ttnn.erf

ttnn.erf = FastOperation(python_fully_qualified_name='ttnn.erf', function=<ttnn._ttnn.operations.unary.erf_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 erf to input_tensor element-wise.

\[\mathrm{output\_tensor}_i = erf(\mathrm{input\_tensor}_i)\]

Args:

input_tensor (ttnn.Tensor): the input tensor.

Keyword Args:

fast_and_approximate_mode (bool, optional): Use the fast and approximate mode. Defaults to False. 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.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.erf(tensor, fast_and_approximate_mode=True)