ttnn.sigmoid

ttnn.sigmoid = Operation(python_fully_qualified_name='ttnn.sigmoid', function=<ttnn._ttnn.operations.unary.sigmoid_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function register_ttnn_cpp_unary_function.<locals>._golden_function>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Applies sigmoid to input_tensor element-wise.

\[\mathrm{output\_tensor}_i = \verb|sigmoid|(\mathrm{input\_tensor}_i)\]
Parameters:

input_tensor (ttnn.Tensor) – the input tensor.

Keyword Arguments:

  • vector_mode (int, optional) – Use vector mode to get better performance. Defaults to 4. Use 2 or 4 for different vector modes (2 -> Vector Mode C and 4 -> Vector Mode RC)”.

  • fast_and_approximate_mode (bool, optional) – Use the fast and approximate mode. Defaults to False.

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

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

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

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16

TILE

2, 3, 4

Example

>>> tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.sigmoid(tensor, vector_mode=4, fast_and_approximate_mode=True)