Merge pull request #2068 from huggingface/fix-2042

Nicer error message when Bert's input is missing batch size

transformers/modeling_bert.py

@@ -667,11 +667,10 @@ class BertModel(BertPreTrainedModel):
         # ourselves in which case we just need to make it broadcastable to all heads.
         if attention_mask.dim() == 3:
             extended_attention_mask = attention_mask[:, None, :, :]
-
+        elif attention_mask.dim() == 2:
             # Provided a padding mask of dimensions [batch_size, seq_length]
             # - if the model is a decoder, apply a causal mask in addition to the padding mask
             # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if attention_mask.dim() == 2:
             if self.config.is_decoder:
                 batch_size, seq_length = input_shape
                 seq_ids = torch.arange(seq_length, device=device)
@@ -679,6 +678,8 @@ class BertModel(BertPreTrainedModel):
                 extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
             else:
                 extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError("Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(input_shape, attention_mask.shape))
 
         # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
         # masked positions, this operation will create a tensor which is 0.0 for
@@ -696,8 +697,11 @@ class BertModel(BertPreTrainedModel):
 
             if encoder_attention_mask.dim() == 3:
                 encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
-            if encoder_attention_mask.dim() == 2:
+            elif encoder_attention_mask.dim() == 2:
                 encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
+            else:
+                raise ValueError("Wrong shape for input_ids (shape {}) or encoder_attention_mask (shape {})".format(input_shape,
+                                                                                                                    encoder_attention_mask.shape))
 
             encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
             encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0