def __init__(self, model, src_file=None, trg_file=None, dev_every=0,
batcher=bare(SrcBatcher, batch_size=32), loss_calculator=None,
run_for_epochs=None, lr_decay=1.0, lr_decay_times=3, patience=1,
initial_patience=None, dev_tasks=None, restart_trainer=False,
reload_command=None, name=None, sample_train_sents: Optional[int] = None,
max_num_train_sents=None, max_src_len=None, max_trg_len=None):
self.src_file = src_file
self.trg_file = trg_file
self.dev_tasks = dev_tasks
if lr_decay > 1.0 or lr_decay <= 0.0:
raise RuntimeError("illegal lr_decay, must satisfy: 0.0 < lr_decay <= 1.0")
self.lr_decay = lr_decay
self.patience = patience
self.initial_patience = initial_patience
self.lr_decay_times = lr_decay_times
self.restart_trainer = restart_trainer
self.run_for_epochs = run_for_epochs
self.early_stopping_reached = False
# training state
self.training_state = TrainingState()
self.reload_command = reload_command
self.model = model
self.loss_calculator = loss_calculator or LossCalculator(MLELoss())
self.sample_train_sents = sample_train_sents
self.max_num_train_sents = max_num_train_sents
self.max_src_len = max_src_len
self.max_trg_len = max_trg_len
self.batcher = batcher
self.logger = BatchLossTracker(self, dev_every, name)
def test_single(self):
dy.renew_cg()
train_loss = self.model.calc_loss(src=self.src_data[0],
trg=self.trg_data[0],
loss_calculator=LossCalculator()).value()
dy.renew_cg()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(self.src_data[0], 0,
forced_trg_ids=self.trg_data[0])
self.assertAlmostEqual(-outputs[0].score, train_loss, places=4)
def assert_single_loss_equals_batch_loss(self,
model,
pad_src_to_multiple=1):
"""
Tests whether single loss equals batch loss.
Here we don't truncate the target side and use masking.
"""
batch_size = 5
src_sents = self.src_data[:batch_size]
src_min = min([len(x) for x in src_sents])
src_sents_trunc = [s[:src_min] for s in src_sents]
for single_sent in src_sents_trunc:
single_sent[src_min - 1] = Vocab.ES
while len(single_sent) % pad_src_to_multiple != 0:
single_sent.append(Vocab.ES)
trg_sents = self.trg_data[:batch_size]
trg_max = max([len(x) for x in trg_sents])
trg_masks = Mask(np.zeros([batch_size, trg_max]))
for i in range(batch_size):
for j in range(len(trg_sents[i]), trg_max):
trg_masks.np_arr[i, j] = 1.0
trg_sents_padded = [[w for w in s] + [Vocab.ES] * (trg_max - len(s))
for s in trg_sents]
single_loss = 0.0
for sent_id in range(batch_size):
dy.renew_cg()
train_loss = model.calc_loss(
src=src_sents_trunc[sent_id],
trg=trg_sents[sent_id],
loss_calculator=LossCalculator()).value()
single_loss += train_loss
dy.renew_cg()
batched_loss = model.calc_loss(
src=mark_as_batch(src_sents_trunc),
trg=mark_as_batch(trg_sents_padded, trg_masks),
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(single_loss, sum(batched_loss), places=4)
def __init__(self, src_file, ref_file, model=Ref("model"),
batcher=Ref("train.batcher", default=None),
loss_calculator=None, max_src_len=None, max_trg_len=None,
desc=None):
self.model = model
self.loss_calculator = loss_calculator or LossCalculator(MLELoss())
self.src_file = src_file
self.ref_file = ref_file
self.batcher = batcher
self.src_data = None
self.max_src_len = max_src_len
self.max_trg_len = max_trg_len
self.desc=desc
def test_overfitting(self):
layer_dim = 16
batcher = SrcBatcher(batch_size=10, break_ties_randomly=False)
train_args = {}
train_args['src_file'] = "examples/data/head.ja"
train_args['trg_file'] = "examples/data/head.en"
train_args['loss_calculator'] = LossCalculator()
train_args['model'] = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(vocab_size=100, emb_dim=layer_dim),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(vocab_size=100, emb_dim=layer_dim),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn_layer=UniLSTMSeqTransducer(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn_layer"),
mlp_layer=MLP(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_rnn_dim=layer_dim,
vocab_size=100,
yaml_path="model.decoder.rnn_layer"),
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
train_args['dev_tasks'] = [
LossEvalTask(model=train_args['model'],
src_file="examples/data/head.ja",
ref_file="examples/data/head.en",
batcher=batcher)
]
train_args['run_for_epochs'] = 1
train_args['trainer'] = AdamTrainer(alpha=0.1)
train_args['batcher'] = batcher
training_regimen = xnmt.training_regimen.SimpleTrainingRegimen(
**train_args)
for _ in range(50):
training_regimen.run_training(save_fct=lambda: None,
update_weights=True)
self.assertAlmostEqual(0.0,
training_regimen.logger.epoch_loss.sum() /
training_regimen.logger.epoch_words,
places=2)
def assert_single_loss_equals_batch_loss(self,
model,
pad_src_to_multiple=1):
"""
Tests whether single loss equals batch loss.
Truncating src / trg sents to same length so no masking is necessary
"""
batch_size = 5
src_sents = self.src_data[:batch_size]
src_min = min([len(x) for x in src_sents])
src_sents_trunc = [s[:src_min] for s in src_sents]
for single_sent in src_sents_trunc:
single_sent[src_min - 1] = Vocab.ES
while len(single_sent) % pad_src_to_multiple != 0:
single_sent.append(Vocab.ES)
trg_sents = self.trg_data[:batch_size]
trg_min = min([len(x) for x in trg_sents])
trg_sents_trunc = [s[:trg_min] for s in trg_sents]
for single_sent in trg_sents_trunc:
single_sent[trg_min - 1] = Vocab.ES
single_loss = 0.0
for sent_id in range(batch_size):
dy.renew_cg()
train_loss = model.calc_loss(
src=src_sents_trunc[sent_id],
trg=trg_sents_trunc[sent_id],
loss_calculator=LossCalculator()).value()
single_loss += train_loss
dy.renew_cg()
batched_loss = model.calc_loss(
src=mark_as_batch(src_sents_trunc),
trg=mark_as_batch(trg_sents_trunc),
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(single_loss, sum(batched_loss), places=4)
def test_single(self):
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_loss(
src=self.training_corpus.train_src_data[0],
trg=outputs[0].actions,
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)
def test_train_dev_loss_equal(self):
layer_dim = 512
batcher = SrcBatcher(batch_size=5, break_ties_randomly=False)
train_args = {}
train_args['src_file'] = "examples/data/head.ja"
train_args['trg_file'] = "examples/data/head.en"
train_args['loss_calculator'] = LossCalculator()
train_args['model'] = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
lstm_dim=layer_dim,
mlp_hidden_dim=layer_dim,
trg_embed_dim=layer_dim,
vocab_size=100,
bridge=CopyBridge(dec_layers=1,
dec_dim=layer_dim)),
)
train_args['dev_tasks'] = [
LossEvalTask(model=train_args['model'],
src_file="examples/data/head.ja",
ref_file="examples/data/head.en",
batcher=batcher)
]
train_args['trainer'] = None
train_args['batcher'] = batcher
train_args['run_for_epochs'] = 1
training_regimen = xnmt.training_regimen.SimpleTrainingRegimen(
**train_args)
training_regimen.run_training(save_fct=lambda: None,
update_weights=False)
self.assertAlmostEqual(training_regimen.logger.epoch_loss.sum() /
training_regimen.logger.epoch_words,
training_regimen.logger.dev_score.loss,
places=5)
def test_overfitting(self):
self.exp_global = ExpGlobal(
dynet_param_collection=NonPersistentParamCollection(), dropout=0.0)
self.exp_global.default_layer_dim = 16
batcher = SrcBatcher(batch_size=10, break_ties_randomly=False)
train_args = {}
train_args['src_file'] = "examples/data/head.ja"
train_args['trg_file'] = "examples/data/head.en"
train_args['loss_calculator'] = LossCalculator()
train_args['model'] = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
encoder=BiLSTMSeqTransducer(self.exp_global),
attender=MlpAttender(self.exp_global),
trg_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
decoder=MlpSoftmaxDecoder(self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
train_args['dev_tasks'] = [
LossEvalTask(model=train_args['model'],
src_file="examples/data/head.ja",
ref_file="examples/data/head.en",
batcher=batcher)
]
train_args['run_for_epochs'] = 1
train_args['trainer'] = AdamTrainer(self.exp_global, alpha=0.1)
train_args['batcher'] = batcher
training_regimen = xnmt.training_regimen.SimpleTrainingRegimen(
exp_global=self.exp_global, **train_args)
training_regimen.exp_global = self.exp_global
for _ in range(50):
training_regimen.run_training(save_fct=lambda: None,
update_weights=True)
self.assertAlmostEqual(0.0,
training_regimen.logger.epoch_loss.sum() /
training_regimen.logger.epoch_words,
places=2)
def test_overfitting(self):
self.model_context = ModelContext()
self.model_context.dynet_param_collection = NonPersistentParamCollection(
)
self.model_context.default_layer_dim = 16
train_args = {}
training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
train_args['corpus_parser'] = BilingualCorpusParser(
training_corpus=training_corpus,
src_reader=PlainTextReader(),
trg_reader=PlainTextReader())
train_args['loss_calculator'] = LossCalculator()
train_args['model'] = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
train_args['run_for_epochs'] = 1
train_args['trainer'] = AdamTrainer(self.model_context, alpha=0.1)
train_args['batcher'] = SrcBatcher(batch_size=10,
break_ties_randomly=False)
training_regimen = xnmt.training_regimen.SimpleTrainingRegimen(
yaml_context=self.model_context, **train_args)
training_regimen.model_context = self.model_context
for _ in range(50):
training_regimen.run_training(update_weights=True)
self.assertAlmostEqual(
0.0,
training_regimen.logger.epoch_loss.loss_values['loss'] /
training_regimen.logger.epoch_words,
places=2)
def __call__(self,
corpus_parser,
generator,
batcher,
src_file=None,
trg_file=None,
candidate_id_file=None):
"""
:param src_file: path of input src file to be translated
:param trg_file: path of file where trg translatons will be written
:param batcher:
:param candidate_id_file: if we are doing something like retrieval where we select from fixed candidates, sometimes we want to limit our candidates to a certain subset of the full set. this setting allows us to do this.
:param model_elements: If None, the model will be loaded from model_file. If set, should equal (corpus_parser, generator).
"""
args = dict(model_file=self.model_file,
src_file=src_file or self.src_file,
trg_file=trg_file or self.trg_file,
ref_file=self.ref_file,
max_src_len=self.max_src_len,
input_format=self.input_format,
post_process=self.post_process,
candidate_id_file=candidate_id_file,
report_path=self.report_path,
report_type=self.report_type,
beam=self.beam,
max_len=self.max_len,
len_norm_type=self.len_norm_type,
mode=self.mode)
is_reporting = issubclass(
generator.__class__,
Reportable) and args["report_path"] is not None
# Corpus
src_corpus = list(corpus_parser.src_reader.read_sents(
args["src_file"]))
# Get reference if it exists and is necessary
if args["mode"] == "forced" or args["mode"] == "forceddebug":
if args["ref_file"] == None:
raise RuntimeError(
"When performing {} decoding, must specify reference file".
format(args["mode"]))
ref_corpus = list(
corpus_parser.trg_reader.read_sents(args["ref_file"]))
else:
ref_corpus = None
# Vocab
src_vocab = corpus_parser.src_reader.vocab if hasattr(
corpus_parser.src_reader, "vocab") else None
trg_vocab = corpus_parser.trg_reader.vocab if hasattr(
corpus_parser.trg_reader, "vocab") else None
# Perform initialization
generator.set_train(False)
generator.initialize_generator(**args)
# TODO: Structure it better. not only Translator can have post processes
if issubclass(generator.__class__, Translator):
generator.set_post_processor(self.get_output_processor())
generator.set_trg_vocab(trg_vocab)
generator.set_reporting_src_vocab(src_vocab)
if is_reporting:
generator.set_report_resource("src_vocab", src_vocab)
generator.set_report_resource("trg_vocab", trg_vocab)
# If we're debugging, calculate the loss for each target sentence
ref_scores = None
if args["mode"] == 'forceddebug':
some_batcher = xnmt.batcher.InOrderBatcher(32) # Arbitrary
batched_src, batched_ref = some_batcher.pack(
src_corpus, ref_corpus)
ref_scores = []
for src, ref in zip(batched_src, batched_ref):
dy.renew_cg()
loss_expr = generator.calc_loss(
src, ref, loss_calculator=LossCalculator())
ref_scores.extend(loss_expr.value())
ref_scores = [-x for x in ref_scores]
# Perform generation of output
with io.open(args["trg_file"], 'wt', encoding='utf-8'
) as fp: # Saving the translated output to a trg file
src_ret = []
for i, src in enumerate(src_corpus):
batcher.add_single_batch(src_curr=[src],
trg_curr=None,
src_ret=src_ret,
trg_ret=None)
src = src_ret.pop()[0]
# Do the decoding
if args["max_src_len"] is not None and len(
src) > args["max_src_len"]:
output_txt = NO_DECODING_ATTEMPTED
else:
dy.renew_cg()
ref_ids = ref_corpus[i] if ref_corpus != None else None
output = generator.generate_output(src,
i,
forced_trg_ids=ref_ids)
# If debugging forced decoding, make sure it matches
if ref_scores != None and (
abs(output[0].score - ref_scores[i]) /
abs(ref_scores[i])) > 1e-5:
print(
'Forced decoding score {} and loss {} do not match at sentence {}'
.format(output[0].score, ref_scores[i], i))
output_txt = output[0].plaintext
# Printing to trg file
fp.write(u"{}\n".format(output_txt))
def __call__(self,
generator,
src_file=None,
trg_file=None,
candidate_id_file=None):
"""
Args:
generator (GeneratorModel): the model to be used
src_file (str): path of input src file to be translated
trg_file (str): path of file where trg translatons will be written
candidate_id_file (str): if we are doing something like retrieval where we select from fixed candidates, sometimes we want to limit our candidates to a certain subset of the full set. this setting allows us to do this.
"""
args = dict(src_file=src_file or self.src_file,
trg_file=trg_file or self.trg_file,
ref_file=self.ref_file,
max_src_len=self.max_src_len,
post_process=self.post_process,
candidate_id_file=candidate_id_file,
report_path=self.report_path,
report_type=self.report_type,
beam=self.beam,
max_len=self.max_len,
len_norm_type=self.len_norm_type,
mode=self.mode)
is_reporting = issubclass(
generator.__class__,
Reportable) and args["report_path"] is not None
# Corpus
src_corpus = list(generator.src_reader.read_sents(args["src_file"]))
# Get reference if it exists and is necessary
if args["mode"] == "forced" or args["mode"] == "forceddebug" or args[
"mode"] == "score":
if args["ref_file"] == None:
raise RuntimeError(
"When performing {} decoding, must specify reference file".
format(args["mode"]))
score_src_corpus = []
ref_corpus = []
with open(args["ref_file"], "r", encoding="utf-8") as fp:
for line in fp:
if args["mode"] == "score":
nbest = line.split("|||")
assert len(
nbest
) > 1, "When performing scoring, ref_file must have nbest format 'index ||| hypothesis'"
src_index = int(nbest[0].strip())
assert src_index < len(
src_corpus
), "The src_file has only {} instances, nbest file has invalid src_index {}".format(
len(src_corpus), src_index)
score_src_corpus.append(src_corpus[src_index])
trg_input = generator.trg_reader.read_sent(
nbest[1].strip())
else:
trg_input = generator.trg_reader.read_sent(line)
ref_corpus.append(trg_input)
if args["mode"] == "score":
src_corpus = score_src_corpus
else:
if self.max_len and any(
len(s) > self.max_len for s in ref_corpus):
logger.warning(
"Forced decoding with some targets being longer than max_len. Increase max_len to avoid unexpected behavior."
)
else:
ref_corpus = None
# Vocab
src_vocab = generator.src_reader.vocab if hasattr(
generator.src_reader, "vocab") else None
trg_vocab = generator.trg_reader.vocab if hasattr(
generator.trg_reader, "vocab") else None
# Perform initialization
generator.set_train(False)
generator.initialize_generator(**args)
if hasattr(generator, "set_post_processor"):
generator.set_post_processor(self.get_output_processor())
if hasattr(generator, "set_trg_vocab"):
generator.set_trg_vocab(trg_vocab)
if hasattr(generator, "set_reporting_src_vocab"):
generator.set_reporting_src_vocab(src_vocab)
if is_reporting:
generator.set_report_resource("src_vocab", src_vocab)
generator.set_report_resource("trg_vocab", trg_vocab)
# If we're debugging, calculate the loss for each target sentence
ref_scores = None
if args["mode"] == 'forceddebug' or args["mode"] == 'score':
some_batcher = xnmt.batcher.InOrderBatcher(32) # Arbitrary
if not isinstance(some_batcher, xnmt.batcher.InOrderBatcher):
raise ValueError(
f"forceddebug requires InOrderBatcher, got: {some_batcher}"
)
batched_src, batched_ref = some_batcher.pack(
src_corpus, ref_corpus)
ref_scores = []
for src, ref in zip(batched_src, batched_ref):
dy.renew_cg(immediate_compute=settings.IMMEDIATE_COMPUTE,
check_validity=settings.CHECK_VALIDITY)
loss_expr = generator.calc_loss(
src, ref, loss_calculator=LossCalculator())
if isinstance(loss_expr.value(), Iterable):
ref_scores.extend(loss_expr.value())
else:
ref_scores.append(loss_expr.value())
ref_scores = [-x for x in ref_scores]
# Make the parent directory if necessary
make_parent_dir(args["trg_file"])
# Perform generation of output
if args["mode"] != 'score':
with open(args["trg_file"], 'wt', encoding='utf-8'
) as fp: # Saving the translated output to a trg file
src_ret = []
for i, src in enumerate(src_corpus):
# This is necessary when the batcher does some sort of pre-processing, e.g.
# when the batcher pads to a particular number of dimensions
if self.batcher:
self.batcher.add_single_batch(src_curr=[src],
trg_curr=None,
src_ret=src_ret,
trg_ret=None)
src = src_ret.pop()[0]
# Do the decoding
if args["max_src_len"] is not None and len(
src) > args["max_src_len"]:
output_txt = NO_DECODING_ATTEMPTED
else:
dy.renew_cg(
immediate_compute=settings.IMMEDIATE_COMPUTE,
check_validity=settings.CHECK_VALIDITY)
ref_ids = ref_corpus[i] if ref_corpus != None else None
output = generator.generate_output(
src, i, forced_trg_ids=ref_ids)
# If debugging forced decoding, make sure it matches
if ref_scores != None and (
abs(output[0].score - ref_scores[i]) /
abs(ref_scores[i])) > 1e-5:
logger.error(
f'Forced decoding score {output[0].score} and loss {ref_scores[i]} do not match at sentence {i}'
)
output_txt = output[0].plaintext
# Printing to trg file
fp.write(f"{output_txt}\n")
else:
with open(args["trg_file"], 'wt', encoding='utf-8') as fp:
with open(args["ref_file"], "r", encoding="utf-8") as nbest_fp:
for nbest, score in zip(nbest_fp, ref_scores):
fp.write("{} ||| score={}\n".format(
nbest.strip(), score))
def __init__(self,
yaml_context,
corpus_parser,
model,
glob={},
dev_every=0,
batcher=None,
loss_calculator=None,
pretrained_model_file="",
src_format="text",
run_for_epochs=None,
lr_decay=1.0,
lr_decay_times=3,
patience=1,
initial_patience=None,
dev_metrics="",
schedule_metric="loss",
restart_trainer=False,
reload_command=None,
name=None,
inference=None):
"""
:param yaml_context:
:param corpus_parser: an input.InputReader object
:param model: a generator.GeneratorModel object
:param dev_every (int): dev checkpoints every n sentences (0 for only after epoch)
:param batcher: Type of batcher. Defaults to SrcBatcher of batch size 32.
:param loss_calculator:
:param pretrained_model_file: Path of pre-trained model file
:param src_format: Format of input data: text/contvec
:param lr_decay (float):
:param lr_decay_times (int): Early stopping after decaying learning rate a certain number of times
:param patience (int): apply LR decay after dev scores haven't improved over this many checkpoints
:param initial_patience (int): if given, allows adjusting patience for the first LR decay
:param dev_metrics: Comma-separated list of evaluation metrics (bleu/wer/cer)
:param schedule_metric: determine learning schedule based on this dev_metric (loss/bleu/wer/cer)
:param restart_trainer: Restart trainer (useful for Adam) and revert weights to best dev checkpoint when applying LR decay (https://arxiv.org/pdf/1706.09733.pdf)
:param reload_command: Command to change the input data after each epoch.
--epoch EPOCH_NUM will be appended to the command.
To just reload the data after each epoch set the command to 'true'.
:param name: will be prepended to log outputs if given
:param inference: used for inference during dev checkpoints if dev_metrics are specified
"""
assert yaml_context is not None
self.yaml_context = yaml_context
self.model_file = self.yaml_context.dynet_param_collection.model_file
self.yaml_serializer = YamlSerializer()
if lr_decay > 1.0 or lr_decay <= 0.0:
raise RuntimeError(
"illegal lr_decay, must satisfy: 0.0 < lr_decay <= 1.0")
self.lr_decay = lr_decay
self.patience = patience
self.initial_patience = initial_patience
self.lr_decay_times = lr_decay_times
self.restart_trainer = restart_trainer
self.run_for_epochs = run_for_epochs
self.early_stopping_reached = False
# training state
self.training_state = TrainingState()
self.evaluators = [
s.lower() for s in dev_metrics.split(",") if s.strip() != ""
]
if schedule_metric.lower() not in self.evaluators:
self.evaluators.append(schedule_metric.lower())
if "loss" not in self.evaluators: self.evaluators.append("loss")
if dev_metrics:
self.inference = inference or SimpleInference()
self.reload_command = reload_command
if reload_command is not None:
self._augmentation_handle = None
self._augment_data_initial()
self.model = model
self.corpus_parser = corpus_parser
self.loss_calculator = loss_calculator or LossCalculator(MLELoss())
self.pretrained_model_file = pretrained_model_file
if self.pretrained_model_file:
self.yaml_context.dynet_param_collection.load_from_data_file(
self.pretrained_model_file + '.data')
self.batcher = batcher or SrcBatcher(32)
if src_format == "contvec":
self.batcher.pad_token = np.zeros(self.model.src_embedder.emb_dim)
self.pack_batches()
self.logger = BatchLossTracker(self, dev_every, name)
self.schedule_metric = schedule_metric.lower()
def __init__(self, model, src_file=None, trg_file=None, dev_every=0,
batcher=bare(SrcBatcher, batch_size=32), loss_calculator=None,
run_for_epochs=None, lr_decay=1.0, lr_decay_times=3, patience=1,
initial_patience=None, dev_tasks=None, restart_trainer=False,
reload_command=None, name=None, sample_train_sents=None,
max_num_train_sents=None, max_src_len=None, max_trg_len=None,
exp_global=Ref(Path("exp_global"))):
"""
Args:
exp_global:
model: a generator.GeneratorModel object
src_file: The file for the source data.
trg_file: The file for the target data.
dev_every (int): dev checkpoints every n sentences (0 for only after epoch)
batcher: Type of batcher
loss_calculator:
lr_decay (float):
lr_decay_times (int): Early stopping after decaying learning rate a certain number of times
patience (int): apply LR decay after dev scores haven't improved over this many checkpoints
initial_patience (int): if given, allows adjusting patience for the first LR decay
dev_tasks: A list of tasks to run on the development set
restart_trainer: Restart trainer (useful for Adam) and revert weights to best dev checkpoint when applying LR decay (https://arxiv.org/pdf/1706.09733.pdf)
reload_command: Command to change the input data after each epoch.
--epoch EPOCH_NUM will be appended to the command.
To just reload the data after each epoch set the command to 'true'.
sample_train_sents:
max_num_train_sents:
max_src_len:
max_trg_len:
name: will be prepended to log outputs if given
"""
self.exp_global = exp_global
self.model_file = self.exp_global.dynet_param_collection.model_file
self.src_file = src_file
self.trg_file = trg_file
self.dev_tasks = dev_tasks
if lr_decay > 1.0 or lr_decay <= 0.0:
raise RuntimeError("illegal lr_decay, must satisfy: 0.0 < lr_decay <= 1.0")
self.lr_decay = lr_decay
self.patience = patience
self.initial_patience = initial_patience
self.lr_decay_times = lr_decay_times
self.restart_trainer = restart_trainer
self.run_for_epochs = run_for_epochs
self.early_stopping_reached = False
# training state
self.training_state = TrainingState()
self.reload_command = reload_command
self.model = model
self.loss_calculator = loss_calculator or LossCalculator(MLELoss())
self.sample_train_sents = sample_train_sents
self.max_num_train_sents = max_num_train_sents
self.max_src_len = max_src_len
self.max_trg_len = max_trg_len
self.batcher = batcher
self.logger = BatchLossTracker(self, dev_every, name)
