Restore TF embeddings and attention layers to their previous version (#9890)

* Refacto BERT

* Restore all the concerned models

* Remove print

* Update template

* Apply Sylvain's and Morgan's comments

* Fix cast

* Put the cast inside call

* Remove cond in ebds

* Fix funnel

* Restore previous dot product (attention_scores) computation

* Add ConvBERT and BART

* Make all the S2S models ONNX compliant

* Fix test

* Fix check copies

src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py

@@ -94,7 +94,8 @@ def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: i
 
     if past_key_values_length > 0:
         mask = tf.concat([tf.zeros((tgt_len, past_key_values_length), dtype=tf.float32), mask], axis=-1)
-    return tf.broadcast_to(mask[None, None, :, :], (bsz, 1, tgt_len, tgt_len + past_key_values_length))
+
+    return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
 
 
 # Copied from transformers.models.bart.modeling_tf_bart._expand_mask
@@ -102,10 +103,9 @@ def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values
     """
     Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
     """
-    bsz, src_len = shape_list(mask)
+    src_len = shape_list(mask)[1]
     tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = tf.cast(tf.broadcast_to(mask[:, None, None, :], (bsz, 1, tgt_len, src_len)), tf.float32)
+    expanded_mask = tf.cast(tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)), tf.float32)
 
     return (1.0 - expanded_mask) * LARGE_NEGATIVE