Merge pull request #2254 from huggingface/fix-tfroberta

adding positional embeds masking to TFRoBERTa

transformers/modeling_tf_roberta.py

@@ -20,7 +20,6 @@ from __future__ import (absolute_import, division, print_function,
 
 import logging
 
-import numpy as np
 import tensorflow as tf
 
 from .configuration_roberta import RobertaConfig
@@ -46,17 +45,40 @@ class TFRobertaEmbeddings(TFBertEmbeddings):
         super(TFRobertaEmbeddings, self).__init__(config, **kwargs)
         self.padding_idx = 1
 
+    def create_position_ids_from_input_ids(self, x):
+        """ Replace non-padding symbols with their position numbers. Position numbers begin at
+        padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
+        `utils.make_positions`.
+        :param torch.Tensor x:
+        :return torch.Tensor:
+        """
+        mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
+        incremental_indicies = tf.math.cumsum(mask, axis=1) * mask
+        return incremental_indicies + self.padding_idx
+
+    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+        """ We are provided embeddings directly. We cannot infer which are padded so just generate
+        sequential position ids.
+        :param torch.Tensor inputs_embeds:
+        :return torch.Tensor:
+        """
+        seq_length = shape_list(inputs_embeds)[1]
+
+        position_ids = tf.range(self.padding_idx + 1,
+                                seq_length + self.padding_idx + 1,
+                                dtype=tf.int32)[tf.newaxis, :]
+        return position_ids
+
     def _embedding(self, inputs, training=False):
         """Applies embedding based on inputs tensor."""
         input_ids, position_ids, token_type_ids, inputs_embeds = inputs
 
+        if position_ids is None:
             if input_ids is not None:
-            seq_length = shape_list(input_ids)[1]
+                # Create the position ids from the input token ids. Any padded tokens remain padded.
+                position_ids = self.create_position_ids_from_input_ids(input_ids)
             else:
-            seq_length = shape_list(inputs_embeds)[1]
-
-        if position_ids is None:
-            position_ids = tf.range(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=tf.int32)[tf.newaxis, :]
+                position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
 
         return super(TFRobertaEmbeddings, self)._embedding([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)