ttnn.polygamma_bw

ttnn.polygamma_bw = Operation(python_fully_qualified_name='ttnn.polygamma_bw', function=<ttnn._ttnn.operations.unary_backward.polygamma_bw_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)

Performs backward operations for polygamma on input_tensor, scalar with given grad_tensor.

Parameters:

  • grad_tensor (ttnn.Tensor) – the input gradient tensor.

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

  • n (float) – Order of polygamma function.

Keyword Arguments:

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

Returns:

List of ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16

TILE

2, 3, 4

Example

>>> grad_tensor = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> input = ttnn.to_device(ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16)), device=device)
>>> output = ttnn.polygamma_bw(grad_tensor, input, float)