jax-ml/jax · error · ValueError

mode must be one of ['full', 'same', 'valid']

Error message

mode must be one of ['full', 'same', 'valid']

What it means

_convolve_nd (backing convolve, convolve2d, correlate2d) requires mode to be exactly 'full', 'same', or 'valid' before processing.

Source

Thrown at jax/_src/scipy/signal.py:164

    out_shape = full_shape
  elif mode == "same":
    out_shape = in1.shape
  elif mode == "valid":
    out_shape = tuple(s1 - s2 + 1 for s1, s2 in zip(in1.shape, in2.shape))
  else:
    raise ValueError(f"Unrecognized {mode=}")

  start_indices = tuple((full_size - out_size) // 2
                        for full_size, out_size in zip(full_shape, out_shape))
  return lax.dynamic_slice(conv, start_indices, out_shape)


# Note: we do not reuse the code from jax.numpy.convolve here, because the handling
# of padding differs slightly between the two implementations (particularly for
# mode='same').
def _convolve_nd(in1: Array, in2: Array, mode: ModeString, *, precision: PrecisionLike) -> Array:
  if mode not in ["full", "same", "valid"]:
    raise ValueError("mode must be one of ['full', 'same', 'valid']")
  if in1.ndim != in2.ndim:
    raise ValueError("in1 and in2 must have the same number of dimensions")
  if in1.size == 0 or in2.size == 0:
    raise ValueError(f"zero-size arrays not supported in convolutions, got shapes {in1.shape} and {in2.shape}.")
  in1, in2 = promote_dtypes_inexact(in1, in2)

  no_swap = all(s1 >= s2 for s1, s2 in zip(in1.shape, in2.shape))
  swap = all(s1 <= s2 for s1, s2 in zip(in1.shape, in2.shape))
  if not (no_swap or swap):
    raise ValueError("One input must be smaller than the other in every dimension.")

  shape_o = in2.shape
  if swap:
    in1, in2 = in2, in1
  shape = in2.shape
  in2 = jnp.flip(in2)

  if mode == 'valid':

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Use exact lowercase strings 'full'/'same'/'valid'
2. Sanitize mode strings from configs early

Example fix

# before
signal.convolve2d(a, b, mode='Same')
# after
signal.convolve2d(a, b, mode='same')

assert mode in ('full','same','valid'), mode

def is_mode(m: str) -> bool: return m in ('full', 'same', 'valid')

When it happens

Trigger: convolve2d(x, y, mode='same ' ), correlate2d(..., mode='Valid'), or a mode from an unvalidated config.

Common situations: Typos/case; passing numpy string types or enum objects from other frameworks.

Related errors

• mode must be one of ['same', 'full', 'valid']
• For 'valid' mode, One input must be at least as large as the
• Unrecognized {mode=}
• in1 and in2 must have the same number of dimensions
• ind must be a positive integer; got {ind=}

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/9435be741a7c5727. Report an issue: GitHub.