TF-OPT attention mask fixes

src/transformers/models/opt/modeling_tf_opt.py

@@ -61,17 +61,19 @@
 LARGE_NEGATIVE = -1e8
 
 
-# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
 def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
     """
     Make causal mask used for bi-directional self-attention.
     """
     bsz = input_ids_shape[0]
     tgt_len = input_ids_shape[1]
-    mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
-    mask_cond = tf.range(shape_list(mask)[-1])
-
-    mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
+    # mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
+    # mask_cond = tf.range(shape_list(mask)[-1])
+    #
+    # mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
+    # We need triu with k = 1 but TF expects known compile-time dims for that, so we hack around it
+    mask = tf.fill((tgt_len, tgt_len), tf.cast(LARGE_NEGATIVE, tf.float32))
+    mask = tf.linalg.band_part(mask, 0, -1) - tf.linalg.band_part(mask, 0, 0)
 
     if past_key_values_length > 0:
         mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
@@ -93,7 +95,7 @@ def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
     return (one_cst - expanded_mask) * LARGE_NEGATIVE
 
 
-class TFOPTLearnedPositionalEmbedding(TFSharedEmbeddings):
+class TFOPTLearnedPositionalEmbedding(tf.keras.layers.Embedding):
     """
     This module learns positional embeddings up to a fixed maximum size.
     """
@@ -516,16 +518,22 @@ def get_input_embeddings(self):
     def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length):
         # create causal mask
         # # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
-        else:
-            combined_attention_mask = _expand_mask(
-                tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
-            )
+        _, seq_length = input_shape
+        tf.debugging.assert_equal(
+            seq_length + past_key_values_length,
+            shape_list(attention_mask)[1],
+            message="Attention mask shape should be (batch_size, seq_length + past_key_values_length)"
+            f" but is {shape_list(attention_mask)[1]} with input_ids shape {input_shape} and past length"
+            f" {past_key_values_length}.",
+        )
 
-        if attention_mask is not None:
-            combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
+        expanded_attn_mask = _expand_mask(attention_mask, tgt_len=input_shape[-1])
+        if seq_length > 1:
+            combined_attention_mask = (
+                _make_causal_mask(input_shape, past_key_values_length=past_key_values_length) + expanded_attn_mask
+            )
+        else:
+            combined_attention_mask = expanded_attn_mask
 
         return combined_attention_mask
 
@@ -615,17 +623,16 @@ def call(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if attention_mask is None:
-            attention_mask = tf.ones(inputs_embeds.shape[:2], dtype=tf.bool)
+            attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length), dtype=tf.bool)
         else:
             tf.debugging.assert_equal(
-                tf.shape(attention_mask)[1],
+                shape_list(attention_mask)[1],
                 past_key_values_length + input_shape[1],
                 message=(
-                    f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
+                    f"The provided attention mask has length {tf.shape(attention_mask)[1]}, but its length should be "
                     f"{past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)"
                 ),
             )
-
         pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
 
         attention_mask = self._prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length)