Fix XLA and AMP (#10262)

src/transformers/models/t5/modeling_tf_t5.py

@@ -169,14 +169,18 @@ class TFT5Attention(tf.keras.layers.Layer):
         self.o = tf.keras.layers.Dense(self.d_model, use_bias=False, name="o")
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
 
-        if self.has_relative_attention_bias:
-            self.relative_attention_bias = tf.keras.layers.Embedding(
-                self.relative_attention_num_buckets,
-                self.n_heads,
-                name="relative_attention_bias",
-            )
         self.pruned_heads = set()
 
+    def build(self, input_shape):
+        if self.has_relative_attention_bias:
+            with tf.name_scope("relative_attention_bias"):
+                self.relative_attention_bias = self.add_weight(
+                    name="embeddings",
+                    shape=[self.relative_attention_num_buckets, self.n_heads],
+                )
+
+        return super().build(input_shape)
+
     def prune_heads(self, heads):
         raise NotImplementedError
 
@@ -206,18 +210,20 @@ class TFT5Attention(tf.keras.layers.Layer):
         #        n = -relative_position
         if bidirectional:
             num_buckets //= 2
-            relative_buckets += tf.dtypes.cast(tf.math.greater(relative_position, 0), tf.int32) * num_buckets
+            relative_buckets += (
+                tf.cast(tf.math.greater(relative_position, 0), dtype=relative_position.dtype) * num_buckets
+            )
             relative_position = tf.math.abs(relative_position)
         else:
             relative_position = -tf.math.minimum(relative_position, 0)
         # now n is in the range [0, inf)
         max_exact = num_buckets // 2
         is_small = tf.math.less(relative_position, max_exact)
-        relative_position_if_large = max_exact + tf.dtypes.cast(
-            tf.math.log(tf.dtypes.cast(relative_position, tf.float32) / max_exact)
+        relative_position_if_large = max_exact + tf.cast(
+            tf.math.log(relative_position / max_exact)
             / math.log(max_distance / max_exact)
             * (num_buckets - max_exact),
-            tf.int32,
+            dtype=relative_position.dtype,
         )
         relative_position_if_large = tf.math.minimum(relative_position_if_large, num_buckets - 1)
         relative_buckets += tf.where(is_small, relative_position, relative_position_if_large)
@@ -233,7 +239,9 @@ class TFT5Attention(tf.keras.layers.Layer):
             bidirectional=(not self.is_decoder),
             num_buckets=self.relative_attention_num_buckets,
         )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        values = tf.gather(
+            self.relative_attention_bias, relative_position_bucket
+        )  # shape (query_length, key_length, num_heads)
         values = tf.expand_dims(
             tf.transpose(values, [2, 0, 1]), axis=0
         )  # shape (1, num_heads, query_length, key_length)
@@ -326,7 +334,7 @@ class TFT5Attention(tf.keras.layers.Layer):
 
         if position_bias is None:
             if not self.has_relative_attention_bias:
-                position_bias = tf.zeros((1, self.n_heads, real_seq_length, key_length), dtype=tf.float32)
+                position_bias = tf.zeros((1, self.n_heads, real_seq_length, key_length))
             else:
                 position_bias = self.compute_bias(real_seq_length, key_length)
 
@@ -336,6 +344,7 @@ class TFT5Attention(tf.keras.layers.Layer):
                 position_bias = position_bias[:, :, -seq_length:, :]
 
             if mask is not None:
+                position_bias = tf.cast(position_bias, dtype=mask.dtype)
                 position_bias = position_bias + mask  # (batch_size, n_heads, query_length, key_length)
 
         scores += position_bias
@@ -662,7 +671,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
 
         # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
         # ourselves in which case we just need to make it broadcastable to all heads.
-        inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=tf.float32)
+        inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=inputs["inputs_embeds"].dtype)
         num_dims_attention_mask = len(shape_list(inputs["attention_mask"]))
         if num_dims_attention_mask == 3:
             extended_attention_mask = inputs["attention_mask"][:, None, :, :]
@@ -676,7 +685,7 @@ class TFT5MainLayer(tf.keras.layers.Layer):
                     tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
                     seq_ids[None, :, None],
                 )
-                causal_mask = tf.cast(causal_mask, dtype=tf.float32)
+                causal_mask = tf.cast(causal_mask, dtype=inputs["attention_mask"].dtype)
                 extended_attention_mask = causal_mask[:, None, :, :] * inputs["attention_mask"][:, None, None, :]
                 if inputs["past_key_values"][0] is not None:
                     extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
@@ -700,7 +709,9 @@ class TFT5MainLayer(tf.keras.layers.Layer):
             # If a 2D ou 3D attention mask is provided for the cross-attention
             # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
             # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            inputs["encoder_attention_mask"] = tf.cast(inputs["encoder_attention_mask"], dtype=tf.float32)
+            inputs["encoder_attention_mask"] = tf.cast(
+                inputs["encoder_attention_mask"], dtype=extended_attention_mask.dtype
+            )
             num_dims_encoder_attention_mask = len(shape_list(inputs["encoder_attention_mask"]))
             if num_dims_encoder_attention_mask == 3:
                 encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, :, :]
@@ -868,8 +879,7 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
             decoder_start_token_id is not None
         ), "self.model.config.decoder_start_token_id has to be defined. In TF T5 it is usually set to the pad_token_id. See T5 docs for more information"
 
-        shifted_input_ids = tf.cast(input_ids, tf.int32)
-        shifted_input_ids = tf.roll(shifted_input_ids, 1, axis=-1)
+        shifted_input_ids = tf.roll(input_ids, 1, axis=-1)
         start_tokens = tf.fill((shape_list(shifted_input_ids)[0], 1), decoder_start_token_id)
         shifted_input_ids = tf.concat([start_tokens, shifted_input_ids[:, 1:]], -1)
 
@@ -880,7 +890,7 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
         )
 
         # "Verify that `labels` has only positive values and -100"
-        assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.cast(0, tf.int32))
+        assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0))
 
         # Make sure the assertion op is called by wrapping the result in an identity no-op
         with tf.control_dependencies([assert_gte0]):

tests/test_modeling_tf_t5.py

@@ -305,14 +305,6 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
         # This test is too long (>30sec) and makes fail the CI
         pass
 
-    def test_mixed_precision(self):
-        # TODO JP: Make T5 float16 compliant
-        pass
-
-    def test_xla_mode(self):
-        # TODO JP: Make T5 XLA compliant
-        pass
-
     @slow
     def test_model_from_pretrained(self):
         model = TFT5Model.from_pretrained("t5-small")
@@ -442,14 +434,6 @@ class TFT5EncoderOnlyModelTest(TFModelTesterMixin, unittest.TestCase):
     def test_train_pipeline_custom_model(self):
         pass
 
-    def test_mixed_precision(self):
-        # TODO JP: Make T5 float16 compliant
-        pass
-
-    def test_xla_mode(self):
-        # TODO JP: Make T5 XLA compliant
-        pass
-
 
 @require_tf
 @require_sentencepiece