Making TF MobileBert model compliant with AMP (#10259)

* Fix AMP

* Trigger CI

* Rework cast

src/transformers/models/mobilebert/modeling_tf_mobilebert.py

@@ -251,11 +251,12 @@ class TFMobileBertSelfAttention(tf.keras.layers.Layer):
         attention_scores = tf.matmul(
             query_layer, key_layer, transpose_b=True
         )  # (batch size, num_heads, seq_len_q, seq_len_k)
-        dk = tf.cast(shape_list(key_layer)[-1], tf.float32)  # scale attention_scores
+        dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype)  # scale attention_scores
         attention_scores = attention_scores / tf.math.sqrt(dk)
 
         if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
+            # Apply the attention mask is (precomputed for all layers in TFMobileBertModel call() function)
+            attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype)
             attention_scores = attention_scores + attention_mask
 
         # Normalize the attention scores to probabilities.
@@ -726,6 +727,14 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
         if inputs["token_type_ids"] is None:
             inputs["token_type_ids"] = tf.fill(input_shape, 0)
 
+        embedding_output = self.embeddings(
+            inputs["input_ids"],
+            inputs["position_ids"],
+            inputs["token_type_ids"],
+            inputs["inputs_embeds"],
+            training=inputs["training"],
+        )
+
         # We create a 3D attention mask from a 2D tensor mask.
         # Sizes are [batch_size, 1, 1, to_seq_length]
         # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
@@ -738,9 +747,10 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
         # positions we want to attend and -10000.0 for masked positions.
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
-
-        extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
+        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
 
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
@@ -752,13 +762,6 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
         else:
             inputs["head_mask"] = [None] * self.num_hidden_layers
 
-        embedding_output = self.embeddings(
-            inputs["input_ids"],
-            inputs["position_ids"],
-            inputs["token_type_ids"],
-            inputs["inputs_embeds"],
-            training=inputs["training"],
-        )
         encoder_outputs = self.encoder(
             embedding_output,
             extended_attention_mask,

tests/test_modeling_tf_mobilebert.py

@@ -310,10 +310,6 @@ class TFMobileBertModelTest(TFModelTesterMixin, unittest.TestCase):
         # This test is too long (>30sec) and makes fail the CI
         pass
 
-    def test_mixed_precision(self):
-        # TODO JP: Make MobileBert float16 compliant
-        pass
-
     @slow
     def test_model_from_pretrained(self):
         # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: