[NVIDIA] Add FP8 example using TE

examples/tutorial/fp8/mnist/README.md

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+# Basic MNIST Example with optional FP8
+
+```bash
+python main.py
+python main.py --use-te   # Linear layers from TransformerEngine
+python main.py --use-fp8  # FP8 + TransformerEngine for Linear layers
+```

examples/tutorial/fp8/mnist/main.py

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+# 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 torchvision import datasets, transforms
+from torch.optim.lr_scheduler import StepLR
+
+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()