Fix model equivalence tests (#15670)

* Fix model equivalence tests

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/test_modeling_clip.py

@@ -625,15 +625,15 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
                 if type(tensor) == bool:
                     tf_inputs_dict[key] = tensor
                 elif key == "input_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 elif key == "pixel_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 else:
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
 
             # Check we can load pt model in tf and vice-versa with model => model functions
             tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
-            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model).to(torch_device)
 
             # need to rename encoder-decoder "inputs" for PyTorch
             #            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
@@ -650,7 +650,7 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
                     continue
 
                 tf_out = tf_output.numpy()
-                pt_out = pt_output.numpy()
+                pt_out = pt_output.cpu().numpy()
 
                 self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
 
@@ -676,6 +676,7 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
                 tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
                 tf_model.save_weights(tf_checkpoint_path)
                 pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
+                pt_model = pt_model.to(torch_device)
 
             # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
             pt_model.eval()
@@ -686,11 +687,11 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
                     tensor = np.array(tensor, dtype=bool)
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor, dtype=tf.int32)
                 elif key == "input_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 elif key == "pixel_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 else:
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
 
             # need to rename encoder-decoder "inputs" for PyTorch
             #            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
@@ -708,7 +709,7 @@ class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
                     continue
 
                 tf_out = tf_output.numpy()
-                pt_out = pt_output.numpy()
+                pt_out = pt_output.cpu().numpy()
 
                 self.assertEqual(tf_out.shape, pt_out.shape, "Output component shapes differ between TF and PyTorch")
 

tests/test_modeling_common.py

@@ -1475,17 +1475,17 @@ class ModelTesterMixin:
                 if type(tensor) == bool:
                     tf_inputs_dict[key] = tensor
                 elif key == "input_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 elif key == "pixel_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 elif key == "input_features":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 else:
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
 
             # Check we can load pt model in tf and vice-versa with model => model functions
             tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
-            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model).to(torch_device)
 
             # need to rename encoder-decoder "inputs" for PyTorch
             #            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
@@ -1496,7 +1496,7 @@ class ModelTesterMixin:
             tfo = tf_model(tf_inputs_dict, training=False)
 
             tf_hidden_states = tfo[0].numpy()
-            pt_hidden_states = pto[0].numpy()
+            pt_hidden_states = pto[0].cpu().numpy()
 
             tf_nans = np.copy(np.isnan(tf_hidden_states))
             pt_nans = np.copy(np.isnan(pt_hidden_states))
@@ -1518,6 +1518,7 @@ class ModelTesterMixin:
                 tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
                 tf_model.save_weights(tf_checkpoint_path)
                 pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
+                pt_model = pt_model.to(torch_device)
 
             # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
             pt_model.eval()
@@ -1528,13 +1529,13 @@ class ModelTesterMixin:
                     tensor = np.array(tensor, dtype=bool)
                     tf_inputs_dict[key] = tf.convert_to_tensor(tensor, dtype=tf.int32)
                 elif key == "input_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 elif key == "pixel_values":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 elif key == "input_features":
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.float32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.float32)
                 else:
-                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.numpy(), dtype=tf.int32)
+                    tf_inputs_dict[key] = tf.convert_to_tensor(tensor.cpu().numpy(), dtype=tf.int32)
 
             # need to rename encoder-decoder "inputs" for PyTorch
             #            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
@@ -1545,7 +1546,7 @@ class ModelTesterMixin:
 
             tfo = tf_model(tf_inputs_dict)
             tfo = tfo[0].numpy()
-            pto = pto[0].numpy()
+            pto = pto[0].cpu().numpy()
             tf_nans = np.copy(np.isnan(tfo))
             pt_nans = np.copy(np.isnan(pto))
 

tests/test_modeling_lxmert.py

@@ -776,16 +776,16 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
                     else:
                         if isinstance(value, (list, tuple)):
                             return_dict[key] = (
-                                tf.convert_to_tensor(iter_value.numpy(), dtype=tf.int32) for iter_value in value
+                                tf.convert_to_tensor(iter_value.cpu().numpy(), dtype=tf.int32) for iter_value in value
                             )
                         else:
-                            return_dict[key] = tf.convert_to_tensor(value.numpy(), dtype=tf.int32)
+                            return_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.int32)
                 return return_dict
 
             tf_inputs_dict = recursive_numpy_convert(pt_inputs)
 
             tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
-            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model).to(torch_device)
 
             # Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
             pt_model.eval()
@@ -795,12 +795,6 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
             if "obj_labels" in inputs_dict:
                 del inputs_dict["obj_labels"]
 
-            def torch_type(key):
-                if key in ("visual_feats", "visual_pos"):
-                    return torch.float32
-                else:
-                    return torch.long
-
             pt_inputs = self._prepare_for_class(inputs_dict, model_class)
             tf_inputs_dict = recursive_numpy_convert(pt_inputs)
 
@@ -808,7 +802,7 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
                 pto = pt_model(**pt_inputs)
             tfo = tf_model(tf_inputs_dict, training=False)
             tf_hidden_states = tfo[0].numpy()
-            pt_hidden_states = pto[0].numpy()
+            pt_hidden_states = pto[0].cpu().numpy()
 
             tf_nans = np.copy(np.isnan(tf_hidden_states))
             pt_nans = np.copy(np.isnan(pt_hidden_states))
@@ -852,7 +846,7 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
 
             tfo = tf_model(tf_inputs_dict)
             tfo = tfo[0].numpy()
-            pto = pto[0].numpy()
+            pto = pto[0].cpu().numpy()
             tf_nans = np.copy(np.isnan(tfo))
             pt_nans = np.copy(np.isnan(pto))