Fix OpCheckError

✅ Solution

OpCheckError in PyTorch typically arises when a custom operator's implementation (forward and/or backward) doesn't precisely match its declared signature regarding tensor properties like data type, layout, and device. To fix this, carefully review your operator's forward and backward functions, ensuring that the output tensors' characteristics (dtype, shape, device, layout) strictly adhere to what your operator claims to produce, based on the inputs' characteristics. Specifically pay close attention to data alignment and strides which are crucial for CUDA kernels.