ttnn.div

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

Computes div for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\begin{split}\mathrm{output}_i = \begin{cases} \mathrm{\left(\frac{\mathrm{input\_tensor\_a}_i}{\mathrm{input\_tensor\_b}_i}\right)}, & \text{if } \mathrm{round\_mode} = \mathrm{None} \\ \mathrm{\text{floor}\left(\frac{\mathrm{input\_tensor\_a}_i}{\mathrm{input\_tensor\_b}_i}\right)}, & \text{if } \mathrm{round\_mode} = \mathrm{floor} \\ \mathrm{\text{trunc}\left(\frac{\mathrm{input\_tensor\_a}_i}{\mathrm{input\_tensor\_b}_i}\right)}, & \text{if } \mathrm{round\_mode} = \mathrm{trunc} \end{cases}\end{split}\]
Parameters:

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

  • input_tensor_b (ttnn.Tensor or Number) – the input tensor.

Keyword Arguments:

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

  • accurate_mode (bool, optional) – false if input_tensor_b is non-zero, else true (Only if the input tensor is not ComplexTensor). Defaults to false.

  • round_mode (string, optional) – can be None, floor and trunc (only if the input tensor is not ComplexTensor). 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

Example

>>> tensor1 = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.div(tensor1, tensor2, accurate_mode = false, round_mode = None)

>>> tensor = ttnn.from_torch(torch.tensor(([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> scalar = 3
>>> output = ttnn.div(tensor, scalar, round_mode = "floor")