ttnn.hypot_bw

ttnn.hypot_bw(grad_tensor: ttnn.Tensor, input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor, *, memory_config: ttnn.MemoryConfig | None = None) → List of ttnn.Tensor

Performs backward operations for hypot of input_tensor_a and input_tensor_b with given grad_tensor.

Parameters:

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

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

• input_tensor_b (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	2, 3, 4

bfloat8_b/bfloat4_b is only supported on TILE_LAYOUT

Performance of the PCC may degrade when using BFLOAT8_B. For more details, refer to the BFLOAT8_B limitations.

Example

>>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor1 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.hypot_bw(grad_tensor, tensor1, tensor2)