## 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. from __future__ import print_function 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 import numpy as np import os from onnxruntime.capi.ort_trainer import IODescription, ModelDescription, ORTTrainer from mpi4py import MPI from onnxruntime.capi._pybind_state import set_cuda_device_id class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(NeuralNet, self).__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) data = data.reshape(data.shape[0], -1) 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. * 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) data = data.reshape(data.shape[0], -1) 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. * 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() torch.cuda.set_device(args.local_rank) if use_cuda: device = torch.device("cuda", args.local_rank) else: device = torch.device("cpu") args.n_gpu = 1 set_cuda_device_id(args.local_rank) 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 test_with_trainer(args, trainer, device, test_loader) if __name__ == '__main__': main()