ttnn.outer

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

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)