def prune_finetune_test(iteration, model_cpy, pruning_step, test_fn, train_fn,
app_args, param_name, effective_train_size):
pylogger = PythonLogger(msglogger)
zeros_mask_dict = distiller.create_model_masks_dict(model_cpy)
param = model_cpy.state_dict()[param_name]
if 0 == prune_tensor(param, param_name, pruning_step, zeros_mask_dict):
return (-1, -1, -1, zeros_mask_dict) # Did not prune anything
if train_fn is not None:
# Fine-tune
optimizer = torch.optim.SGD(model_cpy.parameters(),
lr=app_args.lr,
momentum=app_args.momentum,
weight_decay=app_args.weight_decay)
app_args.effective_train_size = effective_train_size
train_fn(model=model_cpy,
compression_scheduler=create_scheduler(
model_cpy, zeros_mask_dict),
optimizer=optimizer,
epoch=iteration,
loggers=[pylogger])
# Physically remove filters
dataset = app_args.dataset
arch = app_args.arch
distiller.remove_filters(model_cpy,
zeros_mask_dict,
arch,
dataset,
optimizer=None)
# Test and record the performance of the pruned model
prec1, prec5, loss = test_fn(model=model_cpy, loggers=None)
return (prec1, prec5, loss, zeros_mask_dict)
def main():
global msglogger
check_pytorch_version()
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(sys.argv, gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
best_top1 = 0
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.workers > 1:
msglogger.error(
'ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1'
)
exit(1)
# Use a well-known seed, for repeatability of experiments
torch.manual_seed(0)
random.seed(0)
np.random.seed(0)
cudnn.deterministic = True
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
# Create the model
png_summary = args.summary is not None and args.summary.startswith('png')
is_parallel = not png_summary and args.summary != 'compute' # For PNG summary, parallel graphs are illegible
model = create_model(args.pretrained,
args.dataset,
args.arch,
parallel=is_parallel,
device_ids=args.gpus)
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# We can optionally resume from a checkpoint
if args.resume:
model, compression_scheduler, start_epoch = apputils.load_checkpoint(
model, chkpt_file=args.resume)
if 'resnet' in args.arch and 'preact' not in args.arch and 'cifar' in args.arch:
distiller.resnet_cifar_remove_layers(model)
#model = distiller.resnet_cifar_remove_channels(model, compression_scheduler.zeros_mask_dict)
# Define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
# This sample application can be invoked to produce various summary reports.
if args.summary:
which_summary = args.summary
if which_summary.startswith('png'):
apputils.draw_img_classifier_to_file(
model, 'model.png', args.dataset,
which_summary == 'png_w_params')
else:
distiller.model_summary(model, which_summary, args.dataset)
exit()
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_size, args.deterministic)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
activations_sparsity = None
if args.activation_stats:
# If your model has ReLU layers, then those layers have sparse activations.
# ActivationSparsityCollector will collect information about this sparsity.
# WARNING! Enabling activation sparsity collection will significantly slow down training!
activations_sparsity = ActivationSparsityCollector(model)
if args.sensitivity is not None:
# This sample application can be invoked to execute Sensitivity Analysis on your
# model. The ouptut is saved to CSV and PNG.
msglogger.info("Running sensitivity tests")
test_fnc = partial(test,
test_loader=test_loader,
criterion=criterion,
loggers=[pylogger],
print_freq=args.print_freq)
which_params = [
param_name for param_name, _ in model.named_parameters()
]
sensitivity = distiller.perform_sensitivity_analysis(
model,
net_params=which_params,
sparsities=np.arange(0.0, 0.50, 0.05)
if args.sensitivity == 'filter' else np.arange(0.0, 0.95, 0.05),
test_func=test_fnc,
group=args.sensitivity)
distiller.sensitivities_to_png(sensitivity, 'sensitivity.png')
distiller.sensitivities_to_csv(sensitivity, 'sensitivity.csv')
exit()
if args.evaluate:
# This sample application can be invoked to evaluate the accuracy of your model on
# the test dataset.
# You can optionally quantize the model to 8-bit integer before evaluation.
# For example:
# python3 compress_classifier.py --arch resnet20_cifar ../data.cifar10 -p=50 --resume=checkpoint.pth.tar --evaluate
if args.quantize:
model.cpu()
quantizer = quantization.SymmetricLinearQuantizer(model, 8, 8)
quantizer.prepare_model()
model.cuda()
top1, _, _ = test(test_loader, model, criterion, [pylogger],
args.print_freq)
if args.quantize:
checkpoint_name = 'quantized'
apputils.save_checkpoint(0,
args.arch,
model,
optimizer=None,
best_top1=top1,
name='_'.split(args.name, checkpoint_name)
if args.name else checkpoint_name,
dir=msglogger.logdir)
exit()
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(model, optimizer,
args.compress)
for epoch in range(start_epoch, start_epoch + args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
train(train_loader,
model,
criterion,
optimizer,
epoch,
compression_scheduler,
loggers=[tflogger, pylogger],
print_freq=args.print_freq,
log_params_hist=args.log_params_histograms)
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
if args.activation_stats:
distiller.log_activation_sparsity(epoch,
loggers=[tflogger, pylogger],
collector=activations_sparsity)
# evaluate on validation set
top1, top5, vloss = validate(val_loader, model, criterion, [pylogger],
args.print_freq, epoch)
stats = ('Peformance/Validation/',
OrderedDict([('Loss', vloss), ('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats,
None,
epoch,
steps_completed=0,
total_steps=1,
log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch)
# remember best top1 and save checkpoint
is_best = top1 > best_top1
best_top1 = max(top1, best_top1)
apputils.save_checkpoint(epoch, args.arch, model, optimizer,
compression_scheduler, best_top1, is_best,
args.name, msglogger.logdir)
# Finally run results on the test set
test(test_loader, model, criterion, [pylogger], args.print_freq)
def main():
global msglogger
check_pytorch_version()
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(sys.argv, gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
best_top1 = 0
best_epoch = 0
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.workers > 1:
msglogger.error('ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1')
exit(1)
# Use a well-known seed, for repeatability of experiments
torch.manual_seed(0)
random.seed(0)
np.random.seed(0)
cudnn.deterministic = True
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error('ERROR: Argument --gpus must be a comma-separated list of integers only')
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error('ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = create_model(args.pretrained, args.dataset, args.arch, device_ids=args.gpus)
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
if args.resume:
model, compression_scheduler, start_epoch = apputils.load_checkpoint(
model, chkpt_file=args.resume)
# Define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.ADC:
return automated_deep_compression(model, criterion, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_size, args.deterministic)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
activations_sparsity = None
if args.activation_stats:
# If your model has ReLU layers, then those layers have sparse activations.
# ActivationSparsityCollector will collect information about this sparsity.
# WARNING! Enabling activation sparsity collection will significantly slow down training!
activations_sparsity = ActivationSparsityCollector(model)
if args.sensitivity is not None:
return sensitivity_analysis(model, criterion, test_loader, pylogger, args)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger, args)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(model, optimizer, args.compress)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
model.cuda()
else:
compression_scheduler = distiller.CompressionScheduler(model)
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained, args.dataset, args.kd_teacher, device_ids=args.gpus)
if args.kd_resume:
teacher, _, _ = apputils.load_checkpoint(teacher, chkpt_file=args.kd_resume)
dlw = distiller.DistillationLossWeights(args.kd_distill_wt, args.kd_student_wt, args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy, starting_epoch=args.kd_start_epoch, ending_epoch=args.epochs,
frequency=1)
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
for epoch in range(start_epoch, start_epoch + args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
train(train_loader, model, criterion, optimizer, epoch, compression_scheduler,
loggers=[tflogger, pylogger], args=args)
distiller.log_weights_sparsity(model, epoch, loggers=[tflogger, pylogger])
if args.activation_stats:
distiller.log_activation_sparsity(epoch, loggers=[tflogger, pylogger],
collector=activations_sparsity)
# evaluate on validation set
top1, top5, vloss = validate(val_loader, model, criterion, [pylogger], args, epoch)
stats = ('Peformance/Validation/',
OrderedDict([('Loss', vloss),
('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
# remember best top1 and save checkpoint
is_best = top1 > best_top1
if is_best:
best_epoch = epoch
best_top1 = top1
msglogger.info('==> Best Top1: %.3f On Epoch: %d\n', best_top1, best_epoch)
apputils.save_checkpoint(epoch, args.arch, model, optimizer, compression_scheduler, best_top1, is_best,
args.name, msglogger.logdir)
# Finally run results on the test set
test(test_loader, model, criterion, [pylogger], args=args)
def export_onnx(path, batch_size, seq_len):
msglogger.info('The model is also exported in ONNX format at {}'.format(
os.path.realpath(args.onnx_export)))
model.eval()
dummy_input = torch.LongTensor(seq_len * batch_size).zero_().view(
-1, batch_size).to(device)
hidden = model.init_hidden(batch_size)
torch.onnx.export(model, (dummy_input, hidden), path)
# Distiller loggers
msglogger = apputils.config_pylogger('logging.conf', None)
tflogger = TensorBoardLogger(msglogger.logdir)
tflogger.log_gradients = True
pylogger = PythonLogger(msglogger)
if args.summary:
which_summary = args.summary
if which_summary == 'png':
draw_lang_model_to_file(model, 'rnn.png', 'wikitext2')
elif which_summary == 'percentile':
percentile = 0.9
for name, param in model.state_dict().items():
if param.dim() < 2:
# Skip biases
continue
bottomk, _ = torch.topk(param.abs().view(-1),
int(percentile * param.numel()),
largest=False,
sorted=True)
def train(c, net, compression_scheduler=None):
import distiller.apputils as apputils
from distiller.data_loggers import TensorBoardLogger, PythonLogger
msglogger = apputils.config_pylogger('logging.conf', None)
tflogger = TensorBoardLogger(msglogger.logdir)
tflogger.log_gradients = True
pylogger = PythonLogger(msglogger)
c.setdefault(hebbian=False)
emb_params = count_params(net.embed) + count_params(net.loss.projections) + count_params(net.loss.clusters)
opt = get_opt(c, net)
net, opt, step = c.init_model(net, opt=opt, step='max', train=True)
step_lr = scheduler(c, opt, step)
data_tr = SampleIterator(c, c.train_batch, split='valid' if c.debug else 'train')
iter_tr = iter(data_tr)
data_val = SequentialIterator(c, c.eval_batch, split='valid')
s = Namespace(net=net, opt=opt, step=step)
c.on_train_start(s)
c.log('Embedding has %s parameters' % emb_params)
if c.get("steps_per_epoch"):
steps_per_epoch = c.steps_per_epoch
else:
steps_per_epoch = len(data_tr.tokens) // data_tr.bs // c.train_chunk
print("#### steps per epoch %d ####" % steps_per_epoch)
if c.hebbian:
counters = [torch.ones(end - start, dtype=torch.long, device=c.device) for start, end in zip([0] + c.cutoffs, c.cutoffs + [c.n_vocab])]
temp_counters = [torch.zeros_like(x) for x in counters]
best_val_loss = np.inf
if s.results is not None and 'val_loss' in s.results.columns:
best_val_loss = s.results['val_loss'].dropna().max()
try:
while step < s.step_max:
batch = step % steps_per_epoch
epoch = step // steps_per_epoch
if step % steps_per_epoch == 0:
c.log("====> batch=%d, epoch=%d, step=%d" % (batch, epoch, step))
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
if compression_scheduler:
compression_scheduler.on_minibatch_begin(epoch, minibatch_id=batch, minibatches_per_epoch=steps_per_epoch)
step_lr(step)
x = to_torch(next(iter_tr), c.device).t()
t_s = time()
inputs, labels = x[:-1], x[1:]
preds = net(inputs, labels)
loss = preds['loss']
if compression_scheduler:
_ = compression_scheduler.before_backward_pass(epoch, minibatch_id=batch,
minibatches_per_epoch=steps_per_epoch,
loss=loss, return_loss_components=False)
opt.zero_grad()
if torch.isnan(loss):
raise RuntimeError('Encountered nan loss during training')
loss.backward()
torch.nn.utils.clip_grad_norm_(net.parameters(), c.get('clip_grad', 0.5))
opt.step()
if c.hebbian:
hebbian_weight_update(c, net, preds['hiddens'], counters, temp_counters)
time_model = np.round(time() - t_s, 5)
loss = from_torch(loss)
perplexity = np.nan if loss > 5 else np.e ** loss
step_result = pd.Series(dict(
loss=loss,
perplexity=perplexity,
time=time_model
)).add_prefix('train_')
step_result['lr'] = next(iter(opt.param_groups))['lr']
if c.use_cache:
step_result['theta'] = preds['theta']
step_result['lambda'] = preds['lambda'].item()
if compression_scheduler:
compression_scheduler.on_minibatch_end(epoch, minibatch_id=batch, minibatches_per_epoch=steps_per_epoch)
if step % steps_per_epoch == 0:
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch)
s.step = step = step + 1
if step % c.step_eval == 0:
distiller.log_weights_sparsity(net, epoch, loggers=[tflogger, pylogger])
t, total = distiller.weights_sparsity_tbl_summary(net, return_total_sparsity=True)
c.log("total sparsity: %.3lf" % total)
step_result = step_result.append(
pd.Series(evaluate(c, data_val, net)).add_prefix('val_')
)
s.record_step = step_result['val_loss'] < best_val_loss
clear_gpu_memory()
s.step_result = step_result
c.on_step_end(s)
except Exception as e:
import traceback
err = traceback.format_exc()
if c.main:
c.log(err)
else:
print(err)
finally:
c.on_train_end(s)
def train(args):
SRC = data.Field(tokenize=tokenize_de,
pad_token=BLANK_WORD,
lower=args.lower)
TGT = data.Field(tokenize=tokenize_en,
init_token=BOS_WORD,
eos_token=EOS_WORD,
pad_token=BLANK_WORD,
lower=args.lower)
# Load IWSLT Data ---> German to English Translation
if args.dataset == 'IWSLT':
train, val, test = datasets.IWSLT.splits(
exts=('.de', '.en'),
fields=(SRC, TGT),
filter_pred=lambda x: len(vars(x)['src']) <= args.max_length and
len(vars(x)['trg']) <= args.max_length)
else:
train, val, test = datasets.Multi30k.splits(
exts=('.de', '.en'),
fields=(SRC, TGT),
filter_pred=lambda x: len(vars(x)['src']) <= args.max_length and
len(vars(x)['trg']) <= args.max_length)
# Frequency of words in the vocabulary
SRC.build_vocab(train.src, min_freq=args.min_freq)
TGT.build_vocab(train.trg, min_freq=args.min_freq)
print("Size of source vocabulary:", len(SRC.vocab))
print("Size of target vocabulary:", len(TGT.vocab))
pad_idx = TGT.vocab.stoi[BLANK_WORD]
model = make_model(len(SRC.vocab),
len(TGT.vocab),
n=args.num_blocks,
d_model=args.hidden_dim,
d_ff=args.ff_dim,
h=args.num_heads,
dropout=args.dropout)
print("Model made with n:", args.num_blocks, "hidden_dim:",
args.hidden_dim, "feed forward dim:", args.ff_dim, "heads:",
args.num_heads, "dropout:", args.dropout)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Number of parameters: ", params)
if args.load_model:
print("Loading model from [%s]" % args.load_model)
model.load_state_dict(torch.load(args.load_model))
# UNCOMMENT WHEN RUNNING ON RESEARCH MACHINES - run on GPU
# model.cuda()
# Used by original authors, hurts perplexity but improves BLEU score
criterion = LabelSmoothing(size=len(TGT.vocab),
padding_idx=pad_idx,
smoothing=0.1)
# UNCOMMENT WHEN RUNNING ON RESEARCH MACHINES - run on GPU
# criterion.cuda()
train_iter = MyIterator(train,
batch_size=args.batch_size,
device=0,
repeat=False,
sort_key=lambda x: (len(x.src), len(x.trg)),
batch_size_fn=batch_size_fn,
train=True)
valid_iter = MyIterator(val,
batch_size=args.batch_size,
device=0,
repeat=False,
sort_key=lambda x: (len(x.src), len(x.trg)),
batch_size_fn=batch_size_fn,
train=False,
sort=False)
model_par = nn.DataParallel(model, device_ids=devices)
# model_opt = NoamOpt(model.src_embed[0].d_model, 1, 2000,
# torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
# Use standard optimizer -- As used in the paper
model_opt = get_std_opt(model)
# PRUNING CODE
if args.summary:
df = distiller.weights_sparsity_tbl_summary(model, False)
print(df)
exit(0)
msglogger = apputils.config_pylogger('logging.conf', None)
tflogger = TensorBoardLogger(msglogger.logdir)
tflogger.log_gradients = True
pylogger = PythonLogger(msglogger)
source = args.compress
if args.compress:
compression_scheduler = distiller.config.file_config(
model_par.module, None, args.compress)
print(model_par.module)
best_bleu = 0
best_epoch = 0
steps_per_epoch = math.ceil(len(train_iter.data()) / 60)
for epoch in range(args.epoch):
print("=" * 80)
print("Epoch ", epoch + 1)
print("=" * 80)
print("Training...")
model_par.train()
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# IF PRUNING
run_epoch((rebatch(pad_idx, b) for b in train_iter),
model_par,
MultiGPULossCompute(model.generator,
criterion,
devices=devices,
opt=model_opt),
args,
epoch,
steps_per_epoch,
compression_scheduler,
SRC,
TGT,
valid_iter,
is_valid=False)
# run_epoch((rebatch(pad_idx, b) for b in train_iter), model_par,
# MultiGPULossCompute(model.generator, criterion, devices=devices, opt=model_opt), args,
# SRC, TGT, valid_iter, is_valid=False)
print("Validation...")
model_par.eval()
# IF PRUNING
loss = run_epoch((rebatch(pad_idx, b) for b in valid_iter),
model_par,
MultiGPULossCompute(model.generator,
criterion,
devices=devices,
opt=None),
args,
epoch,
steps_per_epoch,
compression_scheduler,
SRC,
TGT,
valid_iter,
is_valid=True)
# loss = run_epoch((rebatch(pad_idx, b) for b in valid_iter), model_par,
# MultiGPULossCompute(model.generator, criterion, devices=devices, opt=None), args,
# SRC, TGT, valid_iter, is_valid=True)
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch)
print('Validation loss:', loss)
print('Validation perplexity: ', np.exp(loss))
bleu_score = run_validation_bleu_score(model, SRC, TGT, valid_iter)
if best_bleu < bleu_score:
best_bleu = bleu_score
model_file = args.save_to + args.exp_name + 'validation.bin'
print('Saving model without optimizer [%s]' % model_file)
torch.save(model_par.module.state_dict(), model_file)
best_epoch = epoch
model_file = args.save_to + args.exp_name + 'latest.bin'
print('Saving latest model without optimizer [%s]' % model_file)
torch.save(model_par.module.state_dict(), model_file)
print('The best epoch was:', best_epoch)
