ttnn.polyval

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

Computes polyval of all elements of input_tensor_a with coefficients coeffs and returns the tensor with the same layout as input_tensor_a

\[\mathrm{output\_tensor} = \sum_{i=0}^{n} (\mathrm{coeffs}_i) (\mathrm{input\_tensor}^i)\]

Parameters:

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

• Coeffs (Vector of floats) – coefficients of the polynomial.

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, BFLOAT8_B	TILE	2, 3, 4

Example

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