Deprecate ORTTrainer (#13022)

orttraining/orttraining/python/ort_trainer.py

@@ -731,6 +731,10 @@ class ORTTrainer:
             optimized_model_filepath: path to output the optimized training graph.
                Defaults to "" (no output).
         """
+        warnings.warn(
+            "ORTTrainer is deprecated and will be removed in ort release 1.14. Please use ORTModule instead.",
+            FutureWarning,
+        )
         warnings.warn(
             "DISCLAIMER: This is an early version of an experimental training API and it is subject to change. DO NOT create production applications with it"
         )

orttraining/orttraining/python/training/orttrainer.py

@@ -1,19 +1,20 @@
 import copy
 import io
 import os
-import onnx
-import torch
-from inspect import signature
 import warnings
 from functools import partial
+from inspect import signature
+
 import numpy as np
+import onnx
+import torch
 
 import onnxruntime as ort
-from . import _utils, amp, checkpoint, optim, postprocess, ORTTrainerOptions, _checkpoint_storage
-from .model_desc_validation import _ORTTrainerModelDesc
-
 from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference
 
+from . import ORTTrainerOptions, _checkpoint_storage, _utils, amp, checkpoint, optim, postprocess
+from .model_desc_validation import _ORTTrainerModelDesc
+
 
 class TrainStepInfo(object):
     r"""Private class used to store runtime information from current train step.
@@ -119,6 +120,11 @@ class ORTTrainer(object):
     """
 
     def __init__(self, model, model_desc, optim_config, loss_fn=None, options=None):
+        warnings.warn(
+            "ORTTrainer is deprecated and will be removed in ort release 1.14. Please use ORTModule instead.",
+            FutureWarning,
+        )
+
         assert model is not None, "'model' is required and must be either a 'torch.nn.Module' or ONNX model"
         assert isinstance(model_desc, dict), "'model_desc' must be a 'dict'"
         assert isinstance(
@@ -291,10 +297,8 @@ class ORTTrainer(object):
             f.write(self._onnx_model.SerializeToString())
 
     def _check_model_export(self, input):
-        from onnx import helper, TensorProto, numpy_helper
-        import numpy as np
         from numpy.testing import assert_allclose
-        import _test_helpers
+        from onnx import TensorProto, helper, numpy_helper
 
         onnx_model_copy = copy.deepcopy(self._onnx_model)