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extract_features.py
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extract_features.py
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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Extract pre-computed feature vectors from a PyTorch BERT model."""
import argparse
import logging
import torch
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoTokenizer, AutoModel
import numpy
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
import h5py
EOS_NUM=1
def produce_key(sent):
sent='\t'.join(sent.split())
sent = sent.replace('.', '$period$')
sent = sent.replace('/', '$backslash$')
return sent
def read_examples(input_file,example_batch):
examples = []
unique_id = 0
with open(input_file, "r", encoding='utf-8') as reader:
while True:
line = reader.readline()
if not line:
break
line = line.strip().split('\t')[0]
if line == '' or len(line.split())>400:
continue
examples.append(line)
start=0
while start < len(examples):
yield examples[start:start+example_batch]
start+=example_batch
def get_orig_seq(input_mask_batch):
seq=[i for i in input_mask_batch if i!=0]
return seq
def tokenemb2wemb(average_layer_batch,w2token_batch):
wembs_sent_batch = []
for sent_i, sent_embed in enumerate(average_layer_batch):
sent_embed_out = []
w2token=w2token_batch[sent_i]
for start,end in w2token:
sent_embed_out.append(sum(sent_embed[start:end]) / (end-start))
wembs_sent_batch.append(numpy.array(sent_embed_out))
return wembs_sent_batch
def tokenid2wordid(input_ids,tokenizer,examples):
w2token_batch=[]
input_ids_filtered=[]
for i,example in enumerate(examples):
w2token=[]
input_id=input_ids[i]
input_start=0
for w in example.split():
w_ids=tokenizer.encode(w,add_special_tokens=False)
if len(w_ids)==0:
print (w_ids)
continue
if input_start+len(w_ids)+EOS_NUM > len(input_id):
break
while int(w_ids[0])!=int(input_id[input_start]):
input_start+=1
if input_start>=len(input_id):
logger.warning ('WARNING: wrong tokenisation {0}'.format(example))
w2token=None
break
if w2token is None:
continue
input_end=input_start+len(w_ids)
w2token.append((input_start,input_end))
input_start=input_end
w2token_batch.append(w2token)
input_ids_filtered.append(i)
return w2token_batch,input_ids_filtered
def examples2embeds_file(examples,tokenizer,model,device,writer,args):
model.eval()
with torch.no_grad():
wembs_sent_batch= examples2embeds(examples,tokenizer,model,device,args.max_seq_length,args.layers,lg=args.lg)
for i,sent in enumerate(examples):
sent=produce_key(sent)
payload=numpy.array(wembs_sent_batch[i])
print (payload.shape)
try:
if sent in writer:
print ('already exist',sent.encode('utf-8'))
else:
if len(sent.split('\t'))==len(payload):
writer.create_dataset(sent, payload.shape, dtype='float32', compression="gzip", compression_opts=9,
data=payload)
else:
print ('WARNING. Wrong tokenisation')
except OSError as e:
print(e, sent)
def examples2embeds(examples,tokenizer,model,device,max_seq_length,layers,lg=None):
inputs=tokenizer.batch_encode_plus(examples,max_length=max_seq_length,return_attention_mask=True,add_special_tokens=True,pad_to_max_length='right')
input_ids=torch.tensor(inputs['input_ids']).to(device)
attention_mask=torch.tensor(inputs['attention_mask']).to(device)
if lg:
language_id = tokenizer.lang2id[lg]
langs = torch.tensor([[language_id] * input_ids.shape[1]] * len(examples)).to(device)
input_ids=input_ids.to(device)
w2token_batch,ids_filtered=tokenid2wordid(input_ids,tokenizer,examples)
input_ids=input_ids[ids_filtered]
attention_mask=attention_mask[ids_filtered]
if lg:
all_encoder_layers, _ = model(input_ids=input_ids, langs=langs, attention_mask=attention_mask)[-2:]
else:
all_encoder_layers,_=model(input_ids=input_ids,attention_mask=attention_mask)[-2:]
layer_start,layer_end=int(layers.split('-')[0]),int(layers.split('-')[1])
average_layer_batch = sum(all_encoder_layers[layer_start:layer_end]) / (layer_end-layer_start)
try:
wembs_sent_batch=tokenemb2wemb(average_layer_batch.cpu().detach().numpy(),w2token_batch)
except:
print ('ERROR')
print (average_layer_batch)
print (examples)
return None
return wembs_sent_batch
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--input_file", default=None, type=str, required=True)
parser.add_argument("--output_file", default=None, type=str)
parser.add_argument("--model", default=None, type=str, required=True,
help=" pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
## Other parameters
parser.add_argument("--layers", default='1-13', type=str,help='sum over specific layers')
parser.add_argument("--max_seq_length", default=None, type=int,
help="The maximum total input sequence length after WordPiece tokenization. Sequences longer "
"than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size for predictions.")
parser.add_argument("--local_rank",
type=int,
default=-1,
help = "local_rank for distributed training on gpus")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--gpu', type=int,help='specify the gpu to use')
parser.add_argument('--lg',type=str,default='',help='language id')
parser.add_argument('--model_type', type=str,default=None,help='model type')
import os
args = parser.parse_args()
if not args.model_type:
args.model_type=args.model
if args.output_file:
writer= h5py.File(args.output_file, 'w')
else:
model_pre=args.model_type
if args.model_type!=args.model:
model_pre+='_'+os.path.basename(os.path.dirname(args.model))
writer=h5py.File(args.input_file+'__'+model_pre+'.ly_'+str(args.layers)+'__.hdf5','w')
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda:{0}".format(args.gpu) if torch.cuda.is_available() and not args.no_cuda and args.gpu>=0 else "cpu")
n_gpu=1
else:
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: {}".format(device, n_gpu, bool(args.local_rank != -1)))
# layer_indexes = [int(x) for x in args.layers.split(",")]
# assert args.model_type in MODELS
if args.model.startswith('xlnet'):
EOS_NUM=2
tokenizer = AutoTokenizer.from_pretrained(args.model,output_hidden_states=True,output_attentions=True)
model = AutoModel.from_pretrained(args.model,output_hidden_states=True,output_attentions=True)
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank)
example_counter=0
for examples in read_examples(args.input_file,args.batch_size):
example_counter+=1
print ('processed {0} examples'.format (str(args.batch_size*example_counter)))
examples2embeds_file(examples,tokenizer,model,device,writer,args)
writer.close()
if __name__ == "__main__":
main()