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 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 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)