import lib
parser = argparse.ArgumentParser()
parser = lib.arg_all(parser)
args = parser.parse_args()
DIM = args.dim
norm = nn.BatchNorm1d if DIM == 1 else nn.BatchNorm2d
layer = norm(args.channels)
model = nn.Sequential(
layer, nn.Flatten(),
nn.Linear(in_features=args.channels * args.numf**DIM, out_features=10))
train_dataset = give(DIM, args.numf, args.channels)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['batch_norm', 'NativeBatchNormBackward'], model,
train_loader, args.epochs)
import numpy as np
data = np.array([[
parser = argparse.ArgumentParser()
parser.add_argument('-numf', type=int, required=True)
parser.add_argument('-batch', type=int, required=True)
parser.add_argument('-nodes', type=int, required=True)
parser.add_argument('-epochs', type=int, required=True)
parser.add_argument('-units', type=int, required=True)
args = parser.parse_args()
layer = nn.Linear(in_features=args.numf, out_features=args.units)
model = nn.Sequential(
nn.Flatten(), nn.Linear(in_features=args.numf, out_features=args.units))
train_dataset = give(1, args.numf, 1, out_size=args.units)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset,
args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.batch,
shuffle=True)
time = lib_torch.profile(['addmm', 'AddmmBackward'], model, train_loader,
args.epochs)
import numpy as np
data = np.array([[
layer = nn.Linear(
in_features = args.numf,
out_features = args.units
)
model = nn.Sequential(
nn.Flatten(),
layer,
nn.Flatten(),
nn.Linear(
in_features = args.units,
out_features = 10
)
)
train_dataset = give(1, args.numf, 1)
if args.nodes > 1:
model, train_loader = lib_torch.distribute(model, train_dataset, args.nodes, args.batch)
else:
train_loader = torch.utils.data.DataLoader(
dataset = train_dataset,
batch_size = args.batch,
shuffle = True
)
time = lib_torch.profile(['addmm', 'AddmmBackward'],
model, train_loader, args.epochs)
import numpy as np