Fix fetches when eval_step's input is a subset of train_step's input (#4966)

This PR also includes MNIST sample using the new forntend as a sample

orttraining/orttraining/python/experimental/orttrainer.py

@@ -24,7 +24,7 @@ class TrainStepInfo(object):
     Args:
         optimizer_config (optim._OptimizerConfig): reference to optimizer config
         all_finite (bool, default is True): flag that indicates whether all gradients are still finite after last step
-        fetches (list of str, default is []): list of output names to fetch from train_step/eval_step
+        fetches (list of str, default is []): list of output names to fetch from train_step/eval_step. Set it to [] to reset normal behavior.
         optimization_step (int): indicates the number of optimizations performed. Used for learning rate scheduling
         step (int): indicates current training step. Used for gradient accumulation
 
@@ -678,7 +678,8 @@ class ORTTrainer(object):
             input += (loss_scale, )
             extra_inputs += 1
 
-        assert len(self.model_desc.inputs) + extra_inputs == len(input)
+        # Only assert length of input when fetches is not used
+        assert self._train_step_info.fetches or len(self.model_desc.inputs) + extra_inputs == len(input)
         return input
 
     def _resolve_symbolic_dimensions(self, inputs, inputs_desc, outputs_desc):

samples/python/mnist/mnist_training.py

@@ -0,0 +1,142 @@
+# 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.
+
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import datasets, transforms
+
+import onnxruntime
+from onnxruntime.experimental import ORTTrainer, ORTTrainerOptions, optim
+
+
+# Pytorch model
+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, input1):
+        out = self.fc1(input1)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+
+# ONNX Runtime training
+def mnist_model_description():
+    return {'inputs': [('input1', ['batch', 784]),
+                       ('label', ['batch'])],
+            'outputs': [('loss', [], True),
+                        ('probability', ['batch', 10])]}
+
+
+def my_loss(x, target):
+    return F.nll_loss(F.log_softmax(x, dim=1), target)
+
+
+# Helpers
+def train_with_trainer(log_interval, trainer, device, train_loader, epoch):
+    for batch_idx, (data, target) in enumerate(train_loader):
+        # Fetch data
+        data, target = data.to(device), target.to(device)
+        data = data.reshape(data.shape[0], -1)
+
+        # Train step
+        loss, _ = trainer.train_step(data, target)
+
+        # Stats
+        if batch_idx % 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))
+
+
+def test_with_trainer(trainer, device, test_loader):
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            # Fetch data
+            data, target = data.to(device), target.to(device)
+            data = data.reshape(data.shape[0], -1)
+
+            # Eval step
+            # Using fetches around without eval_step to not pass 'target' as input
+            trainer._train_step_info.fetches = ['probability']
+            output = F.log_softmax(trainer.eval_step(data), dim=1)
+            trainer._train_step_info.fetches = []
+
+            # Stats
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            pred = output.argmax(dim=1, keepdim=True)
+            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 main():
+    # Training settings
+    parser = argparse.ArgumentParser(description='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')
+
+    # Basic setup
+    args = parser.parse_args()
+    if not args.no_cuda and torch.cuda.is_available():
+        device = "cuda"
+    else:
+        device = "cpu"
+    torch.manual_seed(args.seed)
+    onnxruntime.set_seed(args.seed)
+
+    # Data loader
+    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)
+    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)
+
+    # Modeling
+    model = NeuralNet(784, 500, 10)
+    model_desc = mnist_model_description()
+    optim_config = optim.SGDConfig(lr=args.lr)
+    opts = ORTTrainerOptions({'device': {'id': device}})
+    trainer = ORTTrainer(model,
+                         model_desc,
+                         optim_config,
+                         loss_fn=my_loss,
+                         options=opts)
+
+    # Train loop
+    for epoch in range(1, args.epochs + 1):
+        train_with_trainer(args.log_interval, trainer,
+                           device, train_loader, epoch)
+        test_with_trainer(trainer, device, test_loader)
+
+
+if __name__ == '__main__':
+    main()