def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_file",
default=None,
type=str,
required=True,
help="The input train corpus.")
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-base-multilingual, bert-base-chinese."
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written."
)
## Other parameters
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("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=3e-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(
"--on_memory",
action='store_true',
help="Whether to load train samples into memory or use disk")
parser.add_argument(
"--do_lower_case",
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
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 accumualte 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")
args = parser.parse_args()
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 = int(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.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
#train_examples = None
num_train_steps = None
if args.do_train:
print("Loading Train Dataset", args.train_file)
train_dataset = BERTDataset(args.train_file,
tokenizer,
seq_len=args.max_seq_length,
corpus_lines=None,
on_memory=args.on_memory)
num_train_steps = int(
len(train_dataset) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
#model = BertForPreTraining.from_pretrained(args.bert_model)
bert_config = BertConfig.from_json_file('bert_config.json')
model = BertForPreTraining(bert_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 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 = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_steps)
global_step = 0
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
#TODO: check if this works with current data generator from disk that relies on file.__next__
# (it doesn't return item back by index)
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
model.train()
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")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
loss = model(input_ids, segment_ids, input_mask, lm_label_ids,
is_next)
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
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_steps, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
logger.info("** ** * Saving fine - tuned 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")
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--bert_model_or_config_file",
default=None,
type=str,
required=True,
help=
"Directory containing pre-trained BERT model or path of configuration file (if no pre-training)."
)
parser.add_argument("--train_file",
default=None,
type=str,
required=True,
help="The input train corpus.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written."
)
## Other parameters
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("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--learning_rate",
default=3e-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(
"--on_memory",
action='store_true',
help="Whether to load train samples into memory or use disk")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
"--num_gpus",
type=int,
default=-1,
help="Num GPUs to use for training (0 for none, -1 for all available)")
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 accumualte 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")
args = parser.parse_args()
# Check whether bert_model_or_config_file is a file or directory
if os.path.isdir(args.bert_model_or_config_file):
pretrained = True
targets = [WEIGHTS_NAME, CONFIG_NAME, "tokenizer.pkl"]
for t in targets:
path = os.path.join(args.bert_model_or_config_file, t)
if not os.path.exists(path):
msg = "File '{}' not found".format(path)
raise ValueError(msg)
fp = os.path.join(args.bert_model_or_config_file, CONFIG_NAME)
config = BertConfig(fp)
else:
pretrained = False
config = BertConfig(args.bert_model_or_config_file)
# What GPUs do we use?
if args.num_gpus == -1:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
device_ids = None
else:
device = torch.device("cuda" if torch.cuda.is_available()
and args.num_gpus > 0 else "cpu")
n_gpu = args.num_gpus
if n_gpu > 1:
device_ids = list(range(n_gpu))
if args.local_rank != -1:
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))
# Check some other args
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
if not args.do_train:
raise ValueError(
"Training is currently the only implemented execution option. Please set `do_train`."
)
# Seed RNGs
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)
# Prepare output directory
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Make tokenizer
if pretrained:
fp = os.path.join(args.bert_model_or_config_file, "tokenizer.pkl")
with open(fp, "rb") as f:
tokenizer = pickle.load(f)
else:
training_data = [
line.strip() for line in open(args.train_file).readlines()
]
tokenizer = CuneiformCharTokenizer(training_data=training_data)
tokenizer.trim_vocab(config.min_freq)
# Adapt vocab size in config
config.vocab_size = len(tokenizer.vocab)
print("Size of vocab: {}".format(len(tokenizer.vocab)))
# Get training data
num_train_optimization_steps = None
if args.do_train:
print("Loading Train Dataset", args.train_file)
train_dataset = BERTDataset(args.train_file,
tokenizer,
seq_len=args.max_seq_length,
corpus_lines=None,
on_memory=args.on_memory)
num_train_optimization_steps = int(
len(train_dataset) / 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
if pretrained:
model = BertForPreTraining.from_pretrained(
args.bert_model_or_config_file)
else:
model = BertForPreTraining(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 n_gpu > 1:
model = torch.nn.DataParallel(model, device_ids=device_ids)
# 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 = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
# Prepare training log
output_log_file = os.path.join(args.output_dir, "training_log.txt")
with open(output_log_file, "w") as f:
f.write("Steps\tTrainLoss\n")
# Start training
global_step = 0
total_tr_steps = 0
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
#TODO: check if this works with current data generator from disk that relies on next(file)
# (it doesn't return item back by index)
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
model.train()
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")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
loss = model(input_ids, segment_ids, input_mask, lm_label_ids,
is_next)
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
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 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(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
avg_loss = tr_loss / nb_tr_examples
# Update training log
total_tr_steps += nb_tr_steps
log_data = [str(total_tr_steps), "{:.5f}".format(avg_loss)]
with open(output_log_file, "a") as f:
f.write("\t".join(log_data) + "\n")
# Save model
logger.info("** ** * Saving 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, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
fn = os.path.join(args.output_dir, "tokenizer.pkl")
with open(fn, "wb") as f:
pickle.dump(tokenizer, f)
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("--dataset_name",
default="top300_kl",
type=str,
required=True,
help="The name of dataset to inference (without extention ex) top300_kl)")
parser.add_argument("--model_type",
default="baseline_tfidf",
type=str,
required=True,
help="baseline, baseline_tfidf, ir-v0, ir-v1")
parser.add_argument("--model_path",
default=None,
type=str,
required=True,
help="path to model dir")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="save_path")
## Other parameters
parser.add_argument("--bert_model",
default="bert-base-multilingual-cased",
type=str,
help="Default: bert-base-multilingual-cased"
"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("--model_file",
default="pytorch_model.bin",
type=str,
help="The file of model (.bin), default is pytorhc_model.bin,\n"
"특정 파일이 필요시 이름 설정 필요")
parser.add_argument("--max_seq_length",
default=384,
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("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
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('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
args = parser.parse_args()
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))
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)
processor = IRProcessor()
label_list = processor.get_labels()
num_labels = len(label_list)
print("model:", args.model_type)
if args.model_type == "baseline": # load model (finetuned baseline on IR)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=False)
config = BertConfig(os.path.join(args.model_path + "bert_config.json"))
model = BertForPreTraining(config)
model.load_state_dict(torch.load(os.path.join(args.model_path, args.model_file)))
elif args.model_type == "baseline_tfidf": # load model (baseline_tfidf)
tokenizer = BertTFIDFTokenizer.from_pretrained(args.bert_model, do_lower_case=False, do_tf_idf=True)
TFIDFconfig = modeling.BertConfig(os.path.join(args.model_path + "bert_config.json"))
model = modeling.BertTFIDFForPreTraining(TFIDFconfig)
model.load_state_dict(torch.load(os.path.join(args.model_path, args.model_file)))
elif args.model_type == "ir-v0": # load model (*-head)
tokenizer = BertTFIDFTokenizer.from_pretrained(args.bert_model, do_lower_case=False, do_tf_idf=True)
head_config = modeling_ir.BertForIRConfig(os.path.join(args.model_path + "bert_config.json"))
model = modeling_ir.BertForIRForPreTraining(head_config)
model.load_state_dict(torch.load(os.path.join(args.model_path, args.model_file)))
elif args.model_type == "ir-v1": # load model (*-head)
tokenizer = BertTFIDFTokenizer.from_pretrained(args.bert_model, do_lower_case=False, do_tf_idf=True)
head_config = modeling_ir_2.BertForIRConfig(os.path.join(args.model_path + "bert_config.json"))
model = modeling_ir_2.BertForIRForPreTraining(head_config)
model.load_state_dict(torch.load(os.path.join(args.model_path, args.model_file)))
if args.fp16:
model.half()
model.to(device)
tfidf_dict = pickle_load(os.path.join(args.data_dir, args.dataset_name + '_tfidf.pkl'))
results_logit = dict()
results_softmax = dict()
eval_set, documents, queries = processor.make_eval_set(args.data_dir, args.dataset_name)
logger.info("***** Running evaluation *****")
logger.info(" Batch size = %d", args.eval_batch_size)
for q_num, query in tqdm(enumerate(queries), total=len(queries), desc="Evaluating"):
# for query in queries[0:1]: # for testing
logger.info(f"Current Query Num : {q_num}")
eval_examples = processor._create_examples(eval_set, query, documents)
# logger.info(" Num examples = %d", len(eval_examples))
if args.model_type == "baseline": # baseline or baseline_finetuned
eval_features = convert_examples_to_features_for_vanilla(
eval_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = 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_ids)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = []
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Query"):
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)
# 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)
else: # baseline_tfidf or *-head model
eval_data = LazyDatasetClassifier(eval_examples, label_list, args.max_seq_length, tokenizer, tfidf_dict)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = []
for batch in tqdm(eval_dataloader, desc="Query"):
batch = tuple(t.to(device) for t in batch)
input_ids, input_weights, input_mask, segment_ids, label_ids = batch
with torch.no_grad():
_, logits = model(input_ids, input_weights, segment_ids, input_mask)
# 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]
results_softmax[query] = []
for i, pred in enumerate(softmax(preds)): # using softmax
pair = dict()
pair["score"] = pred[1]
pair["doc_id"] = list(documents.keys())[i]
results_softmax[query].append(pair)
results_softmax[query].sort(reverse=True, key=lambda x: x["score"])
ranked_doc_list = []
for doc in results_logit[query]:
ranked_doc_list.append(doc["doc_id"])
results_logit[query] = ranked_doc_list
ranked_doc_list = []
for doc in results_softmax[query]:
ranked_doc_list.append(doc["doc_id"])
results_softmax[query] = ranked_doc_list
save_name2 = args.model_path.split('/')[0] + '_' + args.model_file.split('.')[0] \
+ '_' + args.dataset_name + '_output.json'
path2 = os.path.join(args.output_dir,
save_name2)
with open(path2, 'w', encoding="utf8") as f:
json.dump(results_softmax, f, indent=4, sort_keys=True, ensure_ascii=False)
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-base-multilingual, 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(
"--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",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
default=False,
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=1e-4,
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=0.01,
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",
default=False,
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',
default=False,
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('--checkpoint_filename',
default=None,
type=str,
help="checkpoint_filename")
args = parser.parse_args()
os.system("clear")
# os.system('bash /data1/private/linyankai/code/run_create.sh 0 0')
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 = int(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.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
task_name = args.task_name.lower()
train_data = None
num_train_steps = None
if args.do_train:
# TODO
import indexed_dataset
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler, BatchSampler
import iterators
train_data = indexed_dataset.ZhxIndexedDataset(args.data_dir)
if args.local_rank == -1:
# train_sampler = RandomSampler(train_data)
train_sampler = SequentialSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_sampler = BatchSampler(train_sampler, args.train_batch_size,
True)
def collate_fn(x):
x = torch.LongTensor([xx.numpy() for xx in x])
return x[:, :args.
max_seq_length], x[:, args.max_seq_length:2 * args.
max_seq_length], x[:, 2 * args.
max_seq_length:3 *
args.
max_seq_length], x[:,
3
*
args
.
max_seq_length:
4
*
args
.
max_seq_length], x[:,
4
*
args
.
max_seq_length:
4
*
args
.max_seq_length
+
1]
train_iterator = iterators.EpochBatchIterator(train_data, collate_fn,
train_sampler)
num_train_steps = int(
len(train_data) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
config_file = os.path.join(args.bert_model, "bert_config.json")
config = BertConfig.from_json_file(config_file)
model = BertForPreTraining(config)
# model = BertForPreTraining.from_pretrained(args.bert_model,
# cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
logger.info(config.to_json_string())
checkpoint_file = args.checkpoint_filename
old_iter_id = 0
if checkpoint_file != None:
_, global_step, old_iter_id, model, optimizer_dict = load_checkpoint(
checkpoint_file, device, n_gpu)
if args.fp16:
model.half()
model.to(device)
# if True:
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 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
}]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
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 = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if checkpoint_file == None:
global_step = 0
# optimizer.load_state_dict(optimizer_dict)
tid = 0
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
model.train()
import datetime
f_config_out = open(
os.path.join(args.output_dir,
"config.{}".format(datetime.datetime.now())), "w")
f_config_out.write(str(args))
f_config_out.close()
fout = open(os.path.join(args.output_dir, "loss.txt"), 'w')
f_para_out = open(os.path.join(args.output_dir, "para.txt"), 'w')
for iter_id in enumerate(
tqdm(range(old_iter_id, int(args.num_train_epochs)),
desc="Epoch")):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
checkpoint(args, global_step, iter_id[0] + old_iter_id, model,
optimizer)
# tid = 1 - tid
# os.system('bash /data1/private/linyankai/code/run_create.sh '+str(random.randint(0,255))+" "+str(tid+1)+" &")
all_loss = 0
for step, batch in enumerate(
tqdm(train_iterator.next_epoch_itr(), desc="Iteration")):
batch = tuple(t.to(device) - 1 for t in batch)
input_ids, input_mask, segment_ids, masked_lm_labels, next_sentence_label = batch
loss, masked_lm_loss, next_sentence_loss = model(
input_ids, segment_ids, input_mask, masked_lm_labels,
next_sentence_label)
# loss = masked_lm_loss
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if not math.isnan(loss.item()):
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
all_loss += loss.item()
if (global_step % 1000 == 0):
f_para_out.write("global_step:" + str(global_step) + '\n')
f_para_out.write("loss:\t" + str(loss) + "\n")
'''
for param_group in optimizer.param_groups:
f_para_out.write("lr:\t"+str(param_group['lr'])+'\n')
break
param_all= list(model.named_parameters())
for n, p in param_optimizer:
f_para_out.write(str(n)+"\t"+str(p)+"\tGradient:\t"+str(p.grad.data.float()/optimizer.cur_scale)+"\n")
f_para_out.flush()
'''
print_param_stats(model, f_para_out)
if (global_step % 10 == 0):
fout.write("iter_id:" + str(iter_id) + "\t")
fout.write("global_step:" + str(global_step) + '\t')
fout.write("lr_this_step:" +
str(args.learning_rate *
warmup_linear(global_step / t_total,
args.warmup_proportion)) +
'\t')
fout.write("step:" + str(step) + '\t')
fout.write("loss:" + str(loss.item()) + '\t')
fout.write(
str(masked_lm_loss.item()) + '\t' +
str(next_sentence_loss.item()) + '\n')
if global_step % 10000 == 0:
checkpoint(args, global_step, iter_id[0] + old_iter_id,
model, optimizer)
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%d.bin" % global_step)
torch.save(model_to_save.state_dict(), output_model_file)
# fout.flush()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses
lr_this_step = args.learning_rate * warmup_linear(
global_step / t_total, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
'''
train_iterator = None
train_sampler = None
train_data = None
import indexed_dataset
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler,BatchSampler
import iterators
train_data = indexed_dataset.IndexedCachedDataset('/data'+str(tid+1)+args.data_dir[6:])
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_sampler = BatchSampler(train_sampler, args.train_batch_size, True)
def collate_fn(x):
x = torch.LongTensor([xx.numpy() for xx in x])
return x[:,:args.max_seq_length], x[:,args.max_seq_length:2*args.max_seq_length], x[:,2*args.max_seq_length:3*args.max_seq_length], x[:,3*args.max_seq_length:4*args.max_seq_length], x[:,4*args.max_seq_length:4*args.max_seq_length+1]
train_iterator = iterators.EpochBatchIterator(train_data, collate_fn, train_sampler)
'''
fout.close()
f_para_out.close()
# 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)
