ttnn.sign_bw
- ttnn.sign_bw = Operation(python_fully_qualified_name='ttnn.sign_bw', function=<ttnn._ttnn.operations.unary_backward.sign_bw_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function <lambda>>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)
-
Performs backward operations for sign on
input_tensor
with givengrad_tensor
.- Parameters:
-
grad_tensor (ttnn.Tensor) – the input gradient tensor.
input_tensor_a (ttnn.Tensor) – the input tensor.
- Keyword Arguments:
-
memory_config (ttnn.MemoryConfig, optional) – memory configuration for the operation. Defaults to None.
- Returns:
-
List of ttnn.Tensor – the output tensor.
Note
Supported dtypes, layouts, and ranks:
Dtypes
Layouts
Ranks
BFLOAT16
TILE, ROW MAJOR
2, 3, 4
Example
>>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device) >>> input = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device) >>> output = ttnn.sign_bw(grad_tensor, input)