tracel-ai/burn · error

Can't store in u32

Error message

Can't store in u32

What it means

Quantized tensors stored as PackedU32 pack multiple quantized values per u32; this requires the last tensor dimension to be a multiple of the number of values packed per u32 (num_quants). If it isn't, the data cannot be laid out in whole u32 words and the library panics during tensor allocation.

Source

Thrown at crates/burn-cubecl/src/ops/qtensor.rs:88

fn new_quantized(
    shape: impl Into<Shape>,
    scheme: QuantScheme,
    device: &CubeDevice,
    data: Option<Bytes>,
    alloc_kind: MemoryLayoutStrategy,
) -> CubeTensor {
    let client = device.client();
    let shape: Shape = shape.into();
    let mut shape_value: Shape = shape.clone();

    let rank = shape.rank();
    let shape_last = shape[rank - 1];
    let num_quants = scheme.num_quants();

    let data_size = match scheme.store {
        QuantStore::PackedU32(_) => {
            if !shape_last.is_multiple_of(num_quants) {
                panic!("Can't store in u32")
            }
            shape_value[rank - 1] = shape_last.div_ceil(num_quants);
            size_of::<u32>()
        }
        QuantStore::Native => match scheme.value {
            QuantValue::Q8F | QuantValue::Q8S | QuantValue::E4M3 | QuantValue::E5M2 => {
                size_of::<i8>()
            }
            QuantValue::Q4F
            | QuantValue::Q4S
            | QuantValue::Q2F
            | QuantValue::Q2S
            | QuantValue::E2M1 => {
                panic!("Can't store native sub-byte values")
            }
        },
        QuantStore::PackedNative(_) => match scheme.value {
            QuantValue::E2M1 => size_of::<e2m1x2>(),

View on GitHub (pinned to d16f7ba2ed)

Solutions

1. Pad or choose the quantized dimension so the last dim is divisible by num_quants for the scheme
2. Switch the scheme to QuantStore::Native with a byte-aligned value like Q8 if the dim can't change
3. Check num_quants() for your QuantScheme to know the required divisibility
4. Adjust model architecture (e.g. set the last linear out-features divisible by the pack factor)

Example fix

// before: last dim 1002 with Q4 packed in u32 (needs %8==0)
let qt = Tensor::new_quantized(data, scheme); // panics
// after: pad weights to 1008 or use Native store
let scheme = scheme.with_store(QuantStore::Native);
let qt = Tensor::new_quantized(data, scheme);

let num_quants = scheme.num_quants();
assert_eq!(shape[shape.len() - 1] % num_quants, 0,
    "last dim must be divisible by {num_quants} for PackedU32");

When it happens

Trigger: Creating a quantized tensor with QuantStore::PackedU32 (e.g. Q4/Q2 packing into u32) where shape[rank-1] % num_quants != 0, via new_qtensor or empty_qtensor.

Common situations: Quantizing a model whose last linear layer output (or embedding dim) isn't divisible by the pack factor (e.g. dim 1002 with 4-bit values packing 8 per u32), or reshaping packed quant tensors to non-divisible last dims.

Related errors

• Can't store native sub-byte values
• {other:?} doesn't support native packing
• Quantization scheme is not valid for dtype {other:?}
• Can't store native sub-byte values
• {other:?} doesn't support native packing

AI-assisted analysis of tracel-ai/burn@d16f7ba2ed (2026-09-05). Data as JSON: /api/errors/cfcee6920a579d81. Report an issue: GitHub.