_out = ins_out.cuda()
ins_outs = ins_outs.cuda()
ins_outs = torch.cat((ins_outs, ins_out), 0)
del instance, ins_out
if idx >= opt.max_sentence_in_bag:
break
bag_feature = selector_model(ins_outs)
if opt.use_gpu: bag_feature = bag_feature.cuda()
if (torch.max(bag_feature.squeeze(), 0)[1] == label):
train_cor += 1
outs = torch.cat((outs, bag_feature), 0)
del ins_outs, bag_feature
# outs = outs.squeeze()
# print("outs.size(): ", outs.size(), '\n', "labels.size(): ", labels.size())
# print(outs,labels)
loss = criterion(outs, labels)
total_loss += loss.item()
avg_loss = total_loss / (batch_num + 1)
sys.stdout.write(
"\rbatch number: {:6d}\tloss: {:7.4f}\ttrain_acc: {:7.2f}\t".
format(batch_num, avg_loss, train_cor / len(labels)))
sys.stdout.flush()
# sys.stdout.write('\033')
loss.backward()
if opt.encoder == 'BERT':
scheduler.step()
optimizer.step()
del outs, labels
if (opt.skip_predict != True):
with torch.no_grad():
predict(encoder_model, selector_model, test_data_loader)
t = time.strftime('%m_%d_%H_%M.pth')
torch.save(encoder_model.state_dict(),
'checkpoints/{}_{}'.format(opt.encoder, t))
torch.save(selector_model.state_dict(),
'checkpoints/{}_{}'.format(opt.selector, t))
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(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
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(
"--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 WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
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(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=64,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=256,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
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(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
# if args.server_ip and args.server_port:
# # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
# import ptvsd
# print("Waiting for debugger attach")
# ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
# ptvsd.wait_for_attach()
processors = {
# "cola": ColaProcessor,
# "mnli": MnliProcessor,
# "mnli-mm": MnliMismatchedProcessor,
# "mrpc": MrpcProcessor,
# "sst-2": Sst2Processor,
# "sts-b": StsbProcessor,
# "qqp": QqpProcessor,
# "qnli": QnliProcessor,
"rte": RteProcessor
# "wnli": WnliProcessor,
}
output_modes = {
# "cola": "classification",
# "mnli": "classification",
# "mrpc": "classification",
# "sst-2": "classification",
# "sts-b": "regression",
# "qqp": "classification",
# "qnli": "classification",
"rte": "classification"
# "wnli": "classification",
}
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:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels() #[0,1]
num_labels = len(label_list)
train_examples = None
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_TRANSFORMERS_CACHE), 'distributed_{}'.format(
args.local_rank))
global_step = 0
nb_tr_steps = 0
tr_loss = 0
max_test_unseen_acc = 0.0
max_dev_unseen_acc = 0.0
max_dev_seen_acc = 0.0
max_overall_acc = 0.0
'''load test set'''
seen_types = set()
test_examples, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index = processor.get_examples_emotion_test(
'/export/home/Dataset/Stuttgart_Emotion/unify-emotion-datasets-master/zero-shot-split/test.txt',
seen_types)
test_features = convert_examples_to_features(
test_examples, label_list, args.max_seq_length,
BertTokenizer.from_pretrained(
'/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnRTE',
do_lower_case=args.do_lower_case), output_mode)
test_all_input_ids = torch.tensor([f.input_ids for f in test_features],
dtype=torch.long)
test_all_input_mask = torch.tensor([f.input_mask for f in test_features],
dtype=torch.long)
test_all_segment_ids = torch.tensor([f.segment_ids for f in test_features],
dtype=torch.long)
test_all_label_ids = torch.tensor([f.label_id for f in test_features],
dtype=torch.long)
test_data = TensorDataset(test_all_input_ids, test_all_input_mask,
test_all_segment_ids, test_all_label_ids)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=args.eval_batch_size)
'''
start evaluate on test set after this epoch
'''
modelpaths = [
'/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnRTE',
'/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnMNLI',
'/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnFEVER'
]
pred_probs_ensemble = 0.0
for i, modelpath in enumerate(modelpaths):
# pretrain_model_dir = '/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnRTE' #FineTuneOnCombined'# FineTuneOnMNLI
model = BertForSequenceClassification.from_pretrained(
modelpath, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(
modelpath, do_lower_case=args.do_lower_case)
if args.fp16:
model.half()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
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':
0.01
}, {
'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)
model.eval()
logger.info("***** Running testing *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
test_loss = 0
nb_test_steps = 0
preds = []
print('Testing...')
for input_ids, input_mask, segment_ids, label_ids in test_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, segment_ids, input_mask, labels=None)
logits = logits[0]
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
# print('preds:', preds)
preds = preds[0]
pred_probs_i = softmax(preds, axis=1)[:, 0]
pred_binary_labels_harsh = []
pred_binary_labels_loose = []
for i in range(preds.shape[0]):
if preds[i][0] > preds[i][1] + 0.1:
pred_binary_labels_harsh.append(0)
else:
pred_binary_labels_harsh.append(1)
if preds[i][0] > preds[i][1]:
pred_binary_labels_loose.append(0)
else:
pred_binary_labels_loose.append(1)
seen_acc, unseen_acc = evaluate_emotion_zeroshot_TwpPhasePred(
pred_probs_i, pred_binary_labels_harsh, pred_binary_labels_loose,
test_label_list, test_hypo_seen_str_indicator,
test_hypo_2_type_index, seen_types)
print('seen:', seen_acc, 'unseen:', unseen_acc)
print('\n\n this model preds over\n\n\n')
if i == 0:
pred_probs_ensemble = softmax(preds, axis=1)
else:
pred_probs_ensemble += softmax(preds, axis=1)
pred_probs_ensemble = softmax(pred_probs_ensemble, axis=1)
pred_probs = pred_probs_ensemble[:, 0]
pred_binary_labels_harsh = []
pred_binary_labels_loose = []
for i in range(preds.shape[0]):
if pred_probs_ensemble[i][0] > pred_probs_ensemble[i][1] + 0.1:
pred_binary_labels_harsh.append(0)
else:
pred_binary_labels_harsh.append(1)
if pred_probs_ensemble[i][0] > pred_probs_ensemble[i][1]:
pred_binary_labels_loose.append(0)
else:
pred_binary_labels_loose.append(1)
seen_acc, unseen_acc = evaluate_emotion_zeroshot_TwpPhasePred(
pred_probs, pred_binary_labels_harsh, pred_binary_labels_loose,
test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index,
seen_types)
if unseen_acc > max_test_unseen_acc:
max_test_unseen_acc = unseen_acc
print('\n\n\t test seen_f1 & unseen_f1:', seen_acc, unseen_acc,
' max_test_unseen_f1:', max_test_unseen_acc, '\n')
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."
)
parser.add_argument(
'--classifier',
default='guoday',
type=str,
required=True,
help='classifier type, guoday or MLP or GRU_MLP or ...')
parser.add_argument('--optimizer',
default='RAdam',
type=str,
required=True,
help='optimizer we use, RAdam or ...')
parser.add_argument("--do_label_smoothing",
default='yes',
type=str,
required=True,
help="Whether to do label smoothing. yes or no.")
parser.add_argument('--draw_loss_steps',
default=1,
type=int,
required=True,
help='training steps to draw loss')
parser.add_argument('--label_name',
default='label',
type=str,
required=True,
help='label name in original train set index')
## 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",
default='yes',
type=str,
required=True,
help="Whether to run training. yes or no.")
parser.add_argument("--do_test",
default='yes',
type=str,
required=True,
help="Whether to run training. yes or no.")
parser.add_argument("--do_eval",
default='yes',
type=str,
required=True,
help="Whether to run eval on the dev set. yes or no.")
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("--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=200, type=int, help="")
parser.add_argument("--lstm_hidden_size", default=300, type=int, help="")
parser.add_argument("--lstm_layers", default=2, type=int, help="")
parser.add_argument("--dropout", default=0.5, type=float, 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("--split_num", default=3, type=int, help="text split")
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 = BertTokenizer.from_pretrained(args.model_name_or_path,
do_lower_case=args.do_lower_case)
# tensorboard_log_dir = args.output_dir
# loss_now = tf.placeholder(dtype=tf.float32, name='loss_now')
# loss_mean = tf.placeholder(dtype=tf.float32, name='loss_mean')
# loss_now_variable = loss_now
# loss_mean_variable = loss_mean
# train_loss = tf.summary.scalar('train_loss', loss_now_variable)
# dev_loss_mean = tf.summary.scalar('dev_loss_mean', loss_mean_variable)
# merged = tf.summary.merge([train_loss, dev_loss_mean])
config = BertConfig.from_pretrained(args.model_name_or_path, num_labels=3)
config.hidden_dropout_prob = args.dropout
# Prepare model
if args.do_train == 'yes':
model = BertForSequenceClassification.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 == 'yes':
print(
'________________________now training______________________________'
)
# Prepare data loader
train_examples = read_examples(os.path.join(args.data_dir,
'train.csv'),
is_training=True,
label_name=args.label_name)
train_features = convert_examples_to_features(train_examples,
tokenizer,
args.max_seq_length,
args.split_num, True)
# print('train_feature_size=', train_features.__sizeof__())
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)
# print('train_data=',train_data[0])
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
}]
if args.optimizer == 'RAdam':
optimizer = RAdam(optimizer_grouped_parameters,
lr=args.learning_rate)
else:
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=args.train_steps)
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
loss_batch = 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)
# with tf.Session() as sess:
# summary_writer = tf.summary.FileWriter(tensorboard_log_dir, sess.graph)
# sess.run(tf.global_variables_initializer())
list_loss_mean = []
bx = []
eval_F1 = []
ax = []
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.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
loss_batch += 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)
loss.backward()
else:
loss.backward()
# draw loss every n docs
if (step + 1) % int(args.draw_loss_steps /
(args.train_batch_size /
args.gradient_accumulation_steps)) == 0:
list_loss_mean.append(round(loss_batch, 4))
bx.append(step + 1)
plt.plot(bx,
list_loss_mean,
label='loss_mean',
linewidth=1,
color='b',
marker='o',
markerfacecolor='green',
markersize=2)
plt.savefig(args.output_dir + '/labeled.jpg')
loss_batch = 0
# paras update every batch data.
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
# report results every 200 real batch.
if step % (args.eval_steps *
args.gradient_accumulation_steps) == 0 and step > 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))
# do evaluation totally 10 times during training stage.
if args.do_eval == 'yes' and (step + 1) % int(
num_train_optimization_steps / 10) == 0 and step > 450:
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,
label_name=args.label_name)
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_labels = np.concatenate(inference_labels, 0)
inference_logits = np.concatenate(inference_logits, 0)
model.train()
###############################################
num_gold_0 = np.sum(gold_labels == 0)
num_gold_1 = np.sum(gold_labels == 1)
num_gold_2 = np.sum(gold_labels == 2)
right_0 = 0
right_1 = 0
right_2 = 0
error_0 = 0
error_1 = 0
error_2 = 0
for gold_label, inference_label in zip(
gold_labels, inference_labels):
if gold_label == inference_label:
if gold_label == 0:
right_0 += 1
elif gold_label == 1:
right_1 += 1
else:
right_2 += 1
elif inference_label == 0:
error_0 += 1
elif inference_label == 1:
error_1 += 1
else:
error_2 += 1
recall_0 = right_0 / (num_gold_0 + 1e-5)
recall_1 = right_1 / (num_gold_1 + 1e-5)
recall_2 = right_2 / (num_gold_2 + 1e-5)
precision_0 = right_0 / (error_0 + right_0 + 1e-5)
precision_1 = right_1 / (error_1 + right_1 + 1e-5)
precision_2 = right_2 / (error_2 + right_2 + 1e-5)
f10 = 2 * precision_0 * recall_0 / (precision_0 +
recall_0 + 1e-5)
f11 = 2 * precision_1 * recall_1 / (precision_1 +
recall_1 + 1e-5)
f12 = 2 * precision_2 * recall_2 / (precision_2 +
recall_2 + 1e-5)
output_dev_result_file = os.path.join(
args.output_dir, "dev_results.txt")
with open(output_dev_result_file, 'a',
encoding='utf-8') as f:
f.write('precision:' + str(precision_0) + ' ' +
str(precision_1) + ' ' + str(precision_2) +
'\n')
f.write('recall:' + str(recall_0) + ' ' +
str(recall_1) + ' ' + str(recall_2) + '\n')
f.write('f1:' + str(f10) + ' ' + str(f11) + ' ' +
str(f12) + '\n' + '\n')
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = accuracy(inference_logits, gold_labels)
# draw loss.
eval_F1.append(round(eval_accuracy, 4))
ax.append(step)
plt.plot(ax,
eval_F1,
label='eval_F1',
linewidth=1,
color='r',
marker='o',
markerfacecolor='blue',
markersize=2)
for a, b in zip(ax, eval_F1):
plt.text(a, b, b, ha='center', va='bottom', fontsize=8)
plt.savefig(args.output_dir + '/labeled.jpg')
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("more accurate model arises, now best F1 = ",
eval_accuracy)
print("Saving Model......")
best_acc = eval_accuracy
# Save a trained model, only save the model it-self
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = os.path.join(
args.output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(),
output_model_file)
print("=" * 80)
'''
if (step+1) / int(num_train_optimization_steps/10) > 9.5:
print("=" * 80)
print("End of training. Saving Model......")
# Save a trained model, only save the model it-self
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(args.output_dir, "pytorch_model_final_step.bin")
torch.save(model_to_save.state_dict(), output_model_file)
print("=" * 80)
'''
if args.do_test == 'yes':
start_time = time.time()
print(
'___________________now testing for best eval f1 model_________________________'
)
try:
del model
except:
pass
gc.collect()
args.do_train = 'no'
model = BertForSequenceClassification.from_pretrained(os.path.join(
args.output_dir, "pytorch_model.bin"),
args,
config=config)
model.half()
for layer in model.modules():
if isinstance(layer, torch.nn.modules.batchnorm._BatchNorm):
layer.float()
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 [('test.csv', 'test')]:
inference_labels = []
gold_labels = []
eval_examples = read_examples(os.path.join(args.data_dir, file),
is_training=False,
label_name=args.label_name)
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()
# print('test_logits=', logits)
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)
if flag == 'dev':
print(flag, accuracy(logits, gold_labels))
elif 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)
# df[['id', 'label_0', 'label_1']].to_csv(os.path.join(args.output_dir, "sub.csv"), index=False)
else:
raise ValueError('flag not in [dev, test]')
print('inference time usd = {}s'.format(time.time() - start_time))
'''
def train(self):
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
# logger.info(f'Fold {split_index + 1}')
train_dataloader, eval_dataloader, train_examples, eval_examples = self.create_dataloader()
num_train_optimization_steps = self.train_steps
# Prepare model
config = BertConfig.from_pretrained(self.model_name_or_path, num_labels=self.num_labels)
model = BertForSequenceClassification.from_pretrained(self.model_name_or_path,self.args, config=config)
model.to(self.device)
model.train()
# Prepare optimizer
param_optimizer = list(model.named_parameters())
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': self.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=self.learning_rate, eps=self.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=self.warmup_steps, t_total=self.train_steps)
global_step = 0
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", self.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
best_acc = 0
best_MRR = 0
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
train_dataloader = cycle(train_dataloader)
for step in range(num_train_optimization_steps):
batch = next(train_dataloader)
batch = tuple(t.to(self.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)
tr_loss += loss.item()
train_loss = round(tr_loss / (nb_tr_steps + 1), 4)
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
loss.backward()
if (nb_tr_steps + 1) % self.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
scheduler.step()
global_step += 1
if (step + 1) % (self.eval_steps * self.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 self.do_eval and (step + 1) % (self.eval_steps * self.gradient_accumulation_steps) == 0:
for file in ['dev.csv']:
inference_labels = []
gold_labels = []
inference_logits = []
scores = []
ID = [x.guid for x in eval_examples]
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", self.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(self.device)
input_mask = input_mask.to(self.device)
segment_ids = segment_ids.to(self.device)
label_ids = label_ids.to(self.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))
scores.append(logits)
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)
scores = np.concatenate(scores, 0)
model.train()
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = accuracyF1(inference_logits, gold_labels)
print(
'eval_F1',eval_accuracy,
'global_step',global_step,
'loss',train_loss
)
result = {'eval_loss': eval_loss,
'eval_F1': eval_accuracy,
'global_step': global_step,
'loss': train_loss}
output_eval_file = os.path.join(self.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 :
print("=" * 80)
print("Best F1", eval_accuracy)
print("Saving Model......")
# best_acc = eval_accuracy
best_acc = eval_accuracy
# Save a trained model
model_to_save = model.module if hasattr(model,'module') else model
output_model_file = os.path.join(self.output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
print("=" * 80)
else:
print("=" * 80)
def test_eval(self):
data = DATAMultiWOZ(
debug=False,
data_dir=self.data_dir
)
test_examples = data.read_examples(os.path.join(self.data_dir, 'test.tsv'))
print('eval_examples的数量', len(test_examples))
ID = [x.guid for x in test_examples]
test_features = data.convert_examples_to_features(test_examples, self.tokenizer, self.max_seq_length)
all_input_ids = torch.tensor(data.select_field(test_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(data.select_field(test_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(data.select_field(test_features, 'segment_ids'), dtype=torch.long)
all_utterance_mask = torch.tensor(data.select_field(test_features, 'utterance_mask'), dtype=torch.long)
all_response_mask = torch.tensor(data.select_field(test_features, 'response_mask'), dtype=torch.long)
all_history_mask = torch.tensor(data.select_field(test_features, 'history_mask'), dtype=torch.long)
all_label = torch.tensor([f.label for f in test_features], dtype=torch.long)
test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_utterance_mask,all_response_mask,all_history_mask, all_label)
# Run prediction for full data
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=self.eval_batch_size)
config = BertConfig.from_pretrained(self.model_name_or_path, num_labels=self.num_labels)
model = BertForSequenceClassification.from_pretrained(
os.path.join(self.output_dir, "pytorch_model.bin"), self.args, config=config)
model.to(self.device)
model.eval()
inference_labels = []
gold_labels = []
scores = []
for input_ids, input_mask, segment_ids, label_ids in test_dataloader:
input_ids = input_ids.to(self.device)
input_mask = input_mask.to(self.device)
segment_ids = segment_ids.to(self.device)
label_ids = label_ids.to(self.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()
scores.append(logits)
inference_labels.append(np.argmax(logits, axis=1))
gold_labels.append(label_ids)
gold_labels = np.concatenate(gold_labels, 0)
scores = np.concatenate(scores, 0)
logits = np.concatenate(inference_labels, 0)
# 计算评价指标
assert len(ID) == scores.shape[0]== scores.shape[0]
eval_accuracy = accuracyF1(logits, gold_labels)
# eval_DOUBAN_MRR,eval_DOUBAN_mrr,eval_DOUBAN_MAP,eval_Precision1 = compute_DOUBAN(ID,scores,gold_labels)
# print(
# 'eval_MRR',eval_DOUBAN_MRR,eval_DOUBAN_mrr,
# 'eval_MAP',eval_DOUBAN_MAP,
# 'eval_Precision1',eval_Precision1)
print('F1',eval_accuracy)