Merge pull request #2068 from huggingface/fix-2042
Nicer error message when Bert's input is missing batch size
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fc1bb1f867
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@ -667,11 +667,10 @@ class BertModel(BertPreTrainedModel):
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# ourselves in which case we just need to make it broadcastable to all heads.
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if attention_mask.dim() == 3:
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extended_attention_mask = attention_mask[:, None, :, :]
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# Provided a padding mask of dimensions [batch_size, seq_length]
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# - if the model is a decoder, apply a causal mask in addition to the padding mask
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# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
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if attention_mask.dim() == 2:
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elif attention_mask.dim() == 2:
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# Provided a padding mask of dimensions [batch_size, seq_length]
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# - if the model is a decoder, apply a causal mask in addition to the padding mask
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# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
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if self.config.is_decoder:
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batch_size, seq_length = input_shape
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seq_ids = torch.arange(seq_length, device=device)
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@ -679,6 +678,8 @@ class BertModel(BertPreTrainedModel):
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extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
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else:
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extended_attention_mask = attention_mask[:, None, None, :]
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else:
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raise ValueError("Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(input_shape, attention_mask.shape))
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# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
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# masked positions, this operation will create a tensor which is 0.0 for
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@ -696,8 +697,11 @@ class BertModel(BertPreTrainedModel):
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if encoder_attention_mask.dim() == 3:
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encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
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if encoder_attention_mask.dim() == 2:
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elif encoder_attention_mask.dim() == 2:
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encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
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else:
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raise ValueError("Wrong shape for input_ids (shape {}) or encoder_attention_mask (shape {})".format(input_shape,
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encoder_attention_mask.shape))
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encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
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encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
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