Fix PyTorch/TF Auto tests (#17895)

* add loading_info
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/modeling_tf_pytorch_utils.py

@@ -103,7 +103,9 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="",
 #####################
 
 
-def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False):
+def load_pytorch_checkpoint_in_tf2_model(
+    tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False, output_loading_info=False
+):
     """Load pytorch checkpoints in a TF 2.0 model"""
     try:
         import tensorflow as tf  # noqa: F401
@@ -122,7 +124,11 @@ def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_i
     logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters")
 
     return load_pytorch_weights_in_tf2_model(
-        tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys
+        tf_model,
+        pt_state_dict,
+        tf_inputs=tf_inputs,
+        allow_missing_keys=allow_missing_keys,
+        output_loading_info=output_loading_info,
     )
 
 
@@ -135,7 +141,9 @@ def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_mi
     )
 
 
-def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False):
+def load_pytorch_weights_in_tf2_model(
+    tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False, output_loading_info=False
+):
     """Load pytorch state_dict in a TF 2.0 model."""
     try:
         import tensorflow as tf  # noqa: F401
@@ -281,6 +289,10 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             f"you can already use {tf_model.__class__.__name__} for predictions without further training."
         )
 
+    if output_loading_info:
+        loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys}
+        return tf_model, loading_info
+
     return tf_model
 
 
@@ -289,7 +301,9 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
 #####################
 
 
-def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False):
+def load_tf2_checkpoint_in_pytorch_model(
+    pt_model, tf_checkpoint_path, tf_inputs=None, allow_missing_keys=False, output_loading_info=False
+):
     """
     Load TF 2.0 HDF5 checkpoint in a PyTorch model We use HDF5 to easily do transfer learning (see
     https://github.com/tensorflow/tensorflow/blob/ee16fcac960ae660e0e4496658a366e2f745e1f0/tensorflow/python/keras/engine/network.py#L1352-L1357).
@@ -323,17 +337,21 @@ def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs
 
     load_tf_weights(tf_model, tf_checkpoint_path)
 
-    return load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=allow_missing_keys)
+    return load_tf2_model_in_pytorch_model(
+        pt_model, tf_model, allow_missing_keys=allow_missing_keys, output_loading_info=output_loading_info
+    )
 
 
-def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False):
+def load_tf2_model_in_pytorch_model(pt_model, tf_model, allow_missing_keys=False, output_loading_info=False):
     """Load TF 2.0 model in a pytorch model"""
     weights = tf_model.weights
 
-    return load_tf2_weights_in_pytorch_model(pt_model, weights, allow_missing_keys=allow_missing_keys)
+    return load_tf2_weights_in_pytorch_model(
+        pt_model, weights, allow_missing_keys=allow_missing_keys, output_loading_info=output_loading_info
+    )
 
 
-def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False):
+def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=False, output_loading_info=False):
     """Load TF2.0 symbolic weights in a PyTorch model"""
     try:
         import tensorflow as tf  # noqa: F401
@@ -460,4 +478,8 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
     logger.info(f"Weights or buffers not loaded from TF 2.0 model: {all_tf_weights}")
 
+    if output_loading_info:
+        loading_info = {"missing_keys": missing_keys, "unexpected_keys": unexpected_keys}
+        return pt_model, loading_info
+
     return pt_model

src/transformers/modeling_tf_utils.py

@@ -2316,7 +2316,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
             from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
 
             # Load from a PyTorch checkpoint
-            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
+            return load_pytorch_checkpoint_in_tf2_model(
+                model, resolved_archive_file, allow_missing_keys=True, output_loading_info=output_loading_info
+            )
 
         # we might need to extend the variable scope for composite models
         if load_weight_prefix is not None:

src/transformers/modeling_utils.py

@@ -1831,6 +1831,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         is_sharded = False
         sharded_metadata = None
         # Load model
+        loading_info = None
+
         if pretrained_model_name_or_path is not None:
             pretrained_model_name_or_path = str(pretrained_model_name_or_path)
             if os.path.isdir(pretrained_model_name_or_path):
@@ -2086,7 +2088,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 try:
                     from .modeling_tf_pytorch_utils import load_tf2_checkpoint_in_pytorch_model
 
-                    model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
+                    model, loading_info = load_tf2_checkpoint_in_pytorch_model(
+                        model, resolved_archive_file, allow_missing_keys=True, output_loading_info=True
+                    )
                 except ImportError:
                     logger.error(
                         "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed."
@@ -2139,6 +2143,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             dispatch_model(model, device_map=device_map, offload_dir=offload_folder)
 
         if output_loading_info:
+            if loading_info is None:
                 loading_info = {
                     "missing_keys": missing_keys,
                     "unexpected_keys": unexpected_keys,