jax-ml/jax · error · TypeError

can only specify one unknown axis size with a `-1` value, go

Error message

can only specify one unknown axis size with a `-1` value, got {orig_newshape}

What it means

When reshaping with a -1 placeholder, only one axis may be unspecified because its size is inferred from the others. `_compute_newshape` counts int entries equal to -1 and raises if there is more than one. This mirrors NumPy's rule and is checked before any shape arithmetic.

Source

Thrown at jax/_src/numpy/array_methods.py:525

  Refer to :func:`jax.numpy.var` for full documentation.
  """
  return reductions.var(self, axis=axis, dtype=dtype, out=out, ddof=ddof,
                        keepdims=keepdims, where=where, correction=correction)


def _compute_newshape(arr: Array, newshape: DimSize | Shape) -> Shape:
  """Fixes a -1 value in newshape, if present."""
  orig_newshape = newshape  # for error messages
  try:
    iter(newshape)  # pyrefly: ignore[no-matching-overload]
  except TypeError:
    newshape = [newshape]
  else:
    newshape: Sequence[DimSize]  # pyrefly: ignore[redefinition]
  newshape = core.canonicalize_shape(newshape)
  neg1s = [i for i, d in enumerate(newshape) if type(d) is int and d == -1]
  if len(neg1s) > 1:
    raise TypeError("can only specify one unknown axis size with a `-1` value, "
                    f"got {orig_newshape}")
  if neg1s:
    i, = neg1s
    other_sizes = (*newshape[:i], *newshape[i+1:])
    if (all(isinstance(d, int) for d in (*arr.shape, *other_sizes)) and
        arr.size % math.prod(other_sizes) != 0):
      raise TypeError(f"cannot reshape array of shape {arr.shape} (size {arr.size}) "
                      f"into shape {orig_newshape} because the product of "
                      f"specified axis sizes ({math.prod(other_sizes)}) does "
                      f"not evenly divide {arr.size}")
    sz = core.cancel_divide_tracers(arr.shape, other_sizes)
    if sz is not None:
      return (*newshape[:i], sz, *newshape[i+1:])
  else:
    if (all(isinstance(d, int) for d in (*arr.shape, *newshape)) and
        arr.size != math.prod(newshape)):
      raise TypeError(f"cannot reshape array of shape {arr.shape} (size {arr.size}) "
                      f"into shape {orig_newshape} (size {math.prod(newshape)})")

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Specify all but one dimension explicitly
2. Compute the inferred dimension yourself: `arr.reshape(arr.shape[0], -1)` for batch-major data

Example fix

# before
arr.reshape(-1, -1)

# after
arr.reshape(arr.shape[0], -1)

def count_neg1s(shape):
    return sum(1 for d in shape if isinstance(d, int) and d == -1)

def assert_valid_reshape(shape):
    assert count_neg1s(shape) <= 1, 'at most one -1 allowed'

When it happens

Trigger: Calling reshape with two or more -1s, e.g. `arr.reshape(-1, -1)` or `jnp.reshape(arr, (-1, 3, -1))`.

Common situations: Dynamically built reshape shapes where a batch axis and a feature axis are both left as -1; typos when copying shapes.

Related errors

• reshape new_sizes must all be positive, got {}.
• np.reshape order=A is not implemented.
• Unexpected value for 'order' argument: {order}.
• cannot reshape array of shape {arr.shape} (size {arr.size})
• cannot reshape array of shape {arr.shape} (size {arr.size})

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/15c477a3aca0b81c. Report an issue: GitHub.