updating T5

transformers/modeling_t5.py

@@ -281,7 +281,7 @@ class T5Attention(nn.Module):
         context_position = torch.arange(qlen, dtype=torch.long)[:, None]
         memory_position = torch.arange(klen, dtype=torch.long)[None, :]
         relative_position = memory_position - context_position  # shape (qlen, klen)
-        rp_bucket = self._relative_position_bucket(relative_position,
+        rp_bucket = self._relative_position_bucket(relative_position,  # shape (qlen, klen)
                                                    bidirectional=not self.is_decoder,
                                                    num_buckets=self.relative_attention_num_buckets)
         values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
@@ -337,14 +337,10 @@ class T5Attention(nn.Module):
             if not self.has_relative_attention_bias:
                 raise ValueError("No position_bias provided and no weights to compute position_bias")
             position_bias = self.compute_bias(qlen, klen)
-        scores += position_bias
-        special_out = position_bias
-
             if mask is not None:
-            scores += mask
-            # mask = (mask == 0).expand_as(scores)                              # (bs, n_heads, qlen, klen)
-            # scores.masked_fill_(mask, -float('inf'))                          # (bs, n_heads, qlen, klen)
+                position_bias += mask                                         # (bs, n_heads, qlen, klen)
 
+        scores += position_bias
         weights = F.softmax(scores.float(), dim=-1).type_as(scores)           # (bs, n_heads, qlen, klen)
         weights = F.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)
 
@@ -362,7 +358,7 @@ class T5Attention(nn.Module):
             outputs = outputs + (weights,)
         if self.has_relative_attention_bias:
             outputs = outputs + (position_bias,)
-        return outputs + (special_out,)
+        return outputs
 
 
 class T5LayerSelfAttention(nn.Module):
@@ -379,11 +375,9 @@ class T5LayerSelfAttention(nn.Module):
                                               position_bias=position_bias,
                                               head_mask=head_mask)
         y = attention_output[0]
-        special_out = attention_output[-1]
-        attention_output = attention_output[:-1]
         layer_output = hidden_states + self.dropout(y)
         outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
-        return outputs + (special_out,)
+        return outputs
 
 
 class T5LayerCrossAttention(nn.Module):
@@ -426,8 +420,7 @@ class T5Block(nn.Module):
                                                 position_bias=position_bias,
                                                 head_mask=head_mask)
         hidden_states = self_attention_outputs[0]
-        special_out = self_attention_outputs[-1]
-        outputs = self_attention_outputs[1:-1]  # Keep self-attention outputs and relative position weights
+        outputs = self_attention_outputs[1:]  # Keep self-attention outputs and relative position weights
 
         if not self.is_decoder:
             hidden_states = self.layer[1](hidden_states)
@@ -442,7 +435,7 @@ class T5Block(nn.Module):
             hidden_states = self.layer[2](hidden_states)
 
         outputs = (hidden_states,) + outputs  # add attentions if we output them
-        return outputs + (special_out,)  # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
+        return outputs  # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
 
 
 class T5PreTrainedModel(PreTrainedModel):
@@ -536,6 +529,10 @@ class T5Stack(T5PreTrainedModel):
         # positions we want to attend and -1e9 for masked positions.
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
+
+        # T5 has a mask that can compare sequence ids, we simulate this here with this transposistion
+        # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
+        extended_attention_mask = (extended_attention_mask == extended_attention_mask.transpose(-1, -2))
         extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
         extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
 
@@ -584,8 +581,6 @@ class T5Stack(T5PreTrainedModel):
                                          encoder_attention_mask=encoder_extended_attention_mask,
                                          encoder_decoder_position_bias=encoder_decoder_position_bias,
                                          head_mask=head_mask[i])
-            if i == 0:
-                special_out = layer_outputs[-1]
             # layer_outputs is a tuple with:
             # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
             hidden_states = layer_outputs[0]
@@ -610,7 +605,7 @@ class T5Stack(T5PreTrainedModel):
             outputs = outputs + (all_hidden_states,)
         if self.output_attentions:
             outputs = outputs + (all_attentions,)
-        return outputs + (special_out,)  # last-layer hidden state, (all hidden states), (all attentions)
+        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
 
 
 T5_START_DOCSTRING = r"""    The T5 model was proposed in