def __init__(self):
global args
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
print("=> using pre-trained model '{}'".format(args.arch))
self.model = models.__dict__[args.arch](pretrained=True)
# BatchNorm folding
print("Perform BN folding")
search_absorbe_bn(self.model)
self.model.to(args.device)
# define loss function (criterion) and optimizer
self.criterion = nn.CrossEntropyLoss()
self.criterion.to(args.device)
cudnn.benchmark = True
# Data loading code
valdir = os.path.join(args.data, '')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
resize = 256 if args.arch != 'inception_v3' else 299
crop_size = 224 if args.arch != 'inception_v3' else 299
tfs = [
transforms.Resize(resize),
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normalize,
]
self.val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose(tfs)),
batch_size=args.batch_size,
shuffle=(True),
num_workers=args.workers,
pin_memory=True)
def eval_pnorm(p):
args.qtype = 'lp_norm'
args.lp = p
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
inf_model = CnnModel(args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args)
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization"
)
# pdb.set_trace()
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(inf_model.model)
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
loss = inf_model.evaluate_calibration()
point = mq.get_clipping()
# evaluate
acc = inf_model.validate()
del inf_model
del mq
return point, loss, acc
def process_model(arch, num_bits, base_dir, task='quantize'):
model = models.__dict__[arch](pretrained=True)
search_absorbe_bn(model)
# Quantize model by kmeans non uniform quantization
model_qkmeans = copy.deepcopy(model)
if task == 'quantize':
quantize_model_parameters(model_qkmeans, num_bits=num_bits)
elif task == 'clip':
clip_model_parameters(model_qkmeans, num_bits=num_bits)
else:
print("Invalid argument task=%s" % task)
exit(-1)
# Save model to home dir
model_path = os.path.join(base_dir, 'models')
if not os.path.exists(model_path):
os.makedirs(model_path)
model_path = os.path.join(model_path,
arch + ('_kmeans%dbit.pt' % num_bits))
print("Saving quantized model to %s" % model_path)
torch.save(model_qkmeans, model_path)
# Per channel bias correction
model_bcorr = copy.deepcopy(model_qkmeans)
p_km = [np for np in model_bcorr.named_parameters()]
p_orig = [np for np in model.named_parameters()]
for i in tqdm(range(len(p_km))):
if not is_ignored(p_km[i][0], p_km[i][1]):
w_km = p_km[i][1]
w_orig = p_orig[i][1]
mean_delta = w_km.view(w_km.shape[0], -1).mean(
dim=-1) - w_orig.view(w_orig.shape[0], -1).mean(dim=-1)
p_km[i][1].data = (w_km.view(w_km.shape[0], -1) -
mean_delta.view(mean_delta.shape[0], 1)).view(
w_orig.shape)
model_path = model_path.split('.')[0] + '_bcorr.pt'
print("Saving quantized model with bias correction to %s" % model_path)
torch.save(model_bcorr, model_path)
def main_worker(args, ml_logger):
global best_acc1
if args.gpu_ids is not None:
print("Use GPU: {} for training".format(args.gpu_ids))
if args.log_stats:
from utils.stats_trucker import StatsTrucker as ST
ST("W{}A{}".format(args.bit_weights, args.bit_act))
if 'resnet' in args.arch and args.custom_resnet:
model = custom_resnet(arch=args.arch,
pretrained=args.pretrained,
depth=arch2depth(args.arch),
dataset=args.dataset)
elif 'inception_v3' in args.arch and args.custom_inception:
model = custom_inception(pretrained=args.pretrained)
else:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=args.pretrained)
device = torch.device('cuda:{}'.format(args.gpu_ids[0]))
cudnn.benchmark = True
torch.cuda.set_device(args.gpu_ids[0])
model = model.to(device)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, device)
args.start_epoch = checkpoint['epoch']
# best_acc1 = checkpoint['best_acc1']
# best_acc1 may be from a checkpoint from a different GPU
# best_acc1 = best_acc1.to(device)
checkpoint['state_dict'] = {
normalize_module_name(k): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(checkpoint['state_dict'], strict=False)
# optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if len(args.gpu_ids) > 1:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features,
args.gpu_ids)
else:
model = torch.nn.DataParallel(model, args.gpu_ids)
default_transform = {
'train': get_transform(args.dataset, augment=True),
'eval': get_transform(args.dataset, augment=False)
}
val_data = get_dataset(args.dataset, 'val', default_transform['eval'])
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(device)
train_data = get_dataset(args.dataset, 'train', default_transform['train'])
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
# TODO: replace this call by initialization on small subset of training data
# TODO: enable for activations
# validate(val_loader, model, criterion, args, device)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
lr_scheduler = StepLR(optimizer, step_size=args.lr_step, gamma=0.1)
mq = None
if args.quantize:
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization"
)
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(model)
all_convs = [
n for n, m in model.named_modules() if isinstance(m, nn.Conv2d)
]
# all_convs = [l for l in all_convs if 'downsample' not in l]
all_relu = [
n for n, m in model.named_modules() if isinstance(m, nn.ReLU)
]
all_relu6 = [
n for n, m in model.named_modules() if isinstance(m, nn.ReLU6)
]
layers = all_relu[1:-1] + all_relu6[1:-1] + all_convs[1:]
replacement_factory = {
nn.ReLU: ActivationModuleWrapper,
nn.ReLU6: ActivationModuleWrapper,
nn.Conv2d: ParameterModuleWrapper
}
mq = ModelQuantizer(
model, args, layers, replacement_factory,
OptimizerBridge(optimizer,
settings={
'algo': 'SGD',
'dataset': args.dataset
}))
if args.resume:
# Load quantization parameters from state dict
mq.load_state_dict(checkpoint['state_dict'])
mq.log_quantizer_state(ml_logger, -1)
if args.model_freeze:
mq.freeze()
if args.evaluate:
if args.log_stats:
mean = []
var = []
skew = []
kurt = []
for n, p in model.named_parameters():
if n.replace('.weight', '') in all_convs[1:]:
mu = p.mean()
std = p.std()
mean.append((n, mu.item()))
var.append((n, (std**2).item()))
skew.append((n, torch.mean(((p - mu) / std)**3).item()))
kurt.append((n, torch.mean(((p - mu) / std)**4).item()))
for i in range(len(mean)):
ml_logger.log_metric(mean[i][0] + '.mean', mean[i][1])
ml_logger.log_metric(var[i][0] + '.var', var[i][1])
ml_logger.log_metric(skew[i][0] + '.skewness', skew[i][1])
ml_logger.log_metric(kurt[i][0] + '.kurtosis', kurt[i][1])
ml_logger.log_metric('weight_mean', np.mean([s[1] for s in mean]))
ml_logger.log_metric('weight_var', np.mean([s[1] for s in var]))
ml_logger.log_metric('weight_skewness',
np.mean([s[1] for s in skew]))
ml_logger.log_metric('weight_kurtosis',
np.mean([s[1] for s in kurt]))
acc = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc)
if args.log_stats:
stats = ST().get_stats()
for s in stats:
ml_logger.log_metric(s, np.mean(stats[s]))
return
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc1, -1)
# evaluate with k-means quantization
# if args.model_freeze:
# with mq.disable():
# acc1_nq = validate(val_loader, model, criterion, args, device)
# ml_logger.log_metric('Val Acc1 fp32', acc1_nq, -1)
for epoch in range(0, args.epochs):
# train for one epoch
print('Timestamp Start epoch: {:%Y-%m-%d %H:%M:%S}'.format(
datetime.datetime.now()))
train(train_loader, model, criterion, optimizer, epoch, args, device,
ml_logger, val_loader, mq)
print('Timestamp End epoch: {:%Y-%m-%d %H:%M:%S}'.format(
datetime.datetime.now()))
if not args.lr_freeze:
lr_scheduler.step()
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc1, step='auto')
# evaluate with k-means quantization
# if args.model_freeze:
# with mq.quantization_method('kmeans'):
# acc1_kmeans = validate(val_loader, model, criterion, args, device)
# ml_logger.log_metric('Val Acc1 kmeans', acc1_kmeans, epoch)
# with mq.disable():
# acc1_nq = validate(val_loader, model, criterion, args, device)
# ml_logger.log_metric('Val Acc1 fp32', acc1_nq, step='auto')
if args.quantize:
mq.log_quantizer_state(ml_logger, epoch)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint(
{
'epoch':
epoch + 1,
'arch':
args.arch,
'state_dict':
model.state_dict()
if len(args.gpu_ids) == 1 else model.module.state_dict(),
'best_acc1':
best_acc1,
'optimizer':
optimizer.state_dict(),
}, is_best)
def main(args, ml_logger):
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if args.tag is not None:
ml_logger.mlflow.log_param('tag', args.tag)
args.qtype = 'max_static'
# create model
# Always enable shuffling to avoid issues where we get bad results due to weak statistics
custom_resnet = True
custom_inception = True
inf_model = CnnModel(args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args)
layers = []
# TODO: make it more generic
if args.bit_weights is not None:
layers += [
n for n, m in inf_model.model.named_modules()
if isinstance(m, nn.Conv2d)
][1:-1]
if args.bit_act is not None:
layers += [
n for n, m in inf_model.model.named_modules()
if isinstance(m, nn.ReLU)
][1:-1]
if args.bit_act is not None and 'mobilenet' in args.arch:
layers += [
n for n, m in inf_model.model.named_modules()
if isinstance(m, nn.ReLU6)
][1:-1]
replacement_factory = {
nn.ReLU: ActivationModuleWrapperPost,
nn.ReLU6: ActivationModuleWrapperPost,
nn.Conv2d: ParameterModuleWrapperPost
}
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
loss = inf_model.evaluate_calibration()
# evaluate
max_acc = inf_model.validate()
max_point = mq.get_clipping()
ml_logger.log_metric('Loss max', loss.item(), step='auto')
ml_logger.log_metric('Acc max', max_acc, step='auto')
data = {'max': {'alpha': max_point.cpu().numpy(), 'loss': loss.item()}}
print("max loss: {:.4f}, max_acc: {:.4f}".format(loss.item(), max_acc))
def eval_pnorm(p):
args.qtype = 'lp_norm'
args.lp = p
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
inf_model = CnnModel(
args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args,
)
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
loss = inf_model.evaluate_calibration()
point = mq.get_clipping()
# evaluate
acc = inf_model.validate()
del inf_model
del mq
return point, loss, acc
del inf_model
del mq
print("Evaluate L2 norm optimization")
l2_point, l2_loss, l2_acc = eval_pnorm(2.)
print("loss l2: {:.4f}".format(l2_loss.item()))
ml_logger.log_metric('Loss l2', l2_loss.item(), step='auto')
ml_logger.log_metric('Acc l2', l2_acc, step='auto')
data['l2'] = {
'alpha': l2_point.cpu().numpy(),
'loss': l2_loss.item(),
'acc': l2_acc
}
print("Evaluate L2.5 norm optimization")
l25_point, l25_loss, l25_acc = eval_pnorm(2.5)
print("loss l2.5: {:.4f}".format(l25_loss.item()))
ml_logger.log_metric('Loss l2.5', l25_loss.item(), step='auto')
ml_logger.log_metric('Acc l2.5', l25_acc, step='auto')
data['l2.5'] = {
'alpha': l25_point.cpu().numpy(),
'loss': l25_loss.item(),
'acc': l25_acc
}
print("Evaluate L3 norm optimization")
l3_point, l3_loss, l3_acc = eval_pnorm(3.)
print("loss l3: {:.4f}".format(l3_loss.item()))
ml_logger.log_metric('Loss l3', l3_loss.item(), step='auto')
ml_logger.log_metric('Acc l3', l3_acc, step='auto')
data['l3'] = {
'alpha': l3_point.cpu().numpy(),
'loss': l3_loss.item(),
'acc': l3_acc
}
# Interpolate optimal p
xp = np.linspace(1, 5, 50)
z = np.polyfit([2, 2.5, 3], [l2_acc, l25_acc, l3_acc], 2)
y = np.poly1d(z)
p_intr = xp[np.argmax(y(xp))]
print("p intr: {:.2f}".format(p_intr))
ml_logger.log_metric('p intr', p_intr, step='auto')
args.qtype = 'lp_norm'
args.lp = p_intr
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
inf_model = CnnModel(args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args)
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization")
# pdb.set_trace()
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(inf_model.model)
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
# Evaluate with optimal p
lp_loss = inf_model.evaluate_calibration()
lp_point = mq.get_clipping()
# evaluate
lp_acc = inf_model.validate()
print("loss p intr: {:.4f}".format(lp_loss.item()))
ml_logger.log_metric('Loss p intr', lp_loss.item(), step='auto')
ml_logger.log_metric('Acc p intr', lp_acc, step='auto')
global _eval_count, _min_loss
_min_loss = lp_loss.item()
idx = np.argmax([l2_acc, l25_acc, l3_acc, lp_acc])
init = [l2_point, l25_point, l3_point, lp_point][idx]
# run optimizer
min_options = {}
if args.maxiter is not None:
min_options['maxiter'] = args.maxiter
if args.maxfev is not None:
min_options['maxfev'] = args.maxfev
_iter = count(0)
def local_search_callback(x):
it = next(_iter)
mq.set_clipping(x, inf_model.device)
loss = inf_model.evaluate_calibration()
print("\n[{}]: Local search callback".format(it))
print("loss: {:.4f}\n".format(loss.item()))
print(x)
ml_logger.log_metric('Loss {}'.format(args.min_method),
loss.item(),
step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format(args.min_method),
acc,
step='auto')
args.min_method = "Powell"
method = coord_descent if args.min_method == 'CD' else args.min_method
res = opt.minimize(
lambda scales: evaluate_calibration_clipped(scales, inf_model, mq),
init.cpu().numpy(),
method=method,
options=min_options,
callback=local_search_callback)
print(res)
scales = res.x
mq.set_clipping(scales, inf_model.device)
loss = inf_model.evaluate_calibration()
ml_logger.log_metric('Loss {}'.format(args.min_method),
loss.item(),
step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format(args.min_method), acc, step='auto')
data['powell'] = {'alpha': scales, 'loss': loss.item(), 'acc': acc}
print("Starting coordinate descent")
args.min_method = "CD"
min_options[
'maxiter'] = 1 # Perform only one iteration of coordinate descent to avoid divergence
_iter = count(0)
global _eval_count
_eval_count = count(0)
_min_loss = lp_loss.item()
mq.set_clipping(init, inf_model.device)
# Run coordinate descent for comparison
method = coord_descent
res = opt.minimize(
lambda scales: evaluate_calibration_clipped(scales, inf_model, mq),
init.cpu().numpy(),
method=method,
options=min_options,
callback=local_search_callback)
print(res)
scales = res.x
mq.set_clipping(scales, inf_model.device)
loss = inf_model.evaluate_calibration()
ml_logger.log_metric('Loss {}'.format("CD"), loss.item(), step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format("CD"), acc, step='auto')
data['cd'] = {'alpha': scales, 'loss': loss.item(), 'acc': acc}
# save scales
f_name = "scales_{}_W{}A{}.pkl".format(args.arch, args.bit_weights,
args.bit_act)
f = open(os.path.join(proj_root_dir, 'data', f_name), 'wb')
pickle.dump(data, f)
f.close()
print("Data saved to {}".format(f_name))
def main():
global args, best_prec1
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if 'cuda' in args.device and torch.cuda.is_available():
if args.seed is not None:
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
model.to(args.device)
if args.device_ids and len(args.device_ids) > 1 and args.arch != 'shufflenet':
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features, args.device_ids)
else:
model = torch.nn.DataParallel(model, args.device_ids)
# BatchNorm folding
if 'resnet' in args.arch or args.arch == 'vgg16_bn' or args.arch == 'inception_v3':
print("Perform BN folding")
search_absorbe_bn(model)
QM().bn_folding = True
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss()
criterion.to(args.device)
cudnn.benchmark = True
# Data loading code
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
resize = 256 if args.arch != 'inception_v3' else 299
crop_size = 224 if args.arch != 'inception_v3' else 299
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(resize),
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.eval_precision:
elog = EvalLog(['dtype', 'val_prec1', 'val_prec5'])
print("\nFloat32 no quantization")
QM().disable()
val_loss, val_prec1, val_prec5 = validate(val_loader, model, criterion)
elog.log('fp32', val_prec1, val_prec5)
logging.info('\nValidation Loss {val_loss:.4f} \t'
'Validation Prec@1 {val_prec1:.3f} \t'
'Validation Prec@5 {val_prec5:.3f} \n'
.format(val_loss=val_loss, val_prec1=val_prec1, val_prec5=val_prec5))
print("--------------------------------------------------------------------------")
for q in [8, 7, 6, 5, 4]:
args.qtype = 'int{}'.format(q)
print("\nQuantize to %s" % args.qtype)
QM().quantize = True
QM().reload(args, qparams)
val_loss, val_prec1, val_prec5 = validate(val_loader, model, criterion)
elog.log(args.qtype, val_prec1, val_prec5)
logging.info('\nValidation Loss {val_loss:.4f} \t'
'Validation Prec@1 {val_prec1:.3f} \t'
'Validation Prec@5 {val_prec5:.3f} \n'
.format(val_loss=val_loss, val_prec1=val_prec1, val_prec5=val_prec5))
print("--------------------------------------------------------------------------")
print(elog)
elog.save('results/precision/%s_%s_clipping.csv' % (args.arch, args.threshold))
elif args.custom_test:
log_name = 'results/custom_test/%s_max_mse_%s_cliping_layer_selection.csv' % (args.arch, args.threshold)
elog = EvalLog(['num_8bit_layers', 'indexes', 'val_prec1', 'val_prec5'], log_name, auto_save=True)
for i in range(len(max_mse_order_id)+1):
_8bit_layers = ['conv0_activation'] + max_mse_order_id[0:i]
print("it: %d, 8 bit layers: %d" % (i, len(_8bit_layers)))
QM().set_8bit_list(_8bit_layers)
val_loss, val_prec1, val_prec5 = validate(val_loader, model, criterion)
elog.log(i+1, str(_8bit_layers), val_prec1, val_prec5)
print(elog)
else:
val_loss, val_prec1, val_prec5 = validate(val_loader, model, criterion)
def __init__(self, ml_logger=None):
self.ml_logger = ml_logger
global args, best_prec1
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if 'cuda' in args.device and torch.cuda.is_available():
if args.seed is not None:
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
print("=> using pre-trained model '{}'".format(args.arch))
if args.arch == 'shufflenet':
import models.ShuffleNet as shufflenet
self.model = shufflenet.ShuffleNet(groups=8)
params = torch.load('ShuffleNet_1g8_Top1_67.408_Top5_87.258.pth.tar')
self.model = torch.nn.DataParallel(self.model, args.device_ids)
self.model.load_state_dict(params)
# elif args.arch == 'mobilenetv2':
# from models.MobileNetV2 import MobileNetV2 as mobilenetv2
# self.model = mobilenetv2()
# params = torch.load('mobilenetv2_Top1_71.806_Top2_90.410.pth.tar')
# self.model = torch.nn.DataParallel(self.model, args.device_ids)
# self.model.load_state_dict(params)
# elif args.arch not in models.__dict__ and args.arch in pretrainedmodels.model_names:
# self.model = pretrainedmodels.__dict__[args.arch](num_classes=1000, pretrained='imagenet')
else:
self.model = models.__dict__[args.arch](pretrained=True)
set_node_names(self.model)
# Mark layers before relue for fusing
if 'resnet' in args.arch:
resnet_mark_before_relu(self.model)
# BatchNorm folding
if 'resnet' in args.arch or args.arch == 'vgg16_bn' or args.arch == 'inception_v3':
print("Perform BN folding")
search_absorbe_bn(self.model)
QM().bn_folding = True
# if args.qmodel is not None:
# model_q_path = os.path.join(os.path.join(home, 'mxt-sim/models'), args.arch + '_lowp_pcq%dbit%s.pt' % (args.qmodel, ('' if args.no_bias_corr else '_bcorr')))
# model_q = torch.load(model_q_path)
# qldict = set_node_names(model_q, create_ldict=True)
# QM().ql_dict = qldict
# model_q.to(args.device)
# self.model.load_state_dict(model_q.state_dict())
# del model_q
self.model.to(args.device)
QM().quantize_model(self.model)
if args.device_ids and len(args.device_ids) > 1 and args.arch != 'shufflenet' and args.arch != 'mobilenetv2':
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
self.model.features = torch.nn.DataParallel(self.model.features, args.device_ids)
else:
self.model = torch.nn.DataParallel(self.model, args.device_ids)
# define loss function (criterion) and optimizer
self.criterion = nn.CrossEntropyLoss()
self.criterion.to(args.device)
cudnn.benchmark = True
# Data loading code
valdir = os.path.join(args.data, 'val')
if args.arch not in models.__dict__ and args.arch in pretrainedmodels.model_names:
dataparallel = args.device_ids is not None and len(args.device_ids) > 1
tfs = [mutils.TransformImage(self.model.module if dataparallel else self.model)]
else:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
resize = 256 if args.arch != 'inception_v3' else 299
crop_size = 224 if args.arch != 'inception_v3' else 299
tfs = [
transforms.Resize(resize),
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normalize,
]
self.val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose(tfs)),
batch_size=args.batch_size, shuffle=(True if (args.kld_threshold or args.aciq_cal or args.shuffle) else False),
num_workers=args.workers, pin_memory=True)
def main_worker(args, ml_logger):
global best_acc1
datatime_str = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
suf_name = "_" + args.experiment
if args.gpu_ids is not None:
print("Use GPU: {} for training".format(args.gpu_ids))
if args.log_stats:
from utils.stats_trucker import StatsTrucker as ST
ST("W{}A{}".format(args.bit_weights, args.bit_act))
if 'resnet' in args.arch and args.custom_resnet:
# pdb.set_trace()
model = custom_resnet(arch=args.arch,
pretrained=args.pretrained,
depth=arch2depth(args.arch),
dataset=args.dataset)
elif 'inception_v3' in args.arch and args.custom_inception:
model = custom_inception(pretrained=args.pretrained)
else:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=args.pretrained)
device = torch.device('cuda:{}'.format(args.gpu_ids[0]))
cudnn.benchmark = True
torch.cuda.set_device(args.gpu_ids[0])
model = model.to(device)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, device)
args.start_epoch = checkpoint['epoch']
# best_acc1 = checkpoint['best_acc1']
# best_acc1 may be from a checkpoint from a different GPU
# best_acc1 = best_acc1.to(device)
checkpoint['state_dict'] = {
normalize_module_name(k): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(checkpoint['state_dict'], strict=False)
# optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if len(args.gpu_ids) > 1:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features,
args.gpu_ids)
else:
model = torch.nn.DataParallel(model, args.gpu_ids)
default_transform = {
'train': get_transform(args.dataset, augment=True),
'eval': get_transform(args.dataset, augment=False)
}
val_data = get_dataset(args.dataset, 'val', default_transform['eval'])
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(device)
train_data = get_dataset(args.dataset, 'train', default_transform['train'])
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
# TODO: replace this call by initialization on small subset of training data
# TODO: enable for activations
# validate(val_loader, model, criterion, args, device)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
lr_scheduler = StepLR(optimizer, step_size=args.lr_step, gamma=0.1)
# pdb.set_trace()
mq = None
if args.quantize:
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization"
)
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(model)
all_convs = [
n for n, m in model.named_modules() if isinstance(m, nn.Conv2d)
]
# all_convs = [l for l in all_convs if 'downsample' not in l]
all_relu = [
n for n, m in model.named_modules() if isinstance(m, nn.ReLU)
]
all_relu6 = [
n for n, m in model.named_modules() if isinstance(m, nn.ReLU6)
]
layers = all_relu[1:-1] + all_relu6[1:-1] + all_convs[1:]
replacement_factory = {
nn.ReLU: ActivationModuleWrapper,
nn.ReLU6: ActivationModuleWrapper,
nn.Conv2d: ParameterModuleWrapper
}
mq = ModelQuantizer(
model, args, layers, replacement_factory,
OptimizerBridge(optimizer,
settings={
'algo': 'SGD',
'dataset': args.dataset
}))
if args.resume:
# Load quantization parameters from state dict
mq.load_state_dict(checkpoint['state_dict'])
mq.log_quantizer_state(ml_logger, -1)
if args.model_freeze:
mq.freeze()
# pdb.set_trace()
if args.evaluate:
acc = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc)
return
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc1, -1)
# evaluate with k-means quantization
# if args.model_freeze:
# with mq.disable():
# acc1_nq = validate(val_loader, model, criterion, args, device)
# ml_logger.log_metric('Val Acc1 fp32', acc1_nq, -1)
# pdb.set_trace()
# Kurtosis regularization on weights tensors
weight_to_hook = {}
if args.w_kurtosis:
if args.weight_name[0] == 'all':
all_convs = [
n.replace(".wrapped_module", "") + '.weight'
for n, m in model.named_modules() if isinstance(m, nn.Conv2d)
]
weight_name = all_convs[1:]
if args.remove_weight_name:
for rm_name in args.remove_weight_name:
weight_name.remove(rm_name)
else:
weight_name = args.weight_name
for name in weight_name:
# pdb.set_trace()
curr_param = fine_weight_tensor_by_name(model, name)
# if not curr_param:
# name = 'float_' + name # QAT name
# curr_param = fine_weight_tensor_by_name(self.model, name)
# if curr_param is not None:
weight_to_hook[name] = curr_param
for epoch in range(0, args.epochs):
# train for one epoch
print('Timestamp Start epoch: {:%Y-%m-%d %H:%M:%S}'.format(
datetime.datetime.now()))
train(train_loader, model, criterion, optimizer, epoch, args, device,
ml_logger, val_loader, mq, weight_to_hook)
print('Timestamp End epoch: {:%Y-%m-%d %H:%M:%S}'.format(
datetime.datetime.now()))
if not args.lr_freeze:
lr_scheduler.step()
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc1, step='auto')
# evaluate with k-means quantization
# if args.model_freeze:
# with mq.quantization_method('kmeans'):
# acc1_kmeans = validate(val_loader, model, criterion, args, device)
# ml_logger.log_metric('Val Acc1 kmeans', acc1_kmeans, epoch)
# with mq.disable():
# acc1_nq = validate(val_loader, model, criterion, args, device)
# ml_logger.log_metric('Val Acc1 fp32', acc1_nq, step='auto')
if args.quantize:
mq.log_quantizer_state(ml_logger, epoch)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint(
{
'epoch':
epoch + 1,
'arch':
args.arch,
'state_dict':
model.state_dict()
if len(args.gpu_ids) == 1 else model.module.state_dict(),
'best_acc1':
best_acc1,
'optimizer':
optimizer.state_dict(),
},
is_best,
datatime_str=datatime_str,
suf_name=suf_name)
def main_worker(args, ml_logger):
global best_acc1
if args.gpu_ids is not None:
print("Use GPU: {} for training".format(args.gpu_ids))
# create model
if 'resnet' in args.arch and args.custom_resnet:
model = custom_resnet(arch=args.arch,
pretrained=args.pretrained,
depth=arch2depth(args.arch),
dataset=args.dataset)
elif 'inception_v3' in args.arch and args.custom_inception:
model = custom_inception(pretrained=args.pretrained)
elif args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
device = torch.device('cuda:{}'.format(args.gpu_ids[0]))
cudnn.benchmark = True
torch.cuda.set_device(args.gpu_ids[0])
model = model.to(device)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
# mq = ModelQuantizer(model, args)
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, device)
args.start_epoch = checkpoint['epoch']
if 'best_acc1' in checkpoint.keys():
best_acc1 = checkpoint['best_acc1']
else:
best_acc1 = 0
# best_acc1 = checkpoint['best_acc1']
# best_acc1 may be from a checkpoint from a different GPU
# best_acc1 = best_acc1.to(device)
checkpoint['state_dict'] = {
normalize_module_name(k): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(checkpoint['state_dict'], strict=False)
# model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if len(args.gpu_ids) > 1:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features,
args.gpu_ids)
else:
model = torch.nn.DataParallel(model, args.gpu_ids)
val_data = get_dataset(
args.dataset,
'val',
get_transform(args.dataset,
augment=False,
scale_size=299 if 'inception' in args.arch else None,
input_size=299 if 'inception' in args.arch else None),
datasets_path=args.datapath)
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=args.batch_size,
shuffle=args.shuffle,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(device)
if args.quantize:
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization"
)
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(model)
all_convs = [
n for n, m in model.named_modules() if isinstance(m, nn.Conv2d)
]
all_relu = [
n for n, m in model.named_modules() if isinstance(m, nn.ReLU)
]
all_relu6 = [
n for n, m in model.named_modules() if isinstance(m, nn.ReLU6)
]
layers = all_relu[1:-1] + all_relu6[1:-1] + all_convs[1:-1]
replacement_factory = {
nn.ReLU: ActivationModuleWrapperPost,
nn.ReLU6: ActivationModuleWrapperPost,
nn.Conv2d: ParameterModuleWrapperPost
}
mq = ModelQuantizer(model, args, layers, replacement_factory)
mq.log_quantizer_state(ml_logger, -1)
acc = validate(val_loader, model, criterion, args, device)
ml_logger.log_metric('Val Acc1', acc, step='auto')
def load_maybe_calibrate(checkpoint):
try:
model.load_state_dict(checkpoint)
except BaseException as e:
if model_config.get('quantize'):
measure_name = '{}-{}.measure'.format(args.model,
model_config['depth'])
measure_path = os.path.join(save_path, measure_name)
if os.path.exists(measure_path):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(measure_path)
if 'state_dict' in checkpoint:
best_prec1 = checkpoint['best_prec1']
checkpoint = checkpoint['state_dict']
logging.info(
f"Measured checkpoint loaded, reference score top1 {best_prec1:.3f}"
)
model.load_state_dict(checkpoint)
else:
if model_config.get('absorb_bn'):
from utils.absorb_bn import search_absorbe_bn
logging.info('absorbing batch normalization')
model_config.update({
'absorb_bn': False,
'quantize': False
})
model_bn = model_builder(**model_config)
model_bn.load_state_dict(checkpoint)
search_absorbe_bn(model_bn, verbose=True)
model_config.update({
'absorb_bn': True,
'quantize': True
})
checkpoint = model_bn.state_dict()
model.load_state_dict(checkpoint, strict=False)
logging.info("set model measure mode")
# set_bn_is_train(model,False)
set_measure_mode(model, True, logger=logging)
logging.info(
"calibrating apprentice model to get quant params")
model.to(args.device, dtype)
with torch.no_grad():
losses_avg, top1_avg, top5_avg = forward(
val_loader,
model,
criterion,
0,
training=False,
optimizer=None)
logging.info('Measured float resutls:\nLoss {loss:.4f}\t'
'Prec@1 {top1:.3f}\t'
'Prec@5 {top5:.3f}'.format(loss=losses_avg,
top1=top1_avg,
top5=top5_avg))
set_measure_mode(model, False, logger=logging)
# logging.info("test quant model accuracy")
# losses_avg, top1_avg, top5_avg = validate(val_loader, model, criterion, 0)
# logging.info('Quantized results:\nLoss {loss:.4f}\t'
# 'Prec@1 {top1:.3f}\t'
# 'Prec@5 {top5:.3f}'.format(loss=losses_avg, top1=top1_avg, top5=top5_avg))
save_checkpoint(
{
'epoch': 0,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'best_prec1': top1_avg,
'regime': regime
},
True,
path=save_path,
save_all=True,
filename=measure_name)
else:
raise e
def main():
global args, best_prec1, dtype
best_prec1 = 0
args = parser.parse_args()
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = os.path.join(args.results_dir, args.save)
if not os.path.exists(save_path):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'),
resume=args.resume is not '')
results_path = os.path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
logging.info("creating model %s", args.model)
model_builder = models.__dict__[args.model]
model_config = {
'input_size': args.input_size,
'dataset': args.dataset if args.dataset != 'imaginet' else 'imagenet'
}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
model = model_builder(**model_config)
model.to(args.device, dtype)
# Data loading code
default_transform = {
'train':
get_transform(args.dataset, input_size=args.input_size, augment=True),
'eval':
get_transform(args.dataset, input_size=args.input_size, augment=False)
}
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
transform = getattr(model, 'input_transform', default_transform)
regime = getattr(model, 'regime', [{
'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay
}])
# define loss function (criterion) and optimizer
criterion = getattr(model, 'criterion', nn.CrossEntropyLoss)()
criterion.to(args.device, dtype)
train_data = get_dataset(args.dataset, 'train', transform['train'])
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
val_data = get_dataset(args.dataset, 'val', transform['eval'])
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
def load_maybe_calibrate(checkpoint):
try:
model.load_state_dict(checkpoint)
except BaseException as e:
if model_config.get('quantize'):
measure_name = '{}-{}.measure'.format(args.model,
model_config['depth'])
measure_path = os.path.join(save_path, measure_name)
if os.path.exists(measure_path):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(measure_path)
if 'state_dict' in checkpoint:
best_prec1 = checkpoint['best_prec1']
checkpoint = checkpoint['state_dict']
logging.info(
f"Measured checkpoint loaded, reference score top1 {best_prec1:.3f}"
)
model.load_state_dict(checkpoint)
else:
if model_config.get('absorb_bn'):
from utils.absorb_bn import search_absorbe_bn
logging.info('absorbing batch normalization')
model_config.update({
'absorb_bn': False,
'quantize': False
})
model_bn = model_builder(**model_config)
model_bn.load_state_dict(checkpoint)
search_absorbe_bn(model_bn, verbose=True)
model_config.update({
'absorb_bn': True,
'quantize': True
})
checkpoint = model_bn.state_dict()
model.load_state_dict(checkpoint, strict=False)
logging.info("set model measure mode")
# set_bn_is_train(model,False)
set_measure_mode(model, True, logger=logging)
logging.info(
"calibrating apprentice model to get quant params")
model.to(args.device, dtype)
with torch.no_grad():
losses_avg, top1_avg, top5_avg = forward(
val_loader,
model,
criterion,
0,
training=False,
optimizer=None)
logging.info('Measured float resutls:\nLoss {loss:.4f}\t'
'Prec@1 {top1:.3f}\t'
'Prec@5 {top5:.3f}'.format(loss=losses_avg,
top1=top1_avg,
top5=top5_avg))
set_measure_mode(model, False, logger=logging)
# logging.info("test quant model accuracy")
# losses_avg, top1_avg, top5_avg = validate(val_loader, model, criterion, 0)
# logging.info('Quantized results:\nLoss {loss:.4f}\t'
# 'Prec@1 {top1:.3f}\t'
# 'Prec@5 {top5:.3f}'.format(loss=losses_avg, top1=top1_avg, top5=top5_avg))
save_checkpoint(
{
'epoch': 0,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'best_prec1': top1_avg,
'regime': regime
},
True,
path=save_path,
save_all=True,
filename=measure_name)
else:
raise e
# optionally resume from a checkpoint
if args.evaluate:
if not os.path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate)
# model.load_state_dict(checkpoint['state_dict'])
# logging.info("loaded checkpoint '%s' (epoch %s)",
# args.evaluate, checkpoint['epoch'])
load_maybe_calibrate(checkpoint)
elif args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
results.load(os.path.join(checkpoint_file, 'results.csv'))
checkpoint_file = os.path.join(checkpoint_file,
'model_best.pth.tar')
if os.path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file)
if 'state_dict' in checkpoint:
if checkpoint['epoch'] > 0:
args.start_epoch = checkpoint['epoch'] - 1
best_prec1 = checkpoint['best_prec1']
checkpoint = checkpoint['state_dict']
try:
model.load_state_dict(checkpoint)
except BaseException as e:
if model_config.get('quantize'):
if model_config.get('absorb_bn'):
from utils.absorb_bn import search_absorbe_bn
logging.info('absorbing batch normalization')
model_config.update({
'absorb_bn': False,
'quantize': False
})
model_bn = model_builder(**model_config)
model_bn.load_state_dict(checkpoint)
search_absorbe_bn(model_bn, verbose=True)
model_config.update({
'absorb_bn': True,
'quantize': True
})
checkpoint = model_bn.state_dict()
model.load_state_dict(checkpoint, strict=False)
model.to(args.device, dtype)
logging.info("set model measure mode")
# set_bn_is_train(model,False)
set_measure_mode(model, True, logger=logging)
logging.info(
"calibrating apprentice model to get quant params")
model.to(args.device, dtype)
with torch.no_grad():
losses_avg, top1_avg, top5_avg = forward(
val_loader,
model,
criterion,
0,
training=False,
optimizer=None)
logging.info('Measured float resutls:\nLoss {loss:.4f}\t'
'Prec@1 {top1:.3f}\t'
'Prec@5 {top5:.3f}'.format(loss=losses_avg,
top1=top1_avg,
top5=top5_avg))
set_measure_mode(model, False, logger=logging)
logging.info("test quant model accuracy")
losses_avg, top1_avg, top5_avg = validate(
val_loader, model, criterion, 0)
logging.info('Quantized results:\nLoss {loss:.4f}\t'
'Prec@1 {top1:.3f}\t'
'Prec@5 {top5:.3f}'.format(loss=losses_avg,
top1=top1_avg,
top5=top5_avg))
save_checkpoint(
{
'epoch': 0,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'best_prec1': top1_avg,
'regime': regime
},
True,
path=save_path,
save_freq=5)
#save_checkpoint(model.state_dict(), is_best=True, path=save_path, save_all=True)
logging.info(
f'overwriting quantization method with {args.q_method}'
)
set_global_quantization_method(model, args.q_method)
else:
raise e
logging.info("loaded checkpoint '%s' (epoch %s)", checkpoint_file,
args.start_epoch)
else:
logging.error("no checkpoint found at '%s'", args.resume)
if args.evaluate:
if model_config.get('quantize'):
logging.info(
f'overwriting quantization method with {args.q_method}')
set_global_quantization_method(model, args.q_method)
losses_avg, top1_avg, top5_avg = validate(val_loader, model, criterion,
0)
logging.info('Evaluation results:\nLoss {loss:.4f}\t'
'Prec@1 {top1:.3f}\t'
'Prec@5 {top5:.3f}'.format(loss=losses_avg,
top1=top1_avg,
top5=top5_avg))
return
optimizer = OptimRegime(model, regime)
logging.info('training regime: %s', regime)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train_loss, train_prec1, train_prec5 = train(train_loader, model,
criterion, epoch,
optimizer)
# evaluate on validation set
val_loss, val_prec1, val_prec5 = validate(val_loader, model, criterion,
epoch)
# remember best prec@1 and save checkpoint
is_best = val_prec1 > best_prec1
best_prec1 = max(val_prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'regime': regime
},
is_best,
path=save_path)
logging.info('\n Epoch: {0}\t'
'Training Loss {train_loss:.4f} \t'
'Training Prec@1 {train_prec1:.3f} \t'
'Training Prec@5 {train_prec5:.3f} \t'
'Validation Loss {val_loss:.4f} \t'
'Validation Prec@1 {val_prec1:.3f} \t'
'Validation Prec@5 {val_prec5:.3f} \n'.format(
epoch + 1,
train_loss=train_loss,
val_loss=val_loss,
train_prec1=train_prec1,
val_prec1=val_prec1,
train_prec5=train_prec5,
val_prec5=val_prec5))
results.add(epoch=epoch + 1,
train_loss=train_loss,
val_loss=val_loss,
train_error1=100 - train_prec1,
val_error1=100 - val_prec1,
train_error5=100 - train_prec5,
val_error5=100 - val_prec5)
results.plot(x='epoch',
y=['train_loss', 'val_loss'],
legend=['training', 'validation'],
title='Loss',
ylabel='loss')
results.plot(x='epoch',
y=['train_error1', 'val_error1'],
legend=['training', 'validation'],
title='Error@1',
ylabel='error %')
results.plot(x='epoch',
y=['train_error5', 'val_error5'],
legend=['training', 'validation'],
title='Error@5',
ylabel='error %')
results.save()
def main_ratio(args, ml_logger):
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
curr_best_acc = 0
curr_best_scale_point = None
args.qtype = 'max_static'
# create model
# Always enable shuffling to avoid issues where we get bad results due to weak statistics
custom_resnet = True
custom_inception = True
inf_model = CnnModel(args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args)
# pdb.set_trace()
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization")
# pdb.set_trace()
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(inf_model.model)
# pdb.set_trace()
layers = []
# TODO: make it more generic
if args.bit_weights is not None:
layers += [
n for n, m in inf_model.model.named_modules()
if isinstance(m, nn.Conv2d)
][1:-1]
if args.bit_act is not None:
layers += [
n for n, m in inf_model.model.named_modules()
if isinstance(m, nn.ReLU)
][1:-1]
if args.bit_act is not None and 'mobilenet' in args.arch:
layers += [
n for n, m in inf_model.model.named_modules()
if isinstance(m, nn.ReLU6)
][1:-1]
replacement_factory = {
nn.ReLU: ActivationModuleWrapperPost,
nn.ReLU6: ActivationModuleWrapperPost,
nn.Conv2d: ParameterModuleWrapperPost
}
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
loss = inf_model.evaluate_calibration()
# evaluate
max_acc = inf_model.validate()
max_point = mq.get_clipping()
# pdb.set_trace()
if max_acc > curr_best_acc:
curr_best_acc = max_acc
curr_best_scale_point = max_point
ml_logger.log_metric('Loss max', loss.item(), step='auto')
ml_logger.log_metric('Acc max', max_acc, step='auto')
data = {'max': {'alpha': max_point.cpu().numpy(), 'loss': loss.item()}}
print("max loss: {:.4f}, max_acc: {:.4f}".format(loss.item(), max_acc))
def eval_pnorm(p):
args.qtype = 'lp_norm'
args.lp = p
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
inf_model = CnnModel(args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args)
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization"
)
# pdb.set_trace()
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(inf_model.model)
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
loss = inf_model.evaluate_calibration()
point = mq.get_clipping()
# evaluate
acc = inf_model.validate()
del inf_model
del mq
return point, loss, acc
del inf_model
del mq
l2_point, l2_loss, l2_acc = eval_pnorm(2.)
print("loss l2: {:.4f}".format(l2_loss.item()))
ml_logger.log_metric('Loss l2', l2_loss.item(), step='auto')
ml_logger.log_metric('Acc l2', l2_acc, step='auto')
data['l2'] = {
'alpha': l2_point.cpu().numpy(),
'loss': l2_loss.item(),
'acc': l2_acc
}
if l2_acc > curr_best_acc:
curr_best_acc = l2_acc
curr_best_scale_point = l2_point
l25_point, l25_loss, l25_acc = eval_pnorm(2.5)
print("loss l2.5: {:.4f}".format(l25_loss.item()))
ml_logger.log_metric('Loss l2.5', l25_loss.item(), step='auto')
ml_logger.log_metric('Acc l2.5', l25_acc, step='auto')
data['l2.5'] = {
'alpha': l25_point.cpu().numpy(),
'loss': l25_loss.item(),
'acc': l25_acc
}
if l25_acc > curr_best_acc:
curr_best_acc = l25_acc
curr_best_scale_point = l25_point
l3_point, l3_loss, l3_acc = eval_pnorm(3.)
print("loss l3: {:.4f}".format(l3_loss.item()))
ml_logger.log_metric('Loss l3', l3_loss.item(), step='auto')
ml_logger.log_metric('Acc l3', l3_acc, step='auto')
data['l3'] = {
'alpha': l3_point.cpu().numpy(),
'loss': l3_loss.item(),
'acc': l3_acc
}
if l3_acc > curr_best_acc:
curr_best_acc = l3_acc
curr_best_scale_point = l3_point
# Interpolate optimal p
xp = np.linspace(1, 5, 50)
z = np.polyfit([2, 2.5, 3], [l2_acc, l25_acc, l3_acc], 2)
y = np.poly1d(z)
p_intr = xp[np.argmax(y(xp))]
print("p intr: {:.2f}".format(p_intr))
ml_logger.log_metric('p intr', p_intr, step='auto')
args.qtype = 'lp_norm'
args.lp = p_intr
# Fix the seed
random.seed(args.seed)
if not args.dont_fix_np_seed:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
inf_model = CnnModel(args.arch,
custom_resnet,
custom_inception,
args.pretrained,
args.dataset,
args.gpu_ids,
args.datapath,
batch_size=args.batch_size,
shuffle=True,
workers=args.workers,
print_freq=args.print_freq,
cal_batch_size=args.cal_batch_size,
cal_set_size=args.cal_set_size,
args=args)
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization")
# pdb.set_trace()
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(inf_model.model)
mq = ModelQuantizer(inf_model.model, args, layers, replacement_factory)
# Evaluate with optimal p
lp_loss = inf_model.evaluate_calibration()
lp_point = mq.get_clipping()
# evaluate
lp_acc = inf_model.validate()
print("loss p intr: {:.4f}".format(lp_loss.item()))
ml_logger.log_metric('Loss p intr', lp_loss.item(), step='auto')
ml_logger.log_metric('Acc p intr', lp_acc, step='auto')
if lp_acc > curr_best_acc:
curr_best_acc = lp_acc
curr_best_scale_point = lp_point
global _eval_count, _min_loss
_min_loss = lp_loss.item()
idx = np.argmax([l2_acc, l25_acc, l3_acc, lp_acc])
init = [l2_point, l25_point, l3_point, lp_point][idx]
# run optimizer
min_options = {}
if args.maxiter is not None:
min_options['maxiter'] = args.maxiter
if args.maxfev is not None:
min_options['maxfev'] = args.maxfev
_iter = count(0)
def local_search_callback(x):
it = next(_iter)
mq.set_clipping(x, inf_model.device)
loss = inf_model.evaluate_calibration()
print("\n[{}]: Local search callback".format(it))
print("loss: {:.4f}\n".format(loss.item()))
print(x)
ml_logger.log_metric('Loss {}'.format(args.min_method),
loss.item(),
step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format(args.min_method),
acc,
step='auto')
args.min_method = "Powell"
method = coord_descent if args.min_method == 'CD' else args.min_method
res = opt.minimize(
lambda scales: evaluate_calibration_clipped(scales, inf_model, mq),
init.cpu().numpy(),
method=method,
options=min_options,
callback=local_search_callback)
print(res)
scales = res.x
mq.set_clipping(scales, inf_model.device)
loss = inf_model.evaluate_calibration()
ml_logger.log_metric('Loss {}'.format(args.min_method),
loss.item(),
step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format(args.min_method), acc, step='auto')
data['powell'] = {'alpha': scales, 'loss': loss.item(), 'acc': acc}
if acc > curr_best_acc:
curr_best_acc = acc
curr_best_scale_point = scales
print("Starting coordinate descent")
args.min_method = "CD"
_iter = count(0)
global _eval_count
_eval_count = count(0)
_min_loss = lp_loss.item()
mq.set_clipping(init, inf_model.device)
# Run coordinate descent for comparison
method = coord_descent
res = opt.minimize(
lambda scales: evaluate_calibration_clipped(scales, inf_model, mq),
init.cpu().numpy(),
method=method,
options=min_options,
callback=local_search_callback)
print(res)
scales = res.x
mq.set_clipping(scales, inf_model.device)
loss = inf_model.evaluate_calibration()
ml_logger.log_metric('Loss {}'.format("CD"), loss.item(), step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format("CD"), acc, step='auto')
data['cd'] = {'alpha': scales, 'loss': loss.item(), 'acc': acc}
if acc > curr_best_acc:
curr_best_acc = acc
curr_best_scale_point = scales
pdb.set_trace()
if curr_best_scale_point.is_cuda:
curr_best_scale_point = curr_best_scale_point.cpu()
best_point = np.concatenate(
[curr_best_scale_point,
torch.tensor([curr_best_acc])])
print("**** START LOSS GENERATION ****")
print("best point:" + str(best_point))
best_point_values = best_point[:-1]
mq.set_clipping(best_point_values, inf_model.device)
loss = inf_model.evaluate_calibration()
# evaluate
top1 = inf_model.validate()
print("best point: loss, top1: {:.4f}, {}".format(loss.item(), top1))
# best_point = curr_best_scale_point
# best_point = mq.get_clipping()
# best_point_values = curr_best_scale_point[:-1]
# pdb.set_trace()
n = args.grid_resolution
min_ratio = args.min_ratio # 0.8
max_ratio = args.max_ratio # 1.2
x = np.linspace(min_ratio, max_ratio, n)
# y = np.linspace(min_ratio, max_ratio, n)
loss_best = loss
# X, Y = np.meshgrid(x, y)
Z_loss = np.empty(n)
Z_top1 = np.empty(n)
for i, x_ in enumerate(tqdm(x)):
# set clip value to qwrappers
scales_ratio = x_
mq.set_clipping((best_point_values * scales_ratio), inf_model.device)
if scales_ratio == 1.0:
print(best_point_values * scales_ratio)
# evaluate with clipping
loss = inf_model.evaluate_calibration()
Z_loss[i] = loss.item()
Z_top1[i] = inf_model.validate()
str1 = "[x, loss, top1] = [{}, {}, {}]".format(x[i], Z_loss[i],
Z_top1[i])
print(str1)
# pdb.set_trace()
# best_point = np.concatenate([1.0, loss_best.cpu().numpy()])
best_point_ratio = [1.0, loss_best.cpu().numpy()]
print("best_point_ratio: " + str(best_point_ratio))
# best_point = [best_point_values, loss_best.cpu().numpy()]
# print("best point: " + str(best_point))
print("best point values: " + str(best_point_values))
f_name = "loss_generation_lapq_{}_W{}A{}.pkl".format(
args.arch, 'ALL', None)
dir_fullname = os.path.join(os.getcwd(), args.experiment)
if not os.path.exists(dir_fullname):
os.makedirs(dir_fullname)
f = open(os.path.join(dir_fullname, f_name), 'wb')
data = {
'X': x,
'Z_loss': Z_loss,
'Z_top1': Z_top1,
'best_point_ratio': best_point_ratio,
'best_point': best_point_values
}
pickle.dump(data, f)
f.close()
print("Data saved to {}".format(f_name))
def __init__(self, arch, use_custom_resnet, use_custom_inception,
pretrained, dataset, gpu_ids, datapath, batch_size, shuffle,
workers, print_freq, cal_batch_size, cal_set_size, args):
self.arch = arch
self.use_custom_resnet = use_custom_resnet
self.pretrained = pretrained
self.dataset = dataset
self.gpu_ids = gpu_ids
self.datapath = datapath
self.batch_size = batch_size
self.shuffle = shuffle
self.workers = workers
self.print_freq = print_freq
self.cal_batch_size = cal_batch_size
self.cal_set_size = cal_set_size # TODO: pass it as cmd line argument
# create model
if 'resnet' in arch and use_custom_resnet:
model = custom_resnet(arch=arch,
pretrained=pretrained,
depth=arch2depth(arch),
dataset=dataset)
elif 'inception_v3' in arch and use_custom_inception:
model = custom_inception(pretrained=pretrained)
else:
print("=> using pre-trained model '{}'".format(arch))
model = models.__dict__[arch](pretrained=pretrained)
self.device = torch.device('cuda:{}'.format(gpu_ids[0]))
torch.cuda.set_device(gpu_ids[0])
model = model.to(self.device)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, self.device)
args.start_epoch = checkpoint['epoch']
checkpoint['state_dict'] = {
normalize_module_name(k): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(checkpoint['state_dict'], strict=False)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if len(gpu_ids) > 1:
# DataParallel will divide and allocate batch_size to all available GPUs
if arch.startswith('alexnet') or arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features, gpu_ids)
else:
model = torch.nn.DataParallel(model, gpu_ids)
self.model = model
if args.bn_folding:
print(
"Applying batch-norm folding ahead of post-training quantization"
)
from utils.absorb_bn import search_absorbe_bn
search_absorbe_bn(model)
# define loss function (criterion) and optimizer
self.criterion = torch.nn.CrossEntropyLoss().to(self.device)
val_data = get_dataset(
dataset,
'val',
get_transform(dataset,
augment=False,
scale_size=299 if 'inception' in arch else None,
input_size=299 if 'inception' in arch else None),
datasets_path=datapath)
self.val_loader = torch.utils.data.DataLoader(val_data,
batch_size=batch_size,
shuffle=shuffle,
num_workers=workers,
pin_memory=True)
self.cal_loader = torch.utils.data.DataLoader(
val_data,
batch_size=self.cal_batch_size,
shuffle=shuffle,
num_workers=workers,
pin_memory=True)
def main_worker(args):
global best_prec1, dtype
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = os.path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if not os.path.exists(save_path) and not (args.distributed
and args.local_rank > 0):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = os.path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
if not os.path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate, map_location="cpu")
# Overrride configuration with checkpoint info
args.model = checkpoint.get('model', args.model)
args.model_config = checkpoint.get('config', args.model_config)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
# create model
model = models.__dict__[args.model]
model_config = {'dataset': args.dataset}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# load checkpoint
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)", args.evaluate,
checkpoint['epoch'])
if args.absorb_bn:
search_absorbe_bn(model, verbose=True)
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', nn.CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
trainer = Trainer(model,
criterion,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
mixup=args.mixup,
print_freq=args.print_freq)
# Evaluation Data loading code
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'val',
'augment': args.augment,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': False,
'duplicates': args.duplicates,
'autoaugment': args.autoaugment,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
results = trainer.validate(val_data.get_loader(),
duplicates=val_data.get('duplicates'),
average_output=args.avg_out)
logging.info(results)
return results
