jax-ml/jax · error · ValueError

invalid argument side=

Error message

invalid argument side={side!r}, expected 'left' or 'right'

What it means

Raised by the SearchSorted HiJAX primitive when the side argument is not exactly the string 'left' or 'right'. side controls whether ties return the first (left) or last (right) insertion index, mirroring numpy.searchsorted. Any other value, including capitalized or misspelled variants, is rejected at primitive construction.

Solutions

1. Use exactly side='left' or side='right'
2. If side comes from user input, normalize it: side.strip().lower()
3. Never pass an int/bool for side in this API

Example fix

# before
idx = jnp.searchsorted(a, v, side='Left')
# after
idx = jnp.searchsorted(a, v, side='left')

assert side in ('left', 'right'), side

def valid_side(s: str) -> bool:
    return isinstance(s, str) and s in ('left', 'right')

When it happens

Trigger: Passing side='Left', side='RIGHT', side=0/1, or a typo like side='rigth' to jax.numpy searchsorted.

Common situations: Porting code from an API that used booleans or integers for side; building side dynamically from user config with non-normalized casing.

Understand the failure class

Background: "invalid argument", "unknown mode", "not supported": invalid enum-like argument errors explained — this error's family across 19 libraries.

Related errors

• invalid argument , expected one of
• out_dtype should be an integer type; got
• Unsupported method
• a_scale and b_scale must both be present or absent.
• args argument to jax.scipy.optimize.minimize must be a…

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/96445bfc0230bf8b. Report an issue: GitHub.

Appendix: source

Thrown at jax/_src/numpy/hijax.py:84

      out_dtype: np.dtype):
    batch_dims = operator.index(batch_dims)
    if batch_dims < 0 or batch_dims >= sorted_arr_aval.ndim:
      raise ValueError(
          f"batch_dims={batch_dims} must be in range [0, {sorted_arr_aval.ndim})"
      )
    dimension = operator.index(dimension)
    if not batch_dims <= dimension < sorted_arr_aval.ndim:
      raise ValueError(
          f"dimension={dimension} must be in range [{batch_dims},"
          f" {sorted_arr_aval.ndim})"
      )
    if sorted_arr_aval.dtype != query_aval.dtype:
      raise ValueError(
          "dtypes of sorted_arr and query must match; got "
          f"{sorted_arr_aval.dtype} and {query_aval.dtype}"
      )
    if side not in ["left", "right"]:
      raise ValueError(
          f"invalid argument side={side!r}, expected 'left' or 'right'"
      )
    if method not in self.valid_methods:
      raise ValueError(
          f"invalid argument {method=}, expected one of {list(self.valid_methods)}"
      )
    if sorted_arr_aval.shape[:batch_dims] != query_aval.shape[:batch_dims]:
      raise ValueError(
          "batch dimension sizes must match; got"
          f" {sorted_arr_aval.shape[:batch_dims]} != {query_aval.shape[:batch_dims]}"
      )
    if not dtypes.issubdtype(out_dtype, np.integer):
      raise ValueError(f"out_dtype should be an integer type; got {out_dtype}")
    # Attempt this here to catch overflow errors early.
    out_dtype.type(sorted_arr_aval.shape[dimension])
    self.in_avals = (sorted_arr_aval, query_aval)
    self.out_aval = core.typeof(api.eval_shape(
      functools.partial(_searchsorted_impl,

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