ttnn.gelu_bw

ttnn.gelu_bw(grad_tensor: ttnn.Tensor, input_tensor: ttnn.Tensor, *, approximate: string = none, memory_config: ttnn.MemoryConfig | None = None, output_tensor: ttnn.Tensor | None = None, queue_id: uint8 | None = 0) → List of ttnn.Tensor

Performs backward operations for gelu on input_tensor, with given grad_tensor using given approximate mode. approximate mode can be ‘none’, ‘tanh’.

Parameters:

• grad_tensor (ttnn.Tensor) – the input gradient tensor.

• input_tensor (ttnn.Tensor) – the input tensor.

Keyword Arguments:

• approximate (string) – Approximation type. Defaults to none.

• 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 (uint8, optional) – command queue id. Defaults to 0.

Returns:

List of 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 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.gelu_bw(grad_tensor, input, approximate = none)