sgl-project/sglang · error · RuntimeError

duplicate auxiliary PP tensor

Error message

duplicate auxiliary PP tensor {name!r}

What it means

Sampling PP observer error: the same auxiliary tensor name was registered more than once; duplicate names are rejected to keep mappings unambiguous.

Solutions

1. Namespace the tensor names per output (e.g. 'encoder.logits', 'decoder.logits')
2. Only add each output once per batch step

Example fix

# before
return {"logits": a}  # in both outputs
# after
return {"encoder_logits": a}  # and {"decoder_logits": b}

PREFIX = "sglang_aux_output."  # must match module's _OUTPUT_PREFIX
new_keys = {f"{PREFIX}{k}" for k in output.to_pp_tensors()}
assert not (new_keys & set(tensors)), "name collision"

When it happens

Trigger: Calling add_auxiliary_output_to_pp_tensors twice, or multiple outputs whose to_pp_tensors() share a field name.

Common situations: Attaching per-layer auxiliary outputs that each emit e.g. 'logits'; refactoring merged outputs with colliding field names.

Related errors

• auxiliary PP output must contain at least one tensor
• auxiliary PP output is not a tensor
• auxiliary PP tensor names must be non-empty strings
• received a non-tensor auxiliary PP output
• auxiliary output does not support pipeline-parallel…

AI-assisted analysis of sgl-project/sglang@0132848349 (2026-08-28). Data as JSON: /api/errors/42c1609c899c5fb7. Report an issue: GitHub.

Appendix: source

Thrown at python/sglang/srt/sampling/sampling_observer_pp.py:57

    if output is None:
        return
    if not isinstance(output, PipelineParallelAuxiliaryOutput):
        raise RuntimeError(
            "auxiliary output does not support pipeline-parallel transport"
        )

    output_tensors = output.to_pp_tensors()
    if not output_tensors:
        raise RuntimeError("auxiliary PP output must contain at least one tensor")

    for name, tensor in output_tensors.items():
        if not isinstance(name, str) or not name:
            raise RuntimeError("auxiliary PP tensor names must be non-empty strings")
        if not torch.is_tensor(tensor):
            raise RuntimeError(f"auxiliary PP output {name!r} is not a tensor")
        key = f"{_OUTPUT_PREFIX}{name}"
        if key in tensors:
            raise RuntimeError(f"duplicate auxiliary PP tensor {name!r}")
        tensors[key] = tensor


def pop_auxiliary_output_from_pp_tensors(
    tensors: MutableMapping[str, Any],
    observer: Optional[SamplingObserver],
) -> Optional[DeviceAuxiliaryOutput]:
    output_tensors = {
        key.removeprefix(_OUTPUT_PREFIX): value
        for key, value in tensors.items()
        if key.startswith(_OUTPUT_PREFIX)
    }
    if not output_tensors:
        return None
    if observer is None:
        raise RuntimeError("received auxiliary PP output without a sampling observer")
    if not isinstance(observer, PipelineParallelSamplingObserver):
        raise RuntimeError(

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