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predictor.py
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predictor.py
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#!/usr/bin/env python
""" Translator Class and builder """
from __future__ import print_function
import codecs
import os
import math
import torch
from tensorboardX import SummaryWriter
from others.utils import rouge_results_to_str, test_rouge, tile
from translate.beam import GNMTGlobalScorer
def build_predictor(args, tokenizer, symbols, model, logger=None):
scorer = GNMTGlobalScorer(args.alpha,length_penalty='wu')
translator = Translator(args, model, tokenizer, symbols, global_scorer=scorer, logger=logger)
return translator
class Translator(object):
"""
Uses a model to translate a batch of sentences.
Args:
model (:obj:`onmt.modules.NMTModel`):
NMT model to use for translation
fields (dict of Fields): data fields
beam_size (int): size of beam to use
n_best (int): number of translations produced
max_length (int): maximum length output to produce
global_scores (:obj:`GlobalScorer`):
object to rescore final translations
copy_attn (bool): use copy attention during translation
cuda (bool): use cuda
beam_trace (bool): trace beam search for debugging
logger(logging.Logger): logger.
"""
def __init__(self,
args,
model,
vocab,
symbols,
global_scorer=None,
logger=None,
dump_beam=""):
self.logger = logger
self.cuda = args.visible_gpus != '-1'
self.args = args
self.model = model
self.generator = self.model.generator
self.vocab = vocab
self.symbols = symbols
self.start_token = symbols['BOS']
self.end_token = symbols['EOS']
self.global_scorer = global_scorer
self.beam_size = args.beam_size
self.min_length = args.min_length
self.max_length = args.max_length
self.dump_beam = dump_beam
# for debugging
self.beam_trace = self.dump_beam != ""
self.beam_accum = None
tensorboard_log_dir = args.model_path
self.tensorboard_writer = SummaryWriter(tensorboard_log_dir, comment="Unmt")
if self.beam_trace:
self.beam_accum = {
"predicted_ids": [],
"beam_parent_ids": [],
"scores": [],
"log_probs": []}
def _build_target_tokens(self, pred):
# vocab = self.fields["tgt"].vocab
tokens = []
for tok in pred:
tok = int(tok)
tokens.append(tok)
if tokens[-1] == self.end_token:
tokens = tokens[:-1]
break
tokens = [t for t in tokens if t < len(self.vocab)]
tokens = self.vocab.DecodeIds(tokens).split(' ')
return tokens
def from_batch(self, translation_batch):
batch = translation_batch["batch"]
assert (len(translation_batch["gold_score"]) ==
len(translation_batch["predictions"]))
batch_size = batch.batch_size
preds, pred_score, gold_score, tgt_str, src = translation_batch["predictions"],translation_batch["scores"],translation_batch["gold_score"],batch.tgt_str, batch.src
translations = []
for b in range(batch_size):
pred_sents = self.vocab.convert_ids_to_tokens([int(n) for n in preds[b][0]])
pred_sents = ' '.join(pred_sents).replace(' ##','')
gold_sent = ' '.join(tgt_str[b].split())
# translation = Translation(fname[b],src[:, b] if src is not None else None,
# src_raw, pred_sents,
# attn[b], pred_score[b], gold_sent,
# gold_score[b])
# src = self.spm.DecodeIds([int(t) for t in translation_batch['batch'].src[0][5] if int(t) != len(self.spm)])
raw_src = [self.vocab.ids_to_tokens[int(t)] for t in src[b]][:500]
raw_src = ' '.join(raw_src)
translation = (pred_sents, gold_sent, raw_src)
# translation = (pred_sents[0], gold_sent)
translations.append(translation)
return translations
def translate(self,
data_iter, step,
attn_debug=False):
self.model.eval()
gold_path = self.args.result_path + '.%d.gold' % step
can_path = self.args.result_path + '.%d.candidate' % step
self.gold_out_file = codecs.open(gold_path, 'w', 'utf-8')
self.can_out_file = codecs.open(can_path, 'w', 'utf-8')
# raw_gold_path = self.args.result_path + '.%d.raw_gold' % step
# raw_can_path = self.args.result_path + '.%d.raw_candidate' % step
self.gold_out_file = codecs.open(gold_path, 'w', 'utf-8')
self.can_out_file = codecs.open(can_path, 'w', 'utf-8')
raw_src_path = self.args.result_path + '.%d.raw_src' % step
self.src_out_file = codecs.open(raw_src_path, 'w', 'utf-8')
# pred_results, gold_results = [], []
ct = 0
with torch.no_grad():
for batch in data_iter:
if(self.args.recall_eval):
gold_tgt_len = batch.tgt.size(1)
self.min_length = gold_tgt_len + 20
self.max_length = gold_tgt_len + 60
batch_data = self.translate_batch(batch)
translations = self.from_batch(batch_data)
for trans in translations:
pred, gold, src = trans
pred_str = pred.replace('[unused0]', '').replace('[unused3]', '').replace('[PAD]', '').replace('[unused1]', '').replace(r' +', ' ').replace(' [unused2] ', '<q>').replace('[unused2]', '').strip()
gold_str = gold.strip()
if(self.args.recall_eval):
_pred_str = ''
gap = 1e3
for sent in pred_str.split('<q>'):
can_pred_str = _pred_str+ '<q>'+sent.strip()
can_gap = math.fabs(len(_pred_str.split())-len(gold_str.split()))
# if(can_gap>=gap):
if(len(can_pred_str.split())>=len(gold_str.split())+10):
pred_str = _pred_str
break
else:
gap = can_gap
_pred_str = can_pred_str
# pred_str = ' '.join(pred_str.split()[:len(gold_str.split())])
# self.raw_can_out_file.write(' '.join(pred).strip() + '\n')
# self.raw_gold_out_file.write(' '.join(gold).strip() + '\n')
self.can_out_file.write(pred_str + '\n')
self.gold_out_file.write(gold_str + '\n')
self.src_out_file.write(src.strip() + '\n')
ct += 1
self.can_out_file.flush()
self.gold_out_file.flush()
self.src_out_file.flush()
self.can_out_file.close()
self.gold_out_file.close()
self.src_out_file.close()
if (step != -1):
rouges = self._report_rouge(gold_path, can_path)
self.logger.info('Rouges at step %d \n%s' % (step, rouge_results_to_str(rouges)))
if self.tensorboard_writer is not None:
self.tensorboard_writer.add_scalar('test/rouge1-F', rouges['rouge_1_f_score'], step)
self.tensorboard_writer.add_scalar('test/rouge2-F', rouges['rouge_2_f_score'], step)
self.tensorboard_writer.add_scalar('test/rougeL-F', rouges['rouge_l_f_score'], step)
def _report_rouge(self, gold_path, can_path):
self.logger.info("Calculating Rouge")
results_dict = test_rouge(self.args.temp_dir, can_path, gold_path)
return results_dict
def translate_batch(self, batch, fast=False):
"""
Translate a batch of sentences.
Mostly a wrapper around :obj:`Beam`.
Args:
batch (:obj:`Batch`): a batch from a dataset object
data (:obj:`Dataset`): the dataset object
fast (bool): enables fast beam search (may not support all features)
Todo:
Shouldn't need the original dataset.
"""
with torch.no_grad():
return self._fast_translate_batch(
batch,
self.max_length,
min_length=self.min_length)
def _fast_translate_batch(self,
batch,
max_length,
min_length=0):
# TODO: faster code path for beam_size == 1.
# TODO: support these blacklisted features.
assert not self.dump_beam
beam_size = self.beam_size
batch_size = batch.batch_size
src = batch.src
segs = batch.segs
mask_src = batch.mask_src
src_features = self.model.bert(src, segs, mask_src)
dec_states = self.model.decoder.init_decoder_state(src, src_features, with_cache=True)
device = src_features.device
# Tile states and memory beam_size times.
dec_states.map_batch_fn(
lambda state, dim: tile(state, beam_size, dim=dim))
src_features = tile(src_features, beam_size, dim=0)
batch_offset = torch.arange(
batch_size, dtype=torch.long, device=device)
beam_offset = torch.arange(
0,
batch_size * beam_size,
step=beam_size,
dtype=torch.long,
device=device)
alive_seq = torch.full(
[batch_size * beam_size, 1],
self.start_token,
dtype=torch.long,
device=device)
# Give full probability to the first beam on the first step.
topk_log_probs = (
torch.tensor([0.0] + [float("-inf")] * (beam_size - 1),
device=device).repeat(batch_size))
# Structure that holds finished hypotheses.
hypotheses = [[] for _ in range(batch_size)] # noqa: F812
results = {}
results["predictions"] = [[] for _ in range(batch_size)] # noqa: F812
results["scores"] = [[] for _ in range(batch_size)] # noqa: F812
results["gold_score"] = [0] * batch_size
results["batch"] = batch
for step in range(max_length):
decoder_input = alive_seq[:, -1].view(1, -1)
# Decoder forward.
decoder_input = decoder_input.transpose(0,1)
dec_out, dec_states = self.model.decoder(decoder_input, src_features, dec_states,
step=step)
# Generator forward.
log_probs = self.generator.forward(dec_out.transpose(0,1).squeeze(0))
vocab_size = log_probs.size(-1)
if step < min_length:
log_probs[:, self.end_token] = -1e20
# Multiply probs by the beam probability.
log_probs += topk_log_probs.view(-1).unsqueeze(1)
alpha = self.global_scorer.alpha
length_penalty = ((5.0 + (step + 1)) / 6.0) ** alpha
# Flatten probs into a list of possibilities.
curr_scores = log_probs / length_penalty
if(self.args.block_trigram):
cur_len = alive_seq.size(1)
if(cur_len>3):
for i in range(alive_seq.size(0)):
fail = False
words = [int(w) for w in alive_seq[i]]
words = [self.vocab.ids_to_tokens[w] for w in words]
words = ' '.join(words).replace(' ##','').split()
if(len(words)<=3):
continue
trigrams = [(words[i-1],words[i],words[i+1]) for i in range(1,len(words)-1)]
trigram = tuple(trigrams[-1])
if trigram in trigrams[:-1]:
fail = True
if fail:
curr_scores[i] = -10e20
curr_scores = curr_scores.reshape(-1, beam_size * vocab_size)
topk_scores, topk_ids = curr_scores.topk(beam_size, dim=-1)
# Recover log probs.
topk_log_probs = topk_scores * length_penalty
# Resolve beam origin and true word ids.
topk_beam_index = topk_ids.div(vocab_size)
topk_ids = topk_ids.fmod(vocab_size)
# Map beam_index to batch_index in the flat representation.
batch_index = (
topk_beam_index
+ beam_offset[:topk_beam_index.size(0)].unsqueeze(1))
select_indices = batch_index.view(-1)
# Append last prediction.
alive_seq = torch.cat(
[alive_seq.index_select(0, select_indices),
topk_ids.view(-1, 1)], -1)
is_finished = topk_ids.eq(self.end_token)
if step + 1 == max_length:
is_finished.fill_(1)
# End condition is top beam is finished.
end_condition = is_finished[:, 0].eq(1)
# Save finished hypotheses.
if is_finished.any():
predictions = alive_seq.view(-1, beam_size, alive_seq.size(-1))
for i in range(is_finished.size(0)):
b = batch_offset[i]
if end_condition[i]:
is_finished[i].fill_(1)
finished_hyp = is_finished[i].nonzero().view(-1)
# Store finished hypotheses for this batch.
for j in finished_hyp:
hypotheses[b].append((
topk_scores[i, j],
predictions[i, j, 1:]))
# If the batch reached the end, save the n_best hypotheses.
if end_condition[i]:
best_hyp = sorted(
hypotheses[b], key=lambda x: x[0], reverse=True)
score, pred = best_hyp[0]
results["scores"][b].append(score)
results["predictions"][b].append(pred)
non_finished = end_condition.eq(0).nonzero().view(-1)
# If all sentences are translated, no need to go further.
if len(non_finished) == 0:
break
# Remove finished batches for the next step.
topk_log_probs = topk_log_probs.index_select(0, non_finished)
batch_index = batch_index.index_select(0, non_finished)
batch_offset = batch_offset.index_select(0, non_finished)
alive_seq = predictions.index_select(0, non_finished) \
.view(-1, alive_seq.size(-1))
# Reorder states.
select_indices = batch_index.view(-1)
src_features = src_features.index_select(0, select_indices)
dec_states.map_batch_fn(
lambda state, dim: state.index_select(dim, select_indices))
return results
class Translation(object):
"""
Container for a translated sentence.
Attributes:
src (`LongTensor`): src word ids
src_raw ([str]): raw src words
pred_sents ([[str]]): words from the n-best translations
pred_scores ([[float]]): log-probs of n-best translations
attns ([`FloatTensor`]) : attention dist for each translation
gold_sent ([str]): words from gold translation
gold_score ([float]): log-prob of gold translation
"""
def __init__(self, fname, src, src_raw, pred_sents,
attn, pred_scores, tgt_sent, gold_score):
self.fname = fname
self.src = src
self.src_raw = src_raw
self.pred_sents = pred_sents
self.attns = attn
self.pred_scores = pred_scores
self.gold_sent = tgt_sent
self.gold_score = gold_score
def log(self, sent_number):
"""
Log translation.
"""
output = '\nSENT {}: {}\n'.format(sent_number, self.src_raw)
best_pred = self.pred_sents[0]
best_score = self.pred_scores[0]
pred_sent = ' '.join(best_pred)
output += 'PRED {}: {}\n'.format(sent_number, pred_sent)
output += "PRED SCORE: {:.4f}\n".format(best_score)
if self.gold_sent is not None:
tgt_sent = ' '.join(self.gold_sent)
output += 'GOLD {}: {}\n'.format(sent_number, tgt_sent)
output += ("GOLD SCORE: {:.4f}\n".format(self.gold_score))
if len(self.pred_sents) > 1:
output += '\nBEST HYP:\n'
for score, sent in zip(self.pred_scores, self.pred_sents):
output += "[{:.4f}] {}\n".format(score, sent)
return output