ttnn.square

ttnn.square = FastOperation(python_fully_qualified_name='ttnn.square', function=<ttnn._ttnn.operations.unary.square_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function register_ttnn_cpp_unary_function.<locals>._golden_function>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Applies square to input_tensor element-wise.

\[\mathrm{{output\_tensor}}_i = \verb|square|(\mathrm{{input\_tensor}}_i)\]

Args:

input_tensor (ttnn.Tensor): the input tensor.

Keyword Args:

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
BFLOAT16, BFLOAT8_B	TILE	2, 3, 4

Example:

>>> tensor = ttnn.from_torch(torch.rand([2, 2], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.square(tensor)