add logits option to generate artifacts (#17276)

### Description

Adding the ability to export logits as an output for train and eval
graphs in generate_artifacts
it will remain optional..

orttraining/orttraining/python/training/artifacts.py

@@ -65,6 +65,7 @@ def generate_artifacts(
         ort_format (bool): Whether to save the generated artifacts in ORT format or not. Default is False.
         custom_op_library (str | os.PathLike): The path to the custom op library.
                                                If not specified, no custom op library is used.
+        additional_output_names (List[str]): List of additional output names to be added to the training/eval model.
 
     Raises:
         RuntimeError: If the loss provided is neither one of the supported losses nor an instance of `onnxblock.Block`
@@ -104,6 +105,20 @@ def __init__(self, _loss):
             self._loss = _loss
 
         def build(self, *inputs_to_loss):
+            if "additional_output_names" in extra_options:
+                # If additional output names is not a list, raise an error
+                if not isinstance(extra_options["additional_output_names"], list):
+                    raise RuntimeError(
+                        f"Unknown type provided for additional output names {type(extra_options['additional_output_names'])}. "
+                        "Expected additional output names to be a list of strings."
+                    )
+
+                loss_output = self._loss(*inputs_to_loss)
+                if isinstance(loss_output, tuple):
+                    return (*loss_output, *tuple(extra_options["additional_output_names"]))
+                else:
+                    return (loss_output, *tuple(extra_options["additional_output_names"]))
+
             return self._loss(*inputs_to_loss)
 
     training_block = _TrainingBlock(loss_block)

orttraining/orttraining/test/python/orttraining_test_onnxblock.py

@@ -847,6 +847,39 @@ def mse_loss(prediction, target):
             assert np.allclose(ort_grad, _to_numpy(pt_param.grad))
 
 
+def test_additional_output_names():
+    class DropoutModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.dropout = torch.nn.Dropout(p=0.5)
+
+        def forward(self, x):
+            return self.dropout(x)
+
+    model = DropoutModel()
+    onnx_model = _get_onnx_model(model, (torch.randn(1, 3, 224, 224),))
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        artifacts.generate_artifacts(onnx_model, loss=artifacts.LossType.CrossEntropyLoss, artifact_directory=temp_dir)
+
+        eval_model = onnx.load(os.path.join(temp_dir, "eval_model.onnx"))
+
+        # Make sure only loss is the output
+        assert len(eval_model.graph.output) == 1
+
+        # Re-generate artifacts with additional output names
+        artifacts.generate_artifacts(
+            onnx_model,
+            loss=artifacts.LossType.CrossEntropyLoss,
+            artifact_directory=temp_dir,
+            additional_output_names=["output-0"],
+        )
+
+        # Make sure the eval model has two outputs
+        eval_model = onnx.load(os.path.join(temp_dir, "eval_model.onnx"))
+        assert len(eval_model.graph.output) == 2
+
+
 def test_eval_model_has_no_training_mode_dropout():
     class DropoutModel(torch.nn.Module):
         def __init__(self):