diff --git a/examples/tutorial/fp8/mnist/README.md b/examples/tutorial/fp8/mnist/README.md new file mode 100644 index 000000000..308549cd2 --- /dev/null +++ b/examples/tutorial/fp8/mnist/README.md @@ -0,0 +1,7 @@ +# 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 +``` diff --git a/examples/tutorial/fp8/mnist/main.py b/examples/tutorial/fp8/mnist/main.py new file mode 100644 index 000000000..000ded2f1 --- /dev/null +++ b/examples/tutorial/fp8/mnist/main.py @@ -0,0 +1,237 @@ +# 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()