tracel-ai/burn · error

{0} kernel failed (device={1:?}, dtype={2:?}): {3}

Error message

{0} kernel failed (device={1:?}, dtype={2:?}): {3}

What it means

pool_panic is the shared fatal-error handler for all pooling kernels (max/avg pool, adaptive avg pool, and their backward passes) on the CubeCL backend. When the pool kernel launch returns a PoolError, it panics with the operation label, input device, dtype, and the error. One panic site covers six public pool operations.

Source

Thrown at crates/burn-cubecl/src/kernel/pool/base.rs:385

    );

    let mode = PoolMode::from(AdaptiveAvgPoolOptions::new([out_h, out_w]));

    pool2d_backward(
        &output.client,
        input.clone().binding(),
        out_grad.clone().binding(),
        output.clone().binding(),
        mode,
        dtype_to_storage_type(output.dtype),
    )
    .unwrap_or_else(|e| pool_panic("adaptive_avg_pool2d_backward", &input, e));

    permute_nhwc_to_nchw(output)
}

fn pool_panic(label: &str, input: &CubeTensor, error: PoolError) -> ! {
    panic!(
        "{0} kernel failed (device={1:?}, dtype={2:?}): {3}",
        label, input.device, input.dtype, error
    )
}

View on GitHub (pinned to d16f7ba2ed)

Solutions

1. Read the PoolError and the label to identify which pool op failed
2. Validate kernel_size/stride/padding: stride > 0 and window not exceeding padded input size
3. Ensure input is a valid NCHW tensor with the expected dtype on a supported device
4. For backward errors, confirm the output_grad shape equals the forward output shape

Example fix

// before
let out = max_pool2d(x, [3, 3], [0, 0], [1, 1]); // window larger than 2x2 input
// after: pad or shrink the kernel to fit the input
let out = max_pool2d(x, [2, 2], [0, 0], [1, 1]);

assert!(stride.iter().all(|&s| s > 0), "pool stride must be > 0");
let padded = (h + 2 * pad).ge(&kernel[0]);
assert!(padded, "pool kernel larger than (padded) input");

When it happens

Trigger: Calling max_pool2d, avg_pool2d, adaptive_avg_pool2d (or their _backward/_with_indices variants) with kernel_size/stride/padding combinations that produce invalid output shapes, unsupported dtypes/devices, or malformed input channel layout.

Common situations: Pool window larger than the input spatial dims, stride 0, channel-first vs NHWC confusion after permute, f16 pooling on hardware lacking support, or backward grads with mismatched shapes.

Related errors

• irfft kernel launch failed (device={input_device:?}, dtype={
• interpolate_backward kernel failed (device={0:?}, dtype={1:?
• Not a valid DType for tensors.
• Invalid concreate ref layout
• rfft kernel launch failed (device={input_device:?}, dtype={i

AI-assisted analysis of tracel-ai/burn@d16f7ba2ed (2026-09-05). Data as JSON: /api/errors/2088da43f182b4e9. Report an issue: GitHub.