## This code is from https://github.com/pytorch/examples/blob/master/mnist/main.py ## with modification to do training using onnxruntime as backend on cuda device. ## A private PyTorch build from https://aiinfra.visualstudio.com/Lotus/_git/pytorch (ORTTraining branch) is needed to run the demo. ## Model testing is not complete. import argparse import os import numpy as np # noqa: F401 import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # noqa: F401 from mpi4py import MPI from torchvision import datasets, transforms from onnxruntime.capi.ort_trainer import IODescription, ModelDescription, ORTTrainer try: # noqa: SIM105 from onnxruntime.capi._pybind_state import set_cuda_device_id except ImportError: pass class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super().__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out def my_loss(x, target): return F.nll_loss(F.log_softmax(x, dim=1), target) def train_with_trainer(args, trainer, device, train_loader, epoch): for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) # noqa: PLW2901 data = data.reshape(data.shape[0], -1) # noqa: PLW2901 learning_rate = torch.tensor([args.lr]) loss = trainer.train_step(data, target, learning_rate) # Since the output corresponds to [loss_desc, probability_desc], the first value is taken as loss. if batch_idx % args.log_interval == 0: print( "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( epoch, batch_idx * len(data), len(train_loader.dataset), 100.0 * batch_idx / len(train_loader), loss[0], ) ) # TODO: comple this once ORT training can do evaluation. def test_with_trainer(args, trainer, device, test_loader): test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) # noqa: PLW2901 data = data.reshape(data.shape[0], -1) # noqa: PLW2901 output = F.log_softmax(trainer.eval_step(data, fetches=["probability"]), dim=1) 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( "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset) ) ) def mnist_model_description(): input_desc = IODescription("input1", ["batch", 784], torch.float32) label_desc = IODescription( "label", [ "batch", ], torch.int64, num_classes=10, ) loss_desc = IODescription("loss", [], torch.float32) probability_desc = IODescription("probability", ["batch", 10], torch.float32) return ModelDescription([input_desc, label_desc], [loss_desc, probability_desc]) 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=10, metavar="N", help="number of epochs to train (default: 10)") parser.add_argument("--lr", type=float, default=0.01, metavar="LR", help="learning rate (default: 0.01)") parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training") 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", ) args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) kwargs = {"num_workers": 0, "pin_memory": True} train_loader = torch.utils.data.DataLoader( datasets.MNIST( "../data", train=True, download=True, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), ), batch_size=args.batch_size, shuffle=True, **kwargs, ) test_loader = torch.utils.data.DataLoader( datasets.MNIST( "../data", train=False, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]), ), batch_size=args.test_batch_size, shuffle=True, **kwargs, ) comm = MPI.COMM_WORLD args.local_rank = ( int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) if ("OMPI_COMM_WORLD_LOCAL_RANK" in os.environ) else 0 ) args.world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) if ("OMPI_COMM_WORLD_RANK" in os.environ) else 0 args.world_size = comm.Get_size() if use_cuda: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) args.n_gpu = 1 set_cuda_device_id(args.local_rank) else: device = torch.device("cpu") input_size = 784 hidden_size = 500 num_classes = 10 model = NeuralNet(input_size, hidden_size, num_classes) model_desc = mnist_model_description() # use log_interval as gradient accumulate steps trainer = ORTTrainer( model, my_loss, model_desc, "SGDOptimizer", None, IODescription( "Learning_Rate", [ 1, ], torch.float32, ), device, 1, args.world_rank, args.world_size, use_mixed_precision=False, allreduce_post_accumulation=True, ) print("\nBuild ort model done.") for epoch in range(1, args.epochs + 1): train_with_trainer(args, trainer, device, train_loader, epoch) import pdb # noqa: F401 test_with_trainer(args, trainer, device, test_loader) if __name__ == "__main__": main()