ttnn.rpow

ttnn.rpow = Operation(python_fully_qualified_name='ttnn.rpow', function=<ttnn._ttnn.operations.unary.rpow_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function_rpow>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Performs rpow function on input_tensor, exponent.

Parameters:

• input_tensor (ttnn.Tensor) – the input tensor. Supported for input range upto 28

• exponent (float) – exponent value. Non-positive values are not supported.

Keyword Arguments:

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

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16	TILE	2, 3, 4

System memory is not supported.

Example

>>> tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
>>> exponent = 2
>>> output = ttnn.rpow(tensor, exponent)