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trainer.py
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trainer.py
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# Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import math
import time
from logging import getLogger
from collections import OrderedDict
import numpy as np
import torch
from torch.nn import functional as F
from torch.nn.utils import clip_grad_norm_
from apex.fp16_utils import FP16_Optimizer
from .utils import get_optimizer, to_cuda, concat_batches
from .utils import parse_lambda_config, update_lambdas
logger = getLogger()
class Trainer(object):
def __init__(self, data, params):
"""
Initialize trainer.
"""
# epoch / iteration size
self.epoch_size = params.epoch_size
if self.epoch_size == -1:
self.epoch_size = self.data
assert self.epoch_size > 0
# stopping criterion used for early stopping
if params.stopping_criterion != '':
split = params.stopping_criterion.split(',')
assert len(split) == 2 and split[1].isdigit()
self.decrease_counts_max = int(split[1])
self.decrease_counts = 0
if split[0][0] == '_':
self.stopping_criterion = (split[0][1:], False)
else:
self.stopping_criterion = (split[0], True)
self.best_stopping_criterion = -1e12 if self.stopping_criterion[1] else 1e12
else:
self.stopping_criterion = None
self.best_stopping_criterion = None
# data iterators
self.iterators = {}
# probability of masking out / randomize / not modify words to predict
params.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
# probabilty to predict a word
counts = np.array(list(self.data['dico'].counts.values()))
params.mask_scores = np.maximum(counts, 1) ** -params.sample_alpha
params.mask_scores[params.pad_index] = 0 # do not predict <PAD> index
params.mask_scores[counts == 0] = 0 # do not predict special tokens
# validation metrics
self.metrics = []
metrics = [m for m in params.validation_metrics.split(',') if m != '']
for m in metrics:
m = (m[1:], False) if m[0] == '_' else (m, True)
self.metrics.append(m)
self.best_metrics = {metric: (-1e12 if biggest else 1e12) for (metric, biggest) in self.metrics}
# training statistics
self.epoch = 0
self.n_iter = 0
self.n_total_iter = 0
self.n_sentences = 0
self.stats = OrderedDict(
[('processed_s', 0), ('processed_w', 0)] +
[('CLM-%s' % l, []) for l in params.langs] +
[('CLM-%s-%s' % (l1, l2), []) for l1, l2 in data['para'].keys()] +
[('CLM-%s-%s' % (l2, l1), []) for l1, l2 in data['para'].keys()] +
[('MLM-%s' % l, []) for l in params.langs] +
[('MLM-%s-%s' % (l1, l2), []) for l1, l2 in data['para'].keys()] +
[('MLM-%s-%s' % (l2, l1), []) for l1, l2 in data['para'].keys()] +
[('PC-%s-%s' % (l1, l2), []) for l1, l2 in params.pc_steps] +
[('AE-%s' % lang, []) for lang in params.ae_steps] +
[('MT-%s-%s' % (l1, l2), []) for l1, l2 in params.mt_steps] +
[('BT-%s-%s-%s' % (l1, l2, l3), []) for l1, l2, l3 in params.bt_steps]
)
self.last_time = time.time()
# reload potential checkpoints
self.reload_checkpoint()
# initialize lambda coefficients and their configurations
parse_lambda_config(params)
def get_optimizer_fp(self, module):
"""
Build optimizer.
"""
assert module in ['model', 'encoder', 'decoder']
optimizer = get_optimizer(getattr(self, module).parameters(), self.params.optimizer)
if self.params.fp16:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
return optimizer
def optimize(self, loss, modules):
"""
Optimize.
"""
if type(modules) is str:
modules = [modules]
# check NaN
if (loss != loss).data.any():
logger.error("NaN detected")
exit()
# zero grad
for module in modules:
self.optimizers[module].zero_grad()
# backward
if self.params.fp16:
assert len(modules) == 1, "fp16 not implemented for more than one module"
self.optimizers[module].backward(loss)
else:
loss.backward()
# clip gradients
if self.params.clip_grad_norm > 0:
for module in modules:
if self.params.fp16:
self.optimizers[module].clip_master_grads(self.params.clip_grad_norm)
else:
clip_grad_norm_(getattr(self, module).parameters(), self.params.clip_grad_norm)
# optimization step
for module in modules:
self.optimizers[module].step()
def iter(self):
"""
End of iteration.
"""
self.n_iter += 1
self.n_total_iter += 1
update_lambdas(self.params, self.n_total_iter)
self.print_stats()
def print_stats(self):
"""
Print statistics about the training.
"""
if self.n_iter % 5 != 0:
return
s_iter = "%7i - " % self.n_iter
s_stat = ' || '.join([
'{}: {:7.4f}'.format(k, np.mean(v)) for k, v in self.stats.items()
if type(v) is list and len(v) > 0
])
for k in self.stats.keys():
if type(self.stats[k]) is list:
del self.stats[k][:]
# transformer learning rate
lr = self.optimizers[self.MODEL_NAMES[0]].param_groups[0]['lr']
s_lr = " - Transformer LR = {:.4e}".format(lr)
# processing speed
new_time = time.time()
diff = new_time - self.last_time
s_speed = "{:7.2f} sent/s - {:8.2f} words/s - ".format(
self.stats['processed_s'] * 1.0 / diff,
self.stats['processed_w'] * 1.0 / diff
)
self.stats['processed_s'] = 0
self.stats['processed_w'] = 0
self.last_time = new_time
# log speed + stats + learning rate
logger.info(s_iter + s_speed + s_stat + s_lr)
def get_iterator(self, iter_name, lang1, lang2, stream):
"""
Create a new iterator for a dataset.
"""
logger.info("Creating new training data iterator (%s) ..." % ','.join([str(x) for x in [iter_name, lang1, lang2] if x is not None]))
if lang2 is None:
if stream:
iterator = self.data['mono_stream'][lang1]['train'].get_iterator(shuffle=True)
else:
iterator = self.data['mono'][lang1]['train'].get_iterator(
shuffle=True,
group_by_size=self.params.group_by_size,
n_sentences=-1,
)
else:
assert stream is False
_lang1, _lang2 = (lang1, lang2) if lang1 < lang2 else (lang2, lang1)
iterator = self.data['para'][(_lang1, _lang2)]['train'].get_iterator(
shuffle=True,
group_by_size=self.params.group_by_size,
n_sentences=-1,
)
self.iterators[(iter_name, lang1, lang2)] = iterator
return iterator
def get_batch(self, iter_name, lang1, lang2=None, stream=False):
"""
Return a batch of sentences from a dataset.
"""
assert lang1 in self.params.langs
assert lang2 is None or lang2 in self.params.langs
assert stream is False or lang2 is None
iterator = self.iterators.get((iter_name, lang1, lang2), None)
if iterator is None:
iterator = self.get_iterator(iter_name, lang1, lang2, stream)
try:
x = next(iterator)
except StopIteration:
iterator = self.get_iterator(iter_name, lang1, lang2, stream)
x = next(iterator)
return x if lang2 is None or lang1 < lang2 else x[::-1]
def word_shuffle(self, x, l):
"""
Randomly shuffle input words.
"""
if self.params.word_shuffle == 0:
return x, l
# define noise word scores
noise = np.random.uniform(0, self.params.word_shuffle, size=(x.size(0) - 1, x.size(1)))
noise[0] = -1 # do not move start sentence symbol
assert self.params.word_shuffle > 1
x2 = x.clone()
for i in range(l.size(0)):
# generate a random permutation
scores = np.arange(l[i] - 1) + noise[:l[i] - 1, i]
permutation = scores.argsort()
# shuffle words
x2[:l[i] - 1, i].copy_(x2[:l[i] - 1, i][torch.from_numpy(permutation)])
return x2, l
def word_dropout(self, x, l):
"""
Randomly drop input words.
"""
if self.params.word_dropout == 0:
return x, l
assert 0 < self.params.word_dropout < 1
# define words to drop
eos = self.params.eos_index
assert (x[0] == eos).sum() == l.size(0)
keep = np.random.rand(x.size(0) - 1, x.size(1)) >= self.params.word_dropout
keep[0] = 1 # do not drop the start sentence symbol
sentences = []
lengths = []
for i in range(l.size(0)):
assert x[l[i] - 1, i] == eos
words = x[:l[i] - 1, i].tolist()
# randomly drop words from the input
new_s = [w for j, w in enumerate(words) if keep[j, i]]
# we need to have at least one word in the sentence (more than the start / end sentence symbols)
if len(new_s) == 1:
new_s.append(words[np.random.randint(1, len(words))])
new_s.append(eos)
assert len(new_s) >= 3 and new_s[0] == eos and new_s[-1] == eos
sentences.append(new_s)
lengths.append(len(new_s))
# re-construct input
l2 = torch.LongTensor(lengths)
x2 = torch.LongTensor(l2.max(), l2.size(0)).fill_(self.params.pad_index)
for i in range(l2.size(0)):
x2[:l2[i], i].copy_(torch.LongTensor(sentences[i]))
return x2, l2
def word_blank(self, x, l):
"""
Randomly blank input words.
"""
if self.params.word_blank == 0:
return x, l
assert 0 < self.params.word_blank < 1
# define words to blank
eos = self.params.eos_index
assert (x[0] == eos).sum() == l.size(0)
keep = np.random.rand(x.size(0) - 1, x.size(1)) >= self.params.word_blank
keep[0] = 1 # do not blank the start sentence symbol
sentences = []
for i in range(l.size(0)):
assert x[l[i] - 1, i] == eos
words = x[:l[i] - 1, i].tolist()
# randomly blank words from the input
new_s = [w if keep[j, i] else self.params.mask_index for j, w in enumerate(words)]
new_s.append(eos)
assert len(new_s) == l[i] and new_s[0] == eos and new_s[-1] == eos
sentences.append(new_s)
# re-construct input
x2 = torch.LongTensor(l.max(), l.size(0)).fill_(self.params.pad_index)
for i in range(l.size(0)):
x2[:l[i], i].copy_(torch.LongTensor(sentences[i]))
return x2, l
def add_noise(self, words, lengths):
"""
Add noise to the encoder input.
"""
words, lengths = self.word_shuffle(words, lengths)
words, lengths = self.word_dropout(words, lengths)
words, lengths = self.word_blank(words, lengths)
return words, lengths
def mask_out(self, x, lengths):
"""
Decide of random words to mask out, and what target they get assigned.
"""
params = self.params
slen, bs = x.size()
# define target words to predict
if params.sample_alpha == 0:
pred_mask = np.random.rand(slen, bs) <= params.word_pred
pred_mask = torch.from_numpy(pred_mask.astype(np.uint8))
else:
x_prob = params.mask_scores[x.flatten()]
n_tgt = math.ceil(params.word_pred * slen * bs)
tgt_ids = np.random.choice(len(x_prob), n_tgt, replace=False, p=x_prob / x_prob.sum())
pred_mask = torch.zeros(slen * bs, dtype=torch.uint8)
pred_mask[tgt_ids] = 1
pred_mask = pred_mask.view(slen, bs)
# do not predict padding
pred_mask[x == params.pad_index] = 0
pred_mask[0] = 0 # TODO: remove
# mask a number of words == 0 [8] (faster with fp16)
if params.fp16:
pred_mask = pred_mask.view(-1)
n1 = pred_mask.sum().item()
n2 = max(n1 % 8, 8 * (n1 // 8))
if n2 != n1:
pred_mask[torch.nonzero(pred_mask).view(-1)[:n1 - n2]] = 0
pred_mask = pred_mask.view(slen, bs)
assert pred_mask.sum().item() % 8 == 0
# generate possible targets / update x input
_x_real = x[pred_mask]
_x_rand = _x_real.clone().random_(params.n_words)
_x_mask = _x_real.clone().fill_(params.mask_index)
probs = torch.multinomial(params.pred_probs, len(_x_real), replacement=True)
_x = _x_mask * (probs == 0).long() + _x_real * (probs == 1).long() + _x_rand * (probs == 2).long()
x = x.masked_scatter(pred_mask, _x)
assert 0 <= x.min() <= x.max() < params.n_words
assert x.size() == (slen, bs)
assert pred_mask.size() == (slen, bs)
return x, _x_real, pred_mask
def generate_batch(self, lang1, lang2, name):
"""
Prepare a batch (for causal or non-causal mode).
"""
params = self.params
lang1_id = params.lang2id[lang1]
lang2_id = params.lang2id[lang2] if lang2 is not None else None
if lang2 is None:
x, lengths = self.get_batch(name, lang1, stream=True)
positions = None
langs = x.clone().fill_(lang1_id) if params.n_langs > 1 else None
elif lang1 == lang2:
(x1, len1) = self.get_batch(name, lang1)
(x2, len2) = (x1, len1)
(x1, len1) = self.add_noise(x1, len1)
x, lengths, positions, langs = concat_batches(x1, len1, lang1_id, x2, len2, lang2_id, params.pad_index, params.eos_index, reset_positions=False)
else:
(x1, len1), (x2, len2) = self.get_batch(name, lang1, lang2)
x, lengths, positions, langs = concat_batches(x1, len1, lang1_id, x2, len2, lang2_id, params.pad_index, params.eos_index, reset_positions=True)
return x, lengths, positions, langs, (None, None) if lang2 is None else (len1, len2)
def save_model(self, name):
"""
Save the model.
"""
path = os.path.join(self.params.dump_path, '%s.pth' % name)
logger.info('Saving models to %s ...' % path)
data = {}
for name in self.MODEL_NAMES:
if self.params.multi_gpu:
data[name] = getattr(self, name).module.state_dict()
else:
data[name] = getattr(self, name).state_dict()
data['dico_id2word'] = self.data['dico'].id2word
data['dico_word2id'] = self.data['dico'].word2id
data['dico_counts'] = self.data['dico'].counts
data['params'] = {k: v for k, v in self.params.__dict__.items()}
torch.save(data, path)
def save_checkpoint(self):
"""
Checkpoint the experiment.
"""
if not self.params.is_master:
return
data = {
'epoch': self.epoch,
'n_total_iter': self.n_total_iter,
'best_metrics': self.best_metrics,
'best_stopping_criterion': self.best_stopping_criterion,
}
for name in self.MODEL_NAMES:
data[name] = getattr(self, name).state_dict()
data[name + '_optimizer'] = self.optimizers[name].state_dict()
data['dico_id2word'] = self.data['dico'].id2word
data['dico_word2id'] = self.data['dico'].word2id
data['dico_counts'] = self.data['dico'].counts
data['params'] = {k: v for k, v in self.params.__dict__.items()}
checkpoint_path = os.path.join(self.params.dump_path, 'checkpoint.pth')
logger.info("Saving checkpoint to %s ..." % checkpoint_path)
torch.save(data, checkpoint_path)
def reload_checkpoint(self):
"""
Reload a checkpoint if we find one.
"""
checkpoint_path = os.path.join(self.params.dump_path, 'checkpoint.pth')
if not os.path.isfile(checkpoint_path):
return
logger.warning('Reloading checkpoint from %s ...' % checkpoint_path)
data = torch.load(checkpoint_path, map_location=lambda storage, loc: storage.cuda(self.params.local_rank))
# reload model parameters and optimizers
for name in self.MODEL_NAMES:
getattr(self, name).load_state_dict(data[name])
self.optimizers[name].load_state_dict(data[name + '_optimizer'])
# reload main metrics
self.epoch = data['epoch'] + 1
self.n_total_iter = data['n_total_iter']
self.best_metrics = data['best_metrics']
self.best_stopping_criterion = data['best_stopping_criterion']
logger.warning('Checkpoint reloaded. Resuming at epoch %i ...' % self.epoch)
def save_periodic(self):
"""
Save the models periodically.
"""
if not self.params.is_master:
return
if self.params.save_periodic > 0 and self.epoch % self.params.save_periodic == 0:
self.save_model('periodic-%i' % self.epoch)
def save_best_model(self, scores):
"""
Save best models according to given validation metrics.
"""
if not self.params.is_master:
return
for metric, biggest in self.metrics:
if metric not in scores:
logger.warning("Metric \"%s\" not found in scores!" % metric)
continue
factor = 1 if biggest else -1
if factor * scores[metric] > factor * self.best_metrics[metric]:
self.best_metrics[metric] = scores[metric]
logger.info('New best score for %s: %.6f' % (metric, scores[metric]))
self.save_model('best-%s' % metric)
def end_epoch(self, scores):
"""
End the epoch.
"""
# stop if the stopping criterion has not improved after a certain number of epochs
if self.stopping_criterion is not None and (self.params.is_master or not self.stopping_criterion[0].endswith('_mt_bleu')):
metric, biggest = self.stopping_criterion
assert metric in scores, metric
factor = 1 if biggest else -1
if factor * scores[metric] > factor * self.best_stopping_criterion:
self.best_stopping_criterion = scores[metric]
logger.info("New best validation score: %f" % self.best_stopping_criterion)
self.decrease_counts = 0
else:
logger.info("Not a better validation score (%i / %i)."
% (self.decrease_counts, self.decrease_counts_max))
self.decrease_counts += 1
if self.decrease_counts > self.decrease_counts_max:
logger.info("Stopping criterion has been below its best value for more "
"than %i epochs. Ending the experiment..." % self.decrease_counts_max)
if self.params.multi_gpu and 'SLURM_JOB_ID' in os.environ:
os.system('scancel ' + os.environ['SLURM_JOB_ID'])
exit()
self.save_checkpoint()
self.epoch += 1
def round_batch(self, x, lengths, positions, langs):
"""
For float16 only.
Sub-sample sentences in a batch, and add padding,
so that each dimension is a multiple of 8.
"""
params = self.params
if not params.fp16 or len(lengths) < 8:
return x, lengths, positions, langs, None
# number of sentences == 0 [8]
bs1 = len(lengths)
bs2 = 8 * (bs1 // 8)
assert bs2 > 0 and bs2 % 8 == 0
if bs1 != bs2:
idx = torch.randperm(bs1)[:bs2]
lengths = lengths[idx]
slen = lengths.max().item()
x = x[:slen, idx]
positions = None if positions is None else positions[:slen, idx]
langs = None if langs is None else langs[:slen, idx]
else:
idx = None
# sequence length == 0 [8]
ml1 = x.size(0)
if ml1 % 8 != 0:
pad = 8 - (ml1 % 8)
ml2 = ml1 + pad
x = torch.cat([x, torch.LongTensor(pad, bs2).fill_(params.pad_index)], 0)
if positions is not None:
positions = torch.cat([positions, torch.arange(pad)[:, None] + positions[-1][None] + 1], 0)
if langs is not None:
langs = torch.cat([langs, langs[-1][None].expand(pad, bs2)], 0)
assert x.size() == (ml2, bs2)
assert x.size(0) % 8 == 0
assert x.size(1) % 8 == 0
return x, lengths, positions, langs, idx
def clm_step(self, lang1, lang2, lambda_coeff):
"""
Next word prediction step (causal prediction).
CLM objective.
"""
assert lambda_coeff >= 0
if lambda_coeff == 0:
return
params = self.params
name = 'model' if params.encoder_only else 'decoder'
model = getattr(self, name)
model.train()
# generate batch / select words to predict
x, lengths, positions, langs, _ = self.generate_batch(lang1, lang2, 'causal')
x, lengths, positions, langs, _ = self.round_batch(x, lengths, positions, langs)
alen = torch.arange(lengths.max(), dtype=torch.long, device=lengths.device)
pred_mask = alen[:, None] < lengths[None] - 1
if params.context_size > 0: # do not predict without context
pred_mask[:params.context_size] = 0
y = x[1:].masked_select(pred_mask[:-1])
assert pred_mask.sum().item() == y.size(0)
# cuda
x, lengths, langs, pred_mask, y = to_cuda(x, lengths, langs, pred_mask, y)
# forward / loss
tensor = model('fwd', x=x, lengths=lengths, langs=langs, causal=True)
_, loss = model('predict', tensor=tensor, pred_mask=pred_mask, y=y, get_scores=False)
self.stats[('CLM-%s' % lang1) if lang2 is None else ('CLM-%s-%s' % (lang1, lang2))].append(loss.item())
loss = lambda_coeff * loss
# optimize
self.optimize(loss, name)
# number of processed sentences / words
self.n_sentences += params.batch_size
self.stats['processed_s'] += lengths.size(0)
self.stats['processed_w'] += pred_mask.sum().item()
def mlm_step(self, lang1, lang2, lambda_coeff):
"""
Masked word prediction step.
MLM objective is lang2 is None, TLM objective otherwise.
"""
assert lambda_coeff >= 0
if lambda_coeff == 0:
return
params = self.params
name = 'model' if params.encoder_only else 'encoder'
model = getattr(self, name)
model.train()
# generate batch / select words to predict
x, lengths, positions, langs, _ = self.generate_batch(lang1, lang2, 'pred')
x, lengths, positions, langs, _ = self.round_batch(x, lengths, positions, langs)
x, y, pred_mask = self.mask_out(x, lengths)
# cuda
x, y, pred_mask, lengths, positions, langs = to_cuda(x, y, pred_mask, lengths, positions, langs)
# forward / loss
tensor = model('fwd', x=x, lengths=lengths, positions=positions, langs=langs, causal=False)
_, loss = model('predict', tensor=tensor, pred_mask=pred_mask, y=y, get_scores=False)
self.stats[('MLM-%s' % lang1) if lang2 is None else ('MLM-%s-%s' % (lang1, lang2))].append(loss.item())
loss = lambda_coeff * loss
# optimize
self.optimize(loss, name)
# number of processed sentences / words
self.n_sentences += params.batch_size
self.stats['processed_s'] += lengths.size(0)
self.stats['processed_w'] += pred_mask.sum().item()
def pc_step(self, lang1, lang2, lambda_coeff):
"""
Parallel classification step. Predict if pairs of sentences are mutual translations of each other.
"""
assert lambda_coeff >= 0
if lambda_coeff == 0:
return
params = self.params
name = 'model' if params.encoder_only else 'encoder'
model = getattr(self, name)
model.train()
lang1_id = params.lang2id[lang1]
lang2_id = params.lang2id[lang2]
# sample parallel sentences
(x1, len1), (x2, len2) = self.get_batch('align', lang1, lang2)
bs = len1.size(0)
if bs == 1: # can happen (although very rarely), which makes the negative loss fail
self.n_sentences += params.batch_size
return
# associate lang1 sentences with their translations, and random lang2 sentences
y = torch.LongTensor(bs).random_(2)
idx_pos = torch.arange(bs)
idx_neg = ((idx_pos + torch.LongTensor(bs).random_(1, bs)) % bs)
idx = (y == 1).long() * idx_pos + (y == 0).long() * idx_neg
x2, len2 = x2[:, idx], len2[idx]
# generate batch / cuda
x, lengths, positions, langs = concat_batches(x1, len1, lang1_id, x2, len2, lang2_id, params.pad_index, params.eos_index, reset_positions=False)
x, lengths, positions, langs, new_idx = self.round_batch(x, lengths, positions, langs)
if new_idx is not None:
y = y[new_idx]
x, lengths, positions, langs = to_cuda(x, lengths, positions, langs)
# get sentence embeddings
h = model('fwd', x=x, lengths=lengths, positions=positions, langs=langs, causal=False)[0]
# parallel classification loss
CLF_ID1, CLF_ID2 = 8, 9 # very hacky, use embeddings to make weights for the classifier
emb = (model.module if params.multi_gpu else model).embeddings.weight
pred = F.linear(h, emb[CLF_ID1].unsqueeze(0), emb[CLF_ID2, 0])
loss = F.binary_cross_entropy_with_logits(pred.view(-1), y.to(pred.device).type_as(pred))
self.stats['PC-%s-%s' % (lang1, lang2)].append(loss.item())
loss = lambda_coeff * loss
# optimize
self.optimize(loss, name)
# number of processed sentences / words
self.n_sentences += params.batch_size
self.stats['processed_s'] += bs
self.stats['processed_w'] += lengths.sum().item()
class SingleTrainer(Trainer):
def __init__(self, model, data, params):
self.MODEL_NAMES = ['model']
# model / data / params
self.model = model
self.data = data
self.params = params
# optimizers
self.optimizers = {'model': self.get_optimizer_fp('model')}
super().__init__(data, params)
class EncDecTrainer(Trainer):
def __init__(self, encoder, decoder, data, params):
self.MODEL_NAMES = ['encoder', 'decoder']
# model / data / params
self.encoder = encoder
self.decoder = decoder
self.data = data
self.params = params
# optimizers
self.optimizers = {
'encoder': self.get_optimizer_fp('encoder'),
'decoder': self.get_optimizer_fp('decoder'),
}
super().__init__(data, params)
def mt_step(self, lang1, lang2, lambda_coeff):
"""
Machine translation step.
Can also be used for denoising auto-encoding.
"""
assert lambda_coeff >= 0
if lambda_coeff == 0:
return
params = self.params
self.encoder.train()
self.decoder.train()
lang1_id = params.lang2id[lang1]
lang2_id = params.lang2id[lang2]
# generate batch
if lang1 == lang2:
(x1, len1) = self.get_batch('ae', lang1)
(x2, len2) = (x1, len1)
(x1, len1) = self.add_noise(x1, len1)
else:
(x1, len1), (x2, len2) = self.get_batch('mt', lang1, lang2)
langs1 = x1.clone().fill_(lang1_id)
langs2 = x2.clone().fill_(lang2_id)
# target words to predict
alen = torch.arange(len2.max(), dtype=torch.long, device=len2.device)
pred_mask = alen[:, None] < len2[None] - 1 # do not predict anything given the last target word
y = x2[1:].masked_select(pred_mask[:-1])
assert len(y) == (len2 - 1).sum().item()
# cuda
x1, len1, langs1, x2, len2, langs2, y = to_cuda(x1, len1, langs1, x2, len2, langs2, y)
# encode source sentence
enc1 = self.encoder('fwd', x=x1, lengths=len1, langs=langs1, causal=False)
enc1 = enc1.transpose(0, 1)
# decode target sentence
dec2 = self.decoder('fwd', x=x2, lengths=len2, langs=langs2, causal=True, src_enc=enc1, src_len=len1)
# loss
_, loss = self.decoder('predict', tensor=dec2, pred_mask=pred_mask, y=y, get_scores=False)
self.stats[('AE-%s' % lang1) if lang1 == lang2 else ('MT-%s-%s' % (lang1, lang2))].append(loss.item())
loss = lambda_coeff * loss
# optimize
self.optimize(loss, ['encoder', 'decoder'])
# number of processed sentences / words
self.n_sentences += params.batch_size
self.stats['processed_s'] += len2.size(0)
self.stats['processed_w'] += (len2 - 1).sum().item()
def bt_step(self, lang1, lang2, lang3, lambda_coeff):
"""
Back-translation step for machine translation.
"""
assert lambda_coeff >= 0
if lambda_coeff == 0:
return
assert lang1 == lang3 and lang1 != lang2 and lang2 is not None
params = self.params
_encoder = self.encoder.module if params.multi_gpu else self.encoder
_decoder = self.decoder.module if params.multi_gpu else self.decoder
lang1_id = params.lang2id[lang1]
lang2_id = params.lang2id[lang2]
# generate source batch
x1, len1 = self.get_batch('bt', lang1)
langs1 = x1.clone().fill_(lang1_id)
# cuda
x1, len1, langs1 = to_cuda(x1, len1, langs1)
# generate a translation
with torch.no_grad():
# evaluation mode
self.encoder.eval()
self.decoder.eval()
# encode source sentence and translate it
enc1 = _encoder('fwd', x=x1, lengths=len1, langs=langs1, causal=False)
enc1 = enc1.transpose(0, 1)
x2, len2 = _decoder.generate(enc1, len1, lang2_id, max_len=int(1.3 * len1.max().item() + 5))
langs2 = x2.clone().fill_(lang2_id)
# free CUDA memory
del enc1
# training mode
self.encoder.train()
self.decoder.train()
# encode generate sentence
enc2 = self.encoder('fwd', x=x2, lengths=len2, langs=langs2, causal=False)
enc2 = enc2.transpose(0, 1)
# words to predict
alen = torch.arange(len1.max(), dtype=torch.long, device=len1.device)
pred_mask = alen[:, None] < len1[None] - 1 # do not predict anything given the last target word
y1 = x1[1:].masked_select(pred_mask[:-1])
# decode original sentence
dec3 = self.decoder('fwd', x=x1, lengths=len1, langs=langs1, causal=True, src_enc=enc2, src_len=len2)
# loss
_, loss = self.decoder('predict', tensor=dec3, pred_mask=pred_mask, y=y1, get_scores=False)
self.stats[('BT-%s-%s-%s' % (lang1, lang2, lang3))].append(loss.item())
# optimize
self.optimize(loss, ['encoder', 'decoder'])
# number of processed sentences / words
self.n_sentences += params.batch_size
self.stats['processed_s'] += len1.size(0)
self.stats['processed_w'] += (len1 - 1).sum().item()