ttnn.experimental.gelu_bw

ttnn.experimental.gelu_bw = Operation(python_fully_qualified_name='ttnn.experimental.gelu_bw', function=<ttnn._ttnn.operations.experimental.gelu_bw_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function_gelu>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Applies the backward pass of the GELU function using ttnn experimental kernels.

Parameters:
Keyword Arguments:

  • approximate (str, optional) – “tanh” or “none” (default). The gelu approximation algorithm to use.

  • memory_config (ttnn.MemoryConfig, optional) – Memory configuration for this operation. Defaults to None.

  • input_grad (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 - TILE - 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_tensor = ttnn.from_torch(
...     torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True),
...     layout=ttnn.TILE_LAYOUT, device=device
... )
>>> output = ttnn.experimental.gelu_bw(grad_tensor, input_tensor)