ttnn.rdiv

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

Performs the element-wise division of a scalar value by a tensor input and rounds the result using round_mode.

Input tensor must have BFLOAT16 data type.

Output tensor will have BFLOAT16 data type.

Parameters:

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

  • value (int) – denominator value which is actually calculated as numerator float value >= 0.

Keyword Arguments:

  • round_mode (string) – rounding_mode value. Can be None, “trunc”, “floor”. Defaults to None.

  • 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

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)
>>> value = 2
>>> output = ttnn.rdiv(tensor, value, round_mode = None)