jax-ml/jax · error · ValueError

bcoo_slice: invalid indices. Got {start_indices=}, {limit_in

Error message

bcoo_slice: invalid indices. Got {start_indices=}, {limit_indices=} and shape={mat.shape}

What it means

bcoo_slice requires, for every dimension, 0 <= start <= limit <= shape[dim]. Violating any of these (negative start, limit beyond the axis size, or limit < start) raises this ValueError echoing the offending indices and mat.shape.

Source

Thrown at jax/experimental/sparse/bcoo.py:1992

  """
  if not isinstance(mat, BCOO):
    raise TypeError(f"bcoo_slice: input should be BCOO array, got type(mat)={type(mat)}")
  start_indices = [operator.index(i) for i in start_indices]
  limit_indices = [operator.index(i) for i in limit_indices]
  if strides is not None:
    strides = [operator.index(i) for i in strides]
  else:
    strides = [1] * mat.ndim
  if len(start_indices) != len(limit_indices) != len(strides) != mat.ndim:
    raise ValueError(f"bcoo_slice: indices must have size mat.ndim={mat.ndim}")
  if len(strides) != mat.ndim:
    raise ValueError(f"len(strides) = {len(strides)}; expected {mat.ndim}")
  if any(s <= 0 for s in strides):
    raise ValueError(f"strides must be a sequence of positive integers; got {strides}")

  if not all(0 <= start <= end <= size
             for start, end, size in safe_zip(start_indices, limit_indices, mat.shape)):
    raise ValueError(f"bcoo_slice: invalid indices. Got {start_indices=}, "
                     f"{limit_indices=} and shape={mat.shape}")

  start_batch, start_sparse, start_dense = split_list(start_indices, [mat.n_batch, mat.n_sparse])
  end_batch, end_sparse, end_dense = split_list(limit_indices, [mat.n_batch, mat.n_sparse])
  stride_batch, stride_sparse, stride_dense = split_list(strides, [mat.n_batch, mat.n_sparse])

  data_slices = []
  index_slices = []
  for i, (start, end, stride) in enumerate(zip(start_batch, end_batch, stride_batch)):
    data_slices.append(slice(None) if mat.data.shape[i] != mat.shape[i] else slice(start, end, stride))
    index_slices.append(slice(None) if mat.indices.shape[i] != mat.shape[i] else slice(start, end, stride))
  data_slices.append(slice(None))
  index_slices.extend([slice(None), slice(None)])
  for i, (start, end, stride) in enumerate(zip(start_dense, end_dense, stride_dense)):
    data_slices.append(slice(start, end, stride))
  new_data = mat.data[tuple(data_slices)]
  new_indices = mat.indices[tuple(index_slices)]
  new_shape = tuple(

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Clamp indices: start = max(0, start); limit = min(limit, dim_size)
2. Replace NumPy-style negative ends with explicit axis lengths (use mat.shape, not -1)
3. Assert limits >= starts per axis before calling

Example fix

# before
bcoo_slice(mat, start_indices=(0, -1), limit_indices=(4, 10), strides=None)  # shape (4, 10) ok but -1 invalid
# after
bcoo_slice(mat, start_indices=(0, 0), limit_indices=(4, 10))

starts = [max(0, s) for s in start_indices]
limits = [min(l, d) for l, d in zip(limit_indices, mat.shape)]
assert all(s <= l for s, l in zip(starts, limits))

try:
    out = bcoo_slice(mat, starts, limits, strides)
except ValueError as e:
    if 'invalid indices' in str(e):
        limits = [min(l, d) for l, d in zip(limit_indices, mat.shape)]
        out = bcoo_slice(mat, starts, limits, strides)
    else:
        raise

When it happens

Trigger: Calling bcoo_slice with start_indices containing negatives, limit_indices exceeding mat.shape, or limits smaller than starts, e.g. start=(5,), limit=(3,) on an axis of size 4.

Common situations: Using -1 as an end index (NumPy convention) instead of the axis size; computing limits from dynamic values without clamping; off-by-one errors after shape changes.

Related errors

• bcoo_slice: input should be BCOO array, got type(mat)={type(
• Slice {idx} along axis {axis} is out of bounds for shape {sh
• batch_dims must be None or satisfy 0 < dim < n_batch. Got {b
• data batch dimensions not compatible for {data.shape=}, {sha
• Invalid {data.shape=} for {nse=}, {n_batch=}, {n_dense=}

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/f77e0a6a6c760dc2. Report an issue: GitHub.