Fix input embeddings

transformers/tests/modeling_albert_test.py

@@ -49,6 +49,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
                      use_token_type_ids=True,
                      use_labels=True,
                      vocab_size=99,
+                     embedding_size=16,
                      hidden_size=36,
                      num_hidden_layers=6,
                      num_hidden_groups=6,
@@ -73,6 +74,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
             self.use_token_type_ids = use_token_type_ids
             self.use_labels = use_labels
             self.vocab_size = vocab_size
+            self.embedding_size = embedding_size
             self.hidden_size = hidden_size
             self.num_hidden_layers = num_hidden_layers
             self.num_attention_heads = num_attention_heads

transformers/tests/modeling_tf_albert_test.py

@@ -54,6 +54,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
                      use_token_type_ids=True,
                      use_labels=True,
                      vocab_size=99,
+                     embedding_size=16,
                      hidden_size=32,
                      num_hidden_layers=5,
                      num_attention_heads=4,
@@ -77,6 +78,7 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
             self.use_token_type_ids = use_token_type_ids
             self.use_labels = use_labels
             self.vocab_size = vocab_size
+            self.embedding_size = embedding_size
             self.hidden_size = hidden_size
             self.num_hidden_layers = num_hidden_layers
             self.num_attention_heads = num_attention_heads

transformers/tests/modeling_tf_common_test.py

@@ -426,9 +426,10 @@ class TFCommonTestCases:
                     try:
                         x = wte([input_ids], mode="embedding")
                     except:
-                        x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
-                # ^^ In our TF models, the input_embeddings can take slightly different forms,
-                # so we try two of them and fall back to just synthetically creating a dummy tensor of ones.
+                        if hasattr(self.model_tester, "embedding_size"):
+                            x = tf.ones(input_ids.shape + [model.config.embedding_size], dtype=tf.dtypes.float32)
+                        else:
+                            x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
                 inputs_dict["inputs_embeds"] = x
                 outputs = model(inputs_dict)