fix (#4410)

src/transformers/modeling_tf_xlnet.py

@@ -586,8 +586,9 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
 
         if data_mask is not None:
             # all mems can be attended to
-            mems_mask = tf.zeros([shape_list(data_mask)[0], mlen, bsz], dtype=dtype_float)
-            data_mask = tf.concat([mems_mask, data_mask], axis=1)
+            if mlen > 0:
+                mems_mask = tf.zeros([shape_list(data_mask)[0], mlen, bsz], dtype=dtype_float)
+                data_mask = tf.concat([mems_mask, data_mask], axis=1)
             if attn_mask is None:
                 attn_mask = data_mask[:, :, :, None]
             else:
@@ -598,7 +599,8 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
 
         if attn_mask is not None:
             non_tgt_mask = -tf.eye(qlen, dtype=dtype_float)
-            non_tgt_mask = tf.concat([tf.zeros([qlen, mlen], dtype=dtype_float), non_tgt_mask], axis=-1)
+            if mlen > 0:
+                non_tgt_mask = tf.concat([tf.zeros([qlen, mlen], dtype=dtype_float), non_tgt_mask], axis=-1)
             non_tgt_mask = tf.cast((attn_mask + non_tgt_mask[:, :, None, None]) > 0, dtype=dtype_float)
         else:
             non_tgt_mask = None
@@ -621,8 +623,11 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         # Segment embedding
         if token_type_ids is not None:
             # Convert `token_type_ids` to one-hot `seg_mat`
-            mem_pad = tf.zeros([mlen, bsz], dtype=tf.int32)
-            cat_ids = tf.concat([mem_pad, token_type_ids], 0)
+            if mlen > 0:
+                mem_pad = tf.zeros([mlen, bsz], dtype=tf.int32)
+                cat_ids = tf.concat([mem_pad, token_type_ids], 0)
+            else:
+                cat_ids = token_type_ids
 
             # `1` indicates not in the same segment [qlen x klen x bsz]
             seg_mat = tf.cast(tf.logical_not(tf.equal(token_type_ids[:, None], cat_ids[None, :])), tf.int32)
@@ -640,14 +645,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
         # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
         # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
         if head_mask is not None:
-            if head_mask.dim() == 1:
-                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
-                head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
-            elif head_mask.dim() == 2:
-                head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
-            head_mask = head_mask.to(
-                dtype=next(self.parameters()).dtype
-            )  # switch to fload if need + fp16 compatibility
+            raise NotImplementedError
         else:
             head_mask = [None] * self.n_layer