ttnn.logical_or

ttnn.logical_or(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor or Number, *, memory_config: ttnn.MemoryConfig | None = None, dtype: ttnn.DataType | None = None, output_tensor: ttnn.Tensor | None = None, activations: List[str] | None = None, queue_id: int | None = 0) → ttnn.Tensor

Computes logical OR of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\mathrm{{output\_tensor}}_i = \mathrm{{input\_tensor\_a}}_i \, | \, \mathrm{{input\_tensor\_b}}_i\]
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.

  • dtype (ttnn.DataType, optional) – data type for the output tensor. Defaults to None.

  • output_tensor (ttnn.Tensor, optional) – preallocated output tensor. Defaults to None.

  • activations (List[str], optional) – list of activation functions to apply to the output tensor. Defaults to None.

  • queue_id (int, optional) – command queue id. Defaults to 0.

Returns:

ttnn.Tensor – the output tensor.

Supports broadcasting.

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.logical_or(tensor1, tensor2/scalar)