The error you're encountering, `RuntimeError: Unsupported dtype Half`, occurs because the CPU does not support computations with `float16` (Half precision) as it is not natively supported by most CPUs. The `torch.fft.fftn` function cannot operate directly on `float16` tensors on the CPU.
To work around this issue, you can perform the FFT operation using a supported datatype (e.g., `float32`) and then convert the result back to `float16` if needed for memory efficiency or subsequent operations. Here’s how you can modify your code to achieve this:
```python
import torch
from torch.fft import fftn
# Create a random tensor of float16 type
t = torch.rand(16, 16, dtype=torch.float16)
# Convert the tensor to float32 for computation
t_float32 = t.to(dtype=torch.float32)
# Perform the FFT operation
result_float32 = fftn(t_float32)
# Optionally convert the result back to float16
result_float16 = result_float32.to(dtype=torch.float16)
print(result_float16)
```
Here's the breakdown of the changes:
1. Convert your input tensor from `float16` to `float32` before performing the FFT.
2. Perform the FFT operation.
3. Convert the result back to `float16` if needed.
This approach allows you to circumvent the limitation of unsupported dtype on the CPU while still getting your final result in `float16`.