Don't try to bind unused inputs in the Training frontend

orttraining/orttraining/python/training/orttrainer.py

@@ -804,15 +804,20 @@ def _training_session_run_helper(self, is_train, inputs, inputs_desc, outputs_de
         else:
             iobinding = self._eval_io_binding
 
+        # Get the list of the actual session inputs because unused inputs can be removed.
+        input_nodes = self._training_session.get_inputs()
+        input_node_names = [input_node.name for input_node in input_nodes]
+
         # Bind input tensors
         for input, input_desc in zip(inputs, inputs_desc):
-            device_index = _utils.get_device_index_from_input(input)
-            iobinding.bind_input(input_desc.name,
-                                 input.device.type,
-                                 device_index,
-                                 _utils.dtype_torch_to_numpy(input.dtype),
-                                 list(input.size()),
-                                 input.data_ptr())
+            if input_desc.name in input_node_names:
+                device_index = _utils.get_device_index_from_input(input)
+                iobinding.bind_input(input_desc.name,
+                                    input.device.type,
+                                    device_index,
+                                    _utils.dtype_torch_to_numpy(input.dtype),
+                                    list(input.size()),
+                                    input.data_ptr())
 
         # Bind output tensors
         outputs_desc_resolved = self._resolve_symbolic_dimensions(inputs, inputs_desc, outputs_desc)

orttraining/orttraining/test/python/orttraining_test_orttrainer_frontend.py

@@ -1428,3 +1428,21 @@ def testORTTrainerOptionsDisabledAdasumFlag(test_input):
 
     actual_values = orttrainer_options.ORTTrainerOptions(test_input)
     assert actual_values.distributed.enable_adasum == False
+
+def testORTTrainerUnusedInput():
+    class UnusedInputModel(torch.nn.Module):
+        def __init__(self):
+            super(UnusedInputModel, self).__init__()
+        def forward(self, x, y):
+            return torch.mean(x)
+
+    model = UnusedInputModel()
+    model_desc = {'inputs': [('x', [1]), ('y', [1])], 'outputs': [('loss', [], True)]}
+    optim_config = optim.LambConfig(lr=0.001)
+    trainer = orttrainer.ORTTrainer(model, model_desc, optim_config)
+
+    # Run just one step to make sure there are no iobinding errors for the unused input.
+    try:
+        trainer.train_step(torch.FloatTensor([1.0]), torch.FloatTensor([1.0]))
+    except RuntimeError:
+        pytest.fail("RuntimeError doing train_step with an unused input.")