# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
try:
from transformer_engine import pytorch as te
HAVE_TE = True
except (ImportError, ModuleNotFoundError):
HAVE_TE = False
class Net(nn.Module):
def __init__(self, use_te=False):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
if use_te:
self.fc1 = te.Linear(9216, 128)
self.fc2 = te.Linear(128, 16)
else:
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 16)
self.fc3 = nn.Linear(16, 10)
def forward(self, x):
"""FWD"""
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
x = self.fc3(x)
output = F.log_softmax(x, dim=1)
return output
def train(args, model, device, train_loader, optimizer, epoch, use_fp8):
"""Training function."""
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
with te.fp8_autocast(enabled=use_fp8):
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print(f"Train Epoch: {epoch} "
f"[{batch_idx * len(data)}/{len(train_loader.dataset)} "
f"({100. * batch_idx / len(train_loader):.0f}%)]\t"
f"Loss: {loss.item():.6f}")
if args.dry_run:
break
def calibrate(model, device, test_loader):
"""Calibration function."""
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
with te.fp8_autocast(enabled=False, calibrating=True):
output = model(data)
def test(model, device, test_loader, use_fp8):
"""Testing function."""
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
with te.fp8_autocast(enabled=use_fp8):
output = model(data)
test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print(f"\nTest set: Average loss: {test_loss:.4f}, "
f"Accuracy: {correct}/{len(test_loader.dataset)} "
f"({100. * correct / len(test_loader.dataset):.0f}%)\n")
def main():
# Training settings
parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
parser.add_argument(
"--batch-size",
type=int,
default=64,
metavar="N",
help="input batch size for training (default: 64)",
)
parser.add_argument(
"--test-batch-size",
type=int,
default=1000,
metavar="N",
help="input batch size for testing (default: 1000)",
)
parser.add_argument(
"--epochs",
type=int,
default=14,
metavar="N",
help="number of epochs to train (default: 14)",
)
parser.add_argument(
"--lr",
type=float,
default=1.0,
metavar="LR",
help="learning rate (default: 1.0)",
)
parser.add_argument(
"--gamma",
type=float,
default=0.7,
metavar="M",
help="Learning rate step gamma (default: 0.7)",
)
parser.add_argument(
"--dry-run",
action="store_true",
default=False,
help="quickly check a single pass",
)
parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
parser.add_argument(
"--log-interval",
type=int,
default=10,
metavar="N",
help="how many batches to wait before logging training status",
)
parser.add_argument(
"--save-model",
action="store_true",
default=False,
help="For Saving the current Model",
)
parser.add_argument("--use-fp8",
action="store_true",
default=False,
help="Use FP8 for inference and training without recalibration")
parser.add_argument("--use-fp8-infer", action="store_true", default=False, help="Use FP8 inference only")
parser.add_argument("--use-te", action="store_true", default=False, help="Use Transformer Engine")
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
if args.use_te or args.use_fp8 or args.use_fp8_infer:
assert HAVE_TE, "TransformerEngine not installed."
if args.use_fp8 or args.use_fp8_infer:
args.use_te = True
if args.use_te:
assert use_cuda, "CUDA needed for FP8 execution."
if args.use_fp8_infer:
assert not args.use_fp8, "fp8-infer path currently only supports calibration from a bfloat checkpoint"
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs = {"batch_size": args.batch_size}
test_kwargs = {"batch_size": args.test_batch_size}
if use_cuda:
cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
dataset2 = datasets.MNIST("../data", train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
model = Net(use_te=args.use_te).to(device)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch, args.use_fp8)
test(model, device, test_loader, args.use_fp8)
scheduler.step()
if args.use_fp8_infer:
calibrate(model, device, test_loader)
if args.save_model or args.use_fp8_infer:
torch.save(model.state_dict(), "mnist_cnn.pt")
print('Eval with reloaded checkpoint : fp8=' + str(args.use_fp8_infer))
weights = torch.load("mnist_cnn.pt")
model.load_state_dict(weights)
test(model, device, test_loader, args.use_fp8_infer)
if __name__ == "__main__":
main()