def initialize(self, embeddings):
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
vocab_sources = [self.dataset['train_file'], self.dataset['valid_file']]
# TODO: make this optional
if 'test_file' in self.dataset:
vocab_sources.append(self.dataset['test_file'])
vocab1, vocab2 = self.reader.build_vocabs(vocab_sources,
min_f=Task._get_min_f(self.config_params),
vocab_file=self.dataset.get('vocab_file'))
# To keep the config file simple, share a list between source and destination (tgt)
features_src = []
features_tgt = None
for feature in self.config_params['features']:
if feature['name'] == 'tgt':
features_tgt = feature
else:
features_src += [feature]
self.src_embeddings, self.feat2src = self._create_embeddings(embeddings_set, vocab1, features_src)
# For now, dont allow multiple vocabs of output
baseline.save_vocabs(self.get_basedir(), self.feat2src)
self.tgt_embeddings, self.feat2tgt = self._create_embeddings(embeddings_set, {'tgt': vocab2}, [features_tgt])
baseline.save_vocabs(self.get_basedir(), self.feat2tgt)
self.tgt_embeddings = self.tgt_embeddings['tgt']
self.feat2tgt = self.feat2tgt['tgt']
def initialize(self, embeddings):
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
vocabs = self.reader.build_vocab(
[self.dataset['train_file'], self.dataset['valid_file'], self.dataset['test_file']],
min_f=Task._get_min_f(self.config_params),
)
self.embeddings, self.feat2index = self._create_embeddings(embeddings_set, vocabs, self.config_params['features'])
baseline.save_vocabs(self.get_basedir(), self.feat2index)
def initialize(self, embeddings):
embeddings_set = mead.utils.index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset, self.data_download_cache, True).download()
print("[train file]: {}\n[valid file]: {}\n[test file]: {}\n[vocab file]: {}".format(self.dataset['train_file'], self.dataset['valid_file'], self.dataset['test_file'], self.dataset.get('vocab_file',"None")))
vocab_file = self.dataset.get('vocab_file',None)
if vocab_file is not None:
vocab1, vocab2 = self.reader.build_vocabs([vocab_file])
else:
vocab1, vocab2 = self.reader.build_vocabs([self.dataset['train_file'], self.dataset['valid_file'], self.dataset['test_file']])
self.embeddings1, self.feat2index1 = self._create_embeddings(embeddings_set, {'word': vocab1})
self.embeddings2, self.feat2index2 = self._create_embeddings(embeddings_set, {'word': vocab2})
def initialize(self, embeddings):
embeddings_set = mead.utils.index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset,
self.data_download_cache).download()
print("[train file]: {}\n[valid file]: {}\n[test file]: {}".format(
self.dataset['train_file'], self.dataset['valid_file'],
self.dataset['test_file']))
vocab, self.num_words = self.reader.build_vocab([
self.dataset['train_file'], self.dataset['valid_file'],
self.dataset['test_file']
])
self.embeddings, self.feat2index = self._create_embeddings(
embeddings_set, vocab)
def initialize(self, embeddings):
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
vocab_sources = [self.dataset['train_file'], self.dataset['valid_file']]
# TODO: make this optional
if 'test_file' in self.dataset:
vocab_sources.append(self.dataset['test_file'])
vocabs = self.reader.build_vocab(vocab_sources,
min_f=Task._get_min_f(self.config_params),
vocab_file=self.dataset.get('vocab_file'))
self.embeddings, self.feat2index = self._create_embeddings(embeddings_set, vocabs, self.config_params['features'])
baseline.save_vocabs(self.get_basedir(), self.feat2index)
def initialize(self, embeddings):
self.dataset = DataDownloader(self.dataset,
self.data_download_cache).download()
print("[train file]: {}\n[valid file]: {}\n[test file]: {}".format(
self.dataset['train_file'], self.dataset['valid_file'],
self.dataset['test_file']))
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
vocabs = self.reader.build_vocab([
self.dataset['train_file'], self.dataset['valid_file'],
self.dataset['test_file']
])
self.embeddings, self.feat2index = self._create_embeddings(
embeddings_set, vocabs)
def train():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--dataset_key",
type=str,
default='wikitext-2',
help="key from DATASETS global")
parser.add_argument("--train_file",
type=str,
help='Optional file path to use for train file')
parser.add_argument("--valid_file",
type=str,
help='Optional file path to use for valid file')
parser.add_argument("--dataset_cache",
type=str,
default=os.path.expanduser('~/.bl-data'),
help="Path or url of the dataset cache")
parser.add_argument("--cache_features", type=str2bool, default=True)
parser.add_argument("--d_model",
type=int,
default=410,
help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2100, help="FFN dimension")
parser.add_argument("--num_heads",
type=int,
default=10,
help="Number of heads")
parser.add_argument("--num_layers",
type=int,
default=16,
help="Number of layers")
parser.add_argument("--nctx",
type=int,
default=256,
help="Max input length")
parser.add_argument("--batch_size", type=int, default=8, help="Batch Size")
parser.add_argument("--tokens",
choices=["words", "chars", "bpe", "wordpiece"],
default="wordpiece",
help="What tokens to use")
parser.add_argument("--subword_model_file",
type=str,
help="If using subwords, pass this",
default='bert-base-cased')
parser.add_argument(
"--subword_vocab_file",
type=str,
help="If using subwords with separate vocab file, pass here")
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=0.25,
help="Clipping gradient norm")
parser.add_argument("--weight_decay",
type=float,
default=0.0,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=20,
help="Num training epochs")
parser.add_argument(
"--restart_from",
type=str,
help="Option allows you to restart from a previous checkpoint")
parser.add_argument("--warmup_steps",
type=int,
default=1000,
help="Num warmup steps")
parser.add_argument("--mlm",
type=str2bool,
default=False,
help="Use Masked Language Model (MLM) objective")
parser.add_argument("--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device (cuda or cpu)")
parser.add_argument("--distributed",
type=str2bool,
default=False,
help="Are we doing distributed training?")
parser.add_argument(
"--local_rank",
type=int,
default=-1,
help=
"Local rank for distributed training (-1 means use the environment variables to find)"
)
parser.add_argument("--chars_per_word",
type=int,
default=40,
help="How many max characters per word")
args = parser.parse_args()
if args.train_file and not args.valid_file:
logger.error(
"If you provide a train_file, you must provide a valid_file")
return
if not args.train_file and args.valid_file:
logger.error(
"If you provide a valid_file, you must also provide a train_file")
return
if args.tokens == "chars" and args.mlm:
logger.error(
"Character composition cannot currently be used with the MLM objective"
)
if args.basedir is None:
args.basedir = 'transformer-{}-{}-{}'.format(args.dataset_key,
args.tokens, os.getpid())
logging.basicConfig(
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info("Cache directory [%s]", args.dataset_cache)
args.distributed = args.distributed or int(os.environ.get("WORLD_SIZE",
1)) > 1
if args.distributed:
if args.local_rank == -1:
# https://github.com/kubeflow/pytorch-operator/issues/128
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
logger.info("Setting local rank to RANK env variable")
args.local_rank = int(os.environ['RANK'])
logger.warning("Local rank (%d)", args.local_rank)
torch.cuda.set_device(args.local_rank)
args.device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
if args.train_file:
dataset = {
'train_file': args.train_file,
'valid_file': args.valid_file
}
else:
dataset = DataDownloader(DATASETS[args.dataset_key],
args.dataset_cache).download()
reader = create_reader(args.tokens, args.nctx, args.chars_per_word,
args.subword_model_file, args.subword_vocab_file)
preproc_data = load_embed_and_vocab(args.tokens, reader, dataset,
args.dataset_key, args.d_model,
args.cache_features)
vocabs = preproc_data['vocabs']
if args.mlm:
mask_from = vocabs['x']
vocab_size = len(mask_from)
mask_value = mask_from.get("[MASK]", mask_from.get("<MASK>", -1))
if mask_value == -1:
logger.error(
"We could not find a suitable masking token in the vocab")
return
os.makedirs(args.basedir, exist_ok=True)
# We want to make sure to save our input vocab into the basedir for reuse later
write_json(vocabs['x'], os.path.join(args.basedir, 'vocabs.json'))
embeddings = preproc_data['embeddings']
valid_num_words = preproc_data['valid_num_words']
tgt_key = preproc_data['tgt_key']
logger.info("Loaded embeddings")
train_set = load_data(args.tokens, reader, dataset, 'train_file', vocabs,
args.cache_features)
valid_set = load_data(args.tokens, reader, dataset, 'valid_file', vocabs,
args.cache_features)
logger.info("valid. tokens [%s], valid. words [%s]",
valid_set.tensors[-1].numel(), valid_num_words)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set) if args.distributed else None
train_loader = DataLoader(train_set,
sampler=train_sampler,
batch_size=args.batch_size,
shuffle=(not args.distributed))
valid_loader = DataLoader(valid_set,
batch_size=args.batch_size,
shuffle=False)
logger.info("Loaded datasets")
model = TransformerLanguageModel.create(
embeddings,
hsz=args.d_model,
d_ff=args.d_ff,
tie_weights=(args.tokens != 'chars'),
dropout=args.dropout,
gpu=False,
num_heads=args.num_heads,
layers=args.num_layers,
src_keys=['x'],
tgt_key=tgt_key)
model.to(args.device)
loss_function = model.create_loss()
loss_function.to(args.device)
logger.info("Loaded model and loss")
steps_per_epoch = len(train_loader)
update_on = steps_per_epoch // 10
cosine_decay = CosineDecaySchedulerPyTorch(len(train_loader) * args.epochs,
lr=args.lr)
linear_warmup = WarmupLinearSchedulerPyTorch(args.warmup_steps, lr=args.lr)
lr_sched = CompositeLRScheduler(linear_warmup, cosine_decay, lr=args.lr)
global_step = 0
start_epoch = 0
if args.restart_from:
model.load_state_dict(torch.load(args.restart_from))
start_epoch = int(args.restart_from.split("-")[-1].split(".")[0]) - 1
global_step = (start_epoch + 1) * steps_per_epoch
logger.info(
"Restarting from a previous checkpoint %s.\n\tStarting at global_step=%d, epoch=%d",
args.restart_from, global_step, start_epoch + 1)
optimizer = OptimizerManager(model,
global_step,
optim='adam',
lr=args.lr,
lr_function=lr_sched,
weight_decay=args.weight_decay)
logger.info("Model has {:,} parameters".format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
# Prepare model for distributed training if needed
if args.distributed:
model = DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
logger.info("Model located on %d", args.local_rank)
# This is the training loop
for epoch in range(start_epoch, args.epochs):
avg_loss = Average('average_train_loss')
metrics = {}
optimizer.zero_grad()
if args.distributed:
train_sampler.set_epoch(epoch)
start = time.time()
model.train()
for i, batch in enumerate(train_loader):
x, y = batch
inputs = {'x': x.to(args.device)}
labels = y.to(args.device)
if args.mlm:
# Replace 15% of tokens
masked_indices = torch.bernoulli(torch.full(
labels.shape, 0.15)).byte()
# Anything not masked is 0 so no loss
labels[~masked_indices] = 0
# Of the masked items, mask 80% of them with [MASK]
indices_replaced = torch.bernoulli(
torch.full(labels.shape, 0.8)).byte() & masked_indices
inputs[indices_replaced] = mask_value
# Replace 10% of them with random words, rest preserved for auto-encoding
indices_random = torch.bernoulli(torch.full(
labels.shape,
0.5)).byte() & masked_indices & ~indices_replaced
random_words = torch.randint(vocab_size,
labels.shape,
dtype=torch.long,
device=args.device)
inputs[indices_random] = random_words[indices_random]
labels = labels.transpose(0, 1).contiguous()
logits = model(inputs, None)[0].transpose(0, 1).contiguous()
if args.mlm:
loss = loss_function(logits, labels)
else:
shift_logits = logits[:-1]
shift_labels = labels[1:]
loss = loss_function(shift_logits, shift_labels)
loss.backward()
avg_loss.update(loss.item())
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
optimizer.zero_grad()
if (i + 1) % update_on == 0:
logging.info(avg_loss)
# How much time elapsed in minutes
elapsed = (time.time() - start) / 60
train_token_loss = avg_loss.avg
# This is the average training token-level loss across all machines
# This is the token-level training perplexity
train_token_ppl = math.exp(train_token_loss)
metrics['train_elapsed_min'] = elapsed
metrics['average_train_loss'] = train_token_loss
metrics['train_ppl'] = train_token_ppl
model_base = os.path.join(args.basedir, 'checkpoint')
avg_valid_loss = Average('average_valid_loss')
start = time.time()
model.eval()
for batch in valid_loader:
with torch.no_grad():
x, y = batch
inputs = {'x': x.to(args.device)}
labels = y.to(args.device)
if args.mlm:
# Replace 15% of tokens
masked_indices = torch.bernoulli(
torch.full(labels.shape, 0.15)).byte()
# Anything not masked is 0 so no loss
labels[~masked_indices] = 0
# Of the masked items, mask 80% of them with [MASK]
indices_replaced = torch.bernoulli(
torch.full(labels.shape, 0.8)).byte() & masked_indices
inputs[indices_replaced] = mask_value
# Replace 10% of them with random work
indices_random = torch.bernoulli(
torch.full(
labels.shape,
0.5)).byte() & masked_indices & ~indices_replaced
random_words = torch.randint(vocab_size,
labels.shape,
dtype=torch.long,
device=args.device)
inputs[indices_random] = random_words[indices_random]
labels = labels.transpose(0, 1).contiguous()
logits = model(inputs, None)[0].transpose(0, 1).contiguous()
if args.mlm:
loss = loss_function(logits, labels)
else:
shift_logits = logits[:-1]
shift_labels = labels[1:]
loss = loss_function(shift_logits, shift_labels)
avg_valid_loss.update(loss.item())
valid_token_loss = avg_valid_loss.avg
valid_token_ppl = math.exp(valid_token_loss)
elapsed = (time.time() - start) / 60
metrics['valid_elapsed_min'] = elapsed
metrics['average_valid_loss'] = valid_token_loss
if args.tokens in ['bpe', 'wordpiece']:
metrics['valid_token_ppl'] = valid_token_ppl
metrics['average_valid_word_ppl'] = math.exp(
valid_token_loss * valid_set.tensors[-1].numel() /
valid_num_words)
else:
metrics['average_valid_word_ppl'] = valid_token_ppl
logger.info(metrics)
if args.local_rank < 1:
# Should probably do this more often
checkpoint_name = checkpoint_for(model_base, epoch + 1)
logger.info("Creating checkpoint: %s", checkpoint_name)
if args.distributed:
torch.save(model.module.state_dict(), checkpoint_name)
else:
torch.save(model.state_dict(), checkpoint_name)
rm_old_checkpoints(model_base, epoch + 1)
parser.add_argument('--datasets',
help='json library of dataset labels',
default='config/datasets.json',
type=convert_path)
parser.add_argument('--embeddings',
help='json library of embeddings',
default='config/embeddings.json',
type=convert_path)
args = parser.parse_args()
datasets = read_json(args.datasets)
datasets = index_by_label(datasets)
for name, d in datasets.items():
print(name)
try:
DataDownloader(d, args.cache).download()
except Exception as e:
print(e)
emb = read_json(args.embeddings)
emb = index_by_label(emb)
for name, e in emb.items():
print(name)
try:
EmbeddingDownloader(e['file'], e['dsz'], e.get('sha1'),
args.cache).download()
except Exception as e:
print(e)
def train():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--dataset_key",
type=str,
default='wikitext-2',
help="key from DATASETS global")
parser.add_argument("--train_file",
type=str,
help='Optional file path to use for train file')
parser.add_argument("--valid_file",
type=str,
help='Optional file path to use for valid file')
parser.add_argument("--dataset_cache",
type=str,
default=os.path.expanduser('~/.bl-data'),
help="Path or url of the dataset cache")
parser.add_argument("--cache_features", type=str2bool, default=True)
parser.add_argument("--d_model",
type=int,
default=410,
help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2100, help="FFN dimension")
parser.add_argument("--num_heads",
type=int,
default=10,
help="Number of heads")
parser.add_argument("--num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--nctx",
type=int,
default=256,
help="Max input length")
parser.add_argument("--batch_size", type=int, default=8, help="Batch Size")
parser.add_argument("--tokens",
choices=["words", "chars", "subwords"],
default="subwords",
help="What tokens to use")
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=0.25,
help="Clipping gradient norm")
parser.add_argument("--weight_decay",
type=float,
default=0.0,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=20,
help="Num training epochs")
parser.add_argument("--warmup_steps",
type=int,
default=1000,
help="Num warmup steps")
parser.add_argument("--eval_every",
type=int,
default=-1,
help="Evaluate every X steps (-1 => end of epoch)")
parser.add_argument("--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device (cuda or cpu)")
parser.add_argument("--distributed",
type=str2bool,
default=False,
help="Are we doing distributed training?")
parser.add_argument(
"--local_rank",
type=int,
default=-1,
help=
"Local rank for distributed training (-1 means use the environment variables to find)"
)
parser.add_argument("--chars_per_word",
type=int,
default=40,
help="How many max characters per word")
parser.add_argument(
"--accum_grad_steps",
type=int,
default=1,
help="Create effective batch size by accumulating grads without updates"
)
args = parser.parse_args()
if args.train_file and not args.valid_file:
logger.error(
"If you provide a train_file, you must provide a valid_file")
return
if not args.train_file and args.valid_file:
logger.error(
"If you provide a valid_file, you must also provide a train_file")
return
if args.basedir is None:
args.basedir = 'transformer-{}-{}-{}'.format(args.dataset_key,
args.tokens, os.getpid())
logging.basicConfig(
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info("Cache directory [%s]", args.dataset_cache)
args.distributed = args.distributed or int(os.environ.get("WORLD_SIZE",
1)) > 1
if args.distributed:
if args.local_rank == -1:
# https://github.com/kubeflow/pytorch-operator/issues/128
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
logger.info("Setting local rank to RANK env variable")
args.local_rank = int(os.environ['RANK'])
logger.warning("Local rank (%d)", args.local_rank)
torch.cuda.set_device(args.local_rank)
args.device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
if args.train_file:
dataset = {
'train_file': args.train_file,
'valid_file': args.valid_file
}
else:
dataset = DataDownloader(DATASETS[args.dataset_key],
args.dataset_cache).download()
reader = create_reader(args.tokens, args.nctx, args.chars_per_word)
preproc_data = load_embed_and_vocab(args.tokens, reader, dataset,
args.dataset_key, args.d_model,
args.cache_features)
vocabs = preproc_data['vocabs']
os.makedirs(args.basedir, exist_ok=True)
# We want to make sure to save our input vocab into the basedir for reuse later
write_json(vocabs['x'], os.path.join(args.basedir, 'vocabs.json'))
embeddings = preproc_data['embeddings']
valid_num_words = preproc_data['valid_num_words']
tgt_key = preproc_data['tgt_key']
logger.info("Loaded embeddings")
train_set = load_data(args.tokens, reader, dataset, 'train_file', vocabs,
args.cache_features)
valid_set = load_data(args.tokens, reader, dataset, 'valid_file', vocabs,
args.cache_features)
logger.info("valid. tokens [%s], valid. words [%s]",
valid_set.tensors[-1].numel(), valid_num_words)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set) if args.distributed else None
train_loader = DataLoader(train_set,
sampler=train_sampler,
batch_size=args.batch_size,
shuffle=(not args.distributed))
valid_sampler = torch.utils.data.distributed.DistributedSampler(
valid_set) if args.distributed else None
valid_loader = DataLoader(valid_set,
sampler=valid_sampler,
batch_size=args.batch_size,
shuffle=False)
logger.info("Loaded datasets")
model = TransformerLanguageModel.create(
embeddings,
hsz=args.d_model,
d_ff=args.d_ff,
tie_weights=(args.tokens != 'chars'),
dropout=args.dropout,
gpu=False,
num_heads=args.num_heads,
layers=args.num_layers,
src_keys=['x'],
tgt_key=tgt_key)
model.to(args.device)
train_loss = model.create_loss()
train_loss.to(args.device)
logger.info("Loaded model and loss")
optimizer = Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
logger.info("Model has %s parameters",
sum(p.numel() for p in model.parameters() if p.requires_grad))
# Prepare model for distributed training if needed
if args.distributed:
model = DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
logger.info("Model located on %d", args.local_rank)
def update(engine, batch):
model.train()
x, y = batch
inputs = {'x': x.to(args.device)}
labels = y.to(args.device).transpose(0, 1).contiguous()
logits = model(inputs, None)[0].transpose(0, 1).contiguous()
shift_logits = logits[:-1]
shift_labels = labels[1:]
loss = train_loss(shift_logits, shift_labels)
loss = loss / args.accum_grad_steps
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
if engine.state.iteration % args.accum_grad_steps == 0:
optimizer.step()
optimizer.zero_grad()
return loss.item()
trainer = Engine(update)
def inference(_, batch):
model.eval()
with torch.no_grad():
x, y = batch
inputs = {'x': x.to(args.device)}
labels = y.to(args.device).transpose(0, 1).contiguous()
logits = model(inputs, None)[0].transpose(0, 1).contiguous()
shift_logits = logits[:-1]
shift_labels = labels[1:]
return shift_logits.view(-1,
logits.size(-1)), shift_labels.view(-1)
evaluator = Engine(inference)
# Attach evaluation to trainer: we evaluate at the end of each epoch and every 'eval_every' iterations if needed
trainer.add_event_handler(Events.EPOCH_COMPLETED,
lambda _: evaluator.run(valid_loader))
if args.eval_every > 0:
trainer.add_event_handler(
Events.ITERATION_COMPLETED,
lambda engine: evaluator.run(valid_loader)
if engine.state.iteration % args.eval_every == 0 else None)
if args.distributed:
trainer.add_event_handler(
Events.EPOCH_STARTED,
lambda engine: train_sampler.set_epoch(engine.state.epoch))
evaluator.add_event_handler(
Events.EPOCH_STARTED,
lambda engine: valid_sampler.set_epoch(engine.state.epoch))
cos_scheduler = CosineAnnealingScheduler(optimizer, 'lr', args.lr, 0.0,
len(train_loader) * args.epochs)
scheduler = create_lr_scheduler_with_warmup(cos_scheduler, 0.0, args.lr,
args.warmup_steps)
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
metrics = {"nll": Loss(torch.nn.CrossEntropyLoss(ignore_index=-1))}
metrics.update({
"average_nll":
MetricsLambda(average_distributed_scalar, metrics["nll"], args)
})
if args.tokens == 'subwords':
# If we compute subwords, need to renormalize for num words
metrics["average_subword_ppl"] = MetricsLambda(math.exp,
metrics["average_nll"])
metrics["average_word_ppl"] = MetricsLambda(
lambda x: math.exp(x * valid_set.tensors[-1].numel() /
valid_num_words), metrics["average_nll"])
else:
metrics["average_word_ppl"] = MetricsLambda(math.exp,
metrics["average_nll"])
for name, metric in metrics.items():
metric.attach(evaluator, name)
if args.local_rank < 1:
RunningAverage(output_transform=lambda x: x).attach(
trainer, "valid_loss")
trainer.add_event_handler(
Events.EPOCH_COMPLETED, lambda _: print(
"Epoch[{}] Training Loss: {:.2f}, Perplexity {:.2f}".format(
trainer.state.epoch, trainer.state.output,
np.exp(trainer.state.output))))
evaluator.add_event_handler(
Events.COMPLETED, lambda _: print("Validation: %s" % pformat(
evaluator.state.metrics)))
checkpoint_handler = ModelCheckpoint(args.basedir,
'checkpoint',
save_interval=1,
n_saved=3,
create_dir=False)
trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpoint_handler,
{'mymodel': getattr(model, 'module', model)})
trainer.run(train_loader, max_epochs=args.epochs)
