ttnn.hardswish

ttnn.hardswish = FastOperation(python_fully_qualified_name='ttnn.hardswish', function=<ttnn._ttnn.operations.unary.hardswish_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 hardswish to input_tensor element-wise.

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

input_tensor (ttnn.Tensor) – the input tensor.

Keyword Arguments:

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

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

  • sub_core_grids (ttnn.CoreRangeSet, optional) – sub core grids for the operation. Defaults to None.

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes and layouts:

Dtypes

Layouts

BFLOAT16, BFLOAT8_B, FLOAT32

TILE, ROW_MAJOR