jax-ml/jax · error · ValueError

must be a tuple of factors

Error message

{kind} must be a tuple of factors

What it means

check_special_factors validates that reduction_factors, need_replication_factors, or permutation_factors is a tuple of factor names. A non-tuple (list, string, None where a tuple is expected) triggers this error.

Solutions

1. Wrap the factors in a tuple: reduction_factors=('m',)
2. Convert existing lists with tuple(...) before passing

Example fix

# before
reduction_factors = ['m']
# after
reduction_factors = ('m',)

assert isinstance(reduction_factors, tuple) and isinstance(need_replication_factors, tuple) and isinstance(permutation_factors, tuple)

def is_factor_tuple(x) -> bool:
    return isinstance(x, tuple) and all(isinstance(f, str) for f in x)

When it happens

Trigger: Passing reduction_factors=['m'] (a list) or a bare string 'm' instead of ('m',) to def_partition / str_to_sdy_sharding_rule.

Common situations: Using a list literal out of habit; passing a single factor as a string instead of a 1-tuple.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

• : arrays must have same number of dimensions, got .
• Expected unreduced_kind to be of type…
• Expected unreduced_kind to be of type…
• factor_sizes must be a dict of str to int, but got
• argument of ManualAxisType should of type `frozenset` or…

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/7dc815cbbf7db401. Report an issue: GitHub.

Appendix: source

Thrown at jax/_src/custom_partitioning_sharding_rule.py:163

          f"Factor {factor} is not used in the rule, but size is provided")

    # Check that factors that are used for a whole dimension aren't in
    # factor_sizes and factors that are never used for a whole dimension are
    # in factor_sizes.
    for factor, inferable in factors_inferrable.items():
      if factor not in factor_sizes and not inferable:
        raise ValueError(
          f"Factor {factor} is only used in compound factors; must specify"
          " its size")
      if factor in factor_sizes and inferable:
        raise ValueError(
          f"Factor {factor} represents a whole dimension; do not specify its"
          " size")

    special_factors = set()
    def check_special_factors(kind, factors):
      if not isinstance(factors, tuple):
        raise ValueError(f"{kind} must be a tuple of factors")

      if len(factors) != len(set(factors)):
        raise ValueError(f"{kind} contains duplicated factors")

      for factor in factors:
        if factor not in factors_inferrable:
          raise ValueError(
            f"Factor {factor} in {kind} is not used in the rule")
        if factor in special_factors:
          raise ValueError(f"Factor {factor} can only be in one of the "
              f"reduction, need replication, or permutation factor sets.")
        special_factors.add(factor)

    check_special_factors("reduction_factors", reduction_factors)
    check_special_factors("need_replication_factors", need_replication_factors)
    check_special_factors("permutation_factors", permutation_factors)

    self.operand_mappings = operand_mappings

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