[Bug] `LocalNormalizedCrossCorrelationLoss`: kernel not registered as buffer — silent gradient tracking + wrong device placement

[Zeesejo]

Describe the bug

In monai/losses/image_dissimilarity.py, LocalNormalizedCrossCorrelationLoss.__init__ contains two related problems in kernel initialization:

Problem 1: Typo in require_grads (silent no-op)

self.kernel = _kernel(self.kernel_size)
self.kernel.require_grads = False  # BUG: 'require_grads' is NOT a valid tensor attribute!
self.kernel_vol = self.get_kernel_vol()

require_grads (plural, with trailing s) is not a valid PyTorch tensor attribute. The correct attribute is requires_grad. This line silently does nothing — it creates a new Python attribute called require_grads on the tensor object instead of controlling gradient tracking. As a result, the kernel tensor silently tracks gradients in every forward pass, consuming unnecessary memory in the computation graph.

Problem 2: Plain attribute assignment instead of register_buffer

Using self.kernel = ... (plain attribute) instead of self.register_buffer("kernel", ...) means:

• When the user calls loss.to("cuda"), loss.cuda(), or loss.half() on the loss module, the kernel does NOT move to the target device — it stays on CPU. This causes a device mismatch at runtime.
• The kernel is not included in state_dict() / load_state_dict() which leads to silent inconsistencies if checkpointing the loss object.

To Reproduce

import torch
from monai.losses import LocalNormalizedCrossCorrelationLoss

loss = LocalNormalizedCrossCorrelationLoss(spatial_dims=3, kernel_type="gaussian")

# Bug 1: require_grads typo silently does nothing
print(loss.kernel.requires_grad)   # True! Not False as intended
print(hasattr(loss.kernel, 'require_grads'))  # True -- spurious attribute created

# Bug 2: kernel not a registered buffer
print(dict(loss.named_buffers()))  # {} -- kernel is NOT here!
loss.cuda()
print(loss.kernel.device)  # cpu -- kernel did NOT move to GPU!

Expected behavior

• loss.kernel.requires_grad should be False
• loss.kernel and loss.kernel_vol should appear in loss.named_buffers()
• After loss.cuda(), loss.kernel.device should be cuda:0

Fix

Replace both assignments with register_buffer:

self.register_buffer("kernel", _kernel(self.kernel_size))
self.register_buffer("kernel_vol", self.get_kernel_vol())

This is tracked in PR #8818.

Environment

Affects all versions. Reproducible on MONAI dev branch as of 2026-04-11.

Related chain of issues

This bug reveals a broader pattern worth auditing across the MONAI losses module:

1. GlobalMutualInformationLoss — check if bin_centers is properly registered as a buffer
2. Other custom loss classes that use constant tensors initialized in __init__
3. Test coverage for device movement of loss modules (loss.cuda() should not cause device mismatch)