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translate_ensemble.py
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translate_ensemble.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#
# Translate sentences from the input stream.
# The model will be faster is sentences are sorted by length.
# Input sentences must have the same tokenization and BPE codes than the ones used in the model.
# It also supports ensemble multiple models, beam search and length penlty.
#
# Usage:
# cat source_sentences.bpe | \
# python translate.py --exp_name translate \
# --exp_id en-fr \
# --src_lang en --tgt_lang fr \
# --model_path model1.pth,model2.pth --output_path output \
# --beam 10 --length_penalty 1.1
#
import os
import io
import sys
import argparse
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from src.utils import AttrDict
from src.utils import bool_flag, initialize_exp
from src.data.dictionary import Dictionary
from src.model.transformer import TransformerModel
from src.model.transformer import BeamHypotheses
from src.fp16 import network_to_half
def get_parser():
"""
Generate a parameters parser.
"""
# parse parameters
parser = argparse.ArgumentParser(description="Translate sentences")
# main parameters
parser.add_argument("--dump_path", type=str, default="./dumped/", help="Experiment dump path")
parser.add_argument("--exp_name", type=str, default="", help="Experiment name")
parser.add_argument("--exp_id", type=str, default="", help="Experiment ID")
parser.add_argument("--fp16", type=bool_flag, default=False, help="Run model with float16")
parser.add_argument("--batch_size", type=int, default=32, help="Number of sentences per batch")
parser.add_argument("--input_path", type=str, default="", help="Sentences to translate path")
parser.add_argument("--first_model_is_adapter", type=bool_flag, default=False, help="First model uses adapters")
# model / output paths
parser.add_argument("--model_path", type=str, default="", help="Model path")
parser.add_argument("--output_path", type=str, default="", help="Output path")
parser.add_argument("--beam", type=int, default=1, help="Beam size")
parser.add_argument("--length_penalty", type=float, default=1, help="length penalty")
# source language / target language
parser.add_argument("--src_lang", type=str, default="", help="Source language")
parser.add_argument("--tgt_lang", type=str, default="", help="Target language")
return parser
def generate_beam(decoders, src_encodeds, src_len, tgt_lang_id, beam_size, length_penalty, early_stopping, max_len=200, params=None):
assert params is not None
src_encs = []
bs = len(src_len)
n_words = params.n_words
src_len = src_len.unsqueeze(1).expand(bs, beam_size).contiguous().view(-1)
for i in range(len(src_encodeds)):
src_encodeds[i] = src_encodeds[i].unsqueeze(1).expand((bs, beam_size) + src_encodeds[i].shape[1:]).contiguous().view((bs * beam_size,) + src_encodeds[i].shape[1:])
generated = src_len.new(max_len, bs * beam_size)
generated.fill_(params.pad_index)
generated[0].fill_(params.eos_index)
generated_hyps = [BeamHypotheses(beam_size, max_len, length_penalty, early_stopping) for _ in range(bs)]
positions = src_len.new(max_len).long()
positions = torch.arange(max_len, out=positions).unsqueeze(1).expand_as(generated)
langs = positions.clone().fill_(tgt_lang_id)
beam_scores = src_encodeds[0].new(bs, beam_size).fill_(0)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view(-1)
cur_len = 1
caches = [{'slen': 0} for i in range(len(decoders))]
done = [False for _ in range(bs)]
while cur_len < max_len:
avg_scores = []
#avg_scores = None
for i, (src_enc, decoder) in enumerate(zip(src_encodeds, decoders)):
tensor = decoder.forward(
'fwd',
x=generated[:cur_len],
lengths=src_len.new(bs * beam_size).fill_(cur_len),
positions=positions[:cur_len],
langs=langs[:cur_len],
causal=True,
src_enc=src_enc,
src_len=src_len,
cache=caches[i]
)
assert tensor.size() == (1, bs * beam_size, decoder.dim)
tensor = tensor.data[-1, :, :] # (bs * beam_size, dim)
scores = decoder.pred_layer.get_scores(tensor) # (bs * beam_size, n_words)
scores = F.log_softmax(scores, dim=-1) # (bs * beam_size, n_words)
avg_scores.append(scores)
avg_scores = torch.logsumexp(torch.stack(avg_scores, dim=0), dim=0) - math.log(len(decoders))
#avg_scores.div_(len(decoders))
_scores = avg_scores + beam_scores[:, None].expand_as(avg_scores)
_scores = _scores.view(bs, beam_size * n_words)
next_scores, next_words = torch.topk(_scores, 2 * beam_size, dim=1, largest=True, sorted=True)
assert next_scores.size() == next_words.size() == (bs, 2 * beam_size)
next_batch_beam = []
for sent_id in range(bs):
# if we are done with this sentence
done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item())
if done[sent_id]:
next_batch_beam.extend([(0, params.pad_index, 0)] * beam_size) # pad the batch
continue
# next sentence beam content
next_sent_beam = []
# next words for this sentence
for idx, value in zip(next_words[sent_id], next_scores[sent_id]):
# get beam and word IDs
beam_id = idx // n_words
word_id = idx % n_words
# end of sentence, or next word
if word_id == params.eos_index or cur_len + 1 == max_len:
generated_hyps[sent_id].add(generated[:cur_len, sent_id * beam_size + beam_id].clone(), value.item())
else:
next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))
# the beam for next step is full
if len(next_sent_beam) == beam_size:
break
# update next beam content
assert len(next_sent_beam) == 0 if cur_len + 1 == max_len else beam_size
if len(next_sent_beam) == 0:
next_sent_beam = [(0, params.pad_index, 0)] * beam_size # pad the batch
next_batch_beam.extend(next_sent_beam)
assert len(next_batch_beam) == beam_size * (sent_id + 1)
# sanity check / prepare next batch
assert len(next_batch_beam) == bs * beam_size
beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
beam_words = generated.new([x[1] for x in next_batch_beam])
beam_idx = src_len.new([x[2] for x in next_batch_beam])
# re-order batch and internal states
generated = generated[:, beam_idx]
generated[cur_len] = beam_words
for cache in caches:
for k in cache.keys():
if k != 'slen':
cache[k] = (cache[k][0][beam_idx], cache[k][1][beam_idx])
# update current length
cur_len = cur_len + 1
# stop when we are done with each sentence
if all(done):
break
tgt_len = src_len.new(bs)
best = []
for i, hypotheses in enumerate(generated_hyps):
best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]
tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol
best.append(best_hyp)
# generate target batch
decoded = src_len.new(tgt_len.max().item(), bs).fill_(params.pad_index)
for i, hypo in enumerate(best):
decoded[:tgt_len[i] - 1, i] = hypo
decoded[tgt_len[i] - 1, i] = params.eos_index
# sanity check
assert (decoded == params.eos_index).sum() == 2 * bs
return decoded, tgt_len
def main(params):
# initialize the experiment
logger = initialize_exp(params)
parser = get_parser()
params = parser.parse_args()
models_path = params.model_path.split(',')
first_model_is_adapter = params.first_model_is_adapter
# generate parser / parse parameters
models_reloaded = []
for model_path in models_path:
models_reloaded.append(torch.load(model_path))
model_params = AttrDict(models_reloaded[0]['params'])
model_params.use_adapters = False
model_params.use_adapters_enc_dec_attention = False
logger.info("Supported languages: %s" % ", ".join(model_params.lang2id.keys()))
# update dictionary parameters
for name in ['n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index', 'mask_index']:
setattr(params, name, getattr(model_params, name))
# build dictionary / build encoder / build decoder / reload weights
dico = Dictionary(models_reloaded[0]['dico_id2word'], models_reloaded[0]['dico_word2id'], models_reloaded[0]['dico_counts'])
params.src_id = model_params.lang2id[params.src_lang]
params.tgt_id = model_params.lang2id[params.tgt_lang]
encoders = []
decoders = []
def package_module(modules):
state_dict = OrderedDict()
for k, v in modules.items():
if k.startswith('module.'):
state_dict[k[7:]] = v
else:
state_dict[k] = v
return state_dict
for index, reloaded in enumerate(models_reloaded):
if first_model_is_adapter:
if index == 0:
model_params.use_adapters = True
model_params.use_adapters_enc_dec_attention = False
else:
model_params.use_adapters = False
model_params.use_adapters_enc_dec_attention = False
encoder = TransformerModel(model_params, dico, is_encoder=True, with_output=True).cuda().eval()
decoder = TransformerModel(model_params, dico, is_encoder=False, with_output=True).cuda().eval()
encoder.load_state_dict(package_module(reloaded['encoder']))
decoder.load_state_dict(package_module(reloaded['decoder']))
# float16
if params.fp16:
assert torch.backends.cudnn.enabled
encoder = network_to_half(encoder)
decoder = network_to_half(decoder)
encoders.append(encoder)
decoders.append(decoder)
# read sentences from stdin
src_sent = []
with open(params.input_path, 'r') as file1:
for line in file1:
# if 0 < len(line.strip().split()) < 200:
src_sent.append(line)
logger.info("Read %i sentences from sentences file.Writing them to a src file. Translating ..." % len(src_sent))
f = io.open(params.output_path + '.src_sent', 'w', encoding='utf-8')
for sentence in src_sent:
f.write(sentence)
f.close()
logger.info("Wrote them to a src file")
f = io.open(params.output_path, 'w', encoding='utf-8')
for i in range(0, len(src_sent), params.batch_size):
# prepare batch
word_ids = [torch.LongTensor([dico.index(w) for w in s.strip().split()])
for s in src_sent[i:i + params.batch_size]]
lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
batch = torch.LongTensor(lengths.max().item(), lengths.size(0)).fill_(params.pad_index)
batch[0] = params.eos_index
for j, s in enumerate(word_ids):
if lengths[j] > 2: # if sentence not empty
batch[1:lengths[j] - 1, j].copy_(s)
batch[lengths[j] - 1, j] = params.eos_index
langs = batch.clone().fill_(params.src_id)
# encode source batch and translate it
encodeds = []
for encoder in encoders:
encoded = encoder('fwd', x=batch.cuda(), lengths=lengths.cuda(), langs=langs.cuda(), causal=False)
encoded = encoded.transpose(0, 1)
encodeds.append(encoded)
assert encoded.size(0) == lengths.size(0)
decoded, dec_lengths = generate_beam(decoders, encodeds, lengths.cuda(), params.tgt_id,
beam_size=params.beam,
length_penalty=params.length_penalty,
early_stopping=False,
max_len=int(1.5 * lengths.max().item() + 10), params=params)
# convert sentences to words
for j in range(decoded.size(1)):
# remove delimiters
sent = decoded[:, j]
delimiters = (sent == params.eos_index).nonzero().view(-1)
assert len(delimiters) >= 1 and delimiters[0].item() == 0
sent = sent[1:] if len(delimiters) == 1 else sent[1:delimiters[1]]
# output translation
source = src_sent[i + j].strip()
target = " ".join([dico[sent[k].item()] for k in range(len(sent))])
if (i+j)%100 == 0:
logger.info("Translation of %i / %i:\n Source sentence: %s \n Translation: %s\n" % (i + j, len(src_sent), source, target))
f.write(target + "\n")
f.close()
if __name__ == '__main__':
# generate parser / parse parameters
parser = get_parser()
params = parser.parse_args()
# check parameters
#assert os.path.isfile(params.model_path)
assert params.src_lang != '' and params.tgt_lang != '' and params.src_lang != params.tgt_lang
assert params.output_path and not os.path.isfile(params.output_path)
# translate
with torch.no_grad():
main(params)