def _build_fv_from_multifield(multifield, counters, build_fv_args,
size_multiple=1):
for name, field in multifield:
_build_field_vocab(
field,
counters[name],
size_multiple=size_multiple,
**build_fv_args[name])
logger.info(" * %s vocab size: %d." % (name, len(field.vocab)))
def _load_vocab(vocab_path, name, counters):
# counters changes in place
vocab = _read_vocab_file(vocab_path, name)
vocab_size = len(vocab)
logger.info('Loaded %s vocab has %d tokens.' % (name, vocab_size))
for i, token in enumerate(vocab):
# keep the order of tokens specified in the vocab file by
# adding them to the counter with decreasing counting values
counters[name][token] = vocab_size - i
return vocab, vocab_size
def validate_train_opts(cls, opt):
if opt.epochs:
raise AssertionError(
"-epochs is deprecated please use -train_steps.")
if opt.truncated_decoder > 0 and max(opt.accum_count) > 1:
raise AssertionError("BPTT is not compatible with -accum > 1")
if opt.gpuid:
raise AssertionError("gpuid is deprecated \
see world_size and gpu_ranks")
if torch.cuda.is_available() and not opt.gpu_ranks:
logger.info("WARNING: You have a CUDA device, \
should run with -gpu_ranks")
def _read_vocab_file(vocab_path, tag):
"""Loads a vocabulary from the given path.
Args:
vocab_path (str): Path to utf-8 text file containing vocabulary.
Each token should be on a line by itself. Tokens must not
contain whitespace (else only before the whitespace
is considered).
tag (str): Used for logging which vocab is being read.
"""
logger.info("Loading {} vocabulary from {}".format(tag, vocab_path))
if not os.path.exists(vocab_path):
raise RuntimeError(
"{} vocabulary not found at {}".format(tag, vocab_path))
else:
with codecs.open(vocab_path, 'r', 'utf-8') as f:
return [line.strip().split()[0] for line in f if line.strip()]
def _iter_dataset(self, path):
cur_dataset = torch.load(path)
logger.info('Loading dataset from %s, number of examples: %d' %
(path, len(cur_dataset)))
cur_dataset.fields = self.fields
cur_iter = OrderedIterator(
dataset=cur_dataset,
batch_size=self.batch_size,
batch_size_multiple=self.batch_size_multiple,
batch_size_fn=self.batch_size_fn,
device=self.device,
train=self.is_train,
sort=False,
sort_within_batch=True,
repeat=False
)
for batch in cur_iter:
yield batch
cur_dataset.examples = None
gc.collect()
del cur_dataset
gc.collect()
def output(self, step, num_steps, learning_rate, start):
"""Write out statistics to stdout.
Args:
step (int): current step
n_batch (int): total batches
start (int): start time of step.
"""
t = self.elapsed_time()
step_fmt = "%2d" % step
if num_steps > 0:
step_fmt = "%s/%5d" % (step_fmt, num_steps)
logger.info(
("Step %s; acc: %6.2f; ppl: %5.2f; xent: %4.2f; " +
"lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec")
% (step_fmt,
self.accuracy(),
self.ppl(),
self.xent(),
learning_rate,
self.n_src_words / (t + 1e-5),
self.n_words / (t + 1e-5),
time.time() - start))
sys.stdout.flush()
def build_save_dataset(corpus_type, fields, src_reader, tgt_reader, opt):
assert corpus_type in ['train', 'valid']
if corpus_type == 'train':
src = opt.train_src
tgt = opt.train_tgt
else:
src = opt.valid_src
tgt = opt.valid_tgt
logger.info("Reading source and target files: %s %s." % (src, tgt))
src_shards = split_corpus(src, opt.shard_size)
tgt_shards = split_corpus(tgt, opt.shard_size)
shard_pairs = zip(src_shards, tgt_shards)
dataset_paths = []
if (corpus_type == "train" or opt.filter_valid) and tgt is not None:
filter_pred = partial(
inputters.filter_example, use_src_len=opt.data_type == "text",
max_src_len=opt.src_seq_length, max_tgt_len=opt.tgt_seq_length)
else:
filter_pred = None
for i, (src_shard, tgt_shard) in enumerate(shard_pairs):
assert len(src_shard) == len(tgt_shard)
logger.info("Building shard %d." % i)
dataset = inputters.Dataset(
fields,
readers=[src_reader, tgt_reader] if tgt_reader else [src_reader],
data=([("src", src_shard), ("tgt", tgt_shard)]
if tgt_reader else [("src", src_shard)]),
dirs=[opt.src_dir, None] if tgt_reader else [opt.src_dir],
sort_key=inputters.str2sortkey[opt.data_type],
filter_pred=filter_pred
)
data_path = "{:s}.{:s}.{:d}.pt".format(opt.save_data, corpus_type, i)
dataset_paths.append(data_path)
logger.info(" * saving %sth %s data shard to %s."
% (i, corpus_type, data_path))
dataset.save(data_path)
del dataset.examples
gc.collect()
del dataset
gc.collect()
return dataset_paths
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(
vocab, opt.model_type, dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(
opt, device_id, model, fields, optim, model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter(
"valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(
train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def preprocess(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
src_nfeats = count_features(opt.train_src[0]) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt[0]) # tgt always text so far
if len(opt.train_src) > 1 and opt.data_type == 'text':
for src, tgt in zip(opt.train_src[1:], opt.train_tgt[1:]):
assert src_nfeats == count_features(src),\
"%s seems to mismatch features of "\
"the other source datasets" % src
assert tgt_nfeats == count_features(tgt),\
"%s seems to mismatch features of "\
"the other target datasets" % tgt
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
with_align=opt.train_align[0] is not None,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
align_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, tgt_reader, align_reader,
opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader,
align_reader, opt)
def build_base_model(model_opt, fields, gpu, checkpoint=None, gpu_id=None):
"""Build a model from opts.
Args:
model_opt: the option loaded from checkpoint. It's important that
the opts have been updated and validated. See
:class:`onmt.utils.parse.ArgumentParser`.
fields (dict[str, torchtext.data.Field]):
`Field` objects for the model.
gpu (bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
gpu_id (int or NoneType): Which GPU to use.
Returns:
the NMTModel.
"""
# for back compat when attention_dropout was not defined
try:
model_opt.attention_dropout
except AttributeError:
model_opt.attention_dropout = model_opt.dropout
# Build embeddings.
if model_opt.model_type == "text" \
or model_opt.model_type == "vec" \
or model_opt.model_type == "keyphrase":
src_field = fields["src"]
src_emb = build_embeddings(model_opt, src_field)
else:
src_emb = None
# Build encoder.
encoder = build_encoder(model_opt, src_emb)
# Build decoder.
tgt_field = fields["tgt"]
tgt_emb = build_embeddings(model_opt, tgt_field, for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
assert src_field.base_field.vocab == tgt_field.base_field.vocab, \
"preprocess with -share_vocab if you use share_embeddings"
tgt_emb.word_lut.weight = src_emb.word_lut.weight
decoder = build_decoder(model_opt, tgt_emb)
reconstruct_decoder = build_decoder(model_opt, src_emb)
# Build NMTModel(= encoder + decoder).
if gpu and gpu_id is not None:
device = torch.device("cuda", gpu_id)
elif gpu and not gpu_id:
device = torch.device("cuda")
elif not gpu:
device = torch.device("cpu")
#Build memory @shizhe
if model_opt.model_type == "keyphrase":
if model_opt.memory == False:
logger.info("Do not use memory")
model = onmt.models.NMTModel(encoder, decoder)
elif model_opt.memory == True:
logger.info("Do use memory")
all_docs = load_all_docs(model_opt, fields, device)
logger.info("finish load all documents")
src_vocab_size = len(fields["src"].base_field.vocab)
model = onmt.models.NMTModel(encoder, decoder, reconstruct_decoder,
src_vocab_size, all_docs, model_opt,
src_emb)
else:
logger.info("NO MODEL!!!")
else:
model = onmt.models.NMTModel(encoder, decoder)
# Build Generator.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size,
len(fields["tgt"].base_field.vocab)),
Cast(torch.float32), gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
tgt_base_field = fields["tgt"].base_field
vocab_size = len(tgt_base_field.vocab)
pad_idx = tgt_base_field.vocab.stoi[tgt_base_field.pad_token]
generator = CopyGenerator(model_opt.dec_rnn_size, vocab_size, pad_idx)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
# This preserves backward-compat for models using customed layernorm
def fix_key(s):
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2',
r'\1.layer_norm\2.bias', s)
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2',
r'\1.layer_norm\2.weight', s)
return s
checkpoint['model'] = {
fix_key(k): v
for k, v in checkpoint['model'].items()
}
# end of patch for backward compatibility
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec)
model.generator = generator
model.to(device)
# if model_opt.model_dtype == 'fp16' and model_opt.optim == 'fusedadam':
# model.half()
return model
def _gradient_accumulation(self, true_batches, normalization, total_stats,
report_stats):
if self.accum_count > 1:
self.optim.zero_grad()
for k, batch in enumerate(true_batches):
target_size = batch.tgt.size(0)
# Truncated BPTT: reminder not compatible with accum > 1
if self.trunc_size:
trunc_size = self.trunc_size
else:
trunc_size = target_size
src, src_lengths = batch.src if isinstance(batch.src, tuple) \
else (batch.src, None)
if src_lengths is not None:
report_stats.n_src_words += src_lengths.sum().item()
tgt_outer = batch.tgt
bptt = False
for j in range(0, target_size-1, trunc_size):
# 1. Create truncated target.
tgt = tgt_outer[j: j + trunc_size]
# 2. F-prop all but generator.
if self.accum_count == 1:
self.optim.zero_grad()
if self.decoder_rnn_weights:
kwargs = {'dec_weights': pad_sequence([
torch.Tensor(self.decoder_rnn_weights[batch.indices[b].item()])
for b in range(batch.batch_size)
], batch_first=False).to(self._dev)}
else:
kwargs = dict()
outputs, attns = self.model(src, tgt, src_lengths, bptt=bptt,
with_align=self.with_align, **kwargs)
bptt = True
# 3. Compute loss.
try:
loss, batch_stats = self.train_loss(
batch,
outputs,
attns,
normalization=normalization,
shard_size=self.shard_size,
trunc_start=j,
trunc_size=trunc_size)
if loss is not None:
self.optim.backward(loss)
total_stats.update(batch_stats)
report_stats.update(batch_stats)
except Exception:
traceback.print_exc()
logger.info("At step %d, we removed a batch - accum %d",
self.optim.training_step, k)
# 4. Update the parameters and statistics.
if self.accum_count == 1:
# Multi GPU gradient gather
if self.n_gpu > 1:
grads = [p.grad.data for p in self.model.parameters()
if p.requires_grad
and p.grad is not None]
onmt.utils.distributed.all_reduce_and_rescale_tensors(
grads, float(1))
self.optim.step()
# If truncated, don't backprop fully.
# TO CHECK
# if dec_state is not None:
# dec_state.detach()
if self.model.decoder.state is not None:
self.model.decoder.detach_state()
# in case of multi step gradient accumulation,
# update only after accum batches
if self.accum_count > 1:
if self.n_gpu > 1:
grads = [p.grad.data for p in self.model.parameters()
if p.requires_grad
and p.grad is not None]
onmt.utils.distributed.all_reduce_and_rescale_tensors(
grads, float(1))
self.optim.step()
def train(self,
train_iter,
train_steps,
save_checkpoint_steps=5000,
valid_iter=None,
valid_steps=10000):
"""
The main training loop by iterating over `train_iter` and possibly
running validation on `valid_iter`.
Args:
train_iter: A generator that returns the next training batch.
train_steps: Run training for this many iterations.
save_checkpoint_steps: Save a checkpoint every this many
iterations.
valid_iter: A generator that returns the next validation batch.
valid_steps: Run evaluation every this many iterations.
Returns:
The gathered statistics.
"""
if valid_iter is None:
logger.info('Start training loop without validation...')
else:
logger.info('Start training loop and validate every %d steps...',
valid_steps)
total_stats = onmt.utils.Statistics()
report_stats = onmt.utils.Statistics()
self._start_report_manager(start_time=total_stats.start_time)
for i, (batches, normalization) in enumerate(
self._accum_batches(train_iter)):
step = self.optim.training_step
# UPDATE DROPOUT
self._maybe_update_dropout(step)
if self.gpu_verbose_level > 1:
logger.info("GpuRank %d: index: %d", self.gpu_rank, i)
if self.gpu_verbose_level > 0:
logger.info("GpuRank %d: reduce_counter: %d \
n_minibatch %d"
% (self.gpu_rank, i + 1, len(batches)))
if self.n_gpu > 1:
normalization = sum(onmt.utils.distributed
.all_gather_list
(normalization))
self._gradient_accumulation(
batches, normalization, total_stats,
report_stats)
if self.average_decay > 0 and i % self.average_every == 0:
self._update_average(step)
report_stats = self._maybe_report_training(
step, train_steps,
self.optim.learning_rate(),
report_stats)
if valid_iter is not None and step % valid_steps == 0:
if self.gpu_verbose_level > 0:
logger.info('GpuRank %d: validate step %d'
% (self.gpu_rank, step))
valid_stats = self.validate(
valid_iter, moving_average=self.moving_average)
if self.gpu_verbose_level > 0:
logger.info('GpuRank %d: gather valid stat \
step %d' % (self.gpu_rank, step))
valid_stats = self._maybe_gather_stats(valid_stats)
if self.gpu_verbose_level > 0:
logger.info('GpuRank %d: report stat step %d'
% (self.gpu_rank, step))
self._report_step(self.optim.learning_rate(),
step, valid_stats=valid_stats)
# Run patience mechanism
if self.earlystopper is not None:
self.earlystopper(valid_stats, step)
# If the patience has reached the limit, stop training
if self.earlystopper.has_stopped():
break
if (self.model_saver is not None
and (save_checkpoint_steps != 0
and step % save_checkpoint_steps == 0)):
self.model_saver.save(step, moving_average=self.moving_average)
if train_steps > 0 and step >= train_steps:
break
if self.model_saver is not None:
self.model_saver.save(step, moving_average=self.moving_average)
return total_stats
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
if not (opt.overwrite):
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = 0
tgt_nfeats = 0
for src, tgt in zip(opt.train_src, opt.train_tgt):
src_nfeats += count_features(src) if opt.data_type == 'text' \
else 0
tgt_nfeats += count_features(tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
build_save_dataset('train', fields, src_reader, tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
def main(opt, device_id, batch_queue=None, semaphore=None):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
assert len(opt.accum_count) == len(opt.accum_steps), \
'Number of accum_count values must match number of accum_steps'
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(
vocab, opt.model_type, dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(
opt, device_id, model, fields, optim, model_saver=model_saver)
if batch_queue is None:
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
else:
assert semaphore is not None, \
"Using batch_queue requires semaphore as well"
def _train_iter():
while True:
batch = batch_queue.get()
semaphore.release()
yield batch
train_iter = _train_iter()
valid_iter = build_dataset_iter(
"valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(
train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps,
unlearn=opt.unlearn)
if trainer.report_manager.tensorboard_writer is not None:
trainer.report_manager.tensorboard_writer.close()
def process_one_shard(corpus_params, params):
corpus_type, fields, src_reader, cue_reader, tgt_reader, opt, existing_fields,\
src_vocab, tgt_vocab = corpus_params
i, (src_shard, cue_shard, tgt_shard, maybe_id, filter_pred) = params
# create one counter per shard
sub_sub_counter = defaultdict(Counter)
assert len(src_shard) == len(tgt_shard) and len(src_shard) == len(
cue_shard)
logger.info("Building shard %d." % i)
dataset = inputters.Dataset(
fields,
readers=([src_reader, cue_reader, tgt_reader]
if tgt_reader else [src_reader, cue_reader]),
data=([("src", src_shard), ("cue", cue_shard),
("tgt", tgt_shard)] if tgt_reader else [("src", src_shard),
("cue", cue_shard)]),
dirs=([opt.src_dir, None, None]
if tgt_reader else [opt.src_dir, None]),
sort_key=inputters.str2sortkey[opt.data_type],
filter_pred=filter_pred)
if corpus_type == "train" and existing_fields is None:
for ex in dataset.examples:
for name, field in fields.items():
if ((opt.data_type == "audio") and (name == "src")):
continue
try:
f_iter = iter(field)
except TypeError:
f_iter = [(name, field)]
all_data = [getattr(ex, name, None)]
else:
all_data = getattr(ex, name)
for (sub_n, sub_f), fd in zip(f_iter, all_data):
has_vocab = (sub_n == 'src' and
src_vocab is not None) or \
(sub_n == 'tgt' and
tgt_vocab is not None)
if (hasattr(sub_f, 'sequential') and sub_f.sequential
and not has_vocab):
val = fd
if sub_n == 'cue':
val = list(chain.from_iterable(val))
sub_sub_counter[sub_n].update(val)
if maybe_id:
shard_base = corpus_type + "_" + maybe_id
else:
shard_base = corpus_type
data_path = "{:s}.{:s}.{:d}.pt".\
format(opt.save_data, shard_base, i)
logger.info(" * saving %sth %s data shard to %s." %
(i, shard_base, data_path))
dataset.save(data_path)
del dataset.examples
gc.collect()
del dataset
gc.collect()
return sub_sub_counter
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
first_dataset = pickle.load(open('processed_data/all-train/train.pt',
'rb'))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
optim = build_optim(model, opt, checkpoint) # opt.train_from == ''
# Build model saver
if not os.path.exists('experiments/all_train'):
os.mkdir('experiments/all_train')
model_saver = build_model_saver(model_opt, opt.save_model, opt, model,
fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
"text",
model_saver=model_saver)
def _lazy_dataset_loader(pt_file):
# dataset = torch.load(pt_file)
def dataset_loader(pt_file):
with open(pt_file, 'rb') as f:
dataset = pickle.load(f)
# logger.info('Loading task from <{}>, number of examples: {}'.format(pt_file, len(dataset)))
return dataset
yield dataset_loader(pt_file)
train_iter = list(
build_dataset_iter(
_lazy_dataset_loader('processed_data/all-train/train.pt'), fields,
opt))
trainer.train(train_iter, opt.train_epochs)
def train(self,
train_iter,
train_steps,
save_checkpoint_steps=5000,
valid_iter=None,
valid_steps=10000):
"""
The main training loop by iterating over `train_iter` and possibly
running validation on `valid_iter`.
Args:
train_iter: A generator that returns the next training batch.
train_steps: Run training for this many iterations.
save_checkpoint_steps: Save a checkpoint every this many
iterations.
valid_iter: A generator that returns the next validation batch.
valid_steps: Run evaluation every this many iterations.
Returns:
The gathered statistics.
"""
if valid_iter is None:
logger.info('Start training loop without validation...')
else:
logger.info('Start training loop and validate every %d steps...',
valid_steps)
total_stats = RLStatistics()
report_stats = RLStatistics()
self._start_report_manager(start_time=total_stats.start_time)
for i, (batches, normalization) in enumerate(
self._accum_batches(train_iter)):
step = self.optim.training_step
# UPDATE DROPOUT
self._maybe_update_dropout(step)
if self.gpu_verbose_level > 1:
logger.info("GpuRank %d: index: %d", self.gpu_rank, i)
if self.gpu_verbose_level > 0:
logger.info("GpuRank %d: reduce_counter: %d \
n_minibatch %d"
% (self.gpu_rank, i + 1, len(batches)))
self._gradient_accumulation(
batches, normalization, total_stats,
report_stats)
report_stats = self._maybe_report_training(
step, train_steps,
self.optim.learning_rate(),
report_stats)
if (self.model_saver is not None
and (save_checkpoint_steps != 0
and step % save_checkpoint_steps == 0)):
self.model_saver.save(step, moving_average=self.moving_average)
if train_steps > 0 and step >= train_steps:
break
if self.model_saver is not None:
self.model_saver.save(step, moving_average=self.moving_average)
return total_stats
def _gradient_accumulation(self, true_batches, normalization, total_stats,
report_stats):
if self.accum_count > 1:
self.optim.zero_grad()
for k, batch in enumerate(true_batches):
if self.accum_count == 1:
self.optim.zero_grad()
target_size = batch.tgt.size(0)
src, src_lengths = batch.src
device = src.device
if src_lengths is not None:
report_stats.n_src_words += src_lengths.sum().item()
report_stats.n_batches += 1
# Encoder forward.
enc_states, memory_bank, src_lengths = self.model.encoder(src, src_lengths)
# Teacher forcing
self.model.decoder.init_state(src, memory_bank, enc_states)
ml_outputs, ml_attns = self.model.decoder(batch.tgt[:-1], memory_bank,
memory_lengths=src_lengths)
# Sampling a path
rl_forward = self._forward_model(batch, enc_states, memory_bank, sample="reinforce")
# baseline computing doesn't need gradient
with torch.no_grad():
baseline_forward = self._forward_model(batch, enc_states, memory_bank, sample="topk")
# 3. Compute loss.
try:
rl_loss, rl_stats = self.rl_loss(
batch,
rl_forward,
baseline_forward)
ml_loss, ml_stats = self.ml_loss(
batch,
ml_outputs,
ml_attns,
normalization=normalization,
shard_size=0,
trunc_start=0,
trunc_size=target_size)
loss = rl_loss * self.gamma_loss + ml_loss * (1 - self.gamma_loss)
self.optim.backward(loss)
total_stats.update(rl_stats, ml_stats)
report_stats.update(rl_stats, ml_stats)
except Exception:
traceback.print_exc()
logger.info("At step %d, we removed a batch - accum %d",
self.optim.training_step, k)
# 4. Update the parameters and statistics.
if self.accum_count == 1:
self.optim.step()
# report the grad norms by modules
#GradNorm.output_norms(self.model)
# in case of multi step gradient accumulation,
# update only after accum batches
if self.accum_count > 1:
self.optim.step()
def log_msg(self, msg):
logger.info(msg)
sys.stdout.flush()
def build_model(model_opt, opt, fields, checkpoint):
""" Build the Model """
logger.info("Building model...")
model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint)
logger.info(model)
return model
def rouge_results_to_str(results_dict):
return ">> ROUGE(1/2/3/L/SU4): {:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}".format(
results_dict["rouge_1_f_score"] * 100,
results_dict["rouge_2_f_score"] * 100,
results_dict["rouge_3_f_score"] * 100,
results_dict["rouge_l_f_score"] * 100,
results_dict["rouge_su*_f_score"] * 100)
if __name__ == "__main__":
init_logger('test_rouge.log')
parser = argparse.ArgumentParser()
parser.add_argument('-c',
type=str,
default="candidate.txt",
help='candidate file')
parser.add_argument('-r',
type=str,
default="reference.txt",
help='reference file')
args = parser.parse_args()
if args.c.upper() == "STDIN":
candidates = sys.stdin
else:
candidates = codecs.open(args.c, encoding="utf-8")
references = codecs.open(args.r, encoding="utf-8")
results_dict = test_rouge(candidates, references)
logger.info(rouge_results_to_str(results_dict))
def main(opt):
ArgumentParser.validate_preprocess_args(opt)
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = count_features(opt.train_src) if opt.data_type == 'text' \
else 0
tgt_nfeats = count_features(opt.train_tgt) # tgt always text so far
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(
opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset(
'train', fields, src_reader, tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
def main(opt):
if opt.gpuid:
raise AssertionError("gpuid is deprecated \
see world_size and gpu_ranks")
assert opt.world_size <= 1, "you don't need multi-gpu for morphology"
device_id = 0 if len(opt.gpu_ranks) == 1 else -1
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's useful in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
fields = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
fields = torch.load(opt.data + '.vocab.pt')
for key, values in fields.items():
for name, f in values:
if f.use_vocab:
logger.info(' * %s vocab size = %d' % (name, len(f.vocab)))
# Build model.
logger.info('Building model...')
model = build_model(model_opt, fields, use_gpu(opt), checkpoint)
logger.info(model)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
params = model.parameters()
optim_args = {"lr": opt.learning_rate}
if opt.optim == "adam":
# no need to mess with the default betas
optim_args["eps"] = 1e-9
elif opt.optim == "adagrad":
optim_args["initial_accumulator_value"] = opt.adagrad_accumulator_init
optim = getattr(torch.optim, opt.optim.title())(params, **optim_args)
print(optim)
trainer = build_trainer(opt, model_opt, device_id, model, fields, optim)
# this line is kind of a temporary kludge because different objects expect
# fields to have a different structure
dataset_fields = dict(chain.from_iterable(fields.values()))
device = "cuda" if opt.gpu_ranks else "cpu"
train_dataset = torch.load(opt.data + '.train.pt')
train_dataset.fields = dataset_fields
train_iter = OrderedIterator(train_dataset,
opt.batch_size,
sort_within_batch=True,
device=device,
repeat=False,
shuffle=not opt.no_shuffle)
valid_dataset = torch.load(opt.data + '.valid.pt')
valid_dataset.fields = dataset_fields
valid_iter = OrderedIterator(valid_dataset,
opt.valid_batch_size,
train=False,
sort_within_batch=True,
device=device)
logger.info('Starting training on {}'.format(device))
trainer.train(train_iter, valid_iter, opt.epochs)
def _lazy_dataset_loader(pt_file, corpus_type):
dataset = torch.load(pt_file)
logger.info('Loading %s dataset from %s, number of examples: %d' %
(corpus_type, pt_file, len(dataset)))
return dataset
def build_vocab(train_dataset_files, fields, data_type, share_vocab,
src_vocab_path, src_vocab_size, src_words_min_frequency,
tgt_vocab_path, tgt_vocab_size, tgt_words_min_frequency,
vocab_size_multiple=1):
"""Build the fields for all data sides.
Args:
train_dataset_files: a list of train dataset pt file.
fields (dict[str, Field]): fields to build vocab for.
data_type (str): A supported data type string.
share_vocab (bool): share source and target vocabulary?
src_vocab_path (str): Path to src vocabulary file.
src_vocab_size (int): size of the source vocabulary.
src_words_min_frequency (int): the minimum frequency needed to
include a source word in the vocabulary.
tgt_vocab_path (str): Path to tgt vocabulary file.
tgt_vocab_size (int): size of the target vocabulary.
tgt_words_min_frequency (int): the minimum frequency needed to
include a target word in the vocabulary.
vocab_size_multiple (int): ensure that the vocabulary size is a
multiple of this value.
Returns:
Dict of Fields
"""
counters = defaultdict(Counter)
if src_vocab_path:
try:
logger.info("Using existing vocabulary...")
vocab = torch.load(src_vocab_path)
# return vocab to dump with standard name
return vocab
except torch.serialization.pickle.UnpicklingError:
logger.info("Building vocab from text file...")
# empty train_dataset_files so that vocab is only loaded from
# given paths in src_vocab_path, tgt_vocab_path
train_dataset_files = []
# Load vocabulary
if src_vocab_path:
src_vocab, src_vocab_size = _load_vocab(
src_vocab_path, "src", counters)
else:
src_vocab = None
if tgt_vocab_path:
tgt_vocab, tgt_vocab_size = _load_vocab(
tgt_vocab_path, "tgt", counters)
else:
tgt_vocab = None
for i, path in enumerate(train_dataset_files):
dataset = torch.load(path)
logger.info(" * reloading %s." % path)
for ex in dataset.examples:
for name, field in fields.items():
try:
f_iter = iter(field)
except TypeError:
f_iter = [(name, field)]
all_data = [getattr(ex, name, None)]
else:
all_data = getattr(ex, name)
for (sub_n, sub_f), fd in zip(
f_iter, all_data):
has_vocab = (sub_n == 'src' and src_vocab) or \
(sub_n == 'tgt' and tgt_vocab)
if sub_f.sequential and not has_vocab:
val = fd
counters[sub_n].update(val)
# Drop the none-using from memory but keep the last
if i < len(train_dataset_files) - 1:
dataset.examples = None
gc.collect()
del dataset.examples
gc.collect()
del dataset
gc.collect()
build_fv_args = defaultdict(dict)
build_fv_args["src"] = dict(
max_size=src_vocab_size, min_freq=src_words_min_frequency)
build_fv_args["tgt"] = dict(
max_size=tgt_vocab_size, min_freq=tgt_words_min_frequency)
tgt_multifield = fields["tgt"]
_build_fv_from_multifield(
tgt_multifield,
counters,
build_fv_args,
size_multiple=vocab_size_multiple if not share_vocab else 1)
if data_type == 'text':
src_multifield = fields["src"]
_build_fv_from_multifield(
src_multifield,
counters,
build_fv_args,
size_multiple=vocab_size_multiple if not share_vocab else 1)
if share_vocab:
# `tgt_vocab_size` is ignored when sharing vocabularies
logger.info(" * merging src and tgt vocab...")
src_field = src_multifield.base_field
tgt_field = tgt_multifield.base_field
_merge_field_vocabs(
src_field, tgt_field, vocab_size=src_vocab_size,
min_freq=src_words_min_frequency,
vocab_size_multiple=vocab_size_multiple)
logger.info(" * merged vocab size: %d." % len(src_field.vocab))
return fields # is the return necessary?
def build_save_dataset(corpus_type, fields, src_reader, tgt_reader, opt):
assert corpus_type in ['train', 'valid']
if corpus_type == 'train':
counters = defaultdict(Counter)
srcs = opt.train_src
tgts = opt.train_tgt
ids = opt.train_ids
else:
srcs = [opt.valid_src]
tgts = [opt.valid_tgt]
ids = [None]
for src, tgt, maybe_id in zip(srcs, tgts, ids):
logger.info("Reading source and target files: %s %s." % (src, tgt))
src_shards = split_corpus(src, opt.shard_size)
tgt_shards = split_corpus(tgt, opt.shard_size)
shard_pairs = zip(src_shards, tgt_shards)
dataset_paths = []
if (corpus_type == "train" or opt.filter_valid) and tgt is not None:
filter_pred = partial(inputters.filter_example,
use_src_len=opt.data_type == "text",
max_src_len=opt.src_seq_length,
max_tgt_len=opt.tgt_seq_length)
else:
filter_pred = None
# @shared
if corpus_type == "train":
existing_fields = None
if opt.src_vocab != "":
try:
logger.info("Using existing vocabulary...")
existing_fields = torch.load(opt.src_vocab)
except torch.serialization.pickle.UnpicklingError:
logger.info("Building vocab from text file...")
src_vocab, src_vocab_size = _load_vocab(
opt.src_vocab, "src", counters,
opt.src_words_min_frequency)
else:
src_vocab = None
if opt.tgt_vocab != "":
tgt_vocab, tgt_vocab_size = _load_vocab(
opt.tgt_vocab, "tgt", counters,
opt.tgt_words_min_frequency)
else:
tgt_vocab = None
for i, (src_shard, tgt_shard) in enumerate(shard_pairs):
assert len(src_shard) == len(tgt_shard)
logger.info("Building shard %d." % i)
dataset = inputters.Dataset(
fields,
readers=([src_reader, tgt_reader]
if tgt_reader else [src_reader]),
data=([("src", src_shard),
("tgt", tgt_shard)] if tgt_reader else [("src",
src_shard)]),
dirs=([opt.src_dir, None] if tgt_reader else [opt.src_dir]),
sort_key=inputters.str2sortkey[opt.data_type],
filter_pred=filter_pred)
if corpus_type == "train" and existing_fields is None:
for ex in dataset.examples:
for name, field in fields.items():
try:
f_iter = iter(field)
except TypeError:
f_iter = [(name, field)]
all_data = [getattr(ex, name, None)]
else:
all_data = getattr(ex, name)
for (sub_n, sub_f), fd in zip(f_iter, all_data):
has_vocab = (sub_n == 'src' and
src_vocab is not None) or \
(sub_n == 'tgt' and
tgt_vocab is not None)
if (hasattr(sub_f, 'sequential')
and sub_f.sequential and not has_vocab):
val = fd
counters[sub_n].update(val)
if maybe_id:
shard_base = corpus_type + "_" + maybe_id
else:
shard_base = corpus_type
data_path = "{:s}.{:s}.{:d}.pt".\
format(opt.save_data, shard_base, i)
dataset_paths.append(data_path)
logger.info(" * saving %sth %s data shard to %s." %
(i, shard_base, data_path))
dataset.save(data_path)
del dataset.examples
gc.collect()
del dataset
gc.collect()
if corpus_type == "train":
vocab_path = opt.save_data + '.vocab.pt'
if existing_fields is None:
fields = _build_fields_vocab(
fields, counters, opt.data_type, opt.share_vocab,
opt.vocab_size_multiple, opt.src_vocab_size,
opt.src_words_min_frequency, opt.tgt_vocab_size,
opt.tgt_words_min_frequency)
else:
fields = existing_fields
torch.save(fields, vocab_path)
def build_save_in_shards_using_shards_size(src_corpus, tgt_corpus, fields,
corpus_type, opt):
"""
Divide src_corpus and tgt_corpus into smaller multiples
src_copus and tgt corpus files, then build shards, each
shard will have opt.shard_size samples except last shard.
The reason we do this is to avoid taking up too much memory due
to sucking in a huge corpus file.
"""
with codecs.open(src_corpus, "r", encoding="utf-8") as fsrc:
with codecs.open(tgt_corpus, "r", encoding="utf-8") as ftgt:
src_data = fsrc.readlines()
tgt_data = ftgt.readlines()
src_corpus = "".join(src_corpus.split(".")[:-1])
tgt_corpus = "".join(tgt_corpus.split(".")[:-1])
num_shards = int(len(src_data) / opt.shard_size)
for x in range(num_shards):
f = codecs.open(src_corpus + ".{0}.txt".format(x),
"w",
encoding="utf-8")
f.writelines(src_data[x * opt.shard_size:(x + 1) *
opt.shard_size])
f.close()
f = codecs.open(tgt_corpus + ".{0}.txt".format(x),
"w",
encoding="utf-8")
f.writelines(tgt_data[x * opt.shard_size:(x + 1) *
opt.shard_size])
f.close()
num_written = num_shards * opt.shard_size
if len(src_data) > num_written:
f = codecs.open(src_corpus + ".{0}.txt".format(num_shards),
'w',
encoding="utf-8")
f.writelines(src_data[num_shards * opt.shard_size:])
f.close()
f = codecs.open(tgt_corpus + ".{0}.txt".format(num_shards),
'w',
encoding="utf-8")
f.writelines(tgt_data[num_shards * opt.shard_size:])
f.close()
src_list = sorted(glob.glob(src_corpus + '.*.txt'))
tgt_list = sorted(glob.glob(tgt_corpus + '.*.txt'))
ret_list = []
for index, src in enumerate(src_list):
dataset = inputters.build_dataset(
fields,
opt.data_type,
src_path=src,
tgt_path=tgt_list[index],
src_dir=opt.src_dir,
src_seq_length=opt.src_seq_length,
tgt_seq_length=opt.tgt_seq_length,
src_seq_length_trunc=opt.src_seq_length_trunc,
tgt_seq_length_trunc=opt.tgt_seq_length_trunc,
dynamic_dict=opt.dynamic_dict,
sample_rate=opt.sample_rate,
window_size=opt.window_size,
window_stride=opt.window_stride,
window=opt.window,
image_channel_size=opt.image_channel_size,
reentrancies=opt.reentrancies)
pt_file = "{:s}.{:s}.{:d}.pt".format(opt.save_data, corpus_type, index)
# We save fields in vocab.pt seperately, so make it empty.
dataset.fields = []
logger.info(" * saving %sth %s data shard to %s." %
(index, corpus_type, pt_file))
torch.save(dataset, pt_file)
ret_list.append(pt_file)
del dataset.examples
gc.collect()
del dataset
gc.collect()
return ret_list
def main(opt):
ArgumentParser.validate_train_opts(opt)
ArgumentParser.update_model_opts(opt)
ArgumentParser.validate_model_opts(opt)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
if len(opt.data_ids) > 1:
train_shards = []
for train_id in opt.data_ids:
shard_base = "train_" + train_id
train_shards.append(shard_base)
train_iter = build_dataset_iter_multiple(train_shards, fields, opt)
else:
if opt.data_ids[0] is not None:
shard_base = "train_" + opt.data_ids[0]
else:
shard_base = "train"
train_iter = build_dataset_iter(shard_base, fields, opt)
nb_gpu = len(opt.gpu_ranks)
if opt.world_size > 1:
queues = []
mp = torch.multiprocessing.get_context('spawn')
semaphore = mp.Semaphore(opt.world_size * opt.queue_size)
# Create a thread to listen for errors in the child processes.
error_queue = mp.SimpleQueue()
error_handler = ErrorHandler(error_queue)
# Train with multiprocessing.
procs = []
for device_id in range(nb_gpu):
q = mp.Queue(opt.queue_size)
queues += [q]
procs.append(
mp.Process(target=run,
args=(opt, device_id, error_queue, q, semaphore),
daemon=True))
procs[device_id].start()
logger.info(" Starting process pid: %d " % procs[device_id].pid)
error_handler.add_child(procs[device_id].pid)
producer = mp.Process(target=batch_producer,
args=(
train_iter,
queues,
semaphore,
opt,
),
daemon=True)
producer.start()
error_handler.add_child(producer.pid)
for p in procs:
p.join()
producer.terminate()
elif nb_gpu == 1: # case 1 GPU only
single_main(opt, 0)
else: # case only CPU
single_main(opt, -1)
def main():
opt = parse_args()
if (opt.max_shard_size > 0):
raise AssertionError("-max_shard_size is deprecated, please use \
-shard_size (number of examples) instead.")
init_logger(opt.log_file)
logger.info("Extracting features...")
src_nfeats = inputters.get_num_features(opt.data_type, opt.train_src,
'src')
tgt_nfeats = inputters.get_num_features(opt.data_type, opt.train_tgt,
'tgt')
logger.info(" * number of source features: %d." % src_nfeats)
logger.info(" * number of target features: %d." % tgt_nfeats)
logger.info("Building `Fields` object...")
fields = inputters.get_fields(opt.data_type, src_nfeats, tgt_nfeats)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, opt)
logger.info("Building & saving validation data...")
valid_dataset_files = build_save_dataset('valid', fields, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files + valid_dataset_files, fields, opt)
def _maybe_update_dropout(self, step):
for i in range(len(self.dropout_steps)):
if step > 1 and step == self.dropout_steps[i] + 1:
self.model.update_dropout(self.dropout[i])
logger.info("Updated dropout to %f from step %d"
% (self.dropout[i], step))
def log(self, *args, **kwargs):
logger.info(*args, **kwargs)
def _gradient_accumulation(self, true_batches, normalization, total_stats,
report_stats):
if self.accum_count > 1:
self.optim.zero_grad()
for k, batch in enumerate(true_batches):
target_size = batch.tgt.size(0)
# Truncated BPTT: reminder not compatible with accum > 1
if self.trunc_size:
trunc_size = self.trunc_size
else:
trunc_size = target_size
src, src_lengths = batch.src if isinstance(batch.src, tuple) \
else (batch.src, None)
if src_lengths is not None:
report_stats.n_src_words += src_lengths.sum().item()
tgt_outer = batch.tgt
# TODO(yida) train
if "tag" in self.model.generators.keys():
pos_src, _ = batch.tag_src \
if isinstance(batch.tag_src, tuple) else (batch.tag_src, None)
pos_outer = batch.tag_tgt
else:
pos_src = None
pos_outer = None
# pos_outer = batch.tag_tgt if hasattr(batch, "tag_tgt") else None
bptt = False
for j in range(0, target_size-1, trunc_size):
# 1. Create truncated target.
tgt = tgt_outer[j: j + trunc_size]
# TODO(yida)
pos_tgt = pos_outer[j: j + trunc_size] \
if "tag" in self.model.generators.keys() else None
# 2. F-prop all but generator.
if self.accum_count == 1:
self.optim.zero_grad()
# TODO(yida) trainer
# TODO(yida) temp rl
# with torch.no_grad():
outputs, attns, rnn_outs = self.model(src, tgt, pos_src, pos_tgt, src_lengths, bptt=bptt)
bptt = True
# 3. Compute loss.
try:
# TODO(yida) temp rl
# with torch.no_grad():
loss, batch_stats = self.train_loss(
batch,
outputs,
attns,
# TODO(yida) trainer
rnn_outs,
normalization=normalization,
shard_size=self.shard_size,
trunc_start=j,
trunc_size=trunc_size)
if loss is not None:
self.optim.backward(loss)
total_stats.update(batch_stats)
report_stats.update(batch_stats)
except Exception:
traceback.print_exc()
logger.info("At step %d, we removed a batch - accum %d",
self.optim.training_step, k)
# 4. Update the parameters and statistics.
if self.accum_count == 1:
# Multi GPU gradient gather
if self.n_gpu > 1:
grads = [p.grad.data for p in self.model.parameters()
if p.requires_grad
and p.grad is not None]
onmt.utils.distributed.all_reduce_and_rescale_tensors(
grads, float(1))
self.optim.step()
# If truncated, don't backprop fully.
# TO CHECK
# if dec_state is not None:
# dec_state.detach()
if self.model.decoder.state is not None:
self.model.decoder.detach_state()
# in case of multi step gradient accumulation,
# update only after accum batches
if self.accum_count > 1:
if self.n_gpu > 1:
grads = [p.grad.data for p in self.model.parameters()
if p.requires_grad
and p.grad is not None]
onmt.utils.distributed.all_reduce_and_rescale_tensors(
grads, float(1))
self.optim.step()
def build_model(model_opt, opt, fields, checkpoint):
logger.info('Building model...')
model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint)
logger.info(model)
return model
def process_one_shard(corpus_params, params):
corpus_type, fields, src_reader, tgt_reader, align_reader, opt,\
existing_fields, src_vocab, tgt_vocab = corpus_params
i, (src_shard, tgt_shard, align_shard, maybe_id, filter_pred) = params
# create one counter per shard
sub_sub_counter = defaultdict(Counter)
assert len(src_shard) == len(tgt_shard)
logger.info("Building shard %d." % i)
src_data = {"reader": src_reader, "data": src_shard, "dir": opt.src_dir}
tgt_data = {"reader": tgt_reader, "data": tgt_shard, "dir": None}
align_data = {"reader": align_reader, "data": align_shard, "dir": None}
_readers, _data, _dir = inputters.Dataset.config([('src', src_data),
('tgt', tgt_data),
('align', align_data)])
dataset = inputters.Dataset(fields,
readers=_readers,
data=_data,
dirs=_dir,
sort_key=inputters.str2sortkey[opt.data_type],
filter_pred=filter_pred,
corpus_id=maybe_id)
if corpus_type == "train" and existing_fields is None:
for ex in dataset.examples:
sub_sub_counter['corpus_id'].update(
["train" if maybe_id is None else maybe_id])
for name, field in fields.items():
if (opt.data_type in ["audio", "vec"]) and name == "src":
continue
try:
f_iter = iter(field)
except TypeError:
f_iter = [(name, field)]
all_data = [getattr(ex, name, None)]
else:
all_data = getattr(ex, name)
for (sub_n, sub_f), fd in zip(f_iter, all_data):
has_vocab = (sub_n == 'src' and
src_vocab is not None) or \
(sub_n == 'tgt' and
tgt_vocab is not None)
if (hasattr(sub_f, 'sequential') and sub_f.sequential
and not has_vocab):
val = fd
sub_sub_counter[sub_n].update(val)
if maybe_id:
shard_base = corpus_type + "_" + maybe_id
else:
shard_base = corpus_type
data_path = "{:s}.{:s}.{:d}.pt".\
format(opt.save_data, shard_base, i)
logger.info(" * saving %sth %s data shard to %s." %
(i, shard_base, data_path))
dataset.save(data_path)
del dataset.examples
gc.collect()
del dataset
gc.collect()
return sub_sub_counter
def log(self, *args, **kwargs):
logger.info(*args, **kwargs)
def build_model(model_opt, opt, fields, checkpoint):
logger.info('Building model...')
model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint)
logger.info(model)
return model
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
data_type = opt.model_type
fields = load_old_vocab(vocab, data_type, dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
for name, f in fields[side]:
try:
f_iter = iter(f)
except TypeError:
f_iter = [(name, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
# this line is kind of a temporary kludge because different objects expect
# fields to have a different structure
dataset_fields = dict(chain.from_iterable(fields.values()))
train_iter = build_dataset_iter("train", dataset_fields, opt)
valid_iter = build_dataset_iter("valid",
dataset_fields,
opt,
is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
trainer.train(train_iter,
opt.train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def main(opt, device_id):
opt = training_opt_postprocessing(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
# Load default opts values then overwrite it with opts from
# the checkpoint. It's usefull in order to re-train a model
# after adding a new option (not set in checkpoint)
dummy_parser = configargparse.ArgumentParser()
opts.model_opts(dummy_parser)
default_opt = dummy_parser.parse_known_args([])[0]
model_opt = default_opt
model_opt.__dict__.update(checkpoint['opt'].__dict__)
else:
checkpoint = None
model_opt = opt
# Peek the first dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = load_fields(first_dataset, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
data_type,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt),
fields,
opt,
is_train=False)
# Do training.
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
if opt.no_base == False:
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
if opt.comparable:
logger.info('')
logger.info('Beginning comparable data extraction and training.')
# 1. Initialize Comparable object
comp = Comparable(model, trainer, fields, logger, opt)
# 2. Infer similarity threshold from training data
for epoch in range(opt.comp_epochs):
# 3. Update threshold if dynamic
if opt.threshold_dynamics != 'static' and epoch != 0:
comp.update_threshold(opt.threshold_dynamics,
opt.infer_threshold)
# 4. Extract parallel data and train
#if opt.match_articles:
# comparable_data = comp.match_articles(opt.match_articles)
# train_stats = comp.extract_and_train(comparable_data)
#else:
train_stats = comp.extract_and_train(opt.comparable_data)
# 5. Validate on validation set
if opt.no_valid == False:
valid_iter = build_dataset_iter(
lazily_load_dataset("valid", opt), fields, opt)
valid_stats = comp.validate(valid_iter)
# 6. Drop a checkpoint if needed
comp.trainer.model_saver._save(epoch)
def train(self,
train_iter,
train_steps,
save_checkpoint_steps=5000,
valid_iter=None,
valid_steps=10000):
"""
The main training loop by iterating over `train_iter` and possibly
running validation on `valid_iter`.
Args:
train_iter: A generator that returns the next training batch.
train_steps: Run training for this many iterations.
save_checkpoint_steps: Save a checkpoint every this many
iterations.
valid_iter: A generator that returns the next validation batch.
valid_steps: Run evaluation every this many iterations.
Returns:
The gathered statistics.
"""
if valid_iter is None:
logger.info('Start training loop without validation...')
else:
logger.info('Start training loop and validate every %d steps...',
valid_steps)
total_stats = onmt.utils.Statistics()
report_stats = onmt.utils.Statistics()
self._start_report_manager(start_time=total_stats.start_time)
if self.gpt2_params_std > 0:
total_size = train_iter.total_size
else:
total_size = 0
if self.n_gpu > 1:
train_iter = itertools.islice(train_iter, self.gpu_rank, None,
self.n_gpu)
#torch.cuda.synchronize()
#last_end_time = time.time()
for i, (batches,
normalization) in enumerate(self._accum_batches(train_iter)):
#print('batch time: %0.5f' % (time.time() - last_end_time))
step = self.optim.training_step
if self.gpu_verbose_level > 1:
logger.info("GpuRank %d: index: %d", self.gpu_rank, i)
if self.gpu_verbose_level > 0:
logger.info("GpuRank %d: reduce_counter: %d \
n_minibatch %d" %
(self.gpu_rank, i + 1, len(batches)))
if self.n_gpu > 1:
normalization = sum(
onmt.utils.distributed.all_gather_list(normalization))
#torch.cuda.synchronize()
#tt = time.time()
self._gradient_accumulation(batches, normalization, total_stats,
report_stats, total_size)
#torch.cuda.synchronize()
#print('grad time: %0.5f' % (time.time() - tt))
if self.average_decay > 0 and i % self.average_every == 0:
self._update_average(step)
report_stats = self._maybe_report_training(
step, train_steps, self.optim.learning_rate(), report_stats)
if valid_iter is not None and step % valid_steps == 0:
if self.gpu_verbose_level > 0:
logger.info('GpuRank %d: validate step %d' %
(self.gpu_rank, step))
valid_stats = self.validate(valid_iter,
moving_average=self.moving_average)
if self.gpu_verbose_level > 0:
logger.info('GpuRank %d: gather valid stat \
step %d' % (self.gpu_rank, step))
valid_stats = self._maybe_gather_stats(valid_stats)
if self.gpu_verbose_level > 0:
logger.info('GpuRank %d: report stat step %d' %
(self.gpu_rank, step))
self._report_step(self.optim.learning_rate(),
step,
valid_stats=valid_stats)
if (self.model_saver is not None
and (save_checkpoint_steps != 0
and step % save_checkpoint_steps == 0)):
self.model_saver.save(step, moving_average=self.moving_average)
if train_steps > 0 and step >= train_steps:
break
#torch.cuda.synchronize()
#last_end_time = time.time()
if self.model_saver is not None:
self.model_saver.save(step, moving_average=self.moving_average)
return total_stats
def main(opt, device_id):
# NOTE: It's important that ``opt`` has been validated and updated
# at this point.
configure_process(opt, device_id)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = ArgumentParser.ckpt_model_opts(checkpoint["opt"])
ArgumentParser.update_model_opts(model_opt)
ArgumentParser.validate_model_opts(model_opt)
if opt.fine_tune:
model_opt.learning_rate = opt.learning_rate
model_opt.warmup_steps = opt.warmup_steps
model_opt.n_clusters = opt.n_clusters
logger.info('Loading vocab from checkpoint at %s.' % opt.train_from)
vocab = checkpoint['vocab']
else:
checkpoint = None
model_opt = opt
vocab = torch.load(opt.data + '.vocab.pt')
# check for code where vocab is saved instead of fields
# (in the future this will be done in a smarter way)
if old_style_vocab(vocab):
fields = load_old_vocab(vocab,
opt.model_type,
dynamic_dict=opt.copy_attn)
else:
fields = vocab
# Report src and tgt vocab sizes, including for features
for side in ['src', 'tgt']:
f = fields[side]
try:
f_iter = iter(f)
except TypeError:
f_iter = [(side, f)]
for sn, sf in f_iter:
if sf.use_vocab:
logger.info(' * %s vocab size = %d' % (sn, len(sf.vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = Optimizer.from_opt(model, opt, checkpoint=checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
trainer = build_trainer(opt,
device_id,
model,
fields,
optim,
model_saver=model_saver)
train_iter = build_dataset_iter("train", fields, opt)
valid_iter = build_dataset_iter("valid", fields, opt, is_train=False)
if len(opt.gpu_ranks):
logger.info('Starting training on GPU: %s' % opt.gpu_ranks)
else:
logger.info('Starting training on CPU, could be very slow')
train_steps = opt.train_steps
if opt.single_pass and train_steps > 0:
logger.warning("Option single_pass is enabled, ignoring train_steps.")
train_steps = 0
trainer.train(train_iter,
train_steps,
save_checkpoint_steps=opt.save_checkpoint_steps,
valid_iter=valid_iter,
valid_steps=opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
def build_vocab(train_dataset_files, fields, data_type, share_vocab,
src_vocab_path, src_vocab_size, src_words_min_frequency,
tgt_vocab_path, tgt_vocab_size, tgt_words_min_frequency):
"""
Args:
train_dataset_files: a list of train dataset pt file.
fields (dict): fields to build vocab for.
data_type: "text", "img" or "audio"?
share_vocab(bool): share source and target vocabulary?
src_vocab_path(string): Path to src vocabulary file.
src_vocab_size(int): size of the source vocabulary.
src_words_min_frequency(int): the minimum frequency needed to
include a source word in the vocabulary.
tgt_vocab_path(string): Path to tgt vocabulary file.
tgt_vocab_size(int): size of the target vocabulary.
tgt_words_min_frequency(int): the minimum frequency needed to
include a target word in the vocabulary.
Returns:
Dict of Fields
"""
# Prop src from field to get lower memory using when training with image
if data_type == 'img' or data_type == 'audio':
fields.pop("src")
counters = {k: Counter() for k in fields}
# Load vocabulary
if src_vocab_path:
src_vocab = load_vocabulary(src_vocab_path, "src")
src_vocab_size = len(src_vocab)
logger.info('Loaded source vocab has %d tokens.' % src_vocab_size)
for i, token in enumerate(src_vocab):
# keep the order of tokens specified in the vocab file by
# adding them to the counter with decreasing counting values
counters['src'][token] = src_vocab_size - i
else:
src_vocab = None
if tgt_vocab_path:
tgt_vocab = load_vocabulary(tgt_vocab_path, "tgt")
tgt_vocab_size = len(tgt_vocab)
logger.info('Loaded source vocab has %d tokens.' % tgt_vocab_size)
for i, token in enumerate(tgt_vocab):
counters['tgt'][token] = tgt_vocab_size - i
else:
tgt_vocab = None
for i, path in enumerate(train_dataset_files):
dataset = torch.load(path)
logger.info(" * reloading %s." % path)
for ex in dataset.examples:
for k in fields:
has_vocab = (k == 'src' and src_vocab) or \
(k == 'tgt' and tgt_vocab)
if fields[k].sequential and not has_vocab:
val = getattr(ex, k, None)
counters[k].update(val)
# Drop the none-using from memory but keep the last
if i < len(train_dataset_files) - 1:
dataset.examples = None
gc.collect()
del dataset.examples
gc.collect()
del dataset
gc.collect()
_build_field_vocab(fields["tgt"],
counters["tgt"],
max_size=tgt_vocab_size,
min_freq=tgt_words_min_frequency)
logger.info(" * tgt vocab size: %d." % len(fields["tgt"].vocab))
# All datasets have same num of n_tgt_features,
# getting the last one is OK.
n_tgt_feats = sum('tgt_feat_' in k for k in fields)
for j in range(n_tgt_feats):
key = "tgt_feat_" + str(j)
_build_field_vocab(fields[key], counters[key])
logger.info(" * %s vocab size: %d." % (key, len(fields[key].vocab)))
if data_type == 'text':
_build_field_vocab(fields["src"],
counters["src"],
max_size=src_vocab_size,
min_freq=src_words_min_frequency)
logger.info(" * src vocab size: %d." % len(fields["src"].vocab))
# All datasets have same num of n_src_features,
# getting the last one is OK.
n_src_feats = sum('src_feat_' in k for k in fields)
for j in range(n_src_feats):
key = "src_feat_" + str(j)
_build_field_vocab(fields[key], counters[key])
logger.info(" * %s vocab size: %d." %
(key, len(fields[key].vocab)))
if share_vocab:
# `tgt_vocab_size` is ignored when sharing vocabularies
logger.info(" * merging src and tgt vocab...")
_merge_field_vocabs(fields["src"],
fields["tgt"],
vocab_size=src_vocab_size,
min_freq=src_words_min_frequency)
logger.info(" * merged vocab size: %d." % len(fields["src"].vocab))
return fields
def main(opt):
opt = training_opt_postprocessing(opt)
init_logger(opt.log_file)
# Load checkpoint if we resume from a previous training.
if opt.train_from:
logger.info('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
else:
checkpoint = None
model_opt = opt
# Peek the fisrt dataset to determine the data_type.
# (All datasets have the same data_type).
first_dataset = next(lazily_load_dataset("train", opt))
data_type = first_dataset.data_type
# Load fields generated from preprocess phase.
fields = _load_fields(first_dataset, data_type, opt, checkpoint)
# Report src/tgt features.
src_features, tgt_features = _collect_report_features(fields)
for j, feat in enumerate(src_features):
logger.info(' * src feature %d size = %d' %
(j, len(fields[feat].vocab)))
for j, feat in enumerate(tgt_features):
logger.info(' * tgt feature %d size = %d' %
(j, len(fields[feat].vocab)))
# Build model.
model = build_model(model_opt, opt, fields, checkpoint)
n_params, enc, dec = _tally_parameters(model)
logger.info('encoder: %d' % enc)
logger.info('decoder: %d' % dec)
logger.info('* number of parameters: %d' % n_params)
_check_save_model_path(opt)
# Build optimizer.
optim = build_optim(model, opt, checkpoint)
# Build model saver
model_saver = build_model_saver(model_opt, opt, model, fields, optim)
# todo: notice this
# original trainer
# trainer = build_trainer(
# opt, model, fields, optim, data_type, model_saver=model_saver)
# own trainer
trainer = build_trainer(opt,
model,
fields,
optim,
opt.wpe_pair_size,
model_saver=model_saver)
def train_iter_fct():
return build_dataset_iter(lazily_load_dataset("train", opt), fields,
opt)
def valid_iter_fct():
return build_dataset_iter(lazily_load_dataset("valid", opt), fields,
opt)
# Do training.
trainer.train(train_iter_fct, valid_iter_fct, opt.train_steps,
opt.valid_steps)
if opt.tensorboard:
trainer.report_manager.tensorboard_writer.close()
pass
if os.path.isdir(tmp_dir):
shutil.rmtree(tmp_dir)
def rouge_results_to_str(results_dict):
return ">> ROUGE(1/2/3/L/SU4): {:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}".format(
results_dict["rouge_1_f_score"] * 100,
results_dict["rouge_2_f_score"] * 100,
results_dict["rouge_3_f_score"] * 100,
results_dict["rouge_l_f_score"] * 100,
results_dict["rouge_su*_f_score"] * 100)
if __name__ == "__main__":
init_logger('test_rouge.log')
parser = argparse.ArgumentParser()
parser.add_argument('-c', type=str, default="candidate.txt",
help='candidate file')
parser.add_argument('-r', type=str, default="reference.txt",
help='reference file')
args = parser.parse_args()
if args.c.upper() == "STDIN":
candidates = sys.stdin
else:
candidates = codecs.open(args.c, encoding="utf-8")
references = codecs.open(args.r, encoding="utf-8")
results_dict = test_rouge(candidates, references)
logger.info(rouge_results_to_str(results_dict))
