ttnn.logical_xor

ttnn.logical_xor = Operation(python_fully_qualified_name='ttnn.logical_xor', function=<ttnn._ttnn.operations.binary.logical_xor_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function_logical_xor>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Compute logical_xor input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

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

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

  • input_tensor_b (ttnn.Tensor or Number) – the input tensor.

Keyword Arguments:

  • memory_config (ttnn.MemoryConfig, optional) – memory configuration for the operation. Defaults to None.

  • dtype (ttnn.DataType, optional) – data type for the output tensor. Defaults to None.

  • output_tensor (ttnn.Tensor, optional) – preallocated output tensor. Defaults to None.

  • activations (List[str], optional) – list of activation functions to apply to the output tensor.Defaults to None.

  • queue_id (int, optional) – command queue id. Defaults to 0.

Returns:

ttnn.Tensor – the output tensor.

Supports broadcasting.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16, BFLOAT8_B, INT32

TILE

2, 3, 4

Example

>>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.logical_xor(tensor1, tensor2/scalar)