ttnn.rdiv_bw

ttnn.rdiv_bw = Operation(python_fully_qualified_name='ttnn.rdiv_bw', function=<ttnn._ttnn.operations.unary_backward.rdiv_bw_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function <lambda>>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Performs backward operations for Unary rdiv on input_tensor, scalar with given grad_tensor using given round_mode. round_mode can be ‘None’, ‘trunc’, or ‘floor’.

Parameters:

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

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

  • scalar (float) – divisor.

Keyword Arguments:

  • round_mode (string, optional) – Mode of Rounding. Defaults to None.

  • 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

Performance of the PCC may degrade when using BFLOAT8_B. For more details, refer to the BFLOAT8_B limitations.

Example

>>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> input = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> scalar = 0.5
>>> output = ttnn.rdiv_bw(grad_tensor, input, scalar, round_mode = None)