ttnn.logical_xor_

ttnn.logical_xor_(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor) → ttnn.Tensor

Computes inplace logical XOR of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\mathrm{input\_tensor\_a}_i \land \lnot \mathrm{input\_tensor\_b}_i) \lor (\lnot \mathrm{input\_tensor\_a}_i \land \mathrm{input\_tensor\_b}_i\]

Parameters:

input_tensor_a (ttnn.Tensor) – the input tensor.
input_tensor_b (ttnn.Tensor) – the input tensor.

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16, BFLOAT8_B	TILE	2, 3, 4

Example

>>> tensor1 = ttnn.from_torch(torch.tensor([[2, 2], [2, 2]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor([[1, 1], [1, 1]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> ttnn.logical_xor_(tensor1, tensor2)