tensorflow/models · error · ValueError

At least one input is required for packing

Error message

At least one input is required for packing

What it means

Error "At least one input is required for packing" thrown in tensorflow/models.

Source

Thrown at official/nlp/modeling/layers/text_layers.py:564

        start_of_sequence_id=self.start_of_sequence_id,
        end_of_segment_id=self.end_of_segment_id,
        padding_id=self.padding_id,
        truncator=self.truncator)

  @staticmethod
  def bert_pack_inputs(inputs: Union[tf.RaggedTensor, List[tf.RaggedTensor]],
                       seq_length: Union[int, tf.Tensor],
                       start_of_sequence_id: Union[int, tf.Tensor],
                       end_of_segment_id: Union[int, tf.Tensor],
                       padding_id: Union[int, tf.Tensor],
                       truncator="round_robin"):
    """Freestanding equivalent of the BertPackInputs layer."""
    _check_if_tf_text_installed()
    # Sanitize inputs.
    if not isinstance(inputs, (list, tuple)):
      inputs = [inputs]
    if not inputs:
      raise ValueError("At least one input is required for packing")
    input_ranks = [rt.shape.rank for rt in inputs]
    if None in input_ranks or len(set(input_ranks)) > 1:
      raise ValueError("All inputs for packing must have the same known rank, "
                       "found ranks " + ",".join(input_ranks))
    # Flatten inputs to [batch_size, (tokens)].
    if input_ranks[0] > 2:
      inputs = [rt.merge_dims(1, -1) for rt in inputs]
    # In case inputs weren't truncated (as they should have been),
    # fall back to some ad-hoc truncation.
    num_special_tokens = len(inputs) + 1
    if truncator == "round_robin":
      trimmed_segments = text.RoundRobinTrimmer(seq_length -  # pyrefly: ignore[missing-attribute]
                                                num_special_tokens).trim(inputs)
    elif truncator == "waterfall":
      trimmed_segments = text.WaterfallTrimmer(  # pyrefly: ignore[missing-attribute]
          seq_length - num_special_tokens).trim(inputs)
    else:
      raise ValueError("Unsupported truncator: %s" % truncator)

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/nlp/modeling/layers/text_layers.py:564 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/cb1727240927f721. Report an issue: GitHub.