huggingface/candle · error

Index {} out of bounds for dimension {} with size {}

Error message

Index {} out of bounds for dimension {} with size {}

What it means

During ScatterGD/GatherND-style indexed evaluation, candle-onnx converts each per-dimension index to a flat offset and validates it against the data shape. Negative indices are normalized (dim_size + idx), but if the resulting index is still outside [0, dim_size) this error is thrown, naming the offending index, dimension, and size.

Source

Thrown at candle-onnx/src/eval.rs:2516

                let mut strides: Vec<usize> = vec![1];
                for i in (0..data_shape.len() - 1).rev() {
                    strides.push(strides.last().unwrap() * data_shape[i + 1]);
                }
                strides.reverse();

                // Process each update
                for i in 0..num_updates {
                    let index_slice = flat_indices.narrow(0, i, 1)?;
                    let indices_vec = index_slice.squeeze(0)?.to_vec1::<i64>()?;

                    // Convert multi-dimensional indices to flat index
                    let mut flat_idx: usize = 0;
                    for (dim, &idx) in indices_vec.iter().enumerate() {
                        let dim_size = data_shape[dim] as i64;
                        let norm_idx = if idx < 0 { dim_size + idx } else { idx };

                        if norm_idx < 0 || norm_idx >= dim_size {
                            bail!(
                                "Index {} out of bounds for dimension {} with size {}",
                                idx,
                                dim,
                                dim_size
                            );
                        }

                        flat_idx += (norm_idx as usize) * strides[dim];
                    }

                    // Extract current update
                    let update_slice = if update_element_shape.is_empty() {
                        flat_updates.narrow(0, i, 1)?.squeeze(0)?
                    } else {
                        flat_updates.narrow(0, i, 1)?
                    };

                    match reduction {

View on GitHub (pinned to d5fee525bf)

Solutions

1. Clamp the indices before the op (e.g. via graph surgery or upstream clip node)
2. Fix upstream ops producing out-of-range indices
3. Validate indices against data shape in a preprocessing step before running the graph

Example fix

# before: idx = 10, dim size 8
# after (preprocess)
indices = np.clip(indices, -dim_size, dim_size - 1)

for (dim, &idx) in indices.iter().enumerate() {
    let size = data_shape[dim] as i64;
    let n = if idx < 0 { size + idx } else { idx };
    if n < 0 || n >= size {
        return Err(format!("index {} out of bounds for dim {} (size {})", idx, dim, size));
    }
}

fn indices_in_bounds(indices: &[i64], shape: &[usize]) -> bool {
    indices.iter().zip(shape).all(|(&i, &s)| (i as i64) >= -(s as i64) && (i as i64) < s as i64)
}

match eval(...) {
    Err(e) if e.contains("out of bounds for dimension") => clamp_indices_and_retry(),
    other => other,
}

When it happens

Trigger: Evaluating a ScatterND node whose indices contain a value >= dim_size or < -dim_size for any dimension of the data tensor, e.g. index 10 into a dimension of size 8 or index -9 into size 8.

Common situations: Out-of-range indices generated by upstream ops (argmax on wrong axis, wrong padding values); integer overflow or bad position encodings in exported models.

Related errors

• ScatterND expects k (indices.shape[-1]) to be at most the ra
• attribute {} was of type TENSOR, but no tensor was found
• attribute {} of type TENSOR was an invalid data_type number
• attribute {} of type TENSOR has an unsupported data_type {}
• attribute {} of type TENSOR has a negative dimension, which

AI-assisted analysis of huggingface/candle@d5fee525bf (2026-09-02). Data as JSON: /api/errors/afc5a950c9f7c4cf. Report an issue: GitHub.