def validate(model, loss_function, valid_loader, avg_train_loss, train_timer,
metrics, do_on_demand_masking, mlm, mask_value, vocab_size,
device):
train_token_loss = avg_train_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'] = train_timer.elapsed(True)
metrics['average_train_loss'] = train_token_loss
metrics['train_ppl'] = train_token_ppl
avg_valid_loss = Average('average_valid_loss')
valid_timer = Timer()
valid_timer.start()
model.eval()
valid_steps = len(valid_loader)
valid_itr = iter(valid_loader)
for j in range(valid_steps):
batch = next(valid_itr)
with torch.no_grad():
x, y = batch
inputs = x.to(device)
labels = y.to(device)
if do_on_demand_masking:
inputs, labels, _ = on_demand_mlm_masking(
inputs, labels, mask_value, vocab_size)
inputs = {'x': inputs}
labels = labels.contiguous()
logits = model(inputs, None)[0].contiguous()
if 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)
metrics['valid_elapsed_min'] = valid_timer.elapsed(True)
metrics['average_valid_loss'] = valid_token_loss
metrics['average_valid_word_ppl'] = valid_token_ppl
logger.info(metrics)
return valid_token_loss
def main():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--train_dir",
type=str,
required=True,
help='Training directory')
parser.add_argument("--valid_dir",
type=str,
required=True,
help='Validation directory')
parser.add_argument(
"--train_md",
type=str,
help="Training metadata YAML, defaults to `{train_dir}/md.yml`")
parser.add_argument(
"--valid_md",
type=str,
help="Validation metadata YAML, defaults to `{valid_dir}/md.yml`")
parser.add_argument("--label_file",
type=str,
help="JSON file mapping labels to integers",
default="labels.json")
parser.add_argument("--dataset_key",
default="tlm",
help="dataset key for basedir")
parser.add_argument(
"--embed_type",
type=str,
default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument("--d_model",
type=int,
default=512,
help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2048, help="FFN dimension")
parser.add_argument("--num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--num_train_workers",
type=int,
default=4,
help="Number train workers")
parser.add_argument("--distribute",
type=str,
default="mirror",
choices=["mirror", "tpu", "nccl"])
parser.add_argument("--tpu_ep",
type=str,
help="The TPU endpoint if using `distribute=tpu`")
parser.add_argument("--nctx",
type=int,
default=256,
help="Max input length")
parser.add_argument("--file_type",
default='tfrecord',
choices=['json', 'tfrecord'],
help="Glob pattern for data")
parser.add_argument("--batch_size",
type=int,
default=256,
help="Batch Size")
parser.add_argument("--subword_model_file",
type=str,
help="The BPE model file",
required=True)
parser.add_argument("--subword_vocab_file",
type=str,
help="The BPE subword vocab",
required=True)
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--ffn_pdrop",
type=float,
default=0.0,
help="Dropout in the dense stack")
parser.add_argument("--layer_drop",
type=float,
default=0.0,
help="LayerDrop to apply")
parser.add_argument("--optim",
default="adamw",
type=str,
help="Optimizer to use (defaults to adamw)")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=1.0,
help="Clipping gradient norm")
parser.add_argument("--weight_decay",
type=float,
default=1.0e-2,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=32,
help="Num training epochs")
parser.add_argument(
"--restart",
type=str2bool,
help="Option allows you to restart from a previous checkpoint")
parser.add_argument("--warmup_steps",
type=int,
default=10000,
help="Num warmup steps")
parser.add_argument("--saves_per_epoch",
type=int,
default=10,
help="The number of checkpoints to save per epoch")
parser.add_argument(
'--rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
parser.add_argument(
'--rpr_value_on',
type=str2bool,
default=True,
help=
"In relative attention, whether add positional correction to values in addition to the "
"correction to attention matrix")
parser.add_argument('--windowed_ra',
type=str2bool,
default=False,
help="whether prevent attention beyond rpr_k")
parser.add_argument("--strategy",
help="Training strategy, defaults to `mirror`",
choices=["mirror"])
parser.add_argument("--npz",
help="Should we write out NPZ files?",
type=str2bool,
default=False)
parser.add_argument("--tb",
help="Turn on tensorboard?",
type=str2bool,
default=False)
parser.add_argument(
"--convert_only",
help="Should we just convert this file to NPZ and exit?",
type=str2bool,
default=False)
args = parser.parse_args()
SET_TRAIN_FLAG(True)
if args.convert_only:
args.restart = True
if args.basedir is None:
args.basedir = f'lm-{args.dataset_key}-bpe-{os.getpid()}'
logging.basicConfig(level=logging.INFO)
logger.info(f"Writing results to {args.basedir}")
if args.tb:
logdir = f"logs/scalars/{os.getpid()}"
file_writer = tf.summary.create_file_writer(logdir + "/metrics")
file_writer.set_as_default()
logger.info(f"Set up tensorboard logdir {logdir}")
strategy = create_distribute_strategy(args.distribute, args.tpu_ep)
num_replicas = strategy.num_replicas_in_sync
logger.info(f"Using {num_replicas} replicas in this job.")
vectorizer = BPEVectorizer1D(model_file=args.subword_model_file,
vocab_file=args.subword_vocab_file,
mxlen=args.nctx)
vocab = {'x': vectorizer.vocab}
preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=args.d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=args.embed_type)
vocabs = preproc_data['vocab']
train_md = args.train_md if args.train_md else os.path.join(
args.train_dir, 'md.yml')
num_train_samples = get_num_samples(train_md)
valid_md = args.valid_md if args.valid_md else os.path.join(
args.valid_dir, 'md.yml')
num_valid_samples = get_num_samples(valid_md)
labels = read_json_tf(args.label_file)
num_labels = len(labels)
def dataset_train_fn(input_context):
global_batchsz = args.batch_size
base_batchsz = input_context.get_per_replica_batch_size(global_batchsz)
ds = get_dataset(args.train_dir, args.file_type,
args.num_train_workers).batch(base_batchsz)
return ds.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
train_loader = strategy.experimental_distribute_datasets_from_function(
dataset_train_fn)
def dataset_test_fn(input_context):
global_batchsz = args.batch_size
base_batchsz = input_context.get_per_replica_batch_size(global_batchsz)
ds = get_dataset(args.valid_dir,
args.file_type,
args.num_train_workers,
shuffle=False).batch(base_batchsz)
return ds.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
valid_loader = strategy.experimental_distribute_datasets_from_function(
dataset_test_fn)
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, os.path.join(args.basedir, 'vocabs.json'))
embeddings = {'x': preproc_data['embeddings']}
logger.info("Loaded embeddings")
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
if len(args.rpr_k) == 0 or args.rpr_k[0] < 1:
args.rpr_k = None
elif len(args.rpr_k) == 1:
args.rpr_k = args.rpr_k[0]
model = TransformerTagger(num_labels, embeddings, **vars(args))
logger.info("Loaded model and loss")
steps_per_epoch = num_train_samples // args.batch_size
steps_per_valid_epoch = num_valid_samples // args.batch_size
update_on = steps_per_epoch // args.saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(
f"Steps per epoch: {steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = CosineDecaySchedulerTensorFlow(steps_per_epoch * args.epochs,
lr=args.lr)
linear_warmup = WarmupLinearSchedulerTensorFlow(args.warmup_steps,
lr=args.lr)
lr_sched = CompositeLRSchedulerTensorFlow(linear_warmup, lr_decay)
optimizer = EagerOptimizer(loss_function,
optim=args.optim,
lr_function=lr_sched,
weight_decay=args.weight_decay,
clip=args.clip,
lr=args.lr)
checkpoint = tf.train.Checkpoint(optimizer=optimizer.optimizer,
model=model)
checkpoint_manager = tf.train.CheckpointManager(checkpoint,
directory=args.basedir,
max_to_keep=5)
start_epoch = 0
if args.restart:
# The global step gets automatically updated here
# so we dont have to worry about our LR regimen
checkpoint.restore(checkpoint_manager.latest_checkpoint)
current_step = optimizer.global_step
start_epoch = current_step // steps_per_epoch
def _replicated_train_step(inputs):
"""This runs on a single replica"""
x, y = inputs
per_replica_loss = optimizer.update(model, {'x': x}, y, num_replicas)
return per_replica_loss
@tf.function
def _distributed_train_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
per_replica_loss = strategy.run(_replicated_train_step,
args=(inputs, ))
return strategy.reduce(tf.distribute.ReduceOp.SUM,
per_replica_loss,
axis=None)
def _replicated_test_step(inputs):
"""This runs on a single replica"""
x, y = inputs
per_replica_loss = loss_function(model, {'x': x}, y) / num_replicas
return per_replica_loss
@tf.function
def _distributed_test_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
per_replica_loss = strategy.run(_replicated_test_step, args=(inputs, ))
return strategy.reduce(tf.distribute.ReduceOp.SUM,
per_replica_loss,
axis=None)
timer = Timer()
with strategy.scope():
for epoch in range(start_epoch, args.epochs):
SET_TRAIN_FLAG(True)
logger.info('Starting epoch %d', epoch + 1)
avg_loss = Average('average_train_loss')
metrics = {}
timer.start()
train_iter = iter(train_loader)
for i in range(steps_per_epoch):
try:
loss = _distributed_train_step(next(train_iter))
avg_loss.update(loss.numpy().item())
tf.summary.scalar("train_loss",
data=loss,
step=optimizer.global_step)
except Exception as e:
logger.error(
f"Exception at training step {i+1}/{steps_per_epoch}. Skipping"
)
pass
if args.convert_only:
logger.warning(
"Convert only flag specified. Stopping after one step"
)
steps = optimizer.global_step.numpy()
npz_checkpoint = os.path.join(
args.basedir, f'checkpoint-step-{steps}.npz')
save_tlm_output_npz(model, npz_checkpoint)
return
steps = optimizer.global_step.numpy()
if (steps + 1) % report_on == 0:
logger.info(avg_loss)
if (steps + 1) % update_on == 0:
elapsed = timer.elapsed(True)
logger.info('elapsed time this epoch %d min', elapsed)
logger.info('elapsed step time %f steps/min', i / elapsed)
checkpoint_manager.save()
if args.npz:
npz_checkpoint = os.path.join(
args.basedir, f'checkpoint-step-{steps}.npz')
save_tlm_output_npz(model, npz_checkpoint)
# How much time elapsed in minutes
elapsed = timer.elapsed(True)
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
metrics['lr'] = float(
lr_sched(tf.cast(optimizer.global_step,
tf.float32)).numpy().item())
avg_valid_loss = Average('average_valid_loss')
timer.start()
SET_TRAIN_FLAG(False)
valid_iter = iter(valid_loader)
for i in range(steps_per_valid_epoch):
try:
valid_loss = _distributed_test_step(next(valid_iter))
tf.summary.scalar('valid_loss',
data=valid_loss,
step=optimizer.global_step)
avg_valid_loss.update(valid_loss.numpy().item())
except Exception as e:
logger.error(
f"Exception at validation step {i+1}/{steps_per_valid_epoch}. Skipping"
)
pass
valid_token_loss = avg_valid_loss.avg
valid_token_ppl = math.exp(valid_token_loss)
elapsed = timer.elapsed(True)
metrics['valid_elapsed_min'] = elapsed
metrics['average_valid_loss'] = valid_token_loss
metrics['average_valid_word_ppl'] = valid_token_ppl
logger.info(json.dumps(metrics, indent=4))
return l, h
def repackage_hidden(h):
"""Wraps hidden states in new Variables, to detach them from their history."""
if h is None:
return None
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
optimizer = EagerOptimizer(loss, optim="sgd", lr=args.lr)
timer = Timer()
for epoch in range(args.epochs):
loss_accum = 0.
step = 0
timer.start()
h = None
for x, y in train_set.get_input(training=True):
# Optimize the model
if h is not None:
h = repackage_hidden(h)
loss_value, h = optimizer.update_with_hidden(model, h, x, y)
loss_accum += loss_value
step += 1
print(f'training time {timer.elapsed()}')
def train():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--train_file",
type=str,
required=True,
help='File path to use for train file')
parser.add_argument("--valid_file",
type=str,
required=True,
help='File path to use for valid file')
parser.add_argument("--dataset_key",
default="tlm",
help="dataset key for basedir")
parser.add_argument(
"--embed_type",
type=str,
default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument("--d_model",
type=int,
default=512,
help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2048, help="FFN dimension")
parser.add_argument(
"--d_k",
type=int,
default=None,
help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--num_train_workers",
type=int,
default=4,
help="Number train workers")
parser.add_argument("--nctx",
type=int,
default=256,
help="Max input length")
parser.add_argument("--file_type",
default='json',
help="Glob pattern for data")
parser.add_argument("--batch_size",
type=int,
default=256,
help="Batch Size")
parser.add_argument("--subword_model_file",
type=str,
help="The BPE model file",
required=True)
parser.add_argument("--subword_vocab_file",
type=str,
help="The BPE subword vocab",
required=True)
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--ffn_pdrop",
type=float,
default=0.0,
help="Dropout in the dense stack")
parser.add_argument("--layer_drop",
type=float,
default=0.0,
help="LayerDrop to apply")
parser.add_argument("--lr_scheduler",
type=str,
default='cosine',
help="The type of learning rate decay scheduler")
parser.add_argument("--lr_decay_steps",
type=int,
help="decay steps of lr scheduler")
parser.add_argument("--lr_decay_rate",
type=float,
help="decay rate of lr scheduler")
parser.add_argument("--lr_alpha",
type=float,
help="parameter alpha for cosine decay scheduler")
parser.add_argument("--optim",
default="adamw",
type=str,
help="Optimizer to use (defaults to adamw)")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=1.0,
help="Clipping gradient norm")
parser.add_argument("--weight_decay",
type=float,
default=1.0e-2,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=32,
help="Num training epochs")
parser.add_argument(
"--restart_from",
type=str,
help="Option allows you to restart from a previous checkpoint")
parser.add_argument("--restart_tt",
type=str,
help="Optional param for legacy checkpoints",
choices=['step', 'epoch', 'ignore'])
parser.add_argument("--warmup_steps",
type=int,
default=10000,
help="Num warmup steps")
parser.add_argument("--saves_per_epoch",
type=int,
default=10,
help="The number of checkpoints to save per epoch")
parser.add_argument("--mlm",
type=str2bool,
default=True,
help="Use Masked Language Model (MLM) objective")
parser.add_argument("--preprocessed",
type=str2bool,
default=True,
help="Has the data already been preprocessed?")
parser.add_argument(
'--rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
parser.add_argument(
'--rpr_value_on',
type=str2bool,
default=True,
help=
"In relative attention, whether add positional correction to values in addition to the "
"correction to attention matrix")
parser.add_argument("--windowed_ra",
type=str2bool,
default=False,
help="whether prevent attention beyond rpr_k")
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)"
)
args = parser.parse_args()
if args.basedir is None:
args.basedir = 'lm-{}-bpe-{}'.format(args.dataset_key, os.getpid())
logging.basicConfig(
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
num_gpus = get_num_gpus_multiworker()
args.distributed = args.distributed or num_gpus > 1
logger.info(f"Using {num_gpus} GPUs in this job.")
do_on_demand_masking = args.mlm and not args.preprocessed
if do_on_demand_masking:
logger.info(f"On-demand masking is turned on")
if args.distributed:
args.device, updated_local_rank = init_distributed(args.local_rank)
args.local_rank = updated_local_rank
if args.file_type == 'tfrecord':
reader_type = 'tfrecord'
elif args.preprocessed:
reader_type = 'preprocessed'
else:
reader_type = 'lang'
reader = MultiFileDatasetReader(
src_nctx=args.nctx,
model_file=args.subword_model_file,
vocab_file=args.subword_vocab_file,
file_type=args.file_type,
reader_type=reader_type,
record_keys=['x', 'y'] if args.mlm else ['x'])
# This looks a bit funny but the streaming reader ignores our vocab and gives us the one from the subword_model
# However, we do need to get counts from our dataset for validation so we can calculate the perplexity
vocab = reader.build_vocab([args.valid_file])
# If we are not using chars, then use 'x' for both input and output
preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=args.d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=args.embed_type)
vocabs = preproc_data['vocab']
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, os.path.join(args.basedir, 'vocabs.json'))
embeddings = {'x': preproc_data['embeddings']}
logger.info("Loaded embeddings")
train_set = reader.load(args.train_file, vocabs)
valid_set = reader.load(args.valid_file,
vocabs,
distribute=False,
shuffle=False)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
num_workers=args.num_train_workers)
valid_loader = DataLoader(valid_set, batch_size=args.batch_size)
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
if args.mlm:
mask_from = vocabs
vocab_size = len(mask_from)
mask_value = mask_from.get("[MASK]")
if mask_value == -1:
logger.error(
"We could not find a suitable masking token in the vocab")
return
if len(args.rpr_k) == 0 or args.rpr_k[0] < 1:
rpr_k = None
elif len(args.rpr_k) == 1:
rpr_k = args.rpr_k[0]
else:
rpr_k = args.rpr_k
TLM = TransformerMaskedLanguageModel if args.mlm else TransformerLanguageModel
model = TLM.create(embeddings,
hsz=args.d_model,
d_ff=args.d_ff,
tie_weights=True,
dropout=args.dropout,
gpu=False,
num_heads=args.num_heads,
layers=args.num_layers,
rpr_k=rpr_k,
d_k=args.d_k,
ffn_pdrop=args.ffn_pdrop,
windowed_ra=args.windowed_ra,
rpr_value_on=args.rpr_value_on,
layer_drop=args.layer_drop,
src_keys=['x'],
tgt_key='x')
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) // num_gpus
valid_steps = len(valid_loader)
update_on = steps_per_epoch // args.saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(
f"Steps per epoch per GPU: {steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = get_lr_decay(args.lr_scheduler,
args.lr,
steps_per_epoch,
args.epochs,
logger,
decay_steps=args.lr_decay_steps,
decay_rate=args.lr_decay_rate,
alpha=args.lr_alpha)
linear_warmup = WarmupLinearSchedulerPyTorch(args.warmup_steps, lr=args.lr)
lr_sched = CompositeLRScheduler(linear_warmup, lr_decay, lr=args.lr)
global_step = 0
start_epoch = 0
if args.restart_from:
if args.restart_from.endswith('npz'):
load_tlm_npz(model, args.restart_from)
else:
model.load_state_dict(torch.load(args.restart_from))
vec = args.restart_from.split("-")
if args.restart_tt:
tick_type = args.restart_tt
else:
tick_type = vec[-2]
step_num = int(vec[-1].split(".")[0])
if tick_type == 'epoch':
start_epoch = step_num
global_step = start_epoch * steps_per_epoch
elif tick_type == 'step':
start_epoch = step_num // steps_per_epoch
global_step = step_num
else:
logger.warning(
f"The previous tick was {step_num} but command-line specifies to ignore, setting to 0"
)
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=args.optim,
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:
# This program assume pure data parallelism, each model is on a single gpu
# If we wanted to support model and data parallelism we would need to update
# the selection of gpus based on rank, it would need to select multiple ids
# based on rank, here we select only a single gpu and use it for input and
# output.
model = DistributedDataParallel(model,
device_ids=[args.device],
output_device=args.device)
logger.info("Model located on %s", args.device)
model_base = os.path.join(args.basedir, 'checkpoint')
steps = global_step
timer = Timer()
for epoch in range(start_epoch, args.epochs):
avg_loss = Average('average_train_loss')
metrics = {}
optimizer.zero_grad()
timer.start()
model.train()
train_itr = iter(train_loader)
for i in range(steps_per_epoch):
batch = next(train_itr)
steps += 1
x, y = batch
inputs = x.to(args.device)
labels = y.to(args.device)
if do_on_demand_masking:
inputs, labels, _ = on_demand_mlm_masking(
inputs, labels, mask_value, vocab_size)
inputs = {'x': inputs}
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) % report_on == 0:
logging.info(avg_loss)
if (i + 1) % update_on == 0 and args.local_rank < 1:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
logging.info('LR: %f', optimizer.current_lr)
save_checkpoint(model, model_base, steps, tick_type='step')
# How much time elapsed in minutes
elapsed = timer.elapsed(True)
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
if args.local_rank < 1:
avg_valid_loss = Average('average_valid_loss')
timer.start()
model.eval()
valid_itr = iter(valid_loader)
for j in range(valid_steps):
batch = next(valid_itr)
with torch.no_grad():
x, y = batch
inputs = x.to(args.device)
labels = y.to(args.device)
if do_on_demand_masking:
inputs, labels, _ = on_demand_mlm_masking(
inputs, labels, mask_value, vocab_size)
inputs = {'x': inputs}
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)
metrics['valid_elapsed_min'] = timer.elapsed(True)
metrics['average_valid_loss'] = valid_token_loss
metrics['average_valid_word_ppl'] = valid_token_ppl
logger.info(metrics)
save_checkpoint(model, model_base, epoch, save_npz=True)
def train():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--train_dir",
type=str,
required=True,
help='Training directory')
parser.add_argument("--valid_dir",
type=str,
required=True,
help='Validation directory')
parser.add_argument(
"--train_md",
type=str,
help="Training metadata YAML, defaults to `{train_dir}/md.yml`")
parser.add_argument(
"--valid_md",
type=str,
help="Validation metadata YAML, defaults to `{valid_dir}/md.yml`")
parser.add_argument("--dataset_key",
default="tlm",
help="dataset key for basedir")
parser.add_argument(
"--embed_type",
type=str,
default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument("--gen_d_model",
type=int,
default=256,
help="Model dimension (and embedding dsz)")
parser.add_argument("--gen_d_ff",
type=int,
default=1024,
help="FFN dimension")
parser.add_argument(
"--gen_d_k",
type=int,
default=None,
help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--gen_num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--gen_num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument(
'--gen_rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
parser.add_argument('--windowed_ra',
type=str2bool,
default=False,
help="whether prevent attention beyond rpr_k")
parser.add_argument("--gen_loss_scale",
type=float,
default=50.0,
help="Scaling for loss function")
parser.add_argument("--gen_dropout",
type=float,
default=0.1,
help="Dropout")
parser.add_argument(
'--discrim_rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
parser.add_argument("--discrim_d_model",
type=int,
default=512,
help="Model dimension (and embedding dsz)")
parser.add_argument("--discrim_d_ff",
type=int,
default=2048,
help="FFN dimension")
parser.add_argument(
"--discrim_d_k",
type=int,
default=None,
help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--discrim_num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--discrim_num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--discrim_dropout",
type=float,
default=0.1,
help="Dropout")
parser.add_argument("--num_train_workers",
type=int,
default=4,
help="Number train workers")
parser.add_argument("--distribute",
type=str,
default="mirror",
choices=["mirror", "tpu", "nccl"])
parser.add_argument("--tpu_ep",
type=str,
help="The TPU endpoint if using `distribute=tpu`")
parser.add_argument("--nctx",
type=int,
default=256,
help="Max input length")
parser.add_argument("--file_type",
default='tfrecord',
choices=['json', 'tfrecord'],
help="Glob pattern for data")
parser.add_argument("--batch_size",
type=int,
default=256,
help="Batch Size")
parser.add_argument("--subword_model_file",
type=str,
help="The BPE model file",
required=True)
parser.add_argument("--subword_vocab_file",
type=str,
help="The BPE subword vocab",
required=True)
parser.add_argument("--optim",
default="adam",
type=str,
help="Optimizer to use (defaults to adam)")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=1.0,
help="Clipping gradient norm")
parser.add_argument("--weight_decay",
type=float,
default=1.0e-2,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=32,
help="Num training epochs")
parser.add_argument(
"--restart",
type=str2bool,
help="Option allows you to restart from a previous checkpoint")
parser.add_argument("--warmup_steps",
type=int,
default=10000,
help="Num warmup steps")
parser.add_argument("--causal",
type=str2bool,
default=False,
help="Use CLM (causal) instead of MLM")
parser.add_argument("--saves_per_epoch",
type=int,
default=10,
help="The number of checkpoints to save per epoch")
parser.add_argument("--strategy",
help="Training strategy, defaults to `mirror`",
choices=["mirror"])
parser.add_argument("--npz",
help="Should we write out NPZ files?",
type=str2bool,
default=False)
parser.add_argument("--tb",
help="Turn on tensorboard?",
type=str2bool,
default=False)
parser.add_argument(
"--convert_only",
help="Should we just convert this file to NPZ and exit?",
type=str2bool,
default=False)
args = parser.parse_args()
SET_TRAIN_FLAG(True)
if args.convert_only:
args.restart = True
args.npz = True
if args.basedir is None:
args.basedir = f'discrim-{args.dataset_key}-bpe-{os.getpid()}'
logging.basicConfig(level=logging.INFO)
logger.info(f"Writing results to {args.basedir}")
if args.tb:
logdir = f"logs/scalars/{os.getpid()}"
file_writer = tf.summary.create_file_writer(logdir + "/metrics")
file_writer.set_as_default()
logger.info(f"Set up tensorboard logdir {logdir}")
strategy = create_distribute_strategy(args.distribute, args.tpu_ep)
num_replicas = strategy.num_replicas_in_sync
logger.info(f"Using {num_replicas} replicas in this job.")
vectorizer = BPEVectorizer1D(model_file=args.subword_model_file,
vocab_file=args.subword_vocab_file,
mxlen=args.nctx)
vocab = {'x': vectorizer.vocab}
gen_preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=args.gen_d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=args.embed_type)
vocabs = gen_preproc_data['vocab']
discrim_preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=args.discrim_d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=args.embed_type)
def dataset_train_fn(input_context):
batch_size = input_context.get_per_replica_batch_size(args.batch_size)
ds = get_dataset(args.train_dir, args.file_type,
args.num_train_workers).batch(batch_size)
return ds.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
train_loader = strategy.experimental_distribute_datasets_from_function(
dataset_train_fn)
def dataset_test_fn(input_context):
batch_size = input_context.get_per_replica_batch_size(args.batch_size)
ds = get_dataset(args.valid_dir,
args.file_type,
args.num_train_workers,
shuffle=False).batch(batch_size)
return ds.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
valid_loader = strategy.experimental_distribute_datasets_from_function(
dataset_test_fn)
train_md = args.train_md if args.train_md else os.path.join(
args.train_dir, 'md.yml')
num_train_samples = get_num_samples(train_md)
valid_md = args.valid_md if args.valid_md else os.path.join(
args.valid_dir, 'md.yml')
num_valid_samples = get_num_samples(valid_md)
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, os.path.join(args.basedir, 'vocabs.json'))
gen_embeddings = {'x': gen_preproc_data['embeddings']}
discrim_embeddings = {'x': discrim_preproc_data['embeddings']}
logger.info("Loaded embeddings")
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
if len(args.gen_rpr_k) == 0 or args.gen_rpr_k[0] < 1:
gen_rpr_k = None
elif len(args.gen_rpr_k) == 1:
gen_rpr_k = args.gen_rpr_k[0]
else:
gen_rpr_k = args.gen_rpr_k
if len(args.discrim_rpr_k) == 0 or args.discrim_rpr_k[0] < 1:
discrim_rpr_k = None
elif len(args.gen_rpr_k) == 1:
discrim_rpr_k = args.discrim_rpr_k[0]
else:
discrim_rpr_k = args.discrim_rpr_k
gen_model = TransformerMaskedLanguageModel.create(
gen_embeddings,
hsz=args.gen_d_model,
d_ff=args.gen_d_ff,
tie_weights=True,
dropout=args.gen_dropout,
gpu=False,
num_heads=args.gen_num_heads,
layers=args.gen_num_layers,
rpr_k=gen_rpr_k,
d_k=args.gen_d_k,
windowed_ra=args.windowed_ra,
src_keys=['x'],
tgt_key='x')
discrim_model = TransformerDiscriminator(discrim_embeddings,
d_model=args.discrim_d_model,
d_ff=args.discrim_d_ff,
dropout=args.discrim_dropout,
num_heads=args.discrim_num_heads,
layers=args.discrim_num_layers,
rpr_k=discrim_rpr_k,
d_k=args.discrim_d_k)
logger.info("Loaded model and loss")
steps_per_epoch = num_train_samples // args.batch_size
steps_per_valid_epoch = num_valid_samples // args.batch_size
update_on = steps_per_epoch // args.saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(
f"Steps per epoch: {steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = CosineDecaySchedulerTensorFlow(steps_per_epoch * args.epochs,
lr=args.lr)
linear_warmup = WarmupLinearSchedulerTensorFlow(args.warmup_steps,
lr=args.lr)
lr_sched = CompositeLRSchedulerTensorFlow(linear_warmup, lr_decay)
mask_value = vocabs.get("[MASK]", vocabs.get("<MASK>", -1))
if mask_value == -1:
logger.error("We could not find a suitable masking token in the vocab")
return
optimizer, clip = create_keras_optimizer(**vars(args))
discrim_checkpoint = tf.train.Checkpoint(optimizer=optimizer,
model=discrim_model)
discrim_checkpoint_manager = tf.train.CheckpointManager(
discrim_checkpoint,
directory=os.path.join(args.basedir, 'discrim'),
max_to_keep=5)
gen_checkpoint = tf.train.Checkpoint(optimizer=optimizer,
model=discrim_model)
gen_checkpoint_manager = tf.train.CheckpointManager(gen_checkpoint,
directory=os.path.join(
args.basedir,
'gen'),
max_to_keep=5)
mask_value = vocabs.get("[MASK]", vocabs.get("<MASK>", -1))
if mask_value == -1:
logger.error("We could not find a suitable masking token in the vocab")
return
if args.restart:
# The global step gets automatically updated here
# so we dont have to worry about our LR regimen
gen_checkpoint.restore(gen_checkpoint_manager.latest_checkpoint)
discrim_checkpoint.restore(
discrim_checkpoint_manager.latest_checkpoint)
def _replicated_train_step(inputs):
"""This runs on a single replica"""
noised_x, labels = inputs
with tf.GradientTape() as tape:
gen_loss_step, discrim_loss_step, acc = gen_vs_discrim(
noised_x, labels, gen_model, discrim_model, mask_value)
loss_value = (args.gen_loss_scale * gen_loss_step +
discrim_loss_step) / num_replicas
grads = tape.gradient(
loss_value,
gen_model.trainable_variables + discrim_model.trainable_variables)
grads, _ = tf.clip_by_global_norm(grads, clip)
optimizer.apply_gradients(
zip(
grads, gen_model.trainable_variables +
discrim_model.trainable_variables))
return loss_value, gen_loss_step, discrim_loss_step, acc
@tf.function
def _distributed_train_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
loss, gen_loss, discrim_loss, acc = strategy.run(
_replicated_train_step, args=(inputs, ))
sum_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, loss, axis=None)
sum_gen_loss = strategy.reduce(tf.distribute.ReduceOp.SUM,
gen_loss,
axis=None)
sum_discrim_loss = strategy.reduce(tf.distribute.ReduceOp.SUM,
discrim_loss,
axis=None)
sum_acc = strategy.reduce(tf.distribute.ReduceOp.SUM, acc, axis=None)
return sum_loss, sum_gen_loss, sum_discrim_loss, sum_acc
def _replicated_test_step(inputs):
"""This runs on a single replica"""
noised_x, labels = inputs
gen_loss_step, discrim_loss_step, acc = gen_vs_discrim(
noised_x, labels, gen_model, discrim_model, mask_value)
loss_value = (args.gen_loss_scale * gen_loss_step +
discrim_loss_step) / num_replicas
return loss_value, gen_loss_step, discrim_loss_step, acc
@tf.function
def _distributed_test_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
loss, gen_loss, discrim_loss, acc = strategy.run(_replicated_test_step,
args=(inputs, ))
sum_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, loss, axis=None)
sum_gen_loss = strategy.reduce(tf.distribute.ReduceOp.SUM,
gen_loss,
axis=None)
sum_discrim_loss = strategy.reduce(tf.distribute.ReduceOp.SUM,
discrim_loss,
axis=None)
sum_acc = strategy.reduce(tf.distribute.ReduceOp.SUM, acc, axis=None)
return sum_loss, sum_gen_loss, sum_discrim_loss, sum_acc
# This is the training loop
start_epoch = 0
timer = Timer()
with strategy.scope():
for epoch in range(start_epoch, args.epochs):
SET_TRAIN_FLAG(True)
logger.info('Starting epoch %d', epoch + 1)
avg_loss = Average('average_train_loss')
avg_gen_loss = Average('average_gen_loss')
avg_discrim_loss = Average('average_discrim_loss')
avg_acc = Average('average_train_acc')
metrics = {}
timer.start()
train_iter = iter(train_loader)
for i in range(steps_per_epoch):
loss, gen_loss, discrim_loss, acc = _distributed_train_step(
next(train_iter))
avg_loss.update(loss.numpy().item())
avg_gen_loss.update(gen_loss.numpy().item())
avg_discrim_loss.update(discrim_loss.numpy().item())
avg_acc.update(acc.numpy().item())
tf.summary.scalar("train_loss",
data=loss,
step=optimizer.iterations)
tf.summary.scalar("train_gen_loss",
data=gen_loss,
step=optimizer.iterations)
tf.summary.scalar("train_discrim_loss",
data=discrim_loss,
step=optimizer.iterations)
tf.summary.scalar("train_acc",
data=acc,
step=optimizer.iterations)
if args.convert_only:
logger.warning(
"Convert only flag specified. Stopping after one step"
)
steps = optimizer.iterations.numpy()
npz_checkpoint = os.path.join(args.basedir,
f'discrim-step-{steps}.npz')
save_tlm_npz(discrim_model, npz_checkpoint)
npz_checkpoint = os.path.join(args.basedir,
f'gen-step-{steps}.npz')
save_tlm_npz(gen_model, npz_checkpoint)
return
if (i + 1) % report_on == 0:
logging.info(avg_loss)
logging.info(avg_gen_loss)
logging.info(avg_discrim_loss)
logging.info(avg_acc)
if (i + 1) % update_on == 0:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
gen_checkpoint_manager.save()
discrim_checkpoint_manager.save()
if args.npz:
steps = optimizer.iterations.numpy()
npz_checkpoint = os.path.join(
args.basedir, f'discrim-step-{steps}.npz')
save_tlm_npz(discrim_model, npz_checkpoint)
npz_checkpoint = os.path.join(args.basedir,
f'gen-step-{steps}.npz')
save_tlm_npz(gen_model, npz_checkpoint)
# This is the average training token-level loss across all machines
# This is the token-level training perplexity
metrics['train_elapsed_min'] = timer.elapsed(True)
metrics['average_train_loss'] = avg_loss.avg
metrics['average_gen_loss'] = avg_gen_loss.avg
metrics['average_discrim_loss'] = avg_discrim_loss.avg
metrics['average_train_acc'] = avg_acc.avg
metrics['lr'] = float(
lr_sched(tf.cast(optimizer.global_step,
tf.float32)).numpy().item())
avg_valid_loss = Average('average_valid_loss')
avg_valid_gen_loss = Average('average_valid_gen_loss')
avg_valid_discrim_loss = Average('average_valid_discrim_loss')
avg_valid_acc = Average('average_valid_acc')
timer.start()
SET_TRAIN_FLAG(False)
valid_iter = iter(valid_loader)
for i in range(steps_per_valid_epoch):
valid_loss, valid_gen_loss, valid_discrim_loss, valid_acc = _distributed_test_step(
next(valid_iter))
tf.summary.scalar('valid_loss',
data=valid_loss,
step=optimizer.iterations)
tf.summary.scalar('valid_gen_loss',
data=valid_gen_loss,
step=optimizer.iterations)
tf.summary.scalar('valid_discrim_loss',
data=valid_discrim_loss,
step=optimizer.iterations)
tf.summary.scalar('valid_acc',
data=valid_acc,
step=optimizer.iterations)
avg_valid_loss.update(valid_loss.numpy().item())
avg_valid_gen_loss.update(valid_gen_loss.numpy().item())
avg_valid_discrim_loss.update(
valid_discrim_loss.numpy().item())
avg_valid_acc.update(valid_acc.numpy().item())
metrics['valid_elapsed_min'] = timer.elapsed(True)
metrics['average_valid_loss'] = avg_valid_loss.avg
metrics['average_valid_gen_loss'] = avg_valid_gen_loss.avg
metrics['average_valid_discrim_loss'] = avg_valid_discrim_loss.avg
metrics['average_valid_acc'] = avg_valid_acc.avg
logger.info(json.dumps(metrics, indent=4))
def fit(model_params, ts, vs, es, **kwargs):
"""
Train a classifier using TensorFlow with a `feed_dict`. This
is the previous default behavior for training. To use this, you need to pass
`fit_func: feed_dict` in your MEAD config
:param model_params: The model to train
:param ts: A training data set
:param vs: A validation data set
:param es: A test data set, can be None
:param kwargs:
See below
:Keyword Arguments:
* *do_early_stopping* (``bool``) --
Stop after evaluation data is no longer improving. Defaults to True
* *verbose* (`dict`) A dictionary containing `console` boolean and `file` name if on
* *epochs* (``int``) -- how many epochs. Default to 20
* *outfile* -- Model output file, defaults to classifier-model.pyth
* *patience* --
How many epochs where evaluation is no longer improving before we give up
* *reporting* --
Callbacks which may be used on reporting updates
* *nsteps* (`int`) -- If we should report every n-steps, this should be passed
* *ema_decay* (`float`) -- If we are doing an exponential moving average, what decay to us4e
* *clip* (`int`) -- If we are doing gradient clipping, what value to use
* *optim* (`str`) -- The name of the optimizer we are using
* *lr* (`float`) -- The learning rate we are using
* *mom* (`float`) -- If we are using SGD, what value to use for momentum
* *beta1* (`float`) -- Adam-specific hyper-param, defaults to `0.9`
* *beta2* (`float`) -- Adam-specific hyper-param, defaults to `0.999`
* *epsilon* (`float`) -- Adam-specific hyper-param, defaults to `1e-8
:return: None
"""
epochs = int(kwargs.get('epochs', 5))
patience = int(kwargs.get('patience', epochs))
conll_output = kwargs.get('conll_output', None)
span_type = kwargs.get('span_type', 'iob')
txts = kwargs.get('txts', None)
model_file = get_model_file('tagger', 'tf', kwargs.get('basedir'))
TRAIN_FLAG()
trainer = create_trainer(model_params, **kwargs)
do_early_stopping = bool(kwargs.get('do_early_stopping', True))
verbose = bool(kwargs.get('verbose', False))
best_metric = 0
if do_early_stopping:
early_stopping_metric = kwargs.get('early_stopping_metric', 'acc')
early_stopping_cmp, best_metric = get_metric_cmp(
early_stopping_metric, kwargs.get('early_stopping_cmp'))
patience = kwargs.get('patience', epochs)
print('Doing early stopping on [%s] with patience [%d]' %
(early_stopping_metric, patience))
reporting_fns = listify(kwargs.get('reporting', []))
print('reporting', reporting_fns)
last_improved = 0
for epoch in range(epochs):
trainer.train(ts, reporting_fns)
test_metrics = trainer.test(vs, reporting_fns, phase='Valid')
if do_early_stopping is False:
trainer.checkpoint()
trainer.model.save(model_file)
elif early_stopping_cmp(test_metrics[early_stopping_metric],
best_metric):
last_improved = epoch
best_metric = test_metrics[early_stopping_metric]
print('New best %.3f' % best_metric)
trainer.checkpoint()
trainer.model.save(model_file)
elif (epoch - last_improved) > patience:
print('Stopping due to persistent failures to improve')
break
if do_early_stopping is True:
print('Best performance on %s: %.3f at epoch %d' %
(early_stopping_metric, best_metric, last_improved))
if es is not None:
trainer.recover_last_checkpoint()
# What to do about overloading this??
evaluator = TaggerEvaluatorTf(trainer.model, span_type, verbose)
timer = Timer()
test_metrics = evaluator.test(es, conll_output=conll_output, txts=txts)
duration = timer.elapsed()
for reporting in reporting_fns:
reporting(test_metrics, 0, 'Test')
trainer.log.debug({'phase': 'Test', 'time': duration})
def fit_eager(model_params, ts, vs, es=None, **kwargs):
"""
Train a tagger using TensorFlow with `tf.dataset`. This
is the default behavior for training.
:param model_params: The model (or parameters to create the model) to train
:param ts: A training data set
:param vs: A validation data set
:param es: A test data set, can be None
:param kwargs:
See below
:Keyword Arguments:
* *do_early_stopping* (``bool``) --
Stop after evaluation data is no longer improving. Defaults to True
* *verbose* (`dict`) A dictionary containing `console` boolean and `file` name if on
* *epochs* (``int``) -- how many epochs. Default to 20
* *outfile* -- Model output file, defaults to classifier-model.pyth
* *patience* --
How many epochs where evaluation is no longer improving before we give up
* *reporting* --
Callbacks which may be used on reporting updates
* *nsteps* (`int`) -- If we should report every n-steps, this should be passed
* *ema_decay* (`float`) -- If we are doing an exponential moving average, what decay to us4e
* *clip* (`int`) -- If we are doing gradient clipping, what value to use
* *optim* (`str`) -- The name of the optimizer we are using
* *lr* (`float`) -- The learning rate we are using
* *mom* (`float`) -- If we are using SGD, what value to use for momentum
* *beta1* (`float`) -- Adam-specific hyper-param, defaults to `0.9`
* *beta2* (`float`) -- Adam-specific hyper-param, defaults to `0.999`
* *epsilon* (`float`) -- Adam-specific hyper-param, defaults to `1e-8
:return: None
"""
conll_output = kwargs.get('conll_output', None)
span_type = kwargs.get('span_type', 'iob')
txts = kwargs.get('txts', None)
model_file = get_model_file('tagger', 'tf', kwargs.get('basedir'))
do_early_stopping = bool(kwargs.get('do_early_stopping', True))
verbose = kwargs.get(
'verbose', {
'console': kwargs.get('verbose_console', False),
'file': kwargs.get('verbose_file', None)
})
epochs = int(kwargs.get('epochs', 20))
batchsz = kwargs['batchsz']
test_batchsz = kwargs.get('test_batchsz', batchsz)
lengths_key = model_params.get('lengths_key')
train_dataset = tf.data.Dataset.from_tensor_slices(
to_tensors(ts, lengths_key))
train_dataset = train_dataset.shuffle(buffer_size=SHUF_BUF_SZ)
train_dataset = train_dataset.batch(batchsz, drop_remainder=False)
train_dataset = train_dataset.prefetch(NUM_PREFETCH)
valid_dataset = tf.data.Dataset.from_tensor_slices(
to_tensors(vs, lengths_key))
valid_dataset = valid_dataset.batch(batchsz, drop_remainder=False)
valid_dataset = valid_dataset.prefetch(NUM_PREFETCH)
best_metric = 0
if do_early_stopping:
early_stopping_metric = kwargs.get('early_stopping_metric', 'acc')
early_stopping_cmp, best_metric = get_metric_cmp(
early_stopping_metric, kwargs.get('early_stopping_cmp'))
patience = kwargs.get('patience', epochs)
print('Doing early stopping on [%s] with patience [%d]' %
(early_stopping_metric, patience))
reporting_fns = listify(kwargs.get('reporting', []))
print('reporting', reporting_fns)
trainer = TaggerTrainerEagerTf(model_params, **kwargs)
last_improved = 0
SET_TRAIN_FLAG(True)
for epoch in range(epochs):
trainer.train(train_dataset, reporting_fns, steps=len(ts))
test_metrics = trainer.test(valid_dataset,
reporting_fns,
phase='Valid',
steps=len(vs))
if do_early_stopping is False:
trainer.checkpoint()
trainer.model.save(model_file)
elif early_stopping_cmp(test_metrics[early_stopping_metric],
best_metric):
last_improved = epoch
best_metric = test_metrics[early_stopping_metric]
print('New best %.3f' % best_metric)
trainer.checkpoint()
trainer.model.save(model_file)
elif (epoch - last_improved) > patience:
print('Stopping due to persistent failures to improve')
break
if do_early_stopping is True:
print('Best performance on %s: %.3f at epoch %d' %
(early_stopping_metric, best_metric, last_improved))
if es is not None:
print('Reloading best checkpoint')
trainer.recover_last_checkpoint()
test_dataset = tf.data.Dataset.from_tensor_slices(
to_tensors(es, lengths_key))
test_dataset = test_dataset.batch(test_batchsz, drop_remainder=False)
test_dataset = test_dataset.prefetch(NUM_PREFETCH)
evaluator = TaggerEvaluatorEagerTf(trainer.model, span_type, verbose)
timer = Timer()
test_metrics = evaluator.test(test_dataset,
conll_output=conll_output,
txts=txts,
batches=es,
steps=len(es))
duration = timer.elapsed()
for reporting in reporting_fns:
reporting(test_metrics, 0, 'Test')
trainer.log.debug({'phase': 'Test', 'time': duration})
def run(basedir=None,
train_file=None,
valid_file=None,
dataset_key='paired',
embed_type='default',
d_model=512,
d_ff=2048,
d_k=None,
num_heads=8,
num_layers=8,
num_train_workers=4,
nctx=256,
tgt_nctx=None,
file_type='json',
record_keys=['x', 'y'],
batch_size=256,
subword_model_file=None,
subword_vocab_file=None,
dropout=0.1,
lr_scheduler='cosine',
lr_decay_steps=None,
lr_decay_rate=None,
lr_alpha=None,
optim='adamw',
lr=4.0e-4,
clip=1.0,
weight_decay=1.0e-2,
epochs=32,
restart_from=None,
restart_tt=None,
warmup_steps=10000,
saves_per_epoch=10,
layer_drop=0.0,
reader_type='preprocessed',
src_begin_tok=[],
src_end_tok=['<EOS>'],
tgt_begin_tok=['<GO>'],
tgt_end_tok=['<EOS>'],
lower=False,
rpr_k=[8],
device='cuda',
distributed=False,
local_rank=-1,
save_npz=False,
extra_tokens=["[CLS]", "[MASK]"],
subword_type='bpe',
label_smoothing=None,
ra_type=None,
transformer_type=None,
**kwargs):
if basedir is None:
basedir = f's2s-{reader_type}-paired-{dataset_key}-bpe-{os.getpid()}'
logging.basicConfig(
level=logging.INFO if local_rank in [-1, 0] else logging.WARN)
num_gpus = get_num_gpus_multiworker()
distributed = distributed or num_gpus > 1
logger.info(f"Using {num_gpus} GPUs in this job.")
if distributed:
device, updated_local_rank = init_distributed(local_rank)
local_rank = updated_local_rank
if not tgt_nctx:
tgt_nctx = nctx
reader = MultiFileDatasetReader(nctx,
tgt_nctx,
src_begin_tok,
src_end_tok,
tgt_begin_tok,
tgt_end_tok,
subword_model_file,
subword_vocab_file,
file_type,
reader_type=reader_type,
record_keys=record_keys,
lower=lower,
extra_tokens=extra_tokens,
subword_type=subword_type)
vocab = reader.build_vocab()
# If we are not using chars, then use 'x' for both input and output
preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=embed_type)
vocabs = preproc_data['vocab']
os.makedirs(basedir, exist_ok=True)
# We want to make sure to save our input vocab into the basedir for reuse later
write_json(vocabs, os.path.join(basedir, 'vocabs.json'))
embeddings = preproc_data['embeddings']
logger.info("Loaded embeddings")
train_set = reader.load(train_file, vocabs)
valid_set = reader.load(valid_file,
vocabs,
distribute=False,
shuffle=False)
train_loader = DataLoader(train_set,
batch_size=batch_size,
num_workers=num_train_workers)
valid_loader = DataLoader(valid_set, batch_size=batch_size)
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", embed_type)
if len(rpr_k) == 0 or rpr_k[0] < 1:
rpr_k = None
elif len(rpr_k) == 1:
rpr_k = rpr_k[0]
else:
rpr_k = rpr_k
hps = {
"dsz": d_model,
"hsz": d_model,
"d_ff": d_ff,
"dropout": dropout,
"num_heads": num_heads,
"layers": num_layers,
"encoder_type": "transformer",
"decoder_type": "transformer",
"src_lengths_key": "x_lengths",
"d_k": d_k,
"layer_drop": layer_drop,
"rpr_k": rpr_k,
"ra_type": ra_type,
"transformer_type": transformer_type
}
model = TiedEmbeddingsSeq2SeqModel({'x': embeddings}, None, **hps)
model.to(device)
loss_function = model.create_loss(label_smoothing=label_smoothing)
loss_function.to(device)
logger.info("Created model and loss")
steps_per_epoch = len(train_loader) // num_gpus
valid_steps = len(valid_loader)
update_on = steps_per_epoch // saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(
f"Steps per epoch per GPU: {steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = get_lr_decay(lr_scheduler,
lr,
steps_per_epoch,
epochs,
logger,
decay_steps=lr_decay_steps,
decay_rate=lr_decay_rate,
alpha=lr_alpha)
linear_warmup = WarmupLinearSchedulerPyTorch(warmup_steps, lr=lr)
lr_sched = CompositeLRScheduler(linear_warmup, lr_decay, lr=lr)
global_step = 0
start_epoch = 0
if restart_from:
global_step, start_epoch = reload_from_checkpoint(
restart_from, restart_tt, model, steps_per_epoch)
logger.info(
"Restarting from a previous checkpoint %s.\n\tStarting at global_step=%d, epoch=%d",
restart_from, global_step, start_epoch + 1)
optimizer = OptimizerManager(model,
global_step,
optim=optim,
lr=lr,
lr_function=lr_sched,
weight_decay=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 distributed:
model = DistributedDataParallel(model,
device_ids=[device],
output_device=device)
logger.info("Model located on %d", local_rank)
model_base = os.path.join(basedir, 'checkpoint')
steps = global_step
timer = Timer()
for epoch in range(start_epoch, epochs):
avg_loss = Average('average_train_loss')
metrics = {}
optimizer.zero_grad()
timer.start()
model.train()
train_itr = iter(train_loader)
for i in range(steps_per_epoch):
batch = next(train_itr)
steps += 1
x, y = batch
loss = run_step(x, y, model, loss_function, distributed)
loss.backward()
avg_loss.update(loss.item())
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
optimizer.zero_grad()
if (i + 1) % report_on == 0:
logging.info(avg_loss)
if (i + 1) % update_on == 0 and local_rank < 1:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
logging.info('LR: %f', optimizer.current_lr)
save_checkpoint(model,
model_base,
steps,
tick_type='step',
save_npz=save_npz)
# How much time elapsed in minutes
elapsed = timer.elapsed(True)
train_avg_loss = avg_loss.avg
# This is the average training token-level loss across all machines
# This is the token-level training perplexity
metrics['train_elapsed_min'] = elapsed
metrics['average_train_loss'] = train_avg_loss
if local_rank < 1:
avg_valid_loss = Average('average_valid_loss')
timer.start()
model.eval()
valid_itr = iter(valid_loader)
for j in range(valid_steps):
with torch.no_grad():
batch = next(valid_itr)
x, y = batch
loss = run_step(x, y, model, loss_function, distributed)
avg_valid_loss.update(loss.item())
valid_avg_loss = avg_valid_loss.avg
elapsed = timer.elapsed(True)
metrics['valid_elapsed_min'] = elapsed
metrics['average_valid_loss'] = valid_avg_loss
logger.info(metrics)
save_checkpoint(model,
model_base,
epoch,
tick_type='epoch',
save_npz=save_npz)
def run(basedir=None,
train_file=None,
valid_file=None,
dataset_key='tlm',
embed_type='default',
d_model=512,
d_ff=2048,
d_k=None,
num_heads=8,
num_layers=8,
num_train_workers=4,
nctx=256,
file_type='json',
batch_size=256,
subword_model_file=None,
subword_vocab_file=None,
dropout=0.1,
ffn_pdrop=0.0,
layer_drop=0.0,
lr_scheduler='cosine',
lr_decay_steps=None,
lr_decay_rate=None,
lr_alpha=0.0,
optim='adamw',
lr=4.0e-4,
clip=1.0,
weight_decay=1.0e-2,
epochs=32,
restart_from=None,
restart_tt=None,
warmup_steps=10000,
saves_per_epoch=10,
mlm=True,
preprocessed=True,
rpr_k=[8],
rpr_value_on=False,
windowed_ra=False,
device="cuda",
distributed=False,
local_rank=-1,
extra_tokens=["[CLS]", "[MASK]"],
do_early_stopping=False,
model_type='transformer-mlm',
modules=[],
ra_type=None,
transformer_type=None,
**kwargs):
if basedir is None:
basedir = 'lm-{}-bpe-{}'.format(dataset_key, os.getpid())
logging.basicConfig(
level=logging.INFO if local_rank in [-1, 0] else logging.WARN)
for module in modules:
import_user_module(module)
num_gpus = get_num_gpus_multiworker()
distributed = distributed or num_gpus > 1
logger.info(f"Using {num_gpus} GPUs in this job.")
do_on_demand_masking = mlm and not preprocessed
if do_on_demand_masking:
logger.info(f"On-demand masking is turned on")
if distributed:
device, updated_local_rank = init_distributed(local_rank)
local_rank = updated_local_rank
if file_type == 'tfrecord':
reader_type = 'tfrecord'
elif preprocessed:
reader_type = 'preprocessed'
else:
reader_type = 'lang'
reader = MultiFileDatasetReader(src_nctx=nctx,
model_file=subword_model_file,
vocab_file=subword_vocab_file,
file_type=file_type,
reader_type=reader_type,
record_keys=['x', 'y'] if mlm else ['x'],
extra_tokens=extra_tokens)
# This looks a bit funny but the streaming reader ignores our vocab and gives us the one from the subword_model
# However, we do need to get counts from our dataset for validation so we can calculate the perplexity
vocab = reader.build_vocab([valid_file])
# If we are not using chars, then use 'x' for both input and output
preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=embed_type)
vocabs = preproc_data['vocab']
os.makedirs(basedir, exist_ok=True)
# We want to make sure to save our input vocab into the basedir for reuse later
write_json(vocabs, os.path.join(basedir, 'vocabs.json'))
embeddings = {'x': preproc_data['embeddings']}
logger.info("Loaded embeddings")
train_set = reader.load(train_file, vocabs)
valid_set = reader.load(valid_file,
vocabs,
distribute=False,
shuffle=False)
train_loader = DataLoader(train_set,
batch_size=batch_size,
num_workers=num_train_workers)
valid_loader = DataLoader(valid_set, batch_size=batch_size)
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", embed_type)
if 'mlm' in model_type:
mask_from = vocabs
vocab_size = len(mask_from)
mask_value = mask_from.get("[MASK]")
if mask_value == -1:
logger.error(
"We could not find a suitable masking token in the vocab")
return
if len(rpr_k) == 0 or rpr_k[0] < 1:
rpr_k = None
elif len(rpr_k) == 1:
rpr_k = None if rpr_k[0] == 0 else rpr_k[0]
if ra_type != None and ra_type != 'shaw' and rpr_k is not None:
print(
f"Relative attention mismatch. You requested {ra_type} with rpr set. Setting it to 0"
)
rpr_k = None
model = create_lang_model(
embeddings,
hsz=d_model,
nctx=nctx, # Only for gMLP
d_ff=d_ff,
tie_weights=True,
dropout=dropout,
gpu=False,
num_heads=num_heads,
layers=num_layers,
rpr_k=rpr_k,
d_k=d_k,
ffn_pdrop=ffn_pdrop,
windowed_ra=windowed_ra,
rpr_value_on=rpr_value_on,
layer_drop=layer_drop,
model_type=model_type,
ra_type=ra_type,
transformer_type=transformer_type,
src_keys=['x'],
tgt_key='x')
model.to(device)
loss_function = model.create_loss()
loss_function.to(device)
logger.info("Loaded model and loss")
steps_per_epoch = len(train_loader) // num_gpus
update_on = steps_per_epoch // saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(
f"Steps per epoch per GPU: {steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = get_lr_decay(lr_scheduler,
lr,
steps_per_epoch,
epochs,
logger,
decay_steps=lr_decay_steps,
decay_rate=lr_decay_rate,
alpha=lr_alpha)
linear_warmup = WarmupLinearSchedulerPyTorch(warmup_steps, lr=lr)
lr_sched = CompositeLRScheduler(linear_warmup, lr_decay, lr=lr)
global_step = 0
start_epoch = 0
if restart_from:
if restart_from.endswith('npz'):
load_tlm_npz(model, restart_from)
else:
model.load_state_dict(torch.load(restart_from))
vec = restart_from.split("-")
if restart_tt:
tick_type = restart_tt
else:
tick_type = vec[-2]
step_num = int(vec[-1].split(".")[0])
if tick_type == 'epoch':
start_epoch = step_num
global_step = start_epoch * steps_per_epoch
elif tick_type == 'step':
start_epoch = step_num // steps_per_epoch
global_step = step_num
else:
logger.warning(
f"The previous tick was {step_num} but command-line specifies to ignore, setting to 0"
)
logger.info(
"Restarting from a previous checkpoint %s.\n\tStarting at global_step=%d, epoch=%d",
restart_from, global_step, start_epoch + 1)
optimizer = OptimizerManager(model,
global_step,
optim=optim,
lr=lr,
lr_function=lr_sched,
weight_decay=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 distributed:
# This program assume pure data parallelism, each model is on a single gpu
# If we wanted to support model and data parallelism we would need to update
# the selection of gpus based on rank, it would need to select multiple ids
# based on rank, here we select only a single gpu and use it for input and
# output.
model = DistributedDataParallel(model,
device_ids=[device],
output_device=device,
find_unused_parameters=True)
logger.info("Model located on %s", device)
model_base = os.path.join(basedir, 'checkpoint')
steps = global_step
best_valid_loss = np.inf
timer = Timer()
for epoch in range(start_epoch, epochs):
avg_loss = Average('average_train_loss')
metrics = {}
optimizer.zero_grad()
timer.start()
model.train()
train_itr = iter(train_loader)
for i in range(steps_per_epoch):
batch = next(train_itr)
steps += 1
x, y = batch
inputs = x.to(device)
labels = y.to(device)
if do_on_demand_masking:
inputs, labels, _ = on_demand_mlm_masking(
inputs, labels, mask_value, vocab_size)
inputs = {'x': inputs}
labels = labels.contiguous()
logits = model(inputs, None)[0].contiguous()
if 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(), clip)
optimizer.step()
optimizer.zero_grad()
if (i + 1) % report_on == 0:
logging.info(avg_loss)
if (i + 1) % update_on == 0 and local_rank < 1:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
logging.info('LR: %f', optimizer.current_lr)
if not do_early_stopping:
save_checkpoint(model, model_base, steps, tick_type='step')
else:
valid_token_loss = validate(model, loss_function,
valid_loader, avg_loss, timer,
metrics, do_on_demand_masking,
mlm, mask_value, vocab_size,
device)
if valid_token_loss < best_valid_loss:
best_valid_loss = valid_token_loss
logger.info(
f"New best valid loss: {best_valid_loss}. Saving checkpoint..."
)
save_checkpoint(model,
model_base,
steps,
tick_type='step')
model.train()
if not do_early_stopping:
_ = validate(model, loss_function, valid_loader, avg_loss, timer,
metrics, do_on_demand_masking, mlm, mask_value,
vocab_size, device)
save_checkpoint(model, model_base, epoch, tick_type='epoch')
def main():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--train_dir", type=str, required=True, help='Training directory')
parser.add_argument("--valid_dir", type=str, required=True, help='Validation directory')
parser.add_argument("--train_md", type=str, help="Training metadata YAML, defaults to `{train_dir}/md.yml`")
parser.add_argument("--valid_md", type=str, help="Validation metadata YAML, defaults to `{valid_dir}/md.yml`")
parser.add_argument("--dataset_key", default="tlm",
help="dataset key for basedir")
parser.add_argument("--embed_type", type=str, default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument("--d_model", type=int, default=512, help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2048, help="FFN dimension")
parser.add_argument("--d_k", type=int, default=None, help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--num_heads", type=int, default=8, help="Number of heads")
parser.add_argument("--num_layers", type=int, default=8, help="Number of layers")
parser.add_argument("--num_train_workers", type=int, default=4, help="Number train workers")
parser.add_argument("--distribute", type=str, default="mirror", choices=["mirror", "tpu", "nccl"])
parser.add_argument("--tpu_ep", type=str, help="The TPU endpoint if using `distribute=tpu`")
parser.add_argument("--nctx", type=int, default=256, help="Max input length (x)")
parser.add_argument("--file_type", default='tfrecord', choices=['json', 'jsonl', 'tfrecord'], help="Glob pattern for data")
parser.add_argument("--batch_size", type=int, default=256, help="Batch Size")
parser.add_argument("--subword_model_file", type=str, help="The BPE model file", required=False)
parser.add_argument("--subword_vocab_file", type=str, help="The BPE subword vocab", required=True)
parser.add_argument("--subword_type", type=str, choices=["bpe", "wordpiece", "sentencepiece"], default="bpe")
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--layer_drop", type=float, default=0.0, help="LayerDrop to apply")
parser.add_argument("--ff_pdrop", type=float, default=0.1, help="Dropout in the dense stack")
parser.add_argument("--optim", default="adamw", type=str, help="Optimizer to use (defaults to adamw)")
parser.add_argument("--lr", type=float, default=4.0e-4, help="Learning rate")
parser.add_argument("--clip", type=float, default=1.0, help="Clipping gradient norm")
parser.add_argument("--weight_decay", type=float, default=1.0e-2, help="Weight decay")
parser.add_argument("--epochs", type=int, default=32, help="Num training epochs")
parser.add_argument("--restart", type=str2bool, help="Option allows you to restart from a previous checkpoint")
parser.add_argument("--warmup_steps", type=int, default=10000, help="Num warmup steps")
parser.add_argument("--saves_per_epoch", type=int, default=10, help="The number of checkpoints to save per epoch")
parser.add_argument('--rpr_k',
help='Relative attention positional sizes pass 0 if you dont want relative attention',
type=int, default=[8], nargs='+')
parser.add_argument('--ra_type', type=str, help="Specify a relative attention type")
parser.add_argument("--reduction_d_k", type=int, default=64, help="Dimensions of Key and Query in the single headed"
"reduction layers")
parser.add_argument("--reduction_type", type=str, default="2ha",
help="If using a dual encoder, specifies the reduction type")
parser.add_argument("--stacking_layers", type=int, nargs='+', default=[])
parser.add_argument("--loss", type=str, default='symmetric',
choices=['contrastive', 'symmetric'])
parser.add_argument("--learn_temp", type=str2bool, default=True,
help="If 'constrastive' or 'symmetric' loss, should we learn the temperature scaling")
parser.add_argument("--init_temp", type=float,
help="Initialize the temperature for 'contrastive' or 'symmetric' loss")
parser.add_argument("--npz", help="Should we write out NPZ files?", type=str2bool, default=False)
parser.add_argument("--tb", help="Turn on tensorboard?", type=str2bool, default=False)
parser.add_argument("--convert_only", help="Should we just convert this file to NPZ and exit?", type=str2bool, default=False)
parser.add_argument("--extra_tokens", help="What extra tokens should we use", nargs="+", default=["[CLS]", "[MASK]"])
args = parser.parse_args()
if args.tpu_ep is not None and args.file_type != 'tfrecord':
raise Exception("For TPUs, TFRecord format is required!")
SET_TRAIN_FLAG(True)
if args.convert_only:
args.restart = True
if args.basedir is None:
args.basedir = 'paired-{}-bpe-{}'.format(args.dataset_key, os.getpid())
logging.basicConfig(level=logging.INFO)
logger.info(f"Writing results to {args.basedir}")
if args.tb:
logdir = f"logs/scalars/{os.getpid()}"
file_writer = tf.summary.create_file_writer(logdir + "/metrics")
file_writer.set_as_default()
logger.info(f"Set up tensorboard logdir {logdir}")
strategy = create_distribute_strategy(args.distribute, args.tpu_ep, len(get_env_gpus(None)))
num_replicas = strategy.num_replicas_in_sync
logger.info(f"Using {num_replicas} replicas in this job.")
Vec1D = get_subword_vec1d(args.subword_type)
vectorizer = Vec1D(model_file=args.subword_model_file,
vocab_file=args.subword_vocab_file,
mxlen=args.nctx,
extra_tokens=args.extra_tokens)
preproc_data = baseline.embeddings.load_embeddings('x', dsz=args.d_model, known_vocab=vectorizer.vocab,
preserve_vocab_indices=True,
embed_type=args.embed_type)
vocabs = preproc_data['vocab']
train_md = args.train_md if args.train_md else os.path.join(args.train_dir, 'md.yml')
num_train_samples = get_num_samples(train_md)
valid_md = args.valid_md if args.valid_md else os.path.join(args.valid_dir, 'md.yml')
num_valid_samples = get_num_samples(valid_md)
is_curriculum = True if isinstance(num_train_samples, Mapping) else False
def dataset_train_fn(input_context):
global_batchsz = args.batch_size
base_batchsz = input_context.get_per_replica_batch_size(global_batchsz)
ds = None
num_shards = input_context.num_input_pipelines
index = input_context.input_pipeline_id
if is_curriculum:
for sub in num_train_samples.keys():
train_curr_dir = os.path.join(args.train_dir, str(sub))
batchsz_scale_factor = args.nctx // sub
this_batchsz = base_batchsz * batchsz_scale_factor
curr_ds = get_dataset(train_curr_dir, args.file_type, args.num_train_workers, num_shards, index).batch(this_batchsz, drop_remainder=True)
if ds is None:
ds = curr_ds
else:
ds = ds.concatenate(curr_ds)
else:
ds = get_dataset(args.train_dir, args.file_type, args.num_train_workers, num_shards, index).batch(base_batchsz)
return ds
train_loader = strategy.experimental_distribute_datasets_from_function(dataset_train_fn)
def dataset_test_fn(input_context):
global_batchsz = args.batch_size
base_batchsz = input_context.get_per_replica_batch_size(global_batchsz)
ds = None
num_shards = input_context.num_input_pipelines
index = input_context.input_pipeline_id
if is_curriculum:
for sub in num_valid_samples.keys():
valid_curr_dir = os.path.join(args.valid_dir, str(sub))
batchsz_scale_factor = args.nctx // sub
this_batchsz = base_batchsz * batchsz_scale_factor
curr_ds = get_dataset(valid_curr_dir, args.file_type, args.num_train_workers, num_shards, index, shuffle=False).batch(
this_batchsz, drop_remainder=True)
if ds is None:
ds = curr_ds
else:
ds = ds.concatenate(curr_ds)
else:
ds = get_dataset(args.valid_dir, args.file_type, args.num_train_workers, num_shards, index, shuffle=False).batch(base_batchsz)
return ds
valid_loader = strategy.experimental_distribute_datasets_from_function(dataset_test_fn)
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, os.path.join(args.basedir, 'vocabs.json'))
embeddings = preproc_data['embeddings']
logger.info("Loaded embeddings")
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
if len(args.rpr_k) == 0 or args.rpr_k[0] < 1:
rpr_k = None
elif len(args.rpr_k) == 1:
rpr_k = args.rpr_k[0]
else:
rpr_k = args.rpr_k
logger.info("Creating dual encoder")
model = PairedModel(embeddings, args.d_model, args.d_ff, args.dropout, args.num_heads, args.num_layers, rpr_k=rpr_k,
d_k=args.d_k, reduction_d_k=args.reduction_d_k, stacking_layers=args.stacking_layers,
ffn_pdrop=args.ff_pdrop, reduction_type=args.reduction_type, freeze_encoders=False,
ra_type=args.ra_type)
loss_function = model.create_loss(loss_type=args.loss, init_temp=args.init_temp, learn_temp=args.learn_temp)
logger.info("Loaded model and loss")
if is_curriculum:
steps_per_epoch = 0
steps_per_valid_epoch = 0
for k, v in num_train_samples.items():
steps_per_epoch += int(num_train_samples[k] // (args.batch_size * (args.nctx / k)))
for k, v in num_valid_samples.items():
steps_per_valid_epoch += int(num_valid_samples[k] // (args.batch_size * (args.nctx / k)))
else:
steps_per_epoch = num_train_samples // args.batch_size
steps_per_valid_epoch = num_valid_samples // args.batch_size
update_on = steps_per_epoch // args.saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(f"Steps per epoch: {steps_per_epoch}. Saving checkpoint every {update_on} steps.")
lr_decay = CosineDecaySchedulerTensorFlow(steps_per_epoch * args.epochs, lr=args.lr)
linear_warmup = WarmupLinearSchedulerTensorFlow(args.warmup_steps, lr=args.lr)
lr_sched = CompositeLRSchedulerTensorFlow(linear_warmup, lr_decay)
optimizer = EagerOptimizer(loss_function, optim=args.optim, lr_function=lr_sched, weight_decay=args.weight_decay,
clip=args.clip, lr=args.lr)
checkpoint = tf.train.Checkpoint(optimizer=optimizer.optimizer, model=model)
checkpoint_manager = tf.train.CheckpointManager(checkpoint,
directory=args.basedir,
max_to_keep=5)
if args.restart:
# The global step gets automatically updated here
# so we dont have to worry about our LR regimen
checkpoint.restore(checkpoint_manager.latest_checkpoint)
def _replicated_train_step(inputs):
"""This runs on a single replica"""
x, y = inputs
per_replica_loss = optimizer.update(model, x, y, num_replicas)
return per_replica_loss
@tf.function
def _distributed_train_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
per_replica_loss = strategy.run(_replicated_train_step, args=(inputs,))
return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_loss, axis=None)
def _replicated_test_step(inputs):
"""This runs on a single replica"""
x, y = inputs
per_replica_loss = loss_function(model, x, y) / num_replicas
return per_replica_loss
@tf.function
def _distributed_test_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
per_replica_loss = strategy.run(_replicated_test_step, args=(inputs,))
return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_loss, axis=None)
# This is the training loop
start_epoch = 0
timer = Timer()
with strategy.scope():
for epoch in range(start_epoch, args.epochs):
SET_TRAIN_FLAG(True)
logger.info('Starting epoch %d', epoch + 1)
avg_loss = Average('average_train_loss')
metrics = {}
timer.start()
train_iter = iter(train_loader)
for i in range(steps_per_epoch):
loss = _distributed_train_step(next(train_iter))
avg_loss.update(loss.numpy().item())
tf.summary.scalar("train_loss", data=loss, step=optimizer.global_step)
if args.convert_only:
logger.warning("Convert only flag specified. Stopping after one step")
steps = optimizer.global_step.numpy()
npz_checkpoint = os.path.join(args.basedir, f'checkpoint-step-{steps}.npz')
save_transformer_de_npz(model, npz_checkpoint)
return
if (i + 1) % report_on == 0:
logging.info(avg_loss)
if (i + 1) % update_on == 0:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
checkpoint_manager.save()
if args.npz:
steps = optimizer.global_step.numpy()
npz_checkpoint = os.path.join(args.basedir, f'checkpoint-step-{steps}.npz')
save_transformer_de_npz(model, npz_checkpoint)
# How much time elapsed in minutes
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'] = timer.elapsed(True)
metrics['average_train_loss'] = train_token_loss
metrics['train_ppl'] = train_token_ppl
metrics['lr'] = float(lr_sched(tf.cast(optimizer.global_step, tf.float32)).numpy().item())
avg_valid_loss = Average('average_valid_loss')
timer.start()
SET_TRAIN_FLAG(False)
valid_iter = iter(valid_loader)
for i in range(steps_per_valid_epoch):
valid_loss = _distributed_test_step(next(valid_iter))
tf.summary.scalar('valid_loss', data=valid_loss, step=optimizer.global_step)
avg_valid_loss.update(valid_loss.numpy().item())
valid_token_loss = avg_valid_loss.avg
valid_token_ppl = math.exp(valid_token_loss)
metrics['valid_elapsed_min'] = timer.elapsed(True)
metrics['average_valid_loss'] = valid_token_loss
metrics['average_valid_word_ppl'] = valid_token_ppl
logger.info(json.dumps(metrics, indent=4))
def train():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
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("--preprocessed",
type=str2bool,
default=True,
help="Has the data already been preprocessed?")
parser.add_argument("--gen_d_model",
type=int,
default=256,
help="Model dimension (and embedding dsz)")
parser.add_argument("--gen_d_ff",
type=int,
default=1024,
help="FFN dimension")
parser.add_argument(
"--gen_d_k",
type=int,
default=None,
help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--gen_num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--gen_num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--gen_dropout",
type=float,
default=0.1,
help="Dropout")
parser.add_argument(
'--gen_rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
parser.add_argument("--discrim_d_model",
type=int,
default=512,
help="Model dimension (and embedding dsz)")
parser.add_argument("--discrim_d_ff",
type=int,
default=2048,
help="FFN dimension")
parser.add_argument(
"--discrim_d_k",
type=int,
default=None,
help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--discrim_num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--discrim_num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--discrim_dropout",
type=float,
default=0.1,
help="Dropout")
parser.add_argument(
'--discrim_rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
parser.add_argument("--num_train_workers",
type=int,
default=4,
help="Number train workers")
parser.add_argument(
"--nctx",
type=int,
default=256,
help="Max context length (for both encoder and decoder)")
parser.add_argument(
"--embed_type",
type=str,
default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument(
"--pattern",
default='*.json',
help=
"Glob pattern for files, defaults to *.json if preprocessed, *.txt otherwise"
)
parser.add_argument("--batch_size",
type=int,
default=256,
help="Batch Size")
parser.add_argument("--dataset_key",
default="reddit",
help="dataset key for basedir")
parser.add_argument("--subword_model_file", type=str, required=True)
parser.add_argument("--subword_vocab_file", type=str, required=True)
parser.add_argument("--lr_scheduler",
type=str,
default='cosine',
help="The type of learning rate decay scheduler")
parser.add_argument("--lr_decay_steps",
type=int,
help="decay steps of lr scheduler")
parser.add_argument("--lr_decay_rate",
type=float,
help="decay rate of lr scheduler")
parser.add_argument("--lr_alpha",
type=float,
help="parameter alpha for cosine decay scheduler")
parser.add_argument("--optim",
default="adam",
type=str,
help="Optimizer to use (defaults to adam)")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=1.0,
help="Clipping gradient norm")
parser.add_argument("--gen_loss_scale",
type=float,
default=50.0,
help="Scaling for loss function")
parser.add_argument("--weight_decay",
type=float,
default=0.0,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=32,
help="Num training epochs")
parser.add_argument(
"--restart_from",
type=str,
help=
"Option allows you to restart from the latest checkpoint in a directory"
)
parser.add_argument(
"--restart_tt",
type=str,
choices=['step', 'epoch'],
default='step',
help="Optional param for legacy checkpoints (step|epoch)")
parser.add_argument("--warmup_steps",
type=int,
default=10000,
help="Num warmup steps")
parser.add_argument("--saves_per_epoch",
type=int,
default=100,
help="The number of checkpoints to save per epoch")
parser.add_argument("--print",
type=str2bool,
default=True,
help="Print some output")
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)"
)
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 = 'gd-{}-bpe-{}'.format(args.dataset_key, os.getpid())
logging.basicConfig(
format="%(name)s: %(levelname)s: %(message)s",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
num_gpus = get_num_gpus_multiworker()
args.distributed = args.distributed or num_gpus > 1
logger.info(f"Using {num_gpus} GPUs in this job.")
if args.distributed:
args.device, args.local_rank = init_distributed(args.local_rank)
if not args.preprocessed:
reader_type = "lang"
args.pattern = "*.txt"
else:
reader_type = "preprocessed"
reader = MultiFileDatasetReader(args.nctx,
args.subword_model_file,
args.subword_vocab_file,
args.pattern,
reader_type=reader_type)
# just return the vocab from the BPE vectorizer
vocab = reader.build_vocab([])
gen_embed = baseline.embeddings.load_embeddings('x',
dsz=args.gen_d_model,
known_vocab=vocab['x'],
embed_type=args.embed_type)
vocabs = gen_embed['vocab']
index2word = revlut(vocabs)
discrim_embed = baseline.embeddings.load_embeddings(
'x',
dsz=args.discrim_d_model,
known_vocab=vocab['x'],
embed_type=args.embed_type)
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, os.path.join(args.basedir, 'vocabs.json'))
gen_embeddings = {'x': gen_embed['embeddings']}
discrim_embeddings = {'x': discrim_embed['embeddings']}
logger.info("Loaded embeddings")
train_set = reader.load(args.train_file, vocabs)
valid_set = reader.load(args.valid_file, vocabs)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
num_workers=args.num_train_workers)
valid_loader = DataLoader(valid_set, batch_size=args.batch_size)
train_steps_per_epoch = len(train_loader) // (args.batch_size * num_gpus)
valid_steps_per_epoch = len(valid_loader) // args.batch_size
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
mask_value = vocabs.get("[MASK]", vocabs.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)
vocab_size = len(vocabs)
if len(args.gen_rpr_k) == 0 or args.gen_rpr_k[0] < 1:
gen_rpr_k = None
elif len(args.gen_rpr_k) == 1:
gen_rpr_k = args.gen_rpr_k[0]
else:
gen_rpr_k = args.gen_rpr_k
if len(args.gen_rpr_k) == 0 or args.discrim_rpr_k[0] < 1:
discrim_rpr_k = None
elif len(args.discrim_rpr_k) == 1:
discrim_rpr_k = args.discrim_rpr_k[0]
else:
discrim_rpr_k = args.discrim_rpr_k
gen_model = TransformerMaskedLanguageModel.create(
gen_embeddings,
hsz=args.gen_d_model,
d_ff=args.gen_d_ff,
tie_weights=True,
dropout=args.gen_dropout,
num_heads=args.gen_num_heads,
layers=args.gen_num_layers,
rpr_k=gen_rpr_k,
d_k=args.gen_d_k,
src_keys=['x'],
tgt_key='x')
discrim_model = TransformerDiscriminator(discrim_embeddings,
d_model=args.discrim_d_model,
d_ff=args.discrim_d_ff,
dropout=args.discrim_dropout,
num_heads=args.discrim_num_heads,
layers=args.discrim_num_layers,
activation='gelu',
layer_norm_eps=1.0e-12,
rpr_k=discrim_rpr_k,
d_k=args.discrim_d_k)
gen_model.to(args.device)
gen_loss_fn = gen_model.create_loss()
discrim_model.to(args.device)
discrim_loss_fn = discrim_model.create_loss()
logger.info("Loaded model and loss")
update_on = train_steps_per_epoch // args.saves_per_epoch
report_on = update_on // 10
logger.info(
f"Steps per epoch per GPU: {train_steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = get_lr_decay(args.lr_scheduler,
args.lr,
train_steps_per_epoch,
args.epochs,
logger,
decay_steps=args.lr_decay_steps,
decay_rate=args.lr_decay_rate,
alpha=args.lr_alpha)
linear_warmup = WarmupLinearSchedulerPyTorch(args.warmup_steps, lr=args.lr)
lr_sched = CompositeLRScheduler(linear_warmup, lr_decay, lr=args.lr)
global_step = 0
start_epoch = 0
if args.restart_from:
if not os.path.isdir(args.restart_from):
raise Exception(
f"Cannot restart from {args.restart_from}, directory not found"
)
tick_type = args.restart_tt
discrim_latest, step_num = find_latest_checkpoint(
args.restart_from, wildcard=f'checkpoint-discrim-{tick_type}')
gen_latest, _ = find_latest_checkpoint(
args.restart_from, wildcard=f'checkpoint-gen-{tick_type}')
discrim_model.load_state_dict(torch.load(discrim_latest))
gen_model.load_state_dict(torch.load(gen_latest))
if tick_type == 'step':
start_epoch = step_num // train_steps_per_epoch
global_step = step_num
else:
start_epoch = step_num
global_step = train_steps_per_epoch * start_epoch
parameters = list(discrim_model.parameters()) + list(
gen_model.parameters())
optz = OptimizerManager(parameters,
global_step,
optim=args.optim,
lr=args.lr,
lr_function=lr_sched,
weight_decay=args.weight_decay)
logger.info("Generator has {:,} parameters".format(
sum(p.numel() for p in gen_model.parameters() if p.requires_grad)))
logger.info("Discriminator has {:,} parameters".format(
sum(p.numel() for p in discrim_model.parameters() if p.requires_grad)))
# Prepare model for distributed training if needed
if args.distributed:
# This program assume pure data parallelism, each model is on a single gpu
# If we wanted to support model and data parallelism we would need to update
# the selection of gpus based on rank, it would need to select multiple ids
# based on rank, here we select only a single gpu and use it for input and
# output.
gen_model = DistributedDataParallel(gen_model,
device_ids=[args.device],
output_device=args.device)
discrim_model = DistributedDataParallel(discrim_model,
device_ids=[args.device],
output_device=args.device)
logger.info("Model located on %s", args.device)
# This is the training loop
steps = global_step
model_base = os.path.join(args.basedir, 'checkpoint')
discrim_base = f'{model_base}-discrim'
gen_base = f'{model_base}-gen'
do_on_demand_masking = not args.preprocessed
if do_on_demand_masking:
logger.info(f"On-demand masking is turned on")
timer = Timer()
for epoch in range(start_epoch, args.epochs):
gen_model.train()
discrim_model.train()
avg_gen_loss = Average('average_train_gen_loss')
avg_discrim_loss = Average('average_train_discrim_loss')
avg_discrim_acc = Average('average_train_discrim_acc')
avg_train_loss = Average('average5_train_loss')
metrics = {}
optz.zero_grad()
timer.start()
print(f'Starting epoch {epoch + 1}')
train_iter = iter(train_loader)
valid_iter = iter(valid_loader)
for i in range(train_steps_per_epoch):
steps += 1
x, y = next(train_iter)
do_report = (i + 1) % report_on == 0 and args.print
gen_loss_step, discrim_loss_step, acc = gen_vs_discrim(
x, y, args.device, gen_model, gen_loss_fn, discrim_model,
discrim_loss_fn, mask_value, vocab_size, index2word, do_report,
do_on_demand_masking)
avg_gen_loss.update(gen_loss_step.item())
total_loss_step = gen_loss_step + args.gen_loss_scale * discrim_loss_step
total_loss_step.backward()
avg_discrim_loss.update(discrim_loss_step.item())
avg_train_loss.update(total_loss_step.item())
avg_discrim_acc.update(acc)
torch.nn.utils.clip_grad_norm_(parameters, args.clip)
optz.step()
optz.zero_grad()
if (i + 1) % report_on == 0:
logging.info('Loss g=%f, d=%f total=%f, Per token acc=%f',
avg_gen_loss.avg, avg_discrim_loss.avg,
avg_train_loss.avg, avg_discrim_acc.avg)
if (i + 1) % update_on == 0 and args.local_rank < 1:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
logging.info('LR: %f', optz.current_lr)
save_checkpoint(gen_model, gen_base, steps, tick_type='step')
save_checkpoint(discrim_model,
discrim_base,
steps,
tick_type='step')
# How much time elapsed in minutes
elapsed = timer.elapsed(True)
# This is the average training token-level loss across all machines
# This is the token-level training perplexity
metrics['train_elapsed_min'] = elapsed
metrics['average_train_gen_loss'] = avg_gen_loss.avg
metrics['average_train_discrim_loss'] = avg_discrim_loss.avg
metrics[
'average_train_discrim_per_token_accuracy'] = avg_discrim_acc.avg
metrics['average_train_loss'] = avg_train_loss.avg
if args.local_rank < 1:
avg_valid_gen_loss = Average('average_valid_gen_loss')
avg_valid_discrim_loss = Average('average_valid_discrim_loss')
avg_valid_discrim_acc = Average('average_valid_discrim_acc')
avg_valid_loss = Average('average_valid_loss')
timer.start()
gen_model.eval()
discrim_model.eval()
for i in range(valid_steps_per_epoch):
with torch.no_grad():
x, y = next(valid_iter)
do_report = (i + 1) % report_on == 0 and args.print
gen_loss_step, discrim_loss_step, acc = gen_vs_discrim(
x, y, args.device, gen_model, gen_loss_fn,
discrim_model, discrim_loss_fn, mask_value, vocab_size,
index2word, do_report, do_on_demand_masking)
avg_valid_gen_loss.update(gen_loss_step.item())
avg_valid_discrim_acc.update(acc)
avg_valid_discrim_loss.update(discrim_loss_step.item())
total_loss_step = gen_loss_step + args.gen_loss_scale * discrim_loss_step
avg_valid_loss.update(total_loss_step.item())
elapsed = timer.elapsed(True)
metrics['valid_elapsed_min'] = elapsed
metrics['average_valid_gen_loss'] = avg_valid_gen_loss.avg
metrics['average_valid_discrim_loss'] = avg_valid_discrim_loss.avg
metrics[
'average_valid_discrim_per_token_accuracy'] = avg_valid_discrim_acc.avg
metrics['average_valid_loss'] = avg_valid_loss.avg
logger.info(metrics)
save_checkpoint(discrim_model,
discrim_base,
epoch,
tick_type='epoch',
save_npz=True)
save_checkpoint(gen_model,
gen_base,
epoch,
tick_type='epoch',
save_npz=True)
def main():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--train_dir", type=str, required=True, help='Training directory')
parser.add_argument("--valid_dir", type=str, required=True, help='Validation directory')
parser.add_argument("--train_md", type=str, help="Training metadata YAML, defaults to `{train_dir}/md.yml`")
parser.add_argument("--valid_md", type=str, help="Validation metadata YAML, defaults to `{valid_dir}/md.yml`")
parser.add_argument("--dataset_key", default="tlm",
help="dataset key for basedir")
parser.add_argument("--embed_type", type=str, default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument("--d_model", type=int, default=512, help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2048, help="FFN dimension")
parser.add_argument("--d_k", type=int, default=None, help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--num_heads", type=int, default=8, help="Number of heads")
parser.add_argument("--num_layers", type=int, default=8, help="Number of layers")
parser.add_argument("--num_train_workers", type=int, default=4, help="Number train workers")
parser.add_argument("--distribute", type=str, default="mirror", choices=["mirror", "tpu", "nccl"])
parser.add_argument("--tpu_ep", type=str, help="The TPU endpoint if using `distribute=tpu`")
parser.add_argument("--nctx", type=int, default=512, help="Max input length")
parser.add_argument("--file_type", default='tfrecord', choices=['json', 'tfrecord'], help="Glob pattern for data")
parser.add_argument("--batch_size", type=int, default=256, help="Batch Size")
parser.add_argument("--subword_model_file", type=str, help="The BPE model file", required=False)
parser.add_argument("--subword_vocab_file", type=str, help="The BPE subword vocab", required=False)
parser.add_argument("--subword_type", type=str, choices=["bpe", "wordpiece", "sentencepiece"], default="bpe")
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--ffn_pdrop", type=float, default=0.0, help="Dropout in the dense stack")
parser.add_argument("--layer_drop", type=float, default=0.0, help="LayerDrop to apply")
parser.add_argument("--optim", default="adamw", type=str, help="Optimizer to use (defaults to adamw)")
parser.add_argument("--lr", type=float, default=4.0e-4, help="Learning rate")
parser.add_argument("--clip", type=float, default=1.0, help="Clipping gradient norm")
parser.add_argument("--weight_decay", type=float, default=1.0e-2, help="Weight decay")
parser.add_argument("--epochs", type=int, default=32, help="Num training epochs")
parser.add_argument("--restart", type=str2bool, help="Option allows you to restart from a previous checkpoint")
parser.add_argument("--warmup_steps", type=int, default=10000, help="Num warmup steps")
parser.add_argument("--causal", type=str2bool, default=False, help="Use CLM (causal) instead of MLM")
parser.add_argument("--mlp", type=str2bool, default=False, help="Use Gated MLP")
parser.add_argument("--saves_per_epoch", type=int, default=10, help="The number of checkpoints to save per epoch")
parser.add_argument('--rpr_k',
help='Relative attention positional sizes pass 0 if you dont want relative attention',
type=int, default=[8], nargs='+')
parser.add_argument('--rpr_value_on', type=str2bool, default=True,
help="In relative attention, whether add positional correction to values in addition to the "
"correction to attention matrix")
parser.add_argument('--ra_type', type=str, help="Specify a relative attention type")
parser.add_argument('--windowed_ra', type=str2bool, default=False, help="whether prevent attention beyond rpr_k")
parser.add_argument("--strategy", help="Training strategy, defaults to `mirror`", choices=["mirror"])
parser.add_argument("--npz", help="Should we write out NPZ files?", type=str2bool, default=False)
parser.add_argument("--tb", help="Turn on tensorboard?", type=str2bool, default=False)
parser.add_argument("--convert_only", help="Should we just convert this file to NPZ and exit?", type=str2bool, default=False)
parser.add_argument("--extra_tokens", help="What extra tokens should we use", nargs="+", default=["[CLS]", "[MASK]"])
parser.add_argument("--eps", help="Epsilon", default=1e-6, type=float)
parser.add_argument("--beta2", help="Epsilon", default=0.98, type=float)
parser.add_argument("--grad_accum", help="Number of iterations to accum grads", default=1, type=int)
parser.add_argument("--transformer_type", help="Transformer layer type")
args = parser.parse_args()
SET_TRAIN_FLAG(True)
if args.convert_only:
args.restart = True
if args.basedir is None:
args.basedir = f'lm-{args.dataset_key}-bpe-{os.getpid()}'
logging.basicConfig(level=logging.INFO)
logger.info(f"Writing results to {args.basedir}")
if args.tb:
logdir = f"{args.basedir}/scalars/{os.getpid()}"
file_writer = tf.summary.create_file_writer(logdir + "/metrics")
file_writer.set_as_default()
logger.info(f"Set up tensorboard logdir {logdir}")
strategy = create_distribute_strategy(args.distribute, args.tpu_ep)
num_replicas = strategy.num_replicas_in_sync
logger.info(f"Using {num_replicas} replicas in this job.")
Vec1D = get_subword_vec1d(args.subword_type)
vectorizer = Vec1D(model_file=args.subword_model_file,
vocab_file=args.subword_vocab_file,
mxlen=args.nctx,
extra_tokens=args.extra_tokens)
vocab = {'x': vectorizer.vocab}
preproc_data = baseline.embeddings.load_embeddings('x', dsz=args.d_model, known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=args.embed_type)
vocabs = preproc_data['vocab']
train_md = args.train_md if args.train_md else os.path.join(args.train_dir, 'md.yml')
num_train_samples = get_num_samples(train_md)
valid_md = args.valid_md if args.valid_md else os.path.join(args.valid_dir, 'md.yml')
num_valid_samples = get_num_samples(valid_md)
is_curriculum = True if isinstance(num_train_samples, Mapping) else False
def dataset_train_fn(input_context):
global_batchsz = args.batch_size
base_batchsz = input_context.get_per_replica_batch_size(global_batchsz)
ds = None
num_shards = input_context.num_input_pipelines
index = input_context.input_pipeline_id
if is_curriculum:
for sub in num_train_samples.keys():
train_curr_dir = os.path.join(args.train_dir, str(sub))
batchsz_scale_factor = args.nctx // sub
this_batchsz = base_batchsz * batchsz_scale_factor
curr_ds = get_dataset(train_curr_dir, args.file_type, args.num_train_workers, num_shards, index, causal=args.causal).batch(this_batchsz, drop_remainder=True)
if ds is None:
ds = curr_ds
else:
ds = ds.concatenate(curr_ds)
else:
ds = get_dataset(args.train_dir, args.file_type, args.num_train_workers, num_shards, index, causal=args.causal).batch(base_batchsz)
return ds
train_loader = strategy.experimental_distribute_datasets_from_function(dataset_train_fn)
def dataset_test_fn(input_context):
global_batchsz = args.batch_size
base_batchsz = input_context.get_per_replica_batch_size(global_batchsz)
num_shards = input_context.num_input_pipelines
index = input_context.input_pipeline_id
ds = None
if is_curriculum:
for sub in num_valid_samples.keys():
valid_curr_dir = os.path.join(args.valid_dir, str(sub))
batchsz_scale_factor = args.nctx // sub
this_batchsz = base_batchsz * batchsz_scale_factor
curr_ds = get_dataset(valid_curr_dir, args.file_type, args.num_train_workers, num_shards, index, causal=args.causal).batch(
this_batchsz, drop_remainder=True)
if ds is None:
ds = curr_ds
else:
ds = ds.concatenate(curr_ds)
else:
ds = get_dataset(args.valid_dir, args.file_type, args.num_train_workers, num_shards, index, shuffle=False, causal=args.causal).batch(base_batchsz)
return ds
valid_loader = strategy.experimental_distribute_datasets_from_function(dataset_test_fn)
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, os.path.join(args.basedir, 'vocabs.json'))
embeddings = {'x': preproc_data['embeddings']}
logger.info("Loaded embeddings")
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
model = create_model(args, embeddings)
if isinstance(model, GatedMLPLanguageModel) and is_curriculum:
raise Exception("Variable tensor lengths not currently supported for gMLP")
logger.info("Loaded model and loss")
eff_batch_size = args.batch_size * args.grad_accum
logger.info(f"eff batch size: {eff_batch_size}, {args.batch_size}(b) x {args.grad_accum}(ga)")
if is_curriculum:
steps_per_epoch = 0
steps_per_valid_epoch = 0
for k, v in num_train_samples.items():
steps_per_epoch += int(num_train_samples[k] // (eff_batch_size * (args.nctx / k)))
for k, v in num_valid_samples.items():
steps_per_valid_epoch += int(num_valid_samples[k] // (args.batch_size * (args.nctx / k)))
else:
steps_per_epoch = num_train_samples // eff_batch_size
steps_per_valid_epoch = num_valid_samples // args.batch_size
update_on = steps_per_epoch // args.saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(f"Steps per epoch: {steps_per_epoch}. Saving checkpoint every {update_on} steps.")
lr_decay = CosineDecaySchedulerTensorFlow(steps_per_epoch * args.epochs, lr=args.lr)
linear_warmup = WarmupLinearSchedulerTensorFlow(args.warmup_steps, lr=args.lr)
lr_sched = CompositeLRSchedulerTensorFlow(linear_warmup, lr_decay)
optimizer = EagerOptimizer(loss_function, optim=args.optim, lr_function=lr_sched, weight_decay=args.weight_decay, clip=args.clip,
lr=args.lr, epsilon=args.eps, beta2=args.beta2)
checkpoint = tf.train.Checkpoint(optimizer=optimizer.optimizer, model=model)
checkpoint_manager = tf.train.CheckpointManager(checkpoint,
directory=args.basedir,
max_to_keep=5)
grad_accum = GradientAccumulator()
start_epoch = 0
if args.restart:
# The global step gets automatically updated here
# so we dont have to worry about our LR regimen
checkpoint.restore(checkpoint_manager.latest_checkpoint)
current_step = optimizer.global_step
start_epoch = current_step // steps_per_epoch
def _replicated_forward_step(inputs):
"""This runs on a single replica"""
x, y = inputs
per_replica_grads, per_replica_loss = optimizer.get_grads_and_loss(model, {'x': x}, y, num_replicas * args.grad_accum)
grad_accum(per_replica_grads)
return per_replica_loss
def _replicated_optz_step():
optimizer.apply_grads(model, grad_accum.gradients)
@tf.function
def _distributed_optz_step():
strategy.run(_replicated_optz_step)
@tf.function
def _distributed_forward_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
per_replica_loss = strategy.run(_replicated_forward_step, args=(inputs,))
return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_loss, axis=None)
def _replicated_test_step(inputs):
"""This runs on a single replica"""
x, y = inputs
per_replica_loss = loss_function(model, {'x': x}, y) / num_replicas
return per_replica_loss
@tf.function
def _distributed_test_step(inputs: Tuple[tf.Tensor, tf.Tensor]):
"""Runs across multiple replicas and aggregates the results.
:param inputs:
:return:
"""
per_replica_loss = strategy.run(_replicated_test_step, args=(inputs,))
return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_loss, axis=None)
timer = Timer()
with strategy.scope():
for epoch in range(start_epoch, args.epochs):
timer.start()
SET_TRAIN_FLAG(True)
logger.info('Starting epoch %d', epoch + 1)
avg_loss = Average('average_train_loss')
metrics = {}
train_iter = iter(train_loader)
step_loss = 0
iterations = steps_per_epoch * args.batch_size
for i in range(iterations):
try:
loss = _distributed_forward_step(next(train_iter))
step_loss += loss
if (i + 1) % args.grad_accum == 0:
# This does a gradient update
_distributed_optz_step()
# Now reset the gradient accumulator
grad_accum.reset()
# Now update the loss info
tf.summary.scalar("train_loss", data=step_loss, step=optimizer.global_step)
avg_loss.update(step_loss.numpy().item())
# Now reset the loss
step_loss = 0
steps = optimizer.global_step.numpy()
if (steps + 1) % report_on == 0:
logger.info(avg_loss)
if (steps + 1) % update_on == 0:
elapsed = timer.elapsed(True)
logger.info('elapsed time this epoch %d min', elapsed)
logger.info('elapsed step time %f steps/min', i/elapsed)
checkpoint_manager.save()
if args.npz:
npz_checkpoint = os.path.join(args.basedir, f'checkpoint-step-{steps}.npz')
save_tlm_npz(model, npz_checkpoint)
except Exception as e:
logger.error(e)
logger.error(f"Exception at training iter {i+1}/{iterations}. Skipping")
pass
if args.convert_only:
logger.warning("Convert only flag specified. Stopping after one step")
steps = optimizer.global_step.numpy()
npz_checkpoint = os.path.join(args.basedir, f'checkpoint-step-{steps}.npz')
save_tlm_npz(model, npz_checkpoint)
return
# How much time elapsed in minutes
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'] = timer.elapsed(True)
metrics['average_train_loss'] = train_token_loss
metrics['train_ppl'] = train_token_ppl
metrics['lr'] = float(lr_sched(tf.cast(optimizer.global_step, tf.float32)).numpy().item())
avg_valid_loss = Average('average_valid_loss')
timer.start()
SET_TRAIN_FLAG(False)
valid_iter = iter(valid_loader)
for i in range(steps_per_valid_epoch):
try:
valid_loss = _distributed_test_step(next(valid_iter))
tf.summary.scalar('valid_loss', data=valid_loss, step=optimizer.global_step)
avg_valid_loss.update(valid_loss.numpy().item())
except Exception as e:
logger.error(f"Exception at validation step {i+1}/{steps_per_valid_epoch}. Skipping")
pass
valid_token_loss = avg_valid_loss.avg
valid_token_ppl = math.exp(valid_token_loss)
metrics['valid_elapsed_min'] = timer.elapsed(True)
metrics['average_valid_loss'] = valid_token_loss
metrics['average_valid_word_ppl'] = valid_token_ppl
logger.info(json.dumps(metrics, indent=4))
def run(input_files=[],
input_pattern='*.txt',
codes=None,
vocab=None,
nctx=256,
fmt='json',
fields=['x_str', 'y_str'],
output=None,
x_prefix=None,
x_suffix=None,
y_prefix=None,
y_suffix=None,
max_file_size=100,
cased=True,
mask_type="mlm",
module=None,
pad_y=True,
extra_tokens=['[CLS]', '[MASK]'],
tgt_nctx=None,
world_size=1,
world_offset=0,
subword_type='bpe',
**kwargs):
timer = Timer()
if module:
logger.warning("Loading custom user module %s for masking rules",
module)
baseline.import_user_module(module)
if os.path.isdir(input_files):
import glob
input_files = list(glob.glob(os.path.join(input_files, input_pattern)))
if not output:
output = os.path.join(input_files, 'records')
else:
input_files = [input_files]
if not output:
output = f'{input_files}.records'
logger.info('Output [%s]', output)
if not tgt_nctx:
tgt_nctx = 64
transform = baseline.lowercase if not cased else lambda x: x
Vec1D = get_subword_vec1d(subword_type)
vectorizer = Vec1D(transform_fn=transform,
model_file=codes,
vocab_file=vocab,
mxlen=1024,
extra_tokens=extra_tokens)
if x_prefix:
x_prefix = vectorizer.vocab[x_prefix]
if x_suffix:
x_suffix = vectorizer.vocab[x_suffix]
if y_prefix:
y_prefix = vectorizer.vocab[y_prefix]
if y_suffix:
y_suffix = vectorizer.vocab[y_suffix]
indices2word = baseline.revlut(vectorizer.vocab)
root_dir = os.path.dirname(output)
masking = create_masking(mask_type, vectorizer.vocab, pad_y)
if not os.path.exists(root_dir):
os.makedirs(root_dir)
# Create a file writer for this shard
fw = create_file_writer(fmt, output, fields, max_file_size,
1000 * world_offset)
num_read = -1
num_samples_this_worker = 0
for text in input_files:
with open(text, encoding='utf-8') as rf:
print(f"Reading from {text}...")
for line in rf:
num_read += 1
if num_read % world_size != world_offset:
continue
to_bpe = line.strip().split()
if not to_bpe:
continue
output, available = vectorizer.run(to_bpe, vectorizer.vocab)
x, y = masking(output[:available], False, False)
if x_prefix:
x = [x_prefix] + x
if y_prefix:
y = [y_prefix] + y
if x_suffix:
x += [x_suffix]
if y_suffix:
y += [y_suffix]
x = x[:nctx]
y = y[:tgt_nctx]
x_t = np.zeros(nctx, dtype=output.dtype)
y_t = np.zeros(tgt_nctx, dtype=output.dtype)
x_t[:len(x)] = x
y_t[:len(y)] = y
record = {
'x': x_t,
'y': y_t,
'x_str': [indices2word[s] for s in x_t],
'y_str': [indices2word[s] for s in y_t]
}
if masking.is_valid(record):
fw.write(record)
num_samples_this_worker += 1
fw.close()
duration = timer.elapsed()
print("Processed {:,} samples in {:.2f}s".format(num_samples_this_worker,
duration))
f_name = f'md-{world_offset}.yml' if world_size > 1 else 'md.yml'
write_yaml({'num_samples': num_samples_this_worker},
os.path.join(root_dir, f_name))
def train():
parser = ArgumentParser()
parser.add_argument("--basedir", type=str)
parser.add_argument("--train_file",
type=str,
required=True,
help='File path to use for train file')
parser.add_argument("--valid_file",
type=str,
required=True,
help='File path to use for valid file')
parser.add_argument("--dataset_key",
default="paired",
help="dataset key for basedir")
parser.add_argument(
"--embed_type",
type=str,
default='default',
choices=["default", "positional", "learned-positional"],
help="register label of the embeddings")
parser.add_argument("--d_model",
type=int,
default=512,
help="Model dimension (and embedding dsz)")
parser.add_argument("--d_ff", type=int, default=2048, help="FFN dimension")
parser.add_argument(
"--d_k",
type=int,
default=None,
help="Dimension per head. Use if num_heads=1 to reduce dims")
parser.add_argument("--num_heads",
type=int,
default=8,
help="Number of heads")
parser.add_argument("--num_layers",
type=int,
default=8,
help="Number of layers")
parser.add_argument("--windowed_ra",
type=str2bool,
default=False,
help="whether prevent attention beyond rpr_k")
parser.add_argument("--num_train_workers",
type=int,
default=4,
help="Number train workers")
parser.add_argument("--nctx",
type=int,
default=256,
help="Max input length")
parser.add_argument("--tgt_nctx",
type=int,
help="Max output length, default to args.nctx")
parser.add_argument("--file_type", default='json', help="Suffix for data")
parser.add_argument("--record_keys", default=['x', 'y'], nargs='+')
parser.add_argument("--batch_size",
type=int,
default=256,
help="Batch Size")
parser.add_argument("--subword_model_file",
type=str,
help="The BPE model file",
required=True)
parser.add_argument("--subword_vocab_file",
type=str,
help="The BPE subword vocab",
required=True)
parser.add_argument("--dropout", type=float, default=0.1, help="Dropout")
parser.add_argument("--lr_scheduler",
type=str,
default='cosine',
help="The type of learning rate decay scheduler")
parser.add_argument("--lr_decay_steps",
type=int,
help="decay steps of lr scheduler")
parser.add_argument("--lr_decay_rate",
type=float,
help="decay rate of lr scheduler")
parser.add_argument("--lr_alpha",
type=float,
help="parameter alpha for cosine decay scheduler")
parser.add_argument("--optim",
default="adamw",
type=str,
help="Optimizer to use (defaults to adamw)")
parser.add_argument("--lr",
type=float,
default=4.0e-4,
help="Learning rate")
parser.add_argument("--clip",
type=float,
default=1.0,
help="Clipping gradient norm")
parser.add_argument("--weight_decay",
type=float,
default=1.0e-2,
help="Weight decay")
parser.add_argument("--epochs",
type=int,
default=32,
help="Num training epochs")
parser.add_argument(
"--restart_from",
type=str,
help="Option allows you to restart from a previous checkpoint")
parser.add_argument(
"--restart_tt",
type=str,
help="Optional param for legacy checkpoints (step|epoch)")
parser.add_argument("--warmup_steps",
type=int,
default=10000,
help="Num warmup steps")
parser.add_argument("--saves_per_epoch",
type=int,
default=10,
help="The number of checkpoints to save per epoch")
parser.add_argument("--reduction_d_k",
type=int,
default=64,
help="Dimensions of Key and Query in the single headed"
"reduction layers")
parser.add_argument(
"--reduction_type",
type=str,
default="2ha",
help="If using a dual encoder, specifies the reduction type")
parser.add_argument(
"--unfreeze_after_step",
default=0,
type=int,
help=
"Unfreeze encoders after step, ignored if we dont have a checkpoint")
parser.add_argument(
"--stacking_layers",
type=int,
nargs='+',
default=[],
help="Hidden sizes of the dense stack (ff2 from the convert paper)")
parser.add_argument("--layer_drop",
type=float,
default=0.0,
help="LayerDrop to apply")
parser.add_argument("--ff_pdrop",
type=float,
default=0.1,
help="Dropout in the dense stack")
parser.add_argument("--reader_type",
type=str,
default='preprocessed',
choices=['ntp', 'nsp', 'preprocessed', 'tfrecord'])
parser.add_argument(
"--model_type",
default="dual-encoder",
choices=["dual-encoder", "encoder-decoder", "transformer-bow"])
parser.add_argument("--src_begin_tok", type=str, nargs='+', default=[])
parser.add_argument("--src_end_tok",
type=str,
nargs='+',
default=['<EOS>'])
parser.add_argument("--tgt_begin_tok",
type=str,
nargs='+',
default=['<GO>'])
parser.add_argument("--tgt_end_tok",
type=str,
nargs='+',
default=['<EOS>'])
parser.add_argument('--lower', type=baseline.str2bool, default=False)
parser.add_argument(
"--loss",
type=str,
default='symmetric',
choices=['triplet', 'all', 'all_mean', 'contrastive', 'symmetric'])
parser.add_argument(
"--learn_temp",
type=str2bool,
default=True,
help=
"If 'constrastive' or 'symmetric' loss, should we learn the temperature scaling"
)
parser.add_argument(
"--init_temp",
type=float,
help="Initialize the temperature for 'contrastive' or 'symmetric' loss"
)
parser.add_argument(
'--rpr_k',
help=
'Relative attention positional sizes pass 0 if you dont want relative attention',
type=int,
default=[8],
nargs='+')
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("--save_npz",
type=str2bool,
default=False,
help="Whether save npz checkpoint")
args = parser.parse_args()
if args.basedir is None:
args.basedir = '{}-{}-paired-{}-bpe-{}'.format(args.model_type,
args.reader_type,
args.dataset_key,
os.getpid())
logging.basicConfig(
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
num_gpus = get_num_gpus_multiworker()
args.distributed = args.distributed or num_gpus > 1
logger.info(f"Using {num_gpus} GPUs in this job.")
if args.distributed:
args.device, updated_local_rank = init_distributed(args.local_rank)
args.local_rank = updated_local_rank
if not args.tgt_nctx:
args.tgt_nctx = args.nctx
reader = MultiFileDatasetReader(args.nctx,
args.tgt_nctx,
args.src_begin_tok,
args.src_end_tok,
args.tgt_begin_tok,
args.tgt_end_tok,
args.subword_model_file,
args.subword_vocab_file,
args.file_type,
reader_type=args.reader_type,
record_keys=args.record_keys,
lower=args.lower)
vocab = reader.build_vocab()
# If we are not using chars, then use 'x' for both input and output
preproc_data = baseline.embeddings.load_embeddings(
'x',
dsz=args.d_model,
known_vocab=vocab['x'],
preserve_vocab_indices=True,
embed_type=args.embed_type)
vocabs = preproc_data['vocab']
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, os.path.join(args.basedir, 'vocabs.json'))
embeddings = preproc_data['embeddings']
logger.info("Loaded embeddings")
train_set = reader.load(args.train_file, vocabs)
valid_set = reader.load(args.valid_file,
vocabs,
distribute=False,
shuffle=False)
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
num_workers=args.num_train_workers)
valid_loader = DataLoader(valid_set, batch_size=args.batch_size)
logger.info("Loaded datasets")
logger.info("Using embedding type [%s]", args.embed_type)
if len(args.rpr_k) == 0 or args.rpr_k[0] < 1:
rpr_k = None
elif len(args.rpr_k) == 1:
rpr_k = args.rpr_k[0]
else:
rpr_k = args.rpr_k
model = create_model(embeddings,
d_model=args.d_model,
d_ff=args.d_ff,
dropout=args.dropout,
num_heads=args.num_heads,
num_layers=args.num_layers,
model_type=args.model_type,
rpr_k=rpr_k,
d_k=args.d_k,
reduction_d_k=args.reduction_d_k,
stacking_layers=args.stacking_layers,
ff_pdrop=args.ff_pdrop,
windowed_ra=args.windowed_ra,
reduction_type=args.reduction_type,
layer_drop=args.layer_drop,
logger=logger)
model.to(args.device)
if args.model_type == 'encoder-decoder':
run_step = run_step_s2s
else:
run_step = run_step_dual
logger.info(
f"Creating {args.loss}, init temperature: {args.init_temp}, learnable: {args.learn_temp}"
)
loss_function = model.create_loss(loss_type=args.loss,
init_temp=args.init_temp,
learn_temp=args.learn_temp)
loss_function.to(args.device)
logger.info("Created model and loss")
steps_per_epoch = len(train_loader) // num_gpus
valid_steps = len(valid_loader)
update_on = steps_per_epoch // args.saves_per_epoch
report_on = max(10, update_on) // 10
logger.info(
f"Steps per epoch per GPU: {steps_per_epoch}. Saving checkpoint every {update_on} steps."
)
lr_decay = get_lr_decay(args.lr_scheduler,
args.lr,
steps_per_epoch,
args.epochs,
logger,
decay_steps=args.lr_decay_steps,
decay_rate=args.lr_decay_rate,
alpha=args.lr_alpha)
linear_warmup = WarmupLinearSchedulerPyTorch(args.warmup_steps, lr=args.lr)
lr_sched = CompositeLRScheduler(linear_warmup, lr_decay, lr=args.lr)
global_step = 0
start_epoch = 0
if args.restart_from:
if args.unfreeze_after_step > 0 and args.model_type == "dual-encoder":
logger.info(f"Encoders will be frozen until step %d",
args.unfreeze_after_step)
global_step, start_epoch = reload_from_checkpoint(
args.model_type, args.restart_from, args.restart_tt, model,
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)
target = model if args.model_type == 'encoder-decoder' else loss_function
optimizer = OptimizerManager(target,
global_step,
optim=args.optim,
lr=args.lr,
lr_function=lr_sched,
weight_decay=args.weight_decay)
logger.info("Model has {:,} parameters".format(
sum(p.numel() for p in target.parameters() if p.requires_grad)))
# Prepare model for distributed training if needed
if args.distributed:
model = DistributedDataParallel(model,
device_ids=[args.device],
output_device=args.device)
logger.info("Model located on %d", args.local_rank)
model_base = os.path.join(args.basedir, 'checkpoint')
steps = global_step
timer = Timer()
for epoch in range(start_epoch, args.epochs):
avg_loss = Average('average_train_loss')
metrics = {}
optimizer.zero_grad()
timer.start()
model.train()
train_itr = iter(train_loader)
for i in range(steps_per_epoch):
batch = next(train_itr)
if steps > args.unfreeze_after_step and hasattr(
model, 'freeze') and model.freeze:
logging.info("Unfreezing encoders at step %d", steps)
model.freeze = False
steps += 1
x, y = batch
loss = run_step(x, y, model, loss_function, args.device,
args.distributed)
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) % report_on == 0:
logging.info(avg_loss)
if (i + 1) % update_on == 0 and args.local_rank < 1:
elapsed = timer.elapsed(True)
logging.info('elapsed time this epoch %d min', elapsed)
logging.info('elapsed step time %f steps/min', i / elapsed)
logging.info('LR: %f', optimizer.current_lr)
save_checkpoint(model,
model_base,
steps,
tick_type='step',
save_npz=args.save_npz)
# How much time elapsed in minutes
elapsed = timer.elapsed(True)
train_avg_loss = avg_loss.avg
# This is the average training token-level loss across all machines
# This is the token-level training perplexity
metrics['train_elapsed_min'] = elapsed
metrics['average_train_loss'] = train_avg_loss
if args.local_rank < 1:
avg_valid_loss = Average('average_valid_loss')
timer.start()
model.eval()
valid_itr = iter(valid_loader)
for j in range(valid_steps):
with torch.no_grad():
batch = next(valid_itr)
x, y = batch
loss = run_step(x, y, model, loss_function, args.device,
args.distributed)
avg_valid_loss.update(loss.item())
valid_avg_loss = avg_valid_loss.avg
elapsed = timer.elapsed(True)
metrics['valid_elapsed_min'] = elapsed
metrics['average_valid_loss'] = valid_avg_loss
logger.info(metrics)
save_checkpoint(model,
model_base,
epoch,
tick_type='epoch',
save_npz=args.save_npz)