ttnn.reciprocal

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

Applies reciprocal to input_tensor element-wise.

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

input_tensor (ttnn.Tensor or ComplexTensor) – 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.

  • 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, BFLOAT8_B

TILE

2, 3, 4

BFLOAT8_B is supported only for non-zero inputs. Inputs containing zero may produce inaccurate results due to the characteristics of the block-FP format. More information about the BFLOAT8_B.

Example

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