ttnn.bitwise_and

ttnn.bitwise_and(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor or Integer, *, memory_config: ttnn.MemoryConfig | None = None, output_tensor: ttnn.Tensor | None = None, queue_id: int | None = 0) → ttnn.Tensor

Perform bitwise_and operation on input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a

\[\mathrm{{output\_tensor}}_i = \verb|bitwise_and|(\mathrm{{input\_tensor\_a, input\_tensor\_b}})\]
Parameters:

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

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

Keyword Arguments:

  • 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

INT32

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.bitwise_and(tensor1, tensor2/scalar)