def create_model(embeddings, d_model, d_ff, num_heads, num_layers, rpr_k, rpr_value_on, d_k, checkpoint_name, activation):
rpr_k = listify(rpr_k)
if len(rpr_k) == 0 or rpr_k[0] < 1:
rpr_k = None
elif len(rpr_k) == 1:
rpr_k = rpr_k[0]
logger.info("Creating tied encoder decoder model")
model = TransformerLanguageModel.create({'x': embeddings},
hsz=d_model,
d_ff=d_ff,
tie_weights=True,
dropout=0,
gpu=False,
num_heads=num_heads,
layers=num_layers,
rpr_k=rpr_k,
rpr_value_on=rpr_value_on,
d_k=d_k,
activation=activation,
src_keys=['x'], tgt_key='x')
if checkpoint_name.endswith('npz'):
load_tlm_npz(model, checkpoint_name)
else:
tlm_load_state_dict(model, checkpoint_name)
model.eval()
print(model)
return model
def _round_trip(embed_type, rpr_k=None):
test_file = os.path.join(file_loc, "test_data", "blah.npz")
d_model = 40
vocab_x = {
'a': 1,
'aardvark': 100,
'beandip': 42,
'cheerio': 86,
'dumdum': 129,
'eoyre': 3
}
embeddings = {}
vocabs = {'x': vocab_x}
src_x_embedding = baseline.embeddings.load_embeddings(
'x', dsz=d_model, known_vocab=vocab_x, embed_type=embed_type)
embeddings['x'] = src_x_embedding['embeddings']
src_model = TransformerLanguageModel.create(embeddings,
hsz=d_model,
dropout=0.1,
gpu=False,
num_heads=4,
layers=2,
rpr_k=rpr_k,
src_keys=['x'],
tgt_key='x')
save_tlm_npz(src_model, test_file)
dst_x_embedding = baseline.embeddings.load_embeddings(
'x', dsz=d_model, known_vocab=vocab_x, embed_type=embed_type)
embeddings['x'] = dst_x_embedding['embeddings']
dst_model = TransformerLanguageModel.create(embeddings,
hsz=d_model,
dropout=0.1,
gpu=False,
num_heads=4,
layers=2,
rpr_k=rpr_k,
src_keys=['x'],
tgt_key='x')
load_tlm_npz(dst_model, test_file)
B = 4
T = 7
a_batch = torch.randint(0, 9, (B, T)).long()
a_lengths = torch.randint(0, T, (B, )).long()
out_pyt1 = _call_model(src_model, {
'x': a_batch,
'lengths': a_lengths
}).detach().numpy()
out_pyt2 = _call_model(dst_model, {
'x': a_batch,
'lengths': a_lengths
}).detach().numpy()
return np.allclose(out_pyt1, out_pyt2, atol=1e-6)
def load(cls, embeddings, **kwargs):
c = cls("tlm-words-embed", **kwargs)
if embeddings.endswith('.bin'):
# HuggingFace checkpoint, convert on the fly
from eight_mile.pytorch.serialize import load_tlm_transformers_bin, BERT_HF_FT_LAYER_MAP
unmatch = load_tlm_transformers_bin(
c, embeddings, replace_layers=BERT_HF_FT_LAYER_MAP)
if unmatch['missing'] or unmatch['unexpected']:
raise Exception("Unable to load the HuggingFace checkpoint")
if mime_type(embeddings) == 'application/zip':
load_tlm_npz(c, embeddings)
else:
tlm_load_state_dict(c, embeddings)
return c
def create(cls, embeddings, **kwargs):
lm = cls()
lm.gpu = kwargs.get('gpu', True)
lm.tgt_key = kwargs.get('tgt_key')
if lm.tgt_key is None:
raise Exception('Need a `tgt_key` to know which source vocabulary should be used for destination ')
lm.src_keys = kwargs.get('src_keys', embeddings.keys())
lm.create_layers(embeddings, **kwargs)
checkpoint_name = kwargs.get('checkpoint')
if checkpoint_name is not None:
if checkpoint_name.endswith('npz'):
load_tlm_npz(lm, checkpoint_name)
else:
lm.load_state_dict(torch.load(checkpoint_name))
return lm
def load(cls, embeddings, **kwargs):
c = cls("tlm-words-embed", **kwargs)
if embeddings.endswith('.bin'):
# HuggingFace checkpoint, convert on the fly
from eight_mile.pytorch.serialize import load_tlm_transformers_bin, BERT_HF_FT_LAYER_MAP
unmatch = load_tlm_transformers_bin(c, embeddings, replace_layers=BERT_HF_FT_LAYER_MAP)
if unmatch['missing'] or unmatch['unexpected']:
raise Exception("Unable to load the HuggingFace checkpoint")
if mime_type(embeddings) == 'application/zip' and not embeddings.endswith("pth"):
keys_to_restore = set(list(c.embeddings.keys()))
filtered_keys = keys_to_restore.difference(c.skippable)
if not keys_to_restore:
raise Exception("No keys to restore!")
if len(filtered_keys) < len(keys_to_restore):
logger.warning("Restoring only key [%s]", ' '.join(filtered_keys))
load_tlm_npz(c, embeddings, filtered_keys)
else:
tlm_load_state_dict(c, embeddings)
return c
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 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')
