def load_transformer(model_type):
if model_type == "distilbert":
tokenizer = DistilBertTokenizer.from_pretrained(
'distilbert-base-uncased')
model = TFDistilBertForSequenceClassification.from_pretrained(
"distilbert-base-uncased", num_labels=1)
elif model_type == "bert_x12":
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertForSequenceClassification.from_pretrained(
"bert-base-uncased", num_labels=1)
elif model_type == "bert_x24":
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = TFBertForSequenceClassification.from_pretrained(
"bert-large-uncased", num_labels=1)
elif model_type == "albert_v2_x12":
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
model = TFAlbertForSequenceClassification.from_pretrained(
"albert-base-v2", num_labels=1)
elif model_type == "longformer_x12":
tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
model = TFLongformerForSequenceClassification.from_pretrained(
"allenai/longformer-base-4096", num_labels=1)
elif model_type == "longformer_x24":
tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-large-4096')
model = TFLongformerForSequenceClassification.from_pretrained(
"allenai/longformer-large-4096", num_labels=1)
else:
raise ValueError(model_type + " was invalid")
return model, tokenizer
def main(dataset_directory, jsonlines_filename):
dataset, ids, images = extract_article_list(
os.path.join(dataset_directory, jsonlines_filename))
print(f'Len dataset = {len(dataset)}')
tokenizer = LongformerTokenizer.from_pretrained(
"allenai/longformer-base-4096")
batch_size = 512
all_tokens = np.zeros((len(dataset), 2, 4096), dtype=np.float)
for i, chunk in tqdm(enumerate(chunks(dataset, batch_size)),
total=len(dataset) / batch_size):
with torch.no_grad():
tokenized_text = tokenizer(chunk,
return_tensors="pt",
truncation=True,
padding="max_length")
all_tokens[i * batch_size:i * batch_size + len(chunk),
0, :] = tokenized_text["input_ids"].numpy()
all_tokens[i * batch_size:i * batch_size + len(chunk),
1, :] = tokenized_text["attention_mask"].numpy()
data_df = pd.DataFrame(zip(ids, images, all_tokens.astype(np.int_)))
data_df.to_pickle(
os.path.join(
dataset_directory,
f"longformer_tokens_{jsonlines_filename.split('.')[0]}.pkl"))
def __init__(self, config):
super(LongformerForBinaryClassification, self).__init__()
self.config = config
self.tokenizer = LongformerTokenizer.from_pretrained(
'longformer-base-4096')
self.longformer = LongformerModel(config)
self.classifier = nn.Linear(config.hidden_size, 1)
def get_hotpotqa_longformer_tokenizer(model_name=PRE_TAINED_LONFORMER_BASE, do_lower_case=True):
tokenizer = LongformerTokenizer.from_pretrained(model_name, do_lower_case=do_lower_case)
special_tokens_dict = {'additional_special_tokens': ['<q>', '</q>', '<d>', '<p>']}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('Number of added tokens = {}: {}'.format(num_added_toks, special_tokens_dict))
print('*' * 75)
return tokenizer
def get_tokenizer(lm='bert'):
"""Return the tokenizer. Intiailize it if not initialized.
Args:
lm (string): the name of the language model (bert, albert, or distilbert)
Returns:
BertTokenizer or DistilBertTokenizer or AlbertTokenizer
"""
global tokenizer
if tokenizer is None:
if lm == 'bert':
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
elif lm == 'distilbert':
from transformers import DistilBertTokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
elif lm == 'albert':
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
elif lm == 'roberta':
from transformers import RobertaTokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
elif lm == 'xlnet':
from transformers import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
elif lm == 'longformer':
from transformers import LongformerTokenizer
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
return tokenizer
def main(dataset_directory, jsonlines_filename):
dataset, ids, images = extract_article_list(
os.path.join(dataset_directory, jsonlines_filename))
print(f'Len dataset = {len(dataset)}')
text_model = LongformerModel.from_pretrained(
"allenai/longformer-base-4096").to("cuda")
text_model.eval()
tokenizer = LongformerTokenizer.from_pretrained(
"allenai/longformer-base-4096")
# pool = Pool(processes=48)
# processed_text = list(tqdm(pool.map(process_text, dataset), total=len(dataset)))
# pool.close()
batch_size = 8
all_embeddings_avg = np.zeros((len(dataset), 768), dtype=np.float)
for i, chunk in tqdm(enumerate(chunks(dataset, batch_size)),
total=len(dataset) / batch_size):
with torch.no_grad():
tokenized_text = tokenizer(chunk,
return_tensors="pt",
truncation=True,
padding="max_length")
model_out = text_model(**(tokenized_text.to("cuda")))
all_embeddings_avg[i * batch_size:i * batch_size +
len(chunk), :] = torch.mean(
model_out[0], dim=1).cpu().numpy()
data_df = pd.DataFrame(zip(ids, images, all_embeddings_avg))
data_df.to_pickle(
os.path.join(dataset_directory,
f"longformer_{jsonlines_filename.split('.')[0]}.pkl"))
def __init__(self, config_path):
config = configparser.ConfigParser()
config.read(config_path)
self.save_dir = Path(config.get("general", "save_dir"))
if not self.save_dir.exists():
self.save_dir.mkdir(parents=True)
self.clf_th = config.getfloat("general", "clf_th")
self.mlp_model_path = config.get("model", "mlp")
assert Path(self.mlp_model_path).exists()
self.device = "cuda" if torch.cuda.is_available() else "cpu"
bert_config_path = config.get("bert", "config_path")
assert Path(bert_config_path).exists()
self.bert_config = LongformerConfig.from_json_file(bert_config_path)
self.max_seq_length = self.bert_config.max_position_embeddings - 2
self.bert_tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
# bert_tokenizer_path = config.get("bert", "tokenizer_path")
# assert Path(bert_config_path).exists()
# self.bert_tokenizer = LongformerTokenizer.from_pretrained(bert_tokenizer_path)
bert_model_path = config.get("bert", "model_path")
assert Path(bert_model_path).exists()
self.bert_model = LongformerModel.from_pretrained(
bert_model_path, config=self.bert_config)
self.bert_model.to(self.device)
self.bert_model.eval()
gold_dir = Path(config.get("data", "gold_dir"))
assert Path(gold_dir).exists()
self.gold_dataset = ConllDataset(gold_dir)
target_dir = Path(config.get("data", "target_dir"))
assert Path(target_dir).exists()
self.target_dataset = ConllDataset(target_dir)
def __init__(self):
self.model = LongformerModel.from_pretrained(
'allenai/longformer-base-4096')
self.tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
self.led_tokenizer = LEDTokenizer.from_pretrained(
'allenai/led-base-16384')
self.led_model = LEDModel.from_pretrained('allenai/led-base-16384')
def _test_TFLongformer(self, size, large=False):
from transformers import LongformerTokenizer, TFLongformerModel
tokenizer = LongformerTokenizer.from_pretrained(size)
model = TFLongformerModel.from_pretrained(size)
input_dict = tokenizer("Hello, my dog is cute", return_tensors="tf")
spec, input_dict = self.spec_and_pad(input_dict, max_length=512)
outputs = ["last_hidden_state"]
self.run_test(model, input_dict, input_signature=spec, outputs=outputs, large=large)
def __init__(self, args):
super().__init__()
self.args = args
self.save_hyperparameters(args)
self.tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
self.model = LongformerForSequenceClassification.from_pretrained("allenai/longformer-base-4096")
def __init__(self):
self.train = None
self.test = None
self.tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
self.processor = squad.SquadV2Processor()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
def build_model(self):
super().build_model()
self.tokenizer = LongformerTokenizer.from_pretrained(
"allenai/longformer-base-4096")
self.model = LongformerForSequenceClassification.from_pretrained(
"allenai/longformer-base-4096",
num_labels=self.num_categories,
)
self.model.to("cuda")
def __init__(self, data_path):
super(MafiascumDataset, self).__init__()
tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
config = LongformerConfig()
df = pd.read_pickle(data_path, compression="gzip")
grouped_df = df.groupby(["author", "game_id"])
labels = []
inputs = []
attention_masks = []
for key, item in grouped_df:
posts = grouped_df.get_group(key).content.values # All the posts made by a user in a game
label = grouped_df.get_group(key).scum.values[0] # Boolean
label = 1 if label else 0 # Int
num_sentences_in_game = 0
all_sentences_in_game = []
all_attention_masks_in_game = []
for post in posts:
if len(posts) > 0: # Only consider games where user has spoken at least once
sentences = post.split('\n\n')
for sentence in sentences:
sentence = sentence.strip()
if len(sentence) > 0:
input_ids = tokenizer.encode(sentence, max_length=MAX_SENTENCE_LEN)
# 1 for local attention, 2 for global attention, 0 for none (padding)
# (for our task, mark <s> start of sentence with 2 to have global attention)
attention_mask = [1 for _ in range(len(input_ids))]
attention_mask[0] = 2
input_ids = input_ids
attention_mask = attention_mask
all_sentences_in_game += input_ids
all_attention_masks_in_game += attention_mask
num_sentences_in_game += 1
# If the player said less than 10 sentences in a game, we ignore this sample
if num_sentences_in_game < 10:
continue
input_ids = torch.LongTensor(all_sentences_in_game[:MAX_DOC_LEN])
attention_mask = torch.LongTensor(all_attention_masks_in_game[:MAX_DOC_LEN])
label = torch.FloatTensor([label])
inputs.append(input_ids)
attention_masks.append(attention_mask)
labels.append(label)
self.inputs = inputs
self.attention_masks = attention_masks
self.labels = labels
def __init__(self,
vocab,
unk_token="<unk>",
max_input_chars_per_word=100,
never_split=None):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
self.never_split = never_split
self.tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
def summarise_longformer(long_text_to_summarise):
model_to_load = "patrickvonplaten/longformer2roberta-cnn_dailymail-fp16"
tok_to_load = "allenai/longformer-base-4096"
tokeniser = LongformerTokenizer.from_pretrained(tok_to_load)
model = EncoderDecoderModel.from_pretrained(model_to_load)
input_ids = tokeniser(
long_text_to_summarise,
return_tensors="pt").input_ids #.to(device).input_ids
outputs = model.generate(input_ids) #.to(device)
summary = tokeniser.decode(outputs[0], skip_special_tokens=True)
return summary
def __init__(self, hparams):
#super().__init__()
super(TransformerMarco, self).__init__()
self.hparams = hparams
self.tokenizer = LongformerTokenizer.from_pretrained(
hparams.model_name)
self.model = LongformerForSequenceClassification.from_pretrained(
hparams.model_name)
self.train_dataloader_object = self.val_dataloader_object = self.test_dataloader_object = None
self.DatasetClass = MarcoDataset
def make_dataset(self, data_root: str) -> None:
""" Make Dataset
Make dataset from json files and save it as csv.
Args:
data_root: Root directory for document json files.
"""
log.info(f"Making dataset...")
json_paths = glob.glob(f"{data_root}/**/*.json", recursive=True)
# nltk settings
nltk.download('punkt')
stemmer = PorterStemmer()
cv = CountVectorizer()
texts = [] # A list of tokenized texts separated by half-width characters
# Longformer
feature_matrix = []
device = torch.device('cuda')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
model = LongformerModel.from_pretrained('allenai/longformer-base-4096').to(device)
for json_path in tqdm(json_paths):
with open(json_path) as f:
json_obj = json.load(f)
body = json_obj["body"]
soup = BeautifulSoup(body, "html.parser")
for script in soup(["script", "style"]):
script.decompose()
text = soup.get_text()
with torch.no_grad():
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0).to(device)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device).to(device)
global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device).to(device)
outputs = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
vec = outputs.last_hidden_state[0].cpu().detach().clone().numpy().mean(0)
# np.append(feature_matrix, vec)
feature_matrix.append(list(vec))
# log.info(f"Done: {len(feature_matrix)}")
feature_matrix = np.array(feature_matrix)
log.info(f"Longformer: {feature_matrix.shape}")
# Calculate distance matrix
dist_mat = squareform(pdist(feature_matrix, metric='cosine'))
df = pd.DataFrame(dist_mat)
df.to_csv(join(self.cache_path, "json_document_longformer.csv"), index=False)
log.info(f"Successfully made dataset.")
def set_tokenizer(self, tokenizer = "roberta"):
if tokenizer == "longformer":
from transformers import LongformerTokenizer
self.tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
self.tokenizer_type = tokenizer
elif tokenizer == "roberta":
from transformers import RobertaTokenizer
self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
self.tokenizer_type = tokenizer
elif tokenizer == "bert":
from transformers import BertTokenizer
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.tokenizer_type = tokenizer
else:
print("Error, the tokenizers allowed are 'longformer' , 'roberta' , 'bert' ")
def __init__(self, params):
super(LongEntityLinker, self).__init__()
self.params = params
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.n_gpu = torch.cuda.device_count()
self.use_golden_tags = params['use_golden_tags']
# init tokenizer
if params['use_longformer']:
self.tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
else:
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.pad_id = -1
# init model
self.model = LongEntityLinkerModule(self.params)
self.model = self.model.to(self.device)
def load(self, k):
while self.m.get(k, None) == -1:
time.sleep(1) # loading, wit till ready
if self.m.get(k, None) is not None:
return self.m[k] # it's already loaded
self.m[k] = -1 # tell others it's loading, wait
m = None
if k == 'sentence-encode':
m = SentenceTransformer('roberta-base-nli-stsb-mean-tokens')
# word_embedding_model = models.Transformer('allenai/longformer-base-4096')
# pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
# m = SentenceTransformer(modules=[word_embedding_model, pooling_model])
elif k == 'sentiment-analysis':
tokenizer = AutoTokenizer.from_pretrained(
"mrm8488/t5-base-finetuned-emotion")
model = AutoModelWithLMHead.from_pretrained(
"mrm8488/t5-base-finetuned-emotion").to("cuda")
# TODO we sure it's not ForSequenceClassification? https://huggingface.co/mrm8488/t5-base-finetuned-emotion
m = (tokenizer, model, 512)
elif k == 'summarization':
# Not using pipelines because can't handle >max_tokens
# https://github.com/huggingface/transformers/issues/4501
# https://github.com/huggingface/transformers/issues/4224
max_tokens = 1024 # 4096
tokenizer = BartTokenizer.from_pretrained(
'facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained(
'facebook/bart-large-cnn').to("cuda")
# model = EncoderDecoderModel.from_pretrained("patrickvonplaten/longformer2roberta-cnn_dailymail-fp16").to("cuda")
# tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
m = (tokenizer, model, max_tokens)
elif k == 'question-answering':
tokenizer = LongformerTokenizer.from_pretrained(
"allenai/longformer-large-4096-finetuned-triviaqa")
model = LongformerForQuestionAnswering.from_pretrained(
"allenai/longformer-large-4096-finetuned-triviaqa",
return_dict=True).to("cuda")
# tokenizer = AutoTokenizer.from_pretrained("mrm8488/longformer-base-4096-finetuned-squadv2")
# model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/longformer-base-4096-finetuned-squadv2", return_dict=True).to("cuda")
m = (tokenizer, model, 4096)
self.m[k] = m
return m
def __init__(self, params):
super().__init__()
if 'dropout' in params:
self.dropout = nn.Dropout(p=params['dropout'])
else:
self.dropout = None
# self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=False, do_basic_tokenize=False)
# self.bert = BertModel.from_pretrained("bert-base-uncased")
self.max_length = params['max_length'] if 'max_length' in params else 1024
self.max_memory_size = params['max_memory_size']
self.tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
self.bert = LongformerModel.from_pretrained("allenai/longformer-base-4096", gradient_checkpointing=True)
self.num_labels = params["label_length"] if 'label_length' in params else 2
self.fc = nn.Linear(768, self.num_labels)
def __init__(self, params):
super(LongEncoderRanker, self).__init__()
self.params = params
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.n_gpu = torch.cuda.device_count() # todo
#self.num_tags = 4 if not self.params['end_tag'] else 5
#self.num_tags = 3 if not self.params['end_tag'] else 4
self.num_tags = 9 if self.params['conll'] else 3
self.is_biencoder = params['is_biencoder']
self.use_golden_tags = not params['not_use_golden_tags']
# init tokenizer
if params['use_longformer']:
self.tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
else:
#self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.tokenizer = BertTokenizer.from_pretrained('bert-base-cased', do_lower_case=False)
#self.pad_id = 0
self.pad_id = -1
# init model
self.model = LongEncoderModule(self.params)
self.model = self.model.to(self.device)
def get_par_train_data_loader(rank,
args) -> (DataLoader, DistributedSampler, int):
data_frame = read_train_dev_data_frame(file_path=args.data_path,
json_fileName=args.train_data_name)
data_size = data_frame.shape[0]
if args.train_data_filtered == 1:
data_frame = data_frame[data_frame['level'] != 'easy']
logging.info('Filtered data by removing easy case {} to {}'.format(
data_size, data_frame.shape[0]))
elif args.train_data_filtered == 2:
data_frame = data_frame[data_frame['level'] == 'hard']
logging.info(
'Filtered data by removing easy and medium case {} to {}'.format(
data_size, data_frame.shape[0]))
else:
logging.info('Using all training data {}'.format(data_size))
data_size = data_frame.shape[0]
num_replicas = args.world_size
tokenizer = LongformerTokenizer.from_pretrained(args.pretrained_cfg_name,
do_lower_case=True)
hotpot_tensorizer = LongformerQATensorizer(tokenizer=tokenizer,
max_length=args.max_ctx_len)
dataset = HotpotTrainDataset(data_frame=data_frame,
hotpot_tensorizer=hotpot_tensorizer,
max_sent_num=args.max_sent_num)
batch_size = args.batch_size // num_replicas
logging.info('Each node batch size = {}'.format(batch_size))
train_sampler = torch.utils.data.distributed.DistributedSampler(
dataset=dataset, rank=rank, num_replicas=num_replicas)
train_dataloader = DataLoader(dataset=dataset,
batch_size=batch_size,
num_workers=max(1, args.cpu_num // 2),
collate_fn=HotpotTrainDataset.collate_fn,
shuffle=False,
pin_memory=True,
sampler=train_sampler)
return train_dataloader, train_sampler, data_size
def __init__(self, model_name: str = "allenai/longformer-base-4096"):
self.model = LongformerModel.from_pretrained(model_name)
self.tokenizer = LongformerTokenizer.from_pretrained(model_name)
def main():
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
"or remove the --do_eval argument.")
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO
if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
set_seed(training_args.seed)
model = LongformerForMaskedLM.from_pretrained(
'allenai/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
tokenizer.add_tokens(['<doc-s>'], special_tokens=True)
tokenizer.add_tokens(['</doc-s>'], special_tokens=True)
data_args.block_size = 4096
train_dataset = get_dataset(data_args,
tokenizer=tokenizer,
local_rank=training_args.local_rank)
model.resize_token_embeddings(len(tokenizer))
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=data_args.mlm,
mlm_probability=data_args.mlm_probability,
globalize_special_tokens=data_args.globalize_special_tokens)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
# eval_dataset=eval_dataset,
prediction_loss_only=True,
)
model_path = (model_args.model_name_or_path
if model_args.model_name_or_path is not None
and os.path.isdir(model_args.model_name_or_path) else None)
trainer.train(model_path=model_path)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
results = {}
logger.info("*** Evaluate ***")
eval_output = trainer.evaluate()
perplexity = math.exp(eval_output["loss"])
result = {"perplexity": perplexity}
output_eval_file = os.path.join(training_args.output_dir,
"eval_results_lm.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])))
results.update(result)
return results
'5.0',
'--per_gpu_eval_batch_size',
'2',
'--per_gpu_train_batch_size',
'1', # 32GB gpu with fp32
'--gradient_accumulation_steps',
'32',
#'--evaluate_during_training', # this is removed to reduce training time
'--do_train',
'--do_eval',
])
train_fn = './Preprocessed_Data/Preproc0_clinical_sentences_all_with_number_train.txt'
val_fn = './Preprocessed_Data/Preproc0_clinical_sentences_all_with_number_val.txt'
# these are small file for test
# train_fn = './Preprocessed_Data/test_clinical_sentences_all_with_number_train.txt'
# val_fn = './Preprocessed_Data/test_clinical_sentences_all_with_number_val.txt'
training_args.val_datapath = val_fn
training_args.train_datapath = train_fn
##################### use pretrianed longformer in transformer
longformer_model = LongformerForMaskedLM.from_pretrained(
'allenai/longformer-base-4096')
longformer_tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
logger.info('Train and eval with Longformer pretrained ...')
pretrain_and_evaluate(training_args, longformer_model, longformer_tokenizer, eval_only=False, model_path=None\
#,model_path=training_args.output_dir # Local path to the model if the model to train has been ins tantiated from a local path.
)
def main():
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath('args.json'))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO
if training_args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
)
logger.info("Training/evaluation parameters %s", training_args)
# Set seed
set_seed(training_args.seed)
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
tokenizer = LongformerTokenizer.from_pretrained(
model_args.tokenizer_name
if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
model = LongformerForQuestionAnswering.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
)
# Get datasets
#train_dataset = torch.load(data_args.train_file_path)
#eval_dataset = torch.load(data_args.valid_file_path)
train_examples = DeepThinkDataset(data_args.input_train_file)
train_dataset = DTDataset(tokenizer, train_examples,
data_args.max_seq_length)
eval_examples = DeepThinkDataset(data_args.input_eval_file)
eval_dataset = DTDataset(tokenizer, eval_examples,
data_args.max_seq_length)
# Initialize our Trainer
trainer = Trainer(
model=model,
tokenizer=tokenizer,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=DummyDataCollator(),
prediction_loss_only=True,
)
# Training
if training_args.do_train:
trainer.train(model_path=model_args.model_name_or_path if os.path.
isdir(model_args.model_name_or_path) else None)
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval and training_args.local_rank in [-1, 0]:
logger.info("*** Evaluate ***")
eval_output = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir,
"eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(eval_output.keys()):
logger.info(" %s = %s", key, str(eval_output[key]))
writer.write("%s = %s\n" % (key, str(eval_output[key])))
results.update(eval_output)
return results
tokenizer = ElectraTokenizer.from_pretrained('google/electra-small-generator')
model = ElectraForMaskedLM.from_pretrained('google/electra-small-generator')
input_ids = torch.tensor(
tokenizer.encode("Hello, my dog is cute",
add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
print(prediction_scores)
## Longformer
from transformers import LongformerModel, LongformerTokenizer
model = LongformerModel.from_pretrained('longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000) # long input document
input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(
0) # batch of size 1
# Attention mask values -- 0: no attention, 1: local attention, 2: global attention
attention_mask = torch.ones(
input_ids.shape, dtype=torch.long,
device=input_ids.device) # initialize to local attention
attention_mask[:, [
1,
4,
21,
]] = 2 # Set global attention based on the task. For example,
# classification: the <s> token
def data_consistent_checker():
tokenizer = LongformerTokenizer.from_pretrained(PRE_TAINED_LONFORMER_BASE, do_lower_case=True)
def supp_fact_check(row):
support_facts, filtered_support_facts = row['supporting_facts'], row['supp_facts_filtered']
for x in support_facts:
print('supp {}'.format(x))
for x in filtered_support_facts:
print('filtered supp {}'.format(x))
def answer_check(row):
answer_encode_id = row['answer_encode']
answer_norm = row['norm_answer']
orig_answer = row['answer']
print('Decode = {}\nnorm = {}\norig = {}'.format(tokenizer.decode(answer_encode_id, skip_special_tokens=True), answer_norm, orig_answer))
def support_sentence_checker(row):
filtered_support_facts = row['supp_facts_filtered']
for x in filtered_support_facts:
print(x)
print('=' * 100)
p_ctx = row['p_ctx']
for idx, context in enumerate(p_ctx):
print(context[0])
print('context={}\nnum sents={}'.format(context[1], len(context[1])))
print('*'*75)
print('+'*100)
p_ctx_encode = row['p_ctx_encode']
# print(len(p_ctx_encode), len(p_ctx))
for idx, context in enumerate(p_ctx_encode):
p_doc_encode_ids, p_doc_weight, p_doc_len_i, sent_start_end_pair, supp_sent_labels, ctx_with_answer, answer_positions, p_title_len = context
print('encode {}\nwith len {}\nstore len {}'.format(p_doc_encode_ids, len(p_doc_encode_ids), p_doc_len_i))
print('sent pair = {}\nnum sents ={}'.format(sent_start_end_pair, len(sent_start_end_pair)))
print('sent labels = {}'.format(supp_sent_labels))
print('context len = {}'.format(len(context)))
print('context with answer = {}'.format(ctx_with_answer))
print('title = {}'.format(tokenizer.decode(p_doc_encode_ids[:p_title_len], skip_special_tokens=True)))
print('answer position = {}'.format(answer_positions))
if len(answer_positions) > 0:
sent_start, sent_end = sent_start_end_pair[answer_positions[0][0]]
support_sentence = tokenizer.decode(p_doc_encode_ids[sent_start:(sent_end + 1)], skip_special_tokens=True)
print('sentence idx={}, Decode sentence = {}'.format(answer_positions[0][0], support_sentence))
sentence_ids = p_doc_encode_ids[sent_start:(sent_end + 1)]
decode_answer = tokenizer.decode(sentence_ids[answer_positions[0][1]:(answer_positions[0][2]+1)], skip_special_tokens=True)
print('decode answer = {}, orig answer = {}'.format(decode_answer, row['norm_answer']))
print(context[1])
print('*'*75)
print('+' * 100)
print('p_ctx_lens', row['p_ctx_lens'])
def doc_order_checker(row):
pos_docs = row['p']
'''
_id, answer, question, supporting_facts, context, type, level, norm_query, norm_answer, p_ctx, n_ctx, supp_facts_filtered,
answer_type, p_doc_num, n_doc_num, yes_no, no_found, ques_encode, ques_len, answer_encode, answer_len, p_ctx_encode,
p_ctx_lens, pc_max_len, n_ctx_encode, n_ctx_lens, nc_max_len
:return:
'''
data_frame = loadWikiData(PATH=abs_distractor_wiki_path,
json_fileName='hotpot_train_distractor_wiki_tokenized.json')
print('Data frame size = {}'.format(data_frame.shape))
record_num = data_frame.shape[0]
row_num = 2
random_idx = np.random.choice(record_num, row_num, replace=False)
for idx in range(row_num):
row_i = data_frame.loc[random_idx[idx], :]
# supp_fact_check(row=row_i)
# answer_check(row=row_i)
support_sentence_checker(row=row_i)
print('$' * 90)
def __init__(self, config_path):
config = configparser.ConfigParser()
config.read(config_path)
self.n_epoch = config.getint("general", "n_epoch")
self.batch_size = config.getint("general", "batch_size")
self.train_bert = config.getboolean("general", "train_bert")
self.lr = config.getfloat("general", "lr")
self.cut_frac = config.getfloat("general", "cut_frac")
self.log_dir = Path(config.get("general", "log_dir"))
if not self.log_dir.exists():
self.log_dir.mkdir(parents=True)
self.model_save_freq = config.getint("general", "model_save_freq")
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# bert_config_path = config.get("bert", "config_path")
# bert_tokenizer_path = config.get("bert", "tokenizer_path")
# bert_model_path = config.get("bert", "model_path")
self.bert_tokenizer = LongformerTokenizer.from_pretrained(
'allenai/longformer-base-4096')
# self.bert_tokenizer = BertTokenizer.from_pretrained(bert_tokenizer_path)
tkzer_save_dir = self.log_dir / "tokenizer"
if not tkzer_save_dir.exists():
tkzer_save_dir.mkdir()
self.bert_tokenizer.save_pretrained(tkzer_save_dir)
self.bert_model = LongformerModel.from_pretrained(
'allenai/longformer-base-4096')
self.bert_config = self.bert_model.config
# self.bert_config = BertConfig.from_pretrained(bert_config_path)
# self.bert_model = BertModel.from_pretrained(bert_model_path, config=self.bert_config)
self.max_seq_length = self.bert_config.max_position_embeddings - 2
# self.max_seq_length = self.bert_config.max_position_embeddings
self.bert_model.to(self.device)
if self.train_bert:
self.bert_model.train()
else:
self.bert_model.eval()
train_conll_path = config.get("data", "train_path")
print("train path", train_conll_path)
assert Path(train_conll_path).exists()
dev_conll_path = config.get("data", "dev_path")
print("dev path", dev_conll_path)
assert Path(dev_conll_path).exists()
dev1_conll_path = Path(dev_conll_path) / "1"
print("dev1 path", dev1_conll_path)
assert dev1_conll_path.exists()
dev2_conll_path = Path(dev_conll_path) / "2"
print("dev2 path", dev2_conll_path)
assert dev2_conll_path.exists()
self.train_dataset = ConllDataset(train_conll_path)
# self.dev_dataset = ConllDataset(dev_conll_path)
self.dev1_dataset = ConllDataset(dev1_conll_path)
self.dev2_dataset = ConllDataset(dev2_conll_path)
if self.batch_size == -1:
self.batch_size = len(self.train_dataset)
self.scaler = torch.cuda.amp.GradScaler()
tb_cmt = f"lr_{self.lr}_cut-frac_{self.cut_frac}"
self.writer = SummaryWriter(log_dir=self.log_dir, comment=tb_cmt)