ttnn.nextafter

ttnn.nextafter(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor, *, memory_config: ttnn.MemoryConfig | None = None) → ttnn.Tensor

Computes nextafter input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\begin{split}\mathrm{output\_tensor}_i = \begin{cases} \mathrm{next\_float}(\mathrm{input\_tensor\_a}_i, \mathrm{input\_tensor\_b}_i), & \text{if } \mathrm{input\_tensor\_a}_i \neq \mathrm{input\_tensor\_b}_i \\ \mathrm{input\_tensor\_a}_i, & \text{if } \mathrm{input\_tensor\_a}_i = \mathrm{input\_tensor\_b}_i \end{cases}\end{split}\]

Parameters:

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

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

>>> 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.nextafter(tensor1, tensor2)