def main(args):
log_path = "{}_{}".format(args.log, random.randint(1, 100))
train_writer = SummaryWriter(log_dir=log_path + "/train")
dev_writer = SummaryWriter(log_dir=log_path + "/dev")
train, dev, test, words = read_corpus(args.data)
dev_, test_ = dev, test
train = create_batches(train, args.batch_size)
dev = create_batches(dev, args.batch_size)
test = create_batches(test, args.batch_size)
model = Model(words, args)
if args.load:
model.load_state_dict(torch.load(args.load))
model.cuda()
print(model)
print("vocab size: {}".format(model.n_V))
lr = 1.0 if not args.noam else 1.0 / (args.n_d**0.5) / (args.warmup_steps**
1.5)
if args.prune:
# in place substituion of linear ops in SRU
flop.make_hard_concrete(model.rnn, in_place=True)
model.cuda()
print("model after inserting hardconcrete:")
print(model)
hc_modules = flop.get_hardconcrete_modules(model)
hc_parameters = [
p for m in hc_modules for p in m.parameters() if p.requires_grad
]
optimizer_hc = Adam(hc_parameters,
lr=lr * args.prune_lr,
weight_decay=0)
num_hardconcrete_params = sum(x.numel() for x in hc_parameters)
print("num of hardconcrete paramters: {}".format(
num_hardconcrete_params))
lambda_1 = nn.Parameter(torch.tensor(0.).cuda())
lambda_2 = nn.Parameter(torch.tensor(0.).cuda())
optimizer_max = Adam([lambda_1, lambda_2], lr=lr, weight_decay=0)
optimizer_max.param_groups[0]['lr'] = -lr * args.prune_lr
hc_linear_modules = flop.get_hardconcrete_linear_modules(model)
num_prunable_params = sum(m.num_prunable_parameters()
for m in hc_linear_modules)
print("num of prunable paramters: {}".format(num_prunable_params))
else:
args.prune_start_epoch = args.max_epoch
m_parameters = [
i[1] for i in model.named_parameters()
if i[1].requires_grad and 'log_alpha' not in i[0]
]
optimizer = Adam(m_parameters,
lr=lr * args.lr,
weight_decay=args.weight_decay)
num_params = sum(x.numel() for x in m_parameters if x.requires_grad)
print("num of parameters: {}".format(num_params))
nbatch = 1
niter = 1
best_dev = 1e+8
unroll_size = args.unroll_size
batch_size = args.batch_size
N = (len(train[0]) - 1) // unroll_size + 1
criterion = nn.CrossEntropyLoss()
model.zero_grad()
if args.prune:
optimizer_max.zero_grad()
optimizer_hc.zero_grad()
for epoch in range(args.max_epoch):
start_time = time.time()
model.train()
total_loss = 0.0
hidden = model.init_hidden(batch_size)
start_prune = epoch >= args.prune_start_epoch
for i in range(N):
x = train[0][i * unroll_size:(i + 1) * unroll_size]
y = train[1][i * unroll_size:(i + 1) * unroll_size].view(-1)
hidden.detach_()
# language model forward and backward
output, hidden = model(x, hidden)
loss = criterion(output, y)
(loss / args.update_param_freq).backward()
loss = loss.item()
lagrangian_loss = 0
target_sparsity = 0
expected_sparsity = 0
# add lagrangian loss (regularization) when pruning
if start_prune:
# compute target sparsity with (optionally) linear warmup
target_sparsity = args.prune_sparsity
if args.prune_warmup > 0:
niter_ = niter - args.prune_start_epoch * N
target_sparsity *= min(1.0, niter_ / args.prune_warmup)
# compute expected model size and sparsity
expected_size = sum(
m.num_parameters(train=True) for m in hc_linear_modules)
expected_sparsity = 1.0 - expected_size / num_prunable_params
# compute lagrangian loss
lagrangian_loss = lambda_1 * (expected_sparsity - target_sparsity) + \
lambda_2 * (expected_sparsity - target_sparsity)**2
(lagrangian_loss / args.update_param_freq).backward()
expected_sparsity = expected_sparsity.item()
lagrangian_loss = lagrangian_loss.item()
# log training stats
if (niter - 1) % 100 == 0 and nbatch % args.update_param_freq == 0:
if args.prune:
train_writer.add_scalar('sparsity/expected_sparsity',
expected_sparsity, niter)
train_writer.add_scalar('sparsity/target_sparsity',
target_sparsity, niter)
train_writer.add_scalar('loss/lagrangian_loss',
lagrangian_loss, niter)
train_writer.add_scalar('lambda/1', lambda_1.item(), niter)
train_writer.add_scalar('lambda/2', lambda_2.item(), niter)
if (niter - 1) % 3000 == 0:
for index, layer in enumerate(hc_modules):
train_writer.add_histogram(
'log_alpha/{}'.format(index),
layer.log_alpha,
niter,
bins='sqrt',
)
sys.stderr.write("\r{:.4f} {:.2f} {:.2f}".format(
loss,
lagrangian_loss,
expected_sparsity,
))
train_writer.add_scalar('loss/lm_loss', loss, niter)
train_writer.add_scalar('loss/total_loss',
loss + lagrangian_loss, niter)
train_writer.add_scalar(
'parameter_norm',
calc_norm([x.data for x in m_parameters]), niter)
train_writer.add_scalar(
'gradient_norm',
calc_norm(
[x.grad for x in m_parameters if x.grad is not None]),
niter)
# perform gradient decent every few number of backward()
if nbatch % args.update_param_freq == 0:
if args.clip_grad > 0:
torch.nn.utils.clip_grad_norm(m_parameters, args.clip_grad)
optimizer.step()
if start_prune:
optimizer_max.step()
optimizer_hc.step()
# clear gradient
model.zero_grad()
if args.prune:
optimizer_max.zero_grad()
optimizer_hc.zero_grad()
niter += 1
if nbatch % args.log_period == 0 or i == N - 1:
elapsed_time = (time.time() - start_time) / 60.0
dev_ppl, dev_loss = eval_model(model, dev)
dev_writer.add_scalar('loss/lm_loss', dev_loss, niter)
dev_writer.add_scalar('bpc', np.log2(dev_ppl), niter)
sparsity = 0
if args.prune:
pruned_size = sum(
m.num_parameters(train=False)
for m in hc_linear_modules)
sparsity = 1.0 - pruned_size / num_prunable_params
dev_writer.add_scalar('sparsity/hard_sparsity', sparsity,
niter)
dev_writer.add_scalar('model_size/total_prunable',
num_prunable_params, niter)
dev_writer.add_scalar('model_size/current_prunable',
pruned_size, niter)
dev_writer.add_scalar('model_size/total', num_params,
niter)
dev_writer.add_scalar(
'model_size/current',
num_params - num_prunable_params + pruned_size, niter)
sys.stdout.write(
"\rIter={} lr={:.5f} train_loss={:.4f} dev_loss={:.4f}"
" dev_bpc={:.2f} sparsity={:.2f}\teta={:.1f}m\t[{:.1f}m]\n"
.format(niter, optimizer.param_groups[0]['lr'], loss,
dev_loss, np.log2(dev_ppl), sparsity,
elapsed_time * N / (i + 1), elapsed_time))
if dev_ppl < best_dev:
if (not args.prune
) or sparsity > args.prune_sparsity - 0.02:
best_dev = dev_ppl
checkpoint = copy_model(model)
sys.stdout.write("\n")
sys.stdout.flush()
nbatch += 1
if args.noam:
lr = min(1.0 / (niter**0.5), niter / (args.warmup_steps**1.5))
optimizer.param_groups[0]['lr'] = lr * args.lr / (args.n_d**
0.5)
if args.noam and start_prune:
niter_ = niter - args.prune_start_epoch * N
lr = min(1.0 / (niter_**0.5),
niter_ / (args.warmup_steps**1.5))
optimizer_max.param_groups[0]['lr'] = -lr * args.prune_lr / (
args.n_d**0.5)
optimizer_hc.param_groups[0]['lr'] = lr * args.lr / (args.n_d**
0.5)
if args.save and (epoch + 1) % 10 == 0:
torch.save(
copy_model(model),
"{}.{}.{:.3f}.pt".format(args.save, epoch + 1, sparsity))
train_writer.close()
dev_writer.close()
model.load_state_dict(checkpoint)
model.cuda()
dev = create_batches(dev_, 1)
test = create_batches(test_, 1)
dev_ppl, dev_loss = eval_model(model, dev)
test_ppl, test_loss = eval_model(model, test)
sys.stdout.write("dev_bpc={:.3f} test_bpc={:.3f}\n".format(
np.log2(dev_ppl), np.log2(test_ppl)))
def main(args):
if args.local_rank == 0:
log_path = "{}_{}".format(args.log, random.randint(1, 100))
train_writer = SummaryWriter(log_dir=log_path + "/train")
dev_writer = SummaryWriter(log_dir=log_path + "/dev")
# set up distributed training
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
set_seed(1234)
args.n_gpu = 1
args.device = device
local_rank = args.local_rank
corpus = get_lm_corpus(args.data, "wt103")
n_token = args.n_token = len(corpus.vocab)
args.eval_batch_size = args.eval_batch_size or args.batch_size
args.eval_unroll_size = args.eval_unroll_size or args.unroll_size
unroll_size = args.unroll_size
eval_unroll_size = args.eval_unroll_size
batch_size = args.batch_size
eval_batch_size = args.eval_batch_size
n_nodes = torch.cuda.device_count()
train = corpus.get_distributed_iterator(
"train",
batch_size,
unroll_size,
n_nodes=n_nodes,
rank=local_rank,
device=device,
)
dev = corpus.get_iterator("valid",
eval_batch_size,
eval_unroll_size,
device=device)
if local_rank == 0:
print("vocab size: {}".format(n_token))
model = Model(args)
if args.load:
model.load_state_dict(torch.load(args.load))
lr = 1.0 if not args.noam else 1.0 / (args.n_d**0.5) / (args.warmup_steps**
1.5)
if args.prune:
# in place substituion of linear ops in SRU
flop.make_hard_concrete(model.rnn,
in_place=True,
init_mean=args.prune_init_mean)
model.embedding_layer = HardConcreteAdaptiveEmbedding.from_module(
model.embedding_layer, init_mean=args.prune_init_mean)
model.output_layer = HardConcreteAdaptiveLogSoftmax.from_module(
model.output_layer, init_mean=args.prune_init_mean)
# tie weights again
model.tie_weights()
model.to(device)
hc_modules = flop.get_hardconcrete_modules(
model.rnn) + flop.get_hardconcrete_modules(model.embedding_layer)
# print(len(flop.get_hardconcrete_modules(model)))
# print(len(hc_modules))
hc_parameters = [
p for m in hc_modules for p in m.parameters() if p.requires_grad
]
optimizer_hc = torch.optim.Adam(hc_parameters,
lr=lr * args.prune_lr,
weight_decay=0)
lambda_1 = nn.Parameter(torch.tensor(0.0).cuda())
lambda_2 = nn.Parameter(torch.tensor(0.0).cuda())
optimizer_max = torch.optim.Adam([lambda_1, lambda_2],
lr=lr,
weight_decay=0)
optimizer_max.param_groups[0]["lr"] = -lr * args.prune_lr
hc_linear_modules = flop.get_hardconcrete_linear_modules(model) + [
model.embedding_layer
]
num_hardconcrete_params = sum(x.numel() for x in hc_parameters)
num_prunable_params = sum(m.num_prunable_parameters()
for m in hc_linear_modules)
if local_rank == 0:
print("num of hardconcrete paramters: {}".format(
num_hardconcrete_params))
print("num of prunable paramters: {}".format(num_prunable_params))
else:
model.to(device)
args.prune_start_epoch = args.max_epoch
m_parameters = [
i[1] for i in model.named_parameters()
if i[1].requires_grad and "log_alpha" not in i[0]
]
optimizer = torch.optim.Adam(m_parameters,
lr=lr * args.lr,
weight_decay=args.weight_decay)
num_params = sum(x.numel() for x in m_parameters if x.requires_grad)
model_ = model
model = torch.nn.parallel.DistributedDataParallel(
model,
dim=1,
device_ids=[local_rank],
output_device=local_rank,
)
nbatch = 1
niter = 1
best_dev = 1e8
unroll_size = args.unroll_size
batch_size = args.batch_size
N = train.n_batch
checkpoint = None
if local_rank == 0:
print(model)
print("num of parameters: {}".format(num_params))
print("num of mini-batches: {}".format(N))
model.zero_grad()
if args.prune:
optimizer_max.zero_grad()
optimizer_hc.zero_grad()
for epoch in range(args.max_epoch):
start_time = time.time()
model.train()
total_loss = 0.0
hidden = model_.init_hidden(batch_size)
start_prune = epoch >= args.prune_start_epoch
i = 0
for x, y, seq_len in train:
i += 1
hidden.detach_()
# language model forward and backward
loss, hidden = model(x, y, hidden)
loss = loss.mean()
(loss / args.update_param_freq).backward()
loss = loss.item()
lagrangian_loss = 0
target_sparsity = 0
expected_sparsity = 0
# add lagrangian loss (regularization) when pruning
if start_prune:
# compute target sparsity with (optionally) linear warmup
target_sparsity = args.prune_sparsity
if args.prune_warmup > 0:
niter_ = niter - args.prune_start_epoch * N
target_sparsity *= min(1.0, niter_ / args.prune_warmup)
# compute expected model size and sparsity
expected_size = sum(
m.num_parameters(train=True) for m in hc_linear_modules)
expected_sparsity = 1.0 - expected_size / num_prunable_params
# compute lagrangian loss
lagrangian_loss = (lambda_1 *
(expected_sparsity - target_sparsity) +
lambda_2 *
(expected_sparsity - target_sparsity)**2)
(lagrangian_loss / args.update_param_freq).backward()
expected_sparsity = expected_sparsity.item()
lagrangian_loss = lagrangian_loss.item()
# log training stats
if (local_rank == 0 and (niter - 1) % 100 == 0
and nbatch % args.update_param_freq == 0):
if args.prune:
train_writer.add_scalar("sparsity/expected_sparsity",
expected_sparsity, niter)
train_writer.add_scalar("sparsity/target_sparsity",
target_sparsity, niter)
train_writer.add_scalar("loss/lagrangian_loss",
lagrangian_loss, niter)
train_writer.add_scalar("lambda/1", lambda_1.item(), niter)
train_writer.add_scalar("lambda/2", lambda_2.item(), niter)
if (nbatch - 1) % 3000 == 0:
for index, layer in enumerate(hc_modules):
train_writer.add_histogram(
"log_alpha/{}".format(index),
layer.log_alpha,
niter,
bins="sqrt",
)
sys.stderr.write("\r{:.4f} {:.2f} {:.2f} eta={:.1f}m".format(
math.exp(loss),
lagrangian_loss,
expected_sparsity,
(time.time() - start_time) / 60.0 / (i + 1) * (N - i - 1),
))
train_writer.add_scalar("loss/ppl", math.exp(loss), niter)
train_writer.add_scalar("loss/lm_loss", loss, niter)
train_writer.add_scalar("loss/total_loss",
loss + lagrangian_loss, niter)
train_writer.add_scalar(
"parameter_norm",
calc_norm([x.data for x in m_parameters]), niter)
train_writer.add_scalar(
"gradient_norm",
calc_norm(
[x.grad for x in m_parameters if x.grad is not None]),
niter,
)
# perform gradient decent every few number of backward()
if nbatch % args.update_param_freq == 0:
if args.clip_grad > 0:
torch.nn.utils.clip_grad_norm(m_parameters, args.clip_grad)
optimizer.step()
if start_prune:
optimizer_max.step()
optimizer_hc.step()
# clear gradient
model.zero_grad()
if args.prune:
optimizer_max.zero_grad()
optimizer_hc.zero_grad()
niter += 1
if local_rank == 0 and (nbatch % args.log_period == 0 or i == N):
elapsed_time = (time.time() - start_time) / 60.0
dev_ppl, dev_loss = eval_model(model_, dev)
dev_writer.add_scalar("loss/lm_loss", dev_loss, niter)
dev_writer.add_scalar("loss/ppl", dev_ppl, niter)
dev_writer.add_scalar("ppl", dev_ppl, niter)
sparsity = 0
if args.prune:
pruned_size = sum(
m.num_parameters(train=False)
for m in hc_linear_modules)
sparsity = 1.0 - pruned_size / num_prunable_params
dev_writer.add_scalar("sparsity/hard_sparsity", sparsity,
niter)
dev_writer.add_scalar("model_size/total_prunable",
num_prunable_params, niter)
dev_writer.add_scalar("model_size/current_prunable",
pruned_size, niter)
dev_writer.add_scalar("model_size/total", num_params,
niter)
dev_writer.add_scalar(
"model_size/current",
num_params - num_prunable_params + pruned_size,
niter,
)
dev_writer.add_scalar(
"model_size/current_embedding",
model_.embedding_layer.num_parameters(train=False),
niter,
)
dev_writer.add_scalar(
"model_size/current_output_layer",
model_.output_layer.num_parameters(train=False),
niter,
)
sys.stdout.write(
"\rnum_batches={} lr={:.5f} train_loss={:.4f} dev_loss={:.4f}"
" dev_bpc={:.2f} sparsity={:.2f}\t[{:.1f}m]\n".format(
nbatch,
optimizer.param_groups[0]["lr"],
loss,
dev_loss,
dev_ppl,
sparsity,
elapsed_time,
))
if dev_ppl < best_dev:
if (not args.prune
) or sparsity > args.prune_sparsity - 0.02:
best_dev = dev_ppl
checkpoint = copy_model(model_)
sys.stdout.write("\n")
sys.stdout.flush()
nbatch += 1
if args.noam:
lr = min(1.0 / (niter**0.5), niter / (args.warmup_steps**1.5))
optimizer.param_groups[0]["lr"] = lr * args.lr / (args.n_d**
0.5)
if args.noam and start_prune:
niter_ = niter - args.prune_start_epoch * N
lr = min(1.0 / (niter_**0.5),
niter_ / (args.warmup_steps**1.5))
optimizer_max.param_groups[0]["lr"] = (-lr * args.prune_lr /
(args.n_d**0.5))
optimizer_hc.param_groups[0]["lr"] = lr * args.lr / (args.n_d**
0.5)
if local_rank == 0 and args.save and checkpoint is not None:
torch.save(checkpoint, "{}.pt".format(args.save, ))
if local_rank == 0:
train_writer.close()
dev_writer.close()
if checkpoint is not None:
model_.load_state_dict(checkpoint)
model_.to(device)
# dev = create_batches(dev_, 1)
# test = create_batches(test_, 1)
test = corpus.get_iterator("test",
eval_batch_size,
eval_unroll_size,
device=device)
dev_ppl, dev_loss = eval_model(model_, dev)
test_ppl, test_loss = eval_model(model_, test)
sys.stdout.write("dev_ppl={:.3f} test_ppl={:.3f}\n".format(
dev_ppl, test_ppl))
def main(args):
log_path = "{}_{}".format(args.log, random.randint(1, 100))
train_writer = SummaryWriter(log_dir=log_path + "/train")
dev_writer = SummaryWriter(log_dir=log_path + "/dev")
device = torch.device('cuda')
corpus = get_lm_corpus(args.data, 'wt103')
n_token = args.n_token = len(corpus.vocab)
args.eval_batch_size = args.eval_batch_size or args.batch_size
args.eval_unroll_size = args.eval_unroll_size or args.unroll_size
train = corpus.get_iterator('train',
args.batch_size,
args.unroll_size,
device=device)
dev = corpus.get_iterator('valid',
args.eval_batch_size,
args.eval_unroll_size,
device=device)
test = corpus.get_iterator('test',
args.eval_batch_size,
args.eval_unroll_size,
device=device)
print("vocab size: {}".format(n_token))
model = Model(args)
if args.load:
model.load_state_dict(torch.load(args.load))
model.cuda()
print(model)
if torch.cuda.device_count() > 1:
para_model = torch.nn.DataParallel(model, dim=1) #, output_device=1)
else:
para_model = model
lr = 1.0 if not args.noam else 1.0 / (args.n_d**0.5) / (args.warmup_steps**
1.5)
if args.prune:
# in place substituion of linear ops in SRU
flop.make_hard_concrete(model.rnn,
in_place=True,
init_mean=args.prune_init_mean)
model.embedding_layer = HardConcreteAdaptiveEmbedding.from_module(
model.embedding_layer, init_mean=args.prune_init_mean)
model.output_layer = HardConcreteAdaptiveLogSoftmax.from_module(
model.output_layer, init_mean=args.prune_init_mean)
# tie weights again
model.tie_weights()
model.cuda()
print("model after inserting hardconcrete:")
print(model)
hc_modules = flop.get_hardconcrete_modules(
model.rnn) + flop.get_hardconcrete_modules(model.embedding_layer)
print(len(flop.get_hardconcrete_modules(model)))
print(len(hc_modules))
hc_parameters = [
p for m in hc_modules for p in m.parameters() if p.requires_grad
]
optimizer_hc = RAdam(hc_parameters,
lr=lr * args.prune_lr,
weight_decay=0)
num_hardconcrete_params = sum(x.numel() for x in hc_parameters)
print("num of hardconcrete paramters: {}".format(
num_hardconcrete_params))
lambda_1 = nn.Parameter(torch.tensor(0.).cuda())
lambda_2 = nn.Parameter(torch.tensor(0.).cuda())
optimizer_max = RAdam([lambda_1, lambda_2], lr=lr, weight_decay=0)
optimizer_max.param_groups[0]['lr'] = -lr * args.prune_lr
hc_linear_modules = flop.get_hardconcrete_linear_modules(model) + \
[model.embedding_layer]
num_prunable_params = sum(m.num_prunable_parameters()
for m in hc_linear_modules)
print("num of prunable paramters: {}".format(num_prunable_params))
else:
args.prune_start_epoch = args.max_epoch
m_parameters = [
i[1] for i in model.named_parameters()
if i[1].requires_grad and 'log_alpha' not in i[0]
]
optimizer = RAdam(m_parameters,
lr=lr * args.lr,
weight_decay=args.weight_decay)
num_params = sum(x.numel() for x in m_parameters if x.requires_grad)
print("num of parameters: {}".format(num_params))
nbatch = 1
niter = 1
best_dev = 1e+8
unroll_size = args.unroll_size
batch_size = args.batch_size
N = train.n_batch
checkpoint = None
print("num of mini-batches: {}".format(N))
model.zero_grad()
if args.prune:
optimizer_max.zero_grad()
optimizer_hc.zero_grad()
for epoch in range(args.max_epoch):
start_time = time.time()
model.train()
total_loss = 0.0
hidden = model.init_hidden(batch_size)
start_prune = epoch >= args.prune_start_epoch
i = 0
for x, y, seq_len in train:
i += 1
hidden.detach_()
# language model forward and backward
loss, hidden = para_model(x, y, hidden)
loss = loss.mean()
(loss / args.update_param_freq).backward()
loss = loss.item()
lagrangian_loss = 0
target_sparsity = 0
expected_sparsity = 0
# add lagrangian loss (regularization) when pruning
if start_prune:
# compute target sparsity with (optionally) linear warmup
target_sparsity = args.prune_sparsity
if args.prune_warmup > 0:
niter_ = niter - args.prune_start_epoch * N
target_sparsity *= min(1.0, niter_ / args.prune_warmup)
# compute expected model size and sparsity
expected_size = sum(
m.num_parameters(train=True) for m in hc_linear_modules)
expected_sparsity = 1.0 - expected_size / num_prunable_params
# compute lagrangian loss
lagrangian_loss = lambda_1 * (expected_sparsity - target_sparsity) + \
lambda_2 * (expected_sparsity - target_sparsity)**2
(lagrangian_loss / args.update_param_freq).backward()
expected_sparsity = expected_sparsity.item()
lagrangian_loss = lagrangian_loss.item()
# log training stats
if (niter - 1) % 100 == 0 and nbatch % args.update_param_freq == 0:
if args.prune:
train_writer.add_scalar('sparsity/expected_sparsity',
expected_sparsity, niter)
train_writer.add_scalar('sparsity/target_sparsity',
target_sparsity, niter)
train_writer.add_scalar('loss/lagrangian_loss',
lagrangian_loss, niter)
train_writer.add_scalar('lambda/1', lambda_1.item(), niter)
train_writer.add_scalar('lambda/2', lambda_2.item(), niter)
if (nbatch - 1) % 3000 == 0:
for index, layer in enumerate(hc_modules):
train_writer.add_histogram(
'log_alpha/{}'.format(index),
layer.log_alpha,
niter,
bins='sqrt',
)
sys.stderr.write("\r{:.4f} {:.2f} {:.2f} eta={:.1f}m".format(
math.exp(loss),
lagrangian_loss,
expected_sparsity,
(time.time() - start_time) / 60.0 / (i + 1) * (N - i - 1),
))
train_writer.add_scalar('loss/ppl', math.exp(loss), niter)
train_writer.add_scalar('loss/lm_loss', loss, niter)
train_writer.add_scalar('loss/total_loss',
loss + lagrangian_loss, niter)
train_writer.add_scalar(
'parameter_norm',
calc_norm([x.data for x in m_parameters]), niter)
train_writer.add_scalar(
'gradient_norm',
calc_norm(
[x.grad for x in m_parameters if x.grad is not None]),
niter)
# perform gradient decent every few number of backward()
if nbatch % args.update_param_freq == 0:
if args.clip_grad > 0:
torch.nn.utils.clip_grad_norm(m_parameters, args.clip_grad)
optimizer.step()
if start_prune:
optimizer_max.step()
optimizer_hc.step()
# clear gradient
model.zero_grad()
if args.prune:
optimizer_max.zero_grad()
optimizer_hc.zero_grad()
niter += 1
if nbatch % args.log_period == 0 or i == N:
elapsed_time = (time.time() - start_time) / 60.0
dev_ppl, dev_loss = eval_model(model, dev)
dev_writer.add_scalar('loss/lm_loss', dev_loss, niter)
dev_writer.add_scalar('loss/ppl', dev_ppl, niter)
dev_writer.add_scalar('ppl', dev_ppl, niter)
sparsity = 0
if args.prune:
pruned_size = sum(
m.num_parameters(train=False)
for m in hc_linear_modules)
sparsity = 1.0 - pruned_size / num_prunable_params
dev_writer.add_scalar('sparsity/hard_sparsity', sparsity,
niter)
dev_writer.add_scalar('model_size/total_prunable',
num_prunable_params, niter)
dev_writer.add_scalar('model_size/current_prunable',
pruned_size, niter)
dev_writer.add_scalar('model_size/total', num_params,
niter)
dev_writer.add_scalar(
'model_size/current',
num_params - num_prunable_params + pruned_size, niter)
dev_writer.add_scalar(
'model_size/current_embedding',
model.embedding_layer.num_parameters(train=False),
niter)
dev_writer.add_scalar(
'model_size/current_output_layer',
model.output_layer.num_parameters(train=False), niter)
sys.stdout.write(
"\rnum_batches={} lr={:.5f} train_loss={:.4f} dev_loss={:.4f}"
" dev_bpc={:.2f} sparsity={:.2f}\t[{:.1f}m]\n".format(
nbatch, optimizer.param_groups[0]['lr'], loss,
dev_loss, dev_ppl, sparsity, elapsed_time))
if dev_ppl < best_dev:
best_dev = dev_ppl
checkpoint = copy_model(model)
sys.stdout.write("\n")
sys.stdout.flush()
nbatch += 1
if args.noam:
lr = min(1.0 / (niter**0.5), niter / (args.warmup_steps**1.5))
optimizer.param_groups[0]['lr'] = lr * args.lr / (args.n_d**
0.5)
if args.noam and start_prune:
niter_ = niter - args.prune_start_epoch * N
lr = min(1.0 / (niter_**0.5),
niter_ / (args.warmup_steps**1.5))
optimizer_max.param_groups[0]['lr'] = -lr * args.prune_lr / (
args.n_d**0.5)
optimizer_hc.param_groups[0]['lr'] = lr * args.lr / (args.n_d**
0.5)
if args.save and (epoch + 1) % 5 == 0:
torch.save(
checkpoint,
"{}.{}.{}.pt".format(args.save, epoch + 1,
int(dev_ppl)
#sparsity
))
train_writer.close()
dev_writer.close()
if checkpoint is not None:
model.load_state_dict(checkpoint)
model.cuda()
#dev = create_batches(dev_, 1)
#test = create_batches(test_, 1)
dev_ppl, dev_loss = eval_model(model, dev)
test_ppl, test_loss = eval_model(model, test)
sys.stdout.write("dev_ppl={:.3f} test_ppl={:.3f}\n".format(
dev_ppl, test_ppl))