def test_full_tokenizer(self):
vocab_tokens = [
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un",
"runn", "##ing", ","
]
with open("/tmp/bert_tokenizer_test.txt", "w",
encoding='utf-8') as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
vocab_file = vocab_writer.name
tokenizer = BertTokenizer(vocab_file)
os.remove(vocab_file)
tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
self.assertListEqual(tokens,
["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[7, 4, 5, 10, 8, 9])
vocab_file = tokenizer.save_vocabulary(vocab_path="/tmp/")
tokenizer.from_pretrained(vocab_file)
os.remove(vocab_file)
tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
self.assertListEqual(tokens,
["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[7, 4, 5, 10, 8, 9])
def init_parameters():
tokenizer = BertTokenizer(vocab_file=args.VOCAB_FILE)
tokenizer.save_vocabulary(args.output_dir) # 保存词表文件
processor = MyPro()
return tokenizer, processor
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default="meta",
type=str,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length",
default=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("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=16,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=2,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--random_sampling',
action='store_true',
help="random sampling instead of balanced sampling")
parser.add_argument('--active_sampling',
action='store_true',
help="uses active sampling instead of balanced sampling")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
experiment.log_parameters(vars(args))
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
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')
args.device = device
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels()
full_label_list = label_list[0] + label_list[1] + label_list[2]
num_binary_labels = len(label_list[0])
num_span_labels = len(label_list[1])
num_multi_labels = len(label_list[2])
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
tokenizer = BertTokenizer("savedmodel/vocab.txt", never_split = stopper)
model = BertForMetaClassification.from_pretrained(args.bert_model, num_binary_labels=num_binary_labels, num_span_labels=num_span_labels, num_multi_labels=num_multi_labels)
if args.local_rank == 0:
torch.distributed.barrier()
if args.fp16:
model.half()
model.to(device)
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)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
def save_model(model, outputdir, threshs, score):
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(outputdir, WEIGHTS_NAME)
output_config_file = os.path.join(outputdir, CONFIG_NAME)
TRESH_NAME = "thresholds.txt"
output_thresh_file = os.path.join(outputdir, TRESH_NAME)
print(f"Saving model with score of {score}")
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(outputdir)
with open(output_thresh_file, "w") as text_file:
text_file.write(str(score))
for thresh in threshs:
text_file.write("\n")
text_file.write(str(thresh))
def sigmoid(x):
sigm = 1. / (1. + np.exp(-x))
return sigm
class UnderSampler(Sampler):
def __init__(self, label_mins, label_type_list, label_number_list, order_index_list = None):
self.label_mins = label_mins
self.label_type_list = label_type_list
self.label_number_list = label_number_list
self.order_index_list = order_index_list
label_mins = self.label_mins
label_type_list = self.label_type_list
label_number_list = self.label_number_list
index_list = []
counter_dict = defaultdict(int)
if not order_index_list:
randomlist = list(range(len(label_type_list)))
random.shuffle(randomlist)
else:
randomlist = order_index_list
for i in randomlist:
current_label = label_type_list[i]
current_label_number = label_number_list[i]
current_label_min = label_mins[current_label]
if current_label_min > counter_dict[str(current_label)+ "_" + str(current_label_number)]:
counter_dict[str(current_label)+"_" + str(current_label_number)] += 1
index_list.append(i)
random.shuffle(index_list)
self.index_list_len = len(index_list)
def __iter__(self):
label_mins = self.label_mins
label_type_list = self.label_type_list
label_number_list = self.label_number_list
order_index_list = self.order_index_list
index_list = []
counter_dict = defaultdict(int)
if not order_index_list:
randomlist = list(range(len(label_type_list)))
random.shuffle(randomlist)
else:
randomlist = order_index_list
for i in randomlist:
current_label = label_type_list[i]
current_label_number = label_number_list[i]
current_label_min = label_mins[current_label]
if current_label_min > counter_dict[str(current_label)+ "_" + str(current_label_number)]:
counter_dict[str(current_label)+"_" + str(current_label_number)] += 1
index_list.append(i)
random.shuffle(index_list)
return iter(index_list)
def __len__(self):
return self.index_list_len
### Evaluation
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = eval_examples
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)
input_len = all_input_ids.size(1)
def index2onehot(features, keyname):
returnlist = []
for f in features:
cur_list = []
for position_i in range(input_len):
if position_i in f[keyname]:
cur_list.append(1)
else:
cur_list.append(0)
returnlist.append(cur_list)
return returnlist
def labelindex2binary(label, newline_mask, input_len, ignore= -1):
zeros = [ignore]* input_len
for maskid, mask in enumerate(newline_mask):
zeros[mask] = label[maskid]
return zeros
newline_mask = torch.tensor(index2onehot(eval_features, "[newline]"), dtype=torch.long)
#newline_mask = torch.tensor([f["Newline"] for f in train_features], dtype=torch.long)
list_binary_labels = []
for lb in label_list[0]:
list_binary_labels.append(torch.tensor([labelindex2binary(f[lb], f["[newline]"], input_len=input_len, ignore=-1) for f in eval_features], dtype=torch.long))
list_span_labels = []
for lb in label_list[1]:
list_span_labels.append(torch.tensor([f[lb] for f in eval_features], dtype=torch.long))
list_multi_labels = []
for lb in label_list[2]:
list_multi_labels.append(torch.tensor([labelindex2binary(f[lb[0]], f["[newline]"], input_len=input_len, ignore=-1) for f in eval_features], dtype=torch.long))
pos_weights = []
for lb in label_list[0]:
pos_cases = 0
neg_cases = 0
for example in eval_features:
cur_arr = np.array(example[lb])
cur_arr = cur_arr[cur_arr != -1]
size = cur_arr.size
pos = cur_arr.sum()
pos_cases += pos
neg_cases = neg_cases + size - pos
if pos_cases > 0:
ratio = neg_cases / pos_cases
else:
ratio = 1.0
pos_weights.append(ratio)
experiment.log_metric(f"positive test labels for class: {lb}",pos_cases)
experiment.log_metric(f"negative test labels for class: {lb}",neg_cases)
pos_weights = torch.tensor(pos_weights)
#pos_weights = [pos_weights] * len(eval_features)
pos_weights = pos_weights.expand(all_input_ids.size(0), -1)
# prepare label information for undersampler
label_mins = [defaultdict(int)] * len(full_label_list)
label_type_list = ["x"] * len(eval_features)
label_number_list = ["x"] * len(eval_features)
for lbid, lb in enumerate(full_label_list):
if type(lb) == list:
lb = lb[0]
for exid, example in enumerate(eval_features):
cur_arr = example[lb]
arr_number = sum(cur_arr) + len(cur_arr)
if arr_number != 0:
label_number_list[exid] = arr_number
label_type_list[exid] = lbid
label_mins[lbid][arr_number] += 1
assert ("x" not in label_type_list)
assert ("x" not in label_number_list)
for lid, lm in enumerate(label_mins):
label_mins[lid] = min(lm.values())
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, newline_mask, pos_weights, *list_binary_labels, *list_span_labels, *list_multi_labels)
# Run prediction for full data
if args.local_rank == -1:
# if args.random_sampling:
eval_sampler = SequentialSampler(eval_data)
# else:
#eval_sampler = UnderSampler( label_mins, label_type_list, label_number_list)
else:
eval_sampler = DistributedSampler(eval_data) # Note that this sampler samples randomly
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
def f1_calculate(precision, recall):
num =(2 * precision * recall).astype(float)
den = (precision + recall).astype(float)
try:
f1 = np.divide(num, den, out=np.zeros_like(num), where=den>0.0001)
except:
import pdb; pdb.set_trace()
return f1
def evaluate(number_of_epochs=0, show_examples=True, best_sum_of_scores=0.0):
model.eval()
eval_loss = 0
bce_loss = 0
cross_loss = 0
token_loss = 0
nb_eval_steps = 0
preds = []
out_label_ids = None
result = 0
result = defaultdict(float)
len_bce = len(eval_dataloader)
evaldict = defaultdict(partial(np.ndarray, 0))
thresholds = np.around(np.arange(-10,10, 0.1), decimals=1).tolist()
thresholds= list(dict.fromkeys(thresholds))
bnum = 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
bnum += 1
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, newline_mask, pos_weights, *label_id_list = batch
with torch.no_grad():
logits,loss, loss_list = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, newline_mask= newline_mask, labels=label_id_list, pos_weights=pos_weights)
eval_loss += loss.mean().item()
bce_loss += loss_list[0].mean().item()
cross_loss += loss_list[2].mean().item()
token_loss += loss_list[1].mean().item()
bce_logits = logits[0]
token_logits = logits[1]
evaldict["binary_mask"] = np.append(evaldict["binary_mask"], newline_mask.detach().cpu().numpy())
for l_id, label in enumerate(label_list[0]):
cur_labels = label_id_list[l_id]
bin_label_len = len(cur_labels)
cur_logits = bce_logits[:, :, l_id]
cur_logits_n = cur_logits.detach().cpu().numpy()
cur_labels_n = cur_labels.detach().cpu().numpy()
evaldict[label +"logits"] = np.append(evaldict[label +"logits"], cur_logits_n)
evaldict[label +"labels"]= np.append(evaldict[label +"labels"], cur_labels_n)
if (l_id == 0 or l_id == 1) and bnum < 5:
mask = newline_mask[0].detach().cpu().numpy()
text = " ".join(tokenizer.convert_ids_to_tokens(input_ids.cpu().numpy().tolist()[0]))
print("\n\n1. TEXT:\n")
print(text)
print("\n\n2. LOGITS: \n")
print(sigmoid(cur_logits[0].cpu().numpy())[mask == 1])
print("\n\n3. LABELS: \n")
print(cur_labels[0].cpu().numpy()[mask == 1])
print("\n\n\n")
# for thresh in thresholds:
# threshed_logs = cur_logits > thresh
# threshed_logs = ((cur_logits == 0).float() * -100).float() + (cur_logits != 0).float() * threshed_logs.float()
# cur_labels_cpu = ((cur_logits == 0).float() * -100.0).float() + (cur_logits != 0).float() * cur_labels.float()
# cur_labels_cpu = cur_labels_cpu.detach().numpy()
# threshed_logs = threshed_logs.detach().numpy()
# ignoring= (cur_logits == 0).sum().detach().numpy()
# threshed_logs = threshed_logs[threshed_logs != -100]
# cur_labels_cpu = cur_labels_cpu[cur_labels_cpu != -100]
# # acc = ((cur_labels_cpu == threshed_logs).sum() - ignoring) / (cur_labels_cpu.size - ignoring)
# acc = (cur_labels_cpu == threshed_logs).sum() / cur_labels_cpu.size
# f1= f1_score(cur_labels_cpu, threshed_logs)
# #import pdb; pdb.set_trace()
# #acc = compute_metrics("meta", threshed_logs, cur_labels_cpu, ignoring)
# # if label == "new_topic" and thresh == -1.0:
# #import pdb; pdb.set_trace()
# result[str(thresh)+ "_" + label + "_f1"] += f1 / len_bce
# result[str(thresh)+ "_" + label + "_acc"] += acc / len_bce
for l_id, label in enumerate(label_list[1]):
cur_labels = label_id_list[l_id +len(label_list[0])]
cur_logits = token_logits[:, :, l_id]
cur_logits_n = cur_logits.detach().cpu().numpy()
cur_labels_n = cur_labels.detach().cpu().numpy()
evaldict[label +"logits"] = np.append(evaldict[label +"logits"], cur_logits_n)
evaldict[label +"labels"]= np.append(evaldict[label +"labels"], cur_labels_n)
if (l_id == 0) and bnum < 5:
text = " ".join(tokenizer.convert_ids_to_tokens(input_ids.cpu().numpy().tolist()[0]))
print("\n\n1. TEXT:\n")
print(text)
print("\n\n2. LOGITS: \n")
print(sigmoid(cur_logits[0].cpu().numpy()))
print("\n\n3. LABELS: \n")
print(cur_labels[0].cpu().numpy())
print("\n\n\n")
nb_eval_steps += 1
# if len(preds) == 0:
# preds.append(logits.detach().cpu().numpy())
# out_label_ids = label_ids.detach().cpu().numpy()
# else:
# preds[0] = np.append(
# preds[0], logits.detach().cpu().numpy(), axis=0)
# out_label_ids = np.append(
# out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
# eval_loss = eval_loss / nb_eval_steps
# preds = preds[0]
# if output_mode == "classification":
# preds = np.argmax(preds, axis=1)
# elif output_mode == "regression":
# preds = np.squeeze(preds)
# result = compute_metrics(task_name, preds, out_label_ids)
# bestf1 = 0
# bestf1name = ""
# for key in sorted(result.keys()):
# if "f1" in key and "new_topic" in key:
# if result[key] > bestf1:
# bestf1 = result[key]
# bestf1name = float(key.replace("_f1", "").replace("new_topic", "").replace("_", ""))
# result = 0
# result = defaultdict(float)
# result["zbestf1_"] = bestf1
# result["zbestf1_threshold"] = bestf1name
for l_id, label in enumerate(label_list[0]):
binary_mask = evaldict["binary_mask"]
cur_labels = evaldict[label +"labels"]
cur_preds = evaldict[label+"logits"]
cur_labels = cur_labels[binary_mask == 1]
cur_preds = cur_preds[binary_mask == 1]
cur_ignore = cur_labels != -1
cur_labels = cur_labels[cur_ignore]
cur_preds = cur_preds[cur_ignore]
cur_preds = sigmoid(cur_preds)
try:
if len(cur_labels) == 0:
precision = np.array([0.0])
recall = np.array([0.0])
thresh = np.array([0.0])
else:
precision, recall, thresh = precision_recall_curve(cur_labels, cur_preds)
except:
import pdb; pdb.set_trace()
all_f1 = f1_calculate(precision, recall)
maxindex = np.argmax(all_f1)
result[label+"_best_thresh"] = thresh[maxindex]
best_tresh = thresh[maxindex]
if len(cur_labels) > 0:
threshed_val = cur_preds > best_tresh
conf = confusion_matrix(cur_labels, threshed_val)
print(f"Confusion Matrix for {label}\n")
print(conf)
result[label+"_best_f1"] = all_f1[maxindex]
result[label+"atbf1_best_precision"] = precision[maxindex]
result[label+"atbf1_best_recall"] = recall[maxindex]
if len(cur_labels) == 0:
result[label +"_pr_auc_score"] = 0.0
else:
result[label +"_pr_auc_score"] = auc(recall, precision)
# token metrics
for l_id, label in enumerate(label_list[1]):
cur_labels = evaldict[label +"labels"]
cur_preds = evaldict[label+"logits"]
cur_ignore = cur_labels != -1
cur_labels = cur_labels[cur_ignore]
cur_preds = cur_preds[cur_ignore]
cur_preds = sigmoid(cur_preds)
try:
if len(cur_labels) == 0:
precision = np.array([0.0])
recall = np.array([0.0])
thresh = np.array([0.0])
else:
precision, recall, thresh = precision_recall_curve(cur_labels, cur_preds)
except:
import pdb; pdb.set_trace()
all_f1 = f1_calculate(precision, recall)
maxindex = np.argmax(all_f1)
result[label+"_best_thresh"] = thresh[maxindex]
best_tresh = thresh[maxindex]
if len(cur_labels) > 0:
threshed_val = cur_preds > best_tresh
conf = confusion_matrix(cur_labels, threshed_val)
print(f"Confusion Matrix for {label}\n")
print(conf)
result[label+"_best_f1"] = all_f1[maxindex]
result[label+"_f1_best_precision"] = precision[maxindex]
result[label+"_f1_best_recall"] = recall[maxindex]
if len(cur_labels) == 0:
result[label +"_pr_auc_score"] = 0.0
else:
result[label +"_pr_auc_score"] = auc(recall, precision)
if global_step == 0:
loss = tr_loss/1
else:
loss = tr_loss/global_step if args.do_train else None
#result = {}
result['eval_loss'] = eval_loss
result["bce_loss"] = bce_loss
result["cross_loss"] = cross_loss
result["token_loss"] = token_loss
result['global_step'] = global_step
result['loss'] = loss
for key in sorted(result.keys()):
experiment.log_metric(key,result[key], number_of_epochs)
# output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
# 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])))
important_keys = ["self_con","secondary_relevance", "topic_words"]
sum_of_scores = 0.0
for ikd, ik in enumerate(important_keys):
sum_of_scores += result[ik + "_pr_auc_score"]
if ikd == 0:
sum_of_scores += result[ik + "_pr_auc_score"]
if sum_of_scores > best_sum_of_scores:
threshs = [ result[ts+"_best_thresh"] for ts in important_keys]
save_model(model, args.output_dir, threshs, sum_of_scores/4)
best_sum_of_scores = sum_of_scores
return best_sum_of_scores
if args.do_train:
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
# Prepare data loader
train_features = processor.get_train_examples(args.data_dir)
train_examples = train_features
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)
input_len = all_input_ids.size(1)
def index2onehot(features, keyname):
returnlist = []
for f in features:
cur_list = []
for position_i in range(input_len):
if position_i in f[keyname]:
cur_list.append(1)
else:
cur_list.append(0)
returnlist.append(cur_list)
return returnlist
def labelindex2binary(label, newline_mask, input_len, ignore=-1):
zeros = [ignore]* input_len
for maskid, mask in enumerate(newline_mask):
zeros[mask] = label[maskid]
return zeros
newline_mask = torch.tensor(index2onehot(train_features, "[newline]"), dtype=torch.long)
#newline_mask = torch.tensor([f["Newline"] for f in train_features], dtype=torch.long)
list_binary_labels = []
for lb in label_list[0]:
list_binary_labels.append(torch.tensor([labelindex2binary(f[lb], f["[newline]"], input_len=input_len) for f in train_features], dtype=torch.long))
pos_weights = []
for lbid, lb in enumerate(label_list[0]):
pos_cases = 0
neg_cases = 0
for example in train_features:
cur_arr = np.array(example[lb])
cur_arr = cur_arr[cur_arr != -1]
size = cur_arr.size
pos = cur_arr.sum()
pos_cases += pos
neg_cases = neg_cases + size - pos
if pos_cases > 0:
ratio = neg_cases / pos_cases
else:
ratio = 1.0
pos_weights.append(ratio)
experiment.log_metric(f"positive training labels for class: {lb}",pos_cases)
experiment.log_metric(f"negative training labels for class: {lb}",neg_cases)
pos_weights = torch.tensor(pos_weights)
#pos_weights = [pos_weights] * len(train_features)
#pos_weights = None
pos_weights = pos_weights.expand(all_input_ids.size(0), -1)
list_span_labels = []
for lb in label_list[1]:
list_span_labels.append(torch.tensor([f[lb] for f in train_features], dtype=torch.long))
list_multi_labels = []
for lb in label_list[2]:
list_multi_labels.append(torch.tensor([labelindex2binary(f[lb[0]], f["[newline]"], input_len=input_len, ignore=-1) for f in train_features], dtype=torch.long))
# prepare label information for undersampler
label_mins = [defaultdict(int)] * len(full_label_list)
label_type_list = ["x"] * len(train_features)
label_number_list = ["x"] * len(train_features)
for lbid, lb in enumerate(full_label_list):
if type(lb) == list:
lb = lb[0]
for exid, example in enumerate(train_features):
cur_arr = example[lb]
arr_number = sum(cur_arr) + len(cur_arr)
if arr_number != 0:
label_number_list[exid] = arr_number
label_type_list[exid] = lbid
label_mins[lbid][arr_number] += 1
assert ("x" not in label_type_list)
assert ("x" not in label_number_list)
for lid, lm in enumerate(label_mins):
label_mins[lid] = min(lm.values())
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, newline_mask, pos_weights, *list_binary_labels, *list_span_labels, *list_multi_labels)
if args.local_rank == -1:
# if args.weighted_sampling:
# class_weightings = []
# for f in train_features:
# for lblist in label_list:
# for lb in lblist:
# if type(lb) == list:
# lb = lb[0]
# f["activelist"]
#train_sampler = RandomWeightedSampler(train_data)
#else:
if args.random_sampling:
train_sampler = RandomSampler(train_data)
else:
train_sampler = UnderSampler( label_mins, label_type_list, label_number_list)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
def sample_active(label_mins, label_type_list, label_number_list ,train_data,model):
""" Goes through each train example and evaluates them. The indices of the ranking are then used to create a
new Undersampler and then a new dataloader is returned """
resultlist = []
sample_dataloader = train_dataloader = DataLoader(train_data, sampler=SequentialSampler(train_data), batch_size=1)
for sampleid, batch in enumerate(tqdm(sample_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, newline_mask, pos_weights, *label_id_list = batch
with torch.no_grad():
logits, loss, loss_list = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, newline_mask= newline_mask, labels=label_id_list, pos_weights=pos_weights)
loss = loss.detach().cpu().numpy()
resultlist.append(loss)
sample_dataloader = 0
resultlist = np.array(resultlist)
sorted_resultlist = np.argsort(resultlist).tolist()
sorted_resultlist.reverse()
new_sampler = UnderSampler( label_mins, label_type_list, label_number_list, sorted_resultlist)
return_dataloader = DataLoader(train_data, sampler=new_sampler, batch_size=args.train_batch_size)
return return_dataloader
# 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)
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
number_of_epochs = -1
best_sum_of_scores = 0.0
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
tr_loss = 0
number_of_epochs += 1
nb_tr_examples, nb_tr_steps = 0, 0
if number_of_epochs % 1 == 0:
best_sum_of_scores =evaluate(number_of_epochs=number_of_epochs ,best_sum_of_scores = best_sum_of_scores)
if number_of_epochs > 0 and args.active_sampling:
train_dataloader = sample_active(label_mins, label_type_list, label_number_list ,train_data, model)
model.train()
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, newline_mask, pos_weights, *label_id_list = batch
# define a new function to compute loss values for both output_modes
logits, loss, loss_list = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, newline_mask= newline_mask, labels=label_id_list, pos_weights=pos_weights)
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.get_lr(global_step, 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.local_rank in [-1, 0]:
tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
tb_writer.add_scalar('loss', loss.item(), global_step)
experiment.log_metric("lr",optimizer.get_lr()[0], global_step)
experiment.log_metric("train_loss",loss.item(), global_step)
### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
### Example:
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = BertForMetaClassification.from_pretrained(args.output_dir, num_binary_labels=num_binary_labels, num_span_labels=num_span_labels, num_multi_labels=num_multi_labels)
tokenizer = BertTokenizer("savedmodel/vocab.txt", never_split = stopper)
# Good practice: save your training arguments together with the trained model
output_args_file = os.path.join(args.output_dir, 'training_args.bin')
torch.save(args, output_args_file)
else:
model = BertForMetaClassification.from_pretrained(args.bert_model, num_binary_labels=num_binary_labels, num_span_labels=num_span_labels, num_multi_labels=num_multi_labels)
model.to(device)
