ttnn.outer

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

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

\[\mathrm{output\_tensor} = \mathrm{input\_tensor\_a} \text{ } \otimes \text{ } \mathrm{input\_tensor\_b}\]

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	TILE	4

Example

>>> tensor1 = ttnn.from_torch(torch.rand([1, 1, 32, 1], dtype=torch.bfloat16), device=device)
>>> tensor2 = ttnn.from_torch(torch.rand([1, 1, 1, 32], dtype=torch.bfloat16), device=device)
>>> output = ttnn.outer(tensor1, tensor2)