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Merge pull request #2068 from huggingface/fix-2042

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Nicer error message when Bert's input is missing batch size
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Thomas Wolf 2019-12-06 12:06:42 +01:00 committed by GitHub
commit fc1bb1f867
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1 changed files with 10 additions and 6 deletions
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transformers/modeling_bert.py
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@ -667,11 +667,10 @@ class BertModel(BertPreTrainedModel):
# ourselves in which case we just need to make it broadcastable to all heads. # ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3: if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :] extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
# Provided a padding mask of dimensions [batch_size, seq_length] # 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 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 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: if self.config.is_decoder:
batch_size, seq_length = input_shape batch_size, seq_length = input_shape
seq_ids = torch.arange(seq_length, device=device) 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, :] extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
else: else:
extended_attention_mask = attention_mask[:, None, None, :] 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 # 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 # 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: if encoder_attention_mask.dim() == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] 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, :] 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 = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0