[bugfix][NPU]:Fix bug that correctly obtains device type

[Feng0w0]

Fix: freqs.device->freqs.device.type

[gemini-code-assist]

Summary of Changes

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This pull request resolves a bug where the device type was incorrectly identified, specifically for NPU (Neural Processing Unit) devices. The fix ensures that the type attribute of the device object is properly accessed, allowing for accurate conditional logic related to NPU-specific operations, such as complex number conversions.

Highlights

• NPU Device Type Check: Corrected the logic for identifying NPU devices by accessing the type attribute of the device object (freqs.device.type) instead of directly comparing the device object itself to a string.

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[gemini-code-assist]

Code Review

This pull request fixes a bug in how the device type is checked for NPU devices. The change from some_tensor.device == "npu" to some_tensor.device.type == "npu" is correct, as it properly compares the device type string.

I've added a couple of comments regarding a potential inefficiency in the mixed-precision logic for NPU devices. While the bugfix is correct, the casting to complex64 might be ineffective due to PyTorch's type promotion rules, as other tensors in the operation remain complex128. This is something you might want to look into for optimal performance and correctness on NPUs.

Overall, the change is a good and necessary bugfix.

[gemini-code-assist]

While this change correctly fixes the device type check, there might be an underlying issue with the mixed-precision logic on NPU devices.

On line 94, x_out is created as a complex128 tensor (from torch.float64). With this change, freqs is converted to complex64 on NPU devices.

When x_out * freqs is executed on line 97, PyTorch's type promotion will likely cast freqs back to complex128 to match x_out's dtype. This could render the conversion to complex64 ineffective. If complex128 operations are unsupported on NPU, this could still lead to errors.

For this to work as intended (using complex64 on NPU), x_out should probably also be created as complex64. This would involve changing x.to(torch.float64) on line 94 to use torch.float32 when on an NPU device.

[gemini-code-assist]

Similar to the other file, while this change is correct, there's a potential mixed-precision issue on NPU devices.

x_out is created as complex128 on line 46. Here, freqs_rank is cast to complex64 for NPU devices.

During the multiplication x_out * freqs_rank on line 54, freqs_rank will likely be promoted back to complex128. This makes the cast to complex64 ineffective and might not resolve underlying performance or compatibility issues on NPU if complex128 is the problem.

To fully leverage complex64 on NPU, x_out should also be created as complex64 by changing x.to(torch.float64) on line 46 to use torch.float32 for NPU devices.