# 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() 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): 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()