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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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("--model_name_or_path", default='/data1', type=str)
## 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=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",
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('--threshold', type=float, default=.3)
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))
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 = TypingProcessor()
tokenizer_label = BertTokenizer_label.from_pretrained(
args.ernie_model, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
_, label_list, _ = processor.get_train_examples(args.data_dir)
label_list = sorted(label_list)
#class_weight = [min(d[x], 100) for x in label_list]
#logger.info(class_weight)
# S = []
# for l in label_list:
# s = []
# for ll in label_list:
# if ll in l:
# s.append(1.)
# else:
# s.append(0.)
# S.append(s)
# vecs = []
# vecs.append([0]*100)
# with open("kg_embed/entity2vec.vec", 'r') as fin:
# for line in fin:
# vec = line.strip().split('\t')
# vec = [float(x) for x in vec]
# vecs.append(vec)
# embed = torch.FloatTensor(vecs)
# embed = torch.nn.Embedding.from_pretrained(embed)
# logger.info("Shape of entity embedding: "+str(embed.weight.size()))
# del vecs
filenames = os.listdir(args.output_dir)
filenames = [x for x in filenames if "pytorch_model.bin_" in x]
file_mark = []
for x in filenames:
file_mark.append([x, True])
file_mark.append([x, False])
for x, mark in file_mark:
print(x, mark)
output_model_file = os.path.join(args.output_dir, x)
model_state_dict = torch.load(output_model_file)
bert_model = BertModel.from_pretrained(args.model_name_or_path)
model = BertForEntityTyping(bert_model, len(label_list))
model.load_state_dict(model_state_dict)
#model, _ = BertForEntityTyping.from_pretrained(args.ernie_model, state_dict=model_state_dict, num_labels=len(label_list))
model.to(device)
if mark:
eval_examples = processor.get_dev_examples(args.data_dir)
else:
eval_examples = processor.get_test_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer_label,
tokenizer, args.threshold)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
# zeros = [0 for _ in range(args.max_seq_length)]
# zeros_ent = [0 for _ in range(100)]
# zeros_ent = [zeros_ent for _ in range(args.max_seq_length)]
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_span_mask = torch.tensor([f.span_mask for f in eval_features],
dtype=torch.float)
all_labels = torch.tensor([f.labels for f in eval_features],
dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_span_mask, all_labels)
# 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
pred = []
true = []
for input_ids, input_mask, segment_ids, span_mask, labels in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
span_mask = span_mask.to(device)
labels = labels.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
span_mask, labels)
logits = model(input_ids, segment_ids, input_mask, span_mask)
logits = logits.detach().cpu().numpy()
labels = labels.to('cpu').numpy()
tmp_eval_accuracy, tmp_pred, tmp_true = accuracy(logits, labels)
pred.extend(tmp_pred)
true.extend(tmp_true)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
def f1(p, r):
if r == 0.:
return 0.
return 2 * p * r / float(p + r)
def loose_macro(true, pred):
num_entities = len(true)
p = 0.
r = 0.
for true_labels, predicted_labels in zip(true, pred):
if len(predicted_labels) > 0:
p += len(
set(predicted_labels).intersection(
set(true_labels))) / float(len(predicted_labels))
if len(true_labels):
r += len(
set(predicted_labels).intersection(
set(true_labels))) / float(len(true_labels))
precision = p / num_entities
recall = r / num_entities
return precision, recall, f1(precision, recall)
def loose_micro(true, pred):
num_predicted_labels = 0.
num_true_labels = 0.
num_correct_labels = 0.
for true_labels, predicted_labels in zip(true, pred):
num_predicted_labels += len(predicted_labels)
num_true_labels += len(true_labels)
num_correct_labels += len(
set(predicted_labels).intersection(set(true_labels)))
if num_predicted_labels > 0:
precision = num_correct_labels / num_predicted_labels
else:
precision = 0.
recall = num_correct_labels / num_true_labels
return precision, recall, f1(precision, recall)
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'macro': loose_macro(true, pred),
'micro': loose_micro(true, pred)
}
if mark:
output_eval_file = os.path.join(
args.output_dir,
"eval_results_{}.txt".format(x.split("_")[-1]))
else:
output_eval_file = os.path.join(
args.output_dir,
"test_results_{}.txt".format(x.split("_")[-1]))
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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=16,
type=int,
help="Total batch size for training.")
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",
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('--threshold', type=float, default=.3)
parser.add_argument("--vec_file",
default=None,
type=str,
required=True,
help="File with embeddings")
parser.add_argument("--qid_file",
default=None,
type=str,
required=True,
help="File with qid mapping")
parser.add_argument("--use_lim_ents",
default=None,
type=str,
required=True,
help="Whether to use limited entities")
args = parser.parse_args()
processors = FewrelProcessor
num_labels_task = 80
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:
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) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
processor = processors()
num_labels = num_labels_task
label_list = None
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
train_examples, label_list = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model, _ = BertForSequenceClassification.from_pretrained(
args.ernie_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
num_labels=num_labels)
# 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_grad = [
'bert.encoder.layer.11.output.dense_ent',
'bert.encoder.layer.11.output.LayerNorm_ent'
]
param_optimizer = [(n, p) for n, p in param_optimizer
if not any(nd in n for nd in no_grad)]
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
from apex import amp
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)
if args.loss_scale == 0:
model, optimizer = amp.initialize(model, optimizer, opt_level="O2")
# optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer,
args.threshold, args.qid_file)
# check for limited ents
lim_ents = []
lim_check = (args.use_lim_ents == "y")
if lim_check:
lim_ents = lim_ent_map(0, "kg_embeddings/dbp_eid_2_wd_eid.txt")
logger.info(
"Limited entities flag is on. Count of unique entities considered: "
+ str(len(lim_ents)))
vecs = []
vecs.append([0] * 100) # CLS
lineindex = 1
uid_map = {}
logger.info("Reading embeddings file.")
with open(args.vec_file, 'r') as fin:
for line in fin:
vec = line.strip().split('\t')
# first element is unique id
uniqid = int(vec[0])
# map line index to unique id
uid_map[uniqid] = lineindex
# increment line index
lineindex = lineindex + 1
if (lim_check and (uniqid in lim_ents)) or not lim_check:
vec = [float(x) for x in vec[1:101]]
else:
vec = vecs[0]
vecs.append(vec)
embed = torch.FloatTensor(vecs)
embed = torch.nn.Embedding.from_pretrained(embed)
#embed = torch.nn.Embedding(5041175, 100)
logger.info("Shape of entity embedding: " + str(embed.weight.size()))
del vecs
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_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)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in train_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids)
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)
output_loss_file = os.path.join(args.output_dir, "loss")
loss_fout = open(output_loss_file, 'w')
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) if i != 3 else t for i, t in enumerate(batch))
input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids = batch
input_ent = embed(input_ent + 1).to(device) # -1 -> 0
loss = model(input_ids, segment_ids, input_mask,
input_ent.half(), ent_mask, label_ids)
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:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
loss_fout.write("{}\n".format(loss.item()))
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
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(
args.output_dir, "pytorch_model.bin_{}".format(global_step))
torch.save(model_to_save.state_dict(), output_model_file)
# 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)
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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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=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",
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('--threshold', type=float, default=.3)
parser.add_argument("--vec_file",
default=None,
type=str,
required=True,
help="File with embeddings")
parser.add_argument("--qid_file",
default=None,
type=str,
required=True,
help="File with qid mapping")
parser.add_argument("--use_lim_ents",
default=None,
type=str,
required=True,
help="Whether to use limited entities")
args = parser.parse_args()
processors = FewrelProcessor
num_labels_task = 80
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))
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.")
processor = processors()
num_labels = num_labels_task
label_list = None
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
train_examples, label_list = processor.get_train_examples(args.data_dir)
# check for limited ents
lim_ents = []
lim_check = (args.use_lim_ents == "y")
if lim_check:
lim_ents = lim_ent_map(0, "kg_embeddings/dbp_eid_2_wd_eid.txt")
logger.info(
"Limited entities flag is on. Count of unique entities considered: "
+ str(len(lim_ents)))
vecs = []
vecs.append([0] * 100) # CLS
lineindex = 1
uid_map = {}
logger.info("Reading embeddings file.")
with open(args.vec_file, 'r') as fin:
for line in fin:
vec = line.strip().split('\t')
# first element is unique id
uniqid = int(vec[0])
# map line index to unique id
uid_map[uniqid] = lineindex
# increment line index
lineindex = lineindex + 1
if (lim_check and (uniqid in lim_ents)) or not lim_check:
vec = [float(x) for x in vec[1:101]]
else:
vec = vecs[0]
vecs.append(vec)
embed = torch.FloatTensor(vecs)
embed = torch.nn.Embedding.from_pretrained(embed)
#embed = torch.nn.Embedding(5041175, 100)
logger.info("Shape of entity embedding: " + str(embed.weight.size()))
del vecs
filenames = os.listdir(args.output_dir)
filenames = [x for x in filenames if "pytorch_model.bin_" in x]
file_mark = []
for x in filenames:
file_mark.append([x, True])
file_mark.append([x, False])
eval_examples = processor.get_dev_examples(args.data_dir)
dev = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length, tokenizer,
args.threshold, args.qid_file)
eval_examples = processor.get_test_examples(args.data_dir)
test = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length, tokenizer,
args.threshold, args.qid_file)
for x, mark in file_mark:
print(x, mark)
output_model_file = os.path.join(args.output_dir, x)
model_state_dict = torch.load(output_model_file)
model, _ = BertForSequenceClassification.from_pretrained(
args.ernie_model,
state_dict=model_state_dict,
num_labels=len(label_list))
model.to(device)
if mark:
eval_features = dev
else:
eval_features = test
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
# zeros = [0 for _ in range(args.max_seq_length)]
# zeros_ent = [0 for _ in range(100)]
# zeros_ent = [zeros_ent for _ in range(args.max_seq_length)]
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_id for f in eval_features],
dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in eval_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
if mark:
output_eval_file = os.path.join(
args.output_dir,
"eval_results_{}.txt".format(x.split("_")[-1]))
output_file_pred = os.path.join(
args.output_dir, "eval_pred_{}.txt".format(x.split("_")[-1]))
output_file_glod = os.path.join(
args.output_dir, "eval_gold_{}.txt".format(x.split("_")[-1]))
else:
output_eval_file = os.path.join(
args.output_dir,
"test_results_{}.txt".format(x.split("_")[-1]))
output_file_pred = os.path.join(
args.output_dir, "test_pred_{}.txt".format(x.split("_")[-1]))
output_file_glod = os.path.join(
args.output_dir, "test_gold_{}.txt".format(x.split("_")[-1]))
fpred = open(output_file_pred, "w")
fgold = open(output_file_glod, "w")
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids in eval_dataloader:
input_ent = embed(input_ent + 1) # -1 -> 0
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
input_ent = input_ent.to(device)
ent_mask = ent_mask.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
input_ent, ent_mask, label_ids)
logits = model(input_ids, segment_ids, input_mask, input_ent,
ent_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy, pred = accuracy(logits, label_ids)
for a, b in zip(pred, label_ids):
fgold.write("{}\n".format(b))
fpred.write("{}\n".format(a))
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy}
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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=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",
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('--threshold', type=float, default=.3)
##########ADD##
parser.add_argument("--K_V_dim",
type=int,
default=100,
help="Key and Value dim == KG representation dim")
parser.add_argument(
"--Q_dim",
type=int,
default=768,
help="Query dim == Bert six output layer representation dim")
parser.add_argument(
'--graphsage',
default=False,
action='store_true',
help="Whether to use Attention GraphSage instead of GAT")
parser.add_argument('--self_att',
default=True,
action='store_true',
help="Whether to use GAT")
###############
args = parser.parse_args()
processors = TacredProcessor
num_labels_task = 42
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))
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.")
processor = processors()
num_labels = num_labels_task
label_list = None
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
train_examples, label_list = processor.get_train_examples(args.data_dir)
label_list = sorted(label_list)
'''
vecs = []
vecs.append([0]*100)
with open("kg_embed/entity2vec.vec", 'r') as fin:
for line in fin:
vec = line.strip().split('\t')
vec = [float(x) for x in vec]
vecs.append(vec)
embed = torch.FloatTensor(vecs)
embed = torch.nn.Embedding.from_pretrained(embed)
logger.info("Shape of entity embedding: "+str(embed.weight.size()))
del vecs
'''
filenames = os.listdir(args.output_dir)
filenames = [x for x in filenames if "pytorch_model.bin_" in x]
file_mark = []
for x in filenames:
file_mark.append([x, True])
file_mark.append([x, False])
eval_examples = processor.get_dev_examples(args.data_dir)
dev = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length, tokenizer,
args.threshold)
eval_examples = processor.get_test_examples(args.data_dir)
test = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length, tokenizer,
args.threshold)
for x, mark in file_mark:
print(x, mark)
output_model_file = os.path.join(args.output_dir, x)
model_state_dict = torch.load(output_model_file)
model, _ = BertForSequenceClassification.from_pretrained(
args.ernie_model,
state_dict=model_state_dict,
num_labels=len(label_list),
args=args)
if args.fp16:
model.half()
model.to(device)
if mark:
eval_features = dev
output_file = os.path.join(
args.output_dir, "eval_pred_{}.txt".format(x.split("_")[-1]))
output_file_ = os.path.join(
args.output_dir, "eval_gold_{}.txt".format(x.split("_")[-1]))
else:
eval_features = test
output_file = os.path.join(
args.output_dir, "test_pred_{}.txt".format(x.split("_")[-1]))
output_file_ = os.path.join(
args.output_dir, "test_gold_{}.txt".format(x.split("_")[-1]))
fpred = open(output_file, "w")
fgold = open(output_file_, "w")
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
# zeros = [0 for _ in range(args.max_seq_length)]
# zeros_ent = [0 for _ in range(100)]
# zeros_ent = [zeros_ent for _ in range(args.max_seq_length)]
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_id for f in eval_features],
dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in eval_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids)
# 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, input_ent, ent_mask, label_ids in eval_dataloader:
input_ent = input_ent + 1
#input_ent = embed(input_ent+1) # -1 -> 0
input_ent = input_ent.to(device) # -1 -> 0
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
ent_mask = ent_mask.to(device)
label_ids = label_ids.to(device)
#k,v = load_k_v_queryR_small(input_ent)
k_1, v_1, k_2, v_2 = load_k_v_queryR_small(input_ent)
####test
if int(input_ent[input_ent != 0].shape[0]) == 0: ###open or not?
print("None ent")
#print(input_ent)
print(input_ent.shape)
#print("------------")
#k,v = load_k_v_queryR_small(input_ent)
#print(k)
#print(k.shape)
#print("------------")
#print(v)
#print(v.shape)
#continue
k_1 = torch.zeros(input_ent.shape[0], 1, 1, 100).cuda().half()
v_1 = torch.zeros(input_ent.shape[0], 1, 1, 100).cuda().half()
k_2 = torch.zeros(input_ent.shape[0], 1, 1, 1,
100).cuda().half()
v_2 = torch.zeros(input_ent.shape[0], 1, 1, 1,
100).cuda().half()
'''
else:
#print(k_2)
print(k_1.shape)
print(k_2.shape)
exit()
'''
with torch.no_grad():
#tmp_eval_loss = model(input_ids, segment_ids, input_mask, input_ent, ent_mask, label_ids, k.half(), v.half())
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
input_ent, ent_mask, label_ids,
k_1.half(), v_1.half(), k_2.half(),
v_2.half())
#logits = model(input_ids, segment_ids, input_mask, input_ent, ent_mask, None, k.half(), v.half())
logits = model(input_ids, segment_ids, input_mask, input_ent,
ent_mask, None, k_1.half(), v_1.half(),
k_2.half(), v_2.half())
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy, pred = accuracy(logits, label_ids)
for a, b in zip(pred, label_ids):
fgold.write("{}\n".format(label_list[b]))
fpred.write("{}\n".format(label_list[a]))
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy}
if mark:
output_eval_file = os.path.join(
args.output_dir,
"eval_results_{}.txt".format(x.split("_")[-1]))
else:
output_eval_file = os.path.join(
args.output_dir,
"test_results_{}.txt".format(x.split("_")[-1]))
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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("--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",
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('--threshold', type=float, default=.3)
##########ADD##
parser.add_argument("--K_V_dim",
type=int,
default=100,
help="Key and Value dim == KG representation dim")
parser.add_argument(
"--Q_dim",
type=int,
default=768,
help="Query dim == Bert six output layer representation dim")
parser.add_argument(
'--graphsage',
default=False,
action='store_true',
help="Whether to use Attention GraphSage instead of GAT")
parser.add_argument('--self_att',
default=True,
action='store_true',
help="Whether to use GAT")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight decay 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(
'--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")
###############
args = parser.parse_args()
processors = TacredProcessor
num_labels_task = 80
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) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
processor = processors()
label_list = None
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
train_examples, label_list = processor.get_train_examples(args.data_dir)
num_labels = len(label_list)
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model, _ = BertForSequenceClassification.from_pretrained(
args.ernie_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
num_labels=num_labels,
args=args)
###
'''
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_grad = ['bert.encoder.layer.11.output.dense_ent', 'bert.encoder.layer.11.output.LayerNorm_ent']
param_optimizer = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_grad)]
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)
'''
# Prepare optimizer
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
###
#if args.fp16:
# model.half()
###
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
#no_decay = ['bias', 'LayerNorm.weight']
no_grad = [
'bert.encoder.layer.11.output.dense_ent',
'bert.encoder.layer.11.output.LayerNorm_ent'
]
param_optimizer = [(n, p) for n, p in param_optimizer
if not any(nd in n for nd in no_grad)]
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 = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=int(t_total *
0.1),
num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
###
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer,
args.threshold)
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_steps)
# zeros = [0 for _ in range(args.max_seq_length)]
# zeros_ent = [0 for _ in range(100)]
# zeros_ent = [zeros_ent for _ in range(args.max_seq_length)]
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)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in train_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids)
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)
output_loss_file = os.path.join(args.output_dir, "loss")
loss_fout = open(output_loss_file, 'w')
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) if i != 3 else t for i, t in enumerate(batch))
input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids = batch
#input_ent = embed(input_ent+1).to(device) # -1 -> 0
input_ent = input_ent + 1 # -1 -> 0
#k,v = load_k_v_queryR_small(input_ent)
k_1, v_1, k_2, v_2 = load_k_v_queryR_small(input_ent)
#loss = model(input_ids, segment_ids, input_mask, input_ent.half(), ent_mask, label_ids, k.half(), v.half())
loss = model(input_ids, segment_ids, input_mask,
input_ent.float(), ent_mask, label_ids,
k_1.half(), v_1.half(), k_2.half(), v_2.half())
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)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
###
else:
loss.backward()
loss_fout.write("{}\n".format(loss.item()))
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
'''
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
###
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(
args.output_dir, "pytorch_model.bin_{}".format(global_step))
torch.save(model_to_save.state_dict(), output_model_file)
# 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)
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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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=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",
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('--threshold', type=float, default=.3)
##########ADD##
parser.add_argument("--K_V_dim",
type=int,
default=100,
help="Key and Value dim == KG representation dim")
parser.add_argument(
"--Q_dim",
type=int,
default=768,
help="Query dim == Bert six output layer representation dim")
parser.add_argument(
'--graphsage',
default=False,
action='store_true',
help="Whether to use Attention GraphSage instead of GAT")
parser.add_argument('--self_att',
default=True,
action='store_true',
help="Whether to use GAT")
###############
args = parser.parse_args()
processors = FewrelProcessor
num_labels_task = 80
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))
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.")
processor = processors()
num_labels = num_labels_task
label_list = None
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
#train_examples, label_list = processor.get_train_examples(args.data_dir)
filenames = os.listdir(args.output_dir)
#filenames = [x for x in filenames if "pytorch_model.bin_" in x]
###
#filenames = [x for x in filenames if x in ["pytorch_model.bin_1750", "pytorch_model.bin_2000", "pytorch_model.bin_2250", "pytorch_model.bin_2500", "pytorch_model.bin_2750", "pytorch_model.bin_3000", "pytorch_model.bin_3250", "pytorch_model.bin_3500", "pytorch_model.bin_3750", "pytorch_model.bin_4000", "pytorch_model.bin_4250", "pytorch_model.bin_4500", "pytorch_model.bin_4750", "pytorch_model.bin_5000"] ]
#filenames = [x for x in filenames if x in ["pytorch_model.bin_1750", "pytorch_model.bin_2000", "pytorch_model.bin_2250", "pytorch_model.bin_2500", "pytorch_model.bin_2750", "pytorch_model.bin_3000", "pytorch_model.bin_3250", "pytorch_model.bin_3500", "pytorch_model.bin_3750", "pytorch_model.bin_4000"] ]
filenames = ["pytorch_model.bin"]
###
file_mark = []
for x in filenames:
#file_mark.append([x, True])
file_mark.append([x, False])
###
'''
eval_examples = processor.get_dev_examples(args.data_dir)
dev = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, args.threshold)
'''
###
eval_examples = processor.get_test_examples(args.data_dir)
test = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length, tokenizer,
args.threshold)
for x, mark in file_mark:
print(x, mark)
output_model_file = os.path.join(args.output_dir, x)
model_state_dict = torch.load(output_model_file)
#model, _ = BertForSequenceClassification.from_pretrained(args.ernie_model, state_dict=model_state_dict, num_labels=len(label_list), args=args)
model, _ = BertForSequenceClassification.from_pretrained(
args.ernie_model,
state_dict=model_state_dict,
num_labels=num_labels_task,
args=args)
#model.to(device)
#print(device)
if args.fp16: #
model.half() #
model.to(device)
#print(model)
#print(list(model.named_parameters()))
#print("==")
#print(list(model.bert.word_graph_attention.K_V_linear.weight))
#exit()
#for i in model.parameters():
# print(i)
#exit()
#for name, param in model.named_parameters():
# print(name,param.requires_grad)
if mark:
eval_features = dev
else:
eval_features = test
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
# zeros = [0 for _ in range(args.max_seq_length)]
# zeros_ent = [0 for _ in range(100)]
# zeros_ent = [zeros_ent for _ in range(args.max_seq_length)]
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([1 for f in eval_features],
dtype=torch.long)
#all_text = torch.tensor([f.text for f in eval_features], dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in eval_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in eval_features],
dtype=torch.long)
#eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ent, all_ent_masks, all_label_ids)
###
#output_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
#output_text = torch.tensor([f.text for f in eval_features], dtype=torch.long)
#output_ent = torch.tensor([f.ent for f in eval_features], dtype=torch.long)
#output_ans = torch.tensor([f.ans for f in eval_features], dtype=torch.long)
output_label_map = dict()
output_text_map = dict()
output_ent_map = dict()
output_ans_map = dict()
output_mention_map = dict()
for i, f in enumerate(eval_features):
output_label_map[i] = f.label
output_text_map[i] = f.text
output_ent_map[i] = f.ent
output_ans_map[i] = f.ans
#output_mention_map[i] = f.mention
output_label_id = torch.tensor(
[f[0] for f in enumerate(eval_features)], dtype=torch.long)
output_text_id = torch.tensor([f[0] for f in enumerate(eval_features)],
dtype=torch.long)
output_ent_id = torch.tensor([f[0] for f in enumerate(eval_features)],
dtype=torch.long)
output_ans_id = torch.tensor([f[0] for f in enumerate(eval_features)],
dtype=torch.long)
#output_mention_id = torch.tensor([f[0] for f in enumerate(eval_features)], dtype=torch.long)
#eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ent, all_ent_masks, all_label_ids, output_label_id, output_text_id, output_ent_id, output_ans_id)
#eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ent, all_ent_masks, all_label_ids, output_label_id, output_text_id, output_ent_id, output_mention_id)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids, output_label_id,
output_text_id, output_ent_id, output_ans_id)
###
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
if mark:
#output_eval_file = os.path.join(args.output_dir, "eval_results_{}.txt".format(x.split("_")[-1]))
output_file_pred = os.path.join(
args.output_dir, "eval_pred_{}.txt".format(x.split("_")[-1]))
#output_file_glod = os.path.join(args.output_dir, "eval_gold_{}.txt".format(x.split("_")[-1]))
else:
#output_eval_file = os.path.join(args.output_dir, "test_results_{}.txt".format(x.split("_")[-1]))
output_file_pred = os.path.join(
args.output_dir, "test_pred_{}.txt".format(x.split("_")[-1]))
#output_file_glod = os.path.join(args.output_dir, "test_gold_{}.txt".format(x.split("_")[-1]))
fpred = open(output_file_pred, "w")
#fgold = open(output_file_glod, "w")
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
save_data_list = list()
counter = 0
re_all = 0
pre_all = 0
f1_all = 0
tp_all = 0
fp_all = 0
fn_all = 0
tn_all = 0
#ccc=0
for input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids, output_label_id, output_text_id, output_ent_id, output_ans_id in eval_dataloader:
input_ent = input_ent + 1
output_ans = output_ans_map[int(output_ans_id)]
'''
if output_ans == None:
#ccc+=1
#print(ccc)
print("==1==")
continue
elif len(input_ent[input_ent!=0]) != len(output_ans):
print(len(input_ent[input_ent!=0]),len(output_ans))
#ccc+=1
#print(ccc)
#exit()
print("==2==")
continue
'''
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
input_ent = input_ent.to(device)
ent_mask = ent_mask.to(device)
label_ids = label_ids.to(device)
#k, v = load_k_v_queryR(input_ent)
k_1, v_1, new_input_ent, input_ent_nb, input_ent_r = load_k_v_queryR_small(
input_ent)
with torch.no_grad():
output_gen_ids = model(input_ids, segment_ids, input_mask,
input_ent, ent_mask, None, k_1.half(),
v_1.half(), input_ent_nb)
#output_ans = output_ans_map[int(output_ans_id)]
#if output_gen_ids == None:
# print("None")
# continue
#examples_n = 0
if len(output_gen_ids) != len(output_ans):
#print(len(output_gen_ids),len(output_ans))
#print("++++++")
#print("skip")
#print("++++++")
continue
for i, ids_list_pre in enumerate(output_gen_ids):
ids_list_ans = output_ans[i]
if len(ids_list_pre) != len(ids_list_ans):
print("========")
print(ids_list_pre)
print(ids_list_ans)
print("========")
continue
#print("{}/{}".format(counter,329))
#ids_list_ans
#ids_list_pre
tp = 0
fp = 0
fn = 0
tn = 0
re = 0
pre = 0
f1 = 0
for idx, id in enumerate(ids_list_ans):
if id == -1:
if tp == 0 and fp == 0:
pre = 0
else:
pre = tp / (tp + fp)
if tp == 0 and fn == 0:
re = 0
else:
re = tp / (tp + fn)
if pre == 0 and re == 0:
f1 = 0
else:
f1 = float(2.0 * pre * re / (re + pre))
else:
if ids_list_ans[idx] == 1 and ids_list_pre[
idx] == 1:
tp += 1
elif ids_list_ans[idx] == 0 and ids_list_pre[
idx] == 1:
fp += 1
elif ids_list_ans[idx] == 0 and ids_list_pre[
idx] == 0:
tn += 1
elif ids_list_ans[idx] == 1 and ids_list_pre[
idx] == 0:
fn += 1
counter += 1
tp_all += tp
fp_all += fp
fn_all += fn
tn_all += tn
re_all += re
pre_all += pre
f1_all += f1
pp = tp_all / (tp_all + fp_all)
rr = tp_all / (tp_all + fn_all)
ff = float(2.0 * pp * rr / (pp + rr))
print("==============================")
print("P:", pp)
print("R:", rr)
print("F1-micro:", ff)
#print("---")
#print("P:",re_all/counter)
#print("R:",pre_all/counter)
#print("F1-macro:",f1_all/counter)
#print("F1-micro:",float(2.0*(pre_all/counter)*(re_all/counter)/(pre_all/counter+re_all/counter)))
print("==============================")
with open(output_file_pred, "w") as writer:
logger.info("***** Results*****")
fpred.write("P: {}\n".format(pp))
fpred.write("R: {}\n".format(rr))
fpred.write("F1-micro: {}\n".format(ff))
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("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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("--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",
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('--threshold', type=float, default=.1)
args = parser.parse_args()
processors = SemevalProcessor
num_labels_task = 3
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) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
processor = processors()
num_labels = num_labels_task
label_list = None
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
train_examples, label_list = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model, _ = BertForSequenceClassification.from_pretrained(
args.ernie_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
num_labels=num_labels)
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_grad = [
'bert.encoder.layer.11.output.dense_ent',
'bert.encoder.layer.11.output.LayerNorm_ent'
]
param_optimizer = [(n, p) for n, p in param_optimizer
if not any(nd in n for nd in no_grad)]
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)
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer,
args.threshold)
vecs = []
vecs.append([0] * 100)
logger.info("Loading entity embedding.")
with open("kg_embed/entity2vec.vec", 'r') as fin:
for line in fin:
vec = line.strip().split('\t')
vec = [float(x) for x in vec]
vecs.append(vec)
embed = torch.FloatTensor(vecs)
embed = torch.nn.Embedding.from_pretrained(embed)
# embed = torch.nn.Embedding(5041175, 100)
logger.info("Shape of entity embedding: " + str(embed.weight.size()))
del vecs
if args.do_eval:
eval_examples = processor.get_dev_examples(args.data_dir)
dev = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length, tokenizer,
args.threshold)
eval_features = dev
logger.info("Eval Num examples = %d", len(eval_examples))
logger.info("Eval Batch size = %d", args.train_batch_size)
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_id for f in eval_features],
dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in eval_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.train_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_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)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
all_ent = torch.tensor([f.input_ent for f in train_features],
dtype=torch.long)
all_ent_masks = torch.tensor([f.ent_mask for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_ent, all_ent_masks,
all_label_ids)
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)
output_loss_file = os.path.join(args.output_dir, "loss")
loss_fout = open(output_loss_file, 'w')
model.train()
max_acc = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
batch = tuple(
t.to(device) if i != 3 else t for i, t in enumerate(batch))
input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids = batch
input_ent = embed(input_ent + 1).to(device) # -1 -> 0
loss = model(input_ids, segment_ids, input_mask,
input_ent.half(), ent_mask, label_ids)
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()
loss_fout.write("{}\n".format(loss.item()))
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
if args.do_eval:
logger.info("***** Running evaluation *****")
output_eval_file = os.path.join(
args.output_dir, "eval_results_{}.txt".format(global_step))
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, input_ent, ent_mask, label_ids in eval_dataloader:
input_ent = embed(input_ent + 1) # -1 -> 0
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
input_ent = input_ent.to(device)
ent_mask = ent_mask.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids,
input_mask, input_ent, ent_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask,
input_ent, ent_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
max_acc = max(max_acc, eval_accuracy)
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'max_accuracy': max_acc
}
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
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("--train_file", default=None, type=str, required=True)
parser.add_argument("--ernie_model",
default=None,
type=str,
required=True,
help="Ernie pre-trained model")
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("--ckpt", default='None', type=str)
## 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("--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("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
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",
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('--mean_pool', type=float, default=1)
parser.add_argument("--bert_model", type=str, default='bert')
args = parser.parse_args()
logger.info(args)
print(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) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
processor = TypingProcessor()
tokenizer_label = BertTokenizer_label.from_pretrained(
args.ernie_model, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer.from_pretrained(args.ernie_model,
do_lower_case=args.do_lower_case)
if os.path.exists('***path_to_your_roberta***'):
load_path = '***path_to_your_roberta***'
else:
load_path = '***path_to_your_roberta***'
roberta_tokenizer = RobertaTokenizer.from_pretrained(load_path)
bert_tokenizer_cased = BertTokenizer_cased.from_pretrained(
'***path_to_your_bert_tokenizer_cased***')
train_examples = None
num_train_steps = None
train_examples, label_list, d = processor.get_train_examples(
args.data_dir, args.train_file)
label_list = sorted(label_list)
#class_weight = [min(d[x], 100) for x in label_list]
#logger.info(class_weight)
S = []
for l in label_list:
s = []
for ll in label_list:
if ll in l:
s.append(1.)
else:
s.append(0.)
S.append(s)
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
if args.bert_model == 'bert' and args.do_lower_case:
if os.path.exists('***path_to_your_bert_uncased***'):
bert_model = BertModel.from_pretrained(
'***path_to_your_bert_uncased***')
else:
bert_model = BertModel.from_pretrained(
'***path_to_your_bert_uncased***')
if args.ckpt != 'None':
if os.path.exists('***path_to_your_bert_uncased***'):
load_path = '***path_to_your_trained_checkpoint***' + args.ckpt
else:
load_path = '***path_to_your_trained_checkpoint***' + args.ckpt
ckpt = torch.load(load_path)
bert_model.load_state_dict(ckpt["bert-base"])
elif args.bert_model == 'roberta':
if os.path.exists('***path_to_your_roberta***'):
bert_model = RobertaModel.from_pretrained(
'***path_to_your_roberta***')
else:
bert_model = RobertaModel.from_pretrained(
'***path_to_your_roberta***')
if args.ckpt != 'None':
if os.path.exists('***path_to_your_roberta***'):
load_path = '***path_to_your_trained_checkpoint***' + args.ckpt
else:
load_path = '***path_to_your_trained_checkpoint***' + args.ckpt
ckpt = torch.load(load_path)
bert_model.load_state_dict(ckpt["bert-base"])
else:
bert_model = BertModel.from_pretrained(
'***path_to_your_bert_model_cased***')
model = BertForEntityTyping(bert_model, len(label_list))
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_grad = [
'bert.encoder.layer.11.output.dense_ent',
'bert.encoder.layer.11.output.LayerNorm_ent'
]
param_optimizer = [(n, p) for n, p in param_optimizer
if not any(nd in n for nd in no_grad)]
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)
global_step = 0
if args.do_train:
if args.do_lower_case:
if args.train_file == 'train.json' and os.path.exists(
'train_features_1.0'
) and 'FIGER' in args.data_dir and args.mean_pool == 1:
train_features = torch.load('train_features_1.0')
elif args.train_file == 'train.json' and os.path.exists(
'train_features_1.0_se'
) and 'FIGER' in args.data_dir and args.mean_pool == 0:
train_features = torch.load('train_features_1.0_se')
else:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length,
tokenizer_label, tokenizer, roberta_tokenizer,
bert_tokenizer_cased, args.mean_pool, args.bert_model,
args.do_lower_case)
else:
if args.train_file == 'train.json' and os.path.exists(
'train_features_1.0'
) and 'FIGER' in args.data_dir and args.mean_pool == 1:
train_features = torch.load('train_features_cased')
else:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length,
tokenizer_label, tokenizer, roberta_tokenizer,
bert_tokenizer_cased, args.mean_pool, args.bert_model,
args.do_lower_case)
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_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)
all_span_mask = torch.tensor([f.span_mask for f in train_features],
dtype=torch.float)
all_labels = torch.tensor([f.labels for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_span_mask, all_labels)
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)
output_loss_file = os.path.join(args.output_dir, "loss")
loss_fout = open(output_loss_file, 'w')
model.train()
for epoch 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(train_dataloader):
batch = tuple(
t.to(device) if i != 3 else t for i, t in enumerate(batch))
input_ids, input_mask, segment_ids, span_mask, labels = batch
loss = model(input_ids, args.bert_model, segment_ids,
input_mask, span_mask, labels.half())
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()
loss_fout.write("{}\n".format(
loss.item() * args.gradient_accumulation_steps))
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
if global_step % 150 == 0 and global_step > 0:
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = os.path.join(
args.output_dir,
"pytorch_model.bin_{}".format(global_step))
torch.save(model_to_save.state_dict(),
output_model_file)
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(
args.output_dir, "pytorch_model.bin_{}".format(epoch))
torch.save(model_to_save.state_dict(), output_model_file)
x = "pytorch_model.bin_{}".format(epoch)
for mark in [True, False]:
if mark:
eval_examples = processor.get_dev_examples(args.data_dir)
else:
eval_examples = processor.get_test_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length,
tokenizer_label, tokenizer, roberta_tokenizer,
bert_tokenizer_cased, args.mean_pool, args.bert_model,
args.do_lower_case)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
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_span_mask = torch.tensor(
[f.span_mask for f in eval_features], dtype=torch.float)
all_labels = torch.tensor([f.labels for f in eval_features],
dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_span_mask,
all_labels)
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
pred = []
true = []
for input_ids, input_mask, segment_ids, span_mask, labels in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
span_mask = span_mask.to(device)
labels = labels.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, args.bert_model,
segment_ids, input_mask,
span_mask, labels)
logits = model(input_ids, args.bert_model, segment_ids,
input_mask, span_mask)
logits = logits.detach().cpu().numpy()
labels = labels.to('cpu').numpy()
tmp_eval_accuracy, tmp_pred, tmp_true = accuracy(
logits, labels)
pred.extend(tmp_pred)
true.extend(tmp_true)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
def f1(p, r):
if r == 0.:
return 0.
return 2 * p * r / float(p + r)
def loose_macro(true, pred):
num_entities = len(true)
p = 0.
r = 0.
for true_labels, predicted_labels in zip(true, pred):
if len(predicted_labels) > 0:
p += len(
set(predicted_labels).intersection(
set(true_labels))) / float(
len(predicted_labels))
if len(true_labels):
r += len(
set(predicted_labels).intersection(
set(true_labels))) / float(
len(true_labels))
precision = p / num_entities
recall = r / num_entities
return precision, recall, f1(precision, recall)
def loose_micro(true, pred):
num_predicted_labels = 0.
num_true_labels = 0.
num_correct_labels = 0.
for true_labels, predicted_labels in zip(true, pred):
num_predicted_labels += len(predicted_labels)
num_true_labels += len(true_labels)
num_correct_labels += len(
set(predicted_labels).intersection(
set(true_labels)))
if num_predicted_labels > 0:
precision = num_correct_labels / num_predicted_labels
else:
precision = 0.
recall = num_correct_labels / num_true_labels
return precision, recall, f1(precision, recall)
if False:
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'macro': loose_macro(true, pred),
'micro': loose_micro(true, pred)
}
else:
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'macro': loose_macro(true, pred),
'micro': loose_micro(true, pred)
}
if mark:
output_eval_file = os.path.join(
args.output_dir,
"eval_results_{}.txt".format(x.split("_")[-1]))
else:
output_eval_file = os.path.join(
args.output_dir,
"test_results_{}.txt".format(x.split("_")[-1]))
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
exit(0)
