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doc2query.py
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doc2query.py
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"""doc2query
This file contains Summarizer which loads pretrained ABS model and generate
pseudo query given documents. Generated sentences are wrapped in Translation
class.
"""
import logging
import argparse
import random
from prettytable import PrettyTable
import torch
from transformers import BertTokenizer
from data import DataLoader, load_dataset
from model import AbstractiveSummarizer
from beam_search import BeamSearch
from utils import Tokenizer, get_special_tokens
logger = logging.getLogger('doc2query')
logging.getLogger("transformers").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
class Summarizer:
"""Use a test model to generate fielded query sentences from documents"""
def __init__(self, f_abs, n_best=1, min_length=1, max_length=50,
beam_size=4, bert_model='bert-base-uncased'):
self.n_best = n_best
self.min_length = min_length
self.max_length = max_length
self.beam_size = beam_size
self.abs_model = self.load_abs_model(f_abs)
self.eval()
logger.info(f'Loading BERT Tokenizer [{bert_model}]...')
self.tokenizerB = BertTokenizer.from_pretrained('bert-base-uncased')
self.spt_ids_B, self.spt_ids_C, self.eos_mapping = get_special_tokens()
logger.info('Loading custom Tokenizer for using WBMET embeddings')
self.tokenizerC = Tokenizer(self.abs_model.args.vocab_size)
self.tokenizerC.from_pretrained(self.abs_model.args.file_dec_emb)
@staticmethod
def load_abs_model(f_abs):
"""Load a pre-trained abs model"""
logger.info(f'Loading an abstractive test model from {f_abs}...')
data = torch.load(f_abs, map_location=lambda storage, loc: storage)
mdl = AbstractiveSummarizer(data['args'])
mdl.load_state_dict(data['model']).cuda()
return mdl
def translate(self, docs):
"""Translate a batch of documents."""
batch_size = docs.inp.size(0)
spt_ids = self.spt_ids_C
decode_strategy = BeamSearch(self.beam_size, batch_size, self.n_best,
self.min_length, self.max_length,
spt_ids, self.eos_mapping)
return self._translate_batch_with_strategy(docs, decode_strategy)
def _translate_batch_with_strategy(self, batch, decode_strategy):
"""Translate a batch of documents step by step using cache
:param batch (dict): A batch of documentsj
:param decode_strategy (DecodeStrategy): A decode strategy for
generating translations step by step. I.e., BeamSearch
"""
# (1) Run the encoder on the src
ext_scores, hidden_states = \
self.abs_model.encoder(batch.inp,
attention_mask=batch.mask_inp,
token_type_ids=batch.segs)
# (2) Prepare decoder and decode_strategy
self.abs_model.decoder.init_state(batch.inp)
field_signals = batch.tgt[:, 0]
fn_map_state, memory_bank, memory_pad_mask = \
decode_strategy.initialize(hidden_states[-1], batch.src_lens,
field_signals)
if fn_map_state is not None:
self.abs_model.decoder.map_state(fn_map_state)
# (3) Begin decoding step by step:
for step in range(decode_strategy.max_length):
decoder_input = decode_strategy.current_predictions.unsqueeze(-1)
dec_out, attns = self.abs_model.decoder(
decoder_input, memory_bank, memory_pad_mask, step=step
)
log_probs = self.abs_model.generator(dec_out[:, -1, :].squeeze(1))
# Beam advance
decode_strategy.advance(log_probs, attns)
any_finished = decode_strategy.is_finished.any()
if any_finished:
decode_strategy.update_finished()
if decode_strategy.done:
break
select_indices = decode_strategy.select_indices
if any_finished:
# Reorder states.
memory_bank = memory_bank.index_select(0, select_indices)
memory_pad_mask = memory_pad_mask.index_select(
0, select_indices)
if self.beam_size > 1 or any_finished:
self.abs_model.decoder.map_state(
lambda state, dim: state.index_select(dim, select_indices))
res = {
'batch': batch,
'gold_scores':
self._gold_score(batch, hidden_states[-1], batch.mask_inp),
'scores': decode_strategy.scores,
'predictions': decode_strategy.predictions,
'ext_scores': ext_scores,
'attentions': decode_strategy.attention
}
return res
def results_to_translations(self, data):
"""Convert results into Translation object"""
batch = data['batch']
translations = []
for i, did in enumerate(batch.did):
src_input_ = batch.inp[i]
src_ = self.tokenizerB.decode(src_input_)
topic_ = \
self.tokenizerC.convert_id_to_token(batch.tgt[i][0].item())
pred_sents_ = [
self.tokenizerC.decode(data['predictions'][i][n])
for n in range(self.n_best)
]
gold_sent_ = self.tokenizerC.decode(batch.tgt[i])
x = Translation(did=did, src_input=src_input_, src=src_,
topic=topic_, ext_scores=data['ext_scores'][i],
pred_sents=pred_sents_,
pred_scores=data['scores'][i],
gold_sent=gold_sent_,
gold_score=data['gold_scores'][i],
attentions=data['attentions'][i])
translations.append(x)
return translations
def _gold_score(self, batch, memory_bank, memory_pad_mask):
if hasattr(batch, 'tgt'):
gs = self._score_target(batch, memory_bank, memory_pad_mask)
self.abs_model.decoder.init_state(batch.inp)
else:
gs = [0] * batch.batch_size
return gs
def _score_target(self, batch, memory_bank, memory_pad_mask):
tgt_in = batch.tgt[:, :-1]
dec_out, _ = self.abs_model.decoder(
tgt_in, memory_bank, memory_pad_mask)
log_probs = self.abs_model.generator(dec_out)
gold = batch.tgt[:, 1:]
tgt_pad_mask = gold.eq(self.spt_ids_C['[PAD]'])
log_probs[tgt_pad_mask] = 0
gold_scores = log_probs.gather(2, gold.unsqueeze(-1))
gold_scores = gold_scores.sum(dim=1).view(-1)
return gold_scores.tolist()
def eval(self):
self.abs_model.eval()
class Translation:
"""Container for a translated sentence.
Attributes:
did (str): Source document ID
field_signal (str): Field signal (e.g., [unused0])
src (str): Raw source words
pred_sents (List[str]): Words from the n-best translations
pred_scores (List[float]): Log-probs of n-best translations
attns (List[FloatTensor]): Attention distribution for each translation
highlighted_words (List[str]): Tokens from the source document predicted
as query candidate
gold_sent (str): Words from gold translation
gold_score (float): Log-prob of gold translation
"""
def __init__(self, did, src_input, src, topic, ext_scores,
pred_sents, pred_scores, gold_sent=None, gold_score=None,
attentions=None):
self.did = did
self.src_input = src_input
self.src = src
self.topic = topic
self.ext_scores = ext_scores
self.pred_sents = pred_sents
self.pred_scores = pred_scores
self.gold_sent = gold_sent
self.gold_score = gold_score
self.attentions = attentions
# Print-outs
self.table = PrettyTable()
self.table.field_names = ['Key', 'Values']
self.table.align['Key'] = 'r'
self.table.align['Values'] = 'l'
self.table.max_width['Values'] = 80
def log(self):
"""Pretty Print"""
self.table.clear_rows()
keys = ['did', 'src', 'pred_sents', 'pred_scores']
if self.gold_sent is not None:
keys.extend(['gold_sent', 'gold_score'])
for k in keys:
val = getattr(self, k)
if k == 'pred_sents':
val = '\n'.join(val)
elif k == 'pred_scores':
val = f'{val[0]:.4f}' # best score
# val = ', '.join([str(s.item()) for s in val])
elif k == 'gold_score':
val = f'{val:.4f}'
self.table.add_row([k, val])
print(self.table.get_string())
if __name__ == '__main__':
# Logger
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s %(name)s %(levelname)s: [ %(message)s ]',
datefmt='%b%d %H:%M'
)
parser = argparse.ArgumentParser(
'Topic-attended Summarization for Document Retrieval - Query generation',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
# Doc2Query
d2q = parser.add_argument_group('Doc2Query')
d2q.add_argument('--dir_data', type=str, default='data/tasumm',
help='Path to directory where training datasets are')
d2q.add_argument('--beam_size', type=int, default=4,
help='Number of steps in Beam Search algorithm')
d2q.add_argument('--n_best', type=int, default=2,
help='Number of predictions to produce')
d2q.add_argument('--min_length', type=int, default=1,
help='Minimum number of tokens to predict')
d2q.add_argument('--max_length', type=int, default=50,
help='Maximum number of tokens to predict')
d2q.add_argument('--file_abs_model', type=str, default=None,
help='File path to a pre-trained abs model')
args = parser.parse_args()
# Set defaults
args.model_type = 'abs'
args.cuda = torch.cuda.is_available()
if not args.cuda:
logger.error('It seems like running the pretrained BERT on CPU is not'
' available. You should run this on GPU.')
raise RuntimeError
args.gpu = -1
torch.cuda.set_device(args.gpu)
args.device = torch.device('cuda')
random.seed(1234)
torch.manual_seed(1234)
# Test
summarizer = Summarizer(args.file_abs_model,
n_best=args.n_best,
min_length=args.min_length,
max_length=args.max_length,
beam_size=args.beam_size)
mdl_args = summarizer.abs_model.args
test_iter = DataLoader(load_dataset(args.dir_data, 'test'),
mdl_args.model_type, mdl_args.batch_size,
mdl_args.max_ntokens_src,
summarizer.spt_ids_B, summarizer.spt_ids_C,
summarizer.eos_mapping)
with torch.no_grad():
for batch in test_iter:
results = summarizer.translate(batch)
translations = summarizer.results_to_translations(results)
for x in translations:
x.log()