ttnn.div_bw

ttnn.div_bw(grad_tensor: ComplexTensor or ttnn.Tensor, input_tensor_a: ComplexTensor or ttnn.Tensor, input_tensor_b: ComplexTensor or ttnn.Tensor or Number, *, round_mode: str | None = None, are_required_outputs: List[bool] | None = [True, True], memory_config: ttnn.MemoryConfig | None = None, input_grad: ttnn.Tensor | None = None, other_grad: ttnn.Tensor | None = None, queue_id: int | None = 0) → List of ttnn.Tensor

Performs backward operations for divide on input_tensor, alpha or input_tensor_a, input_tensor_b, round_mode, with given grad_tensor.

Parameters:

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

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

  • input_tensor_b (ComplexTensor or ttnn.Tensor or Number) – the input tensor.

Keyword Arguments:

  • round_mode (str, optional) – Round mode for the operation (when input tensors are not ComplexTensor type). Can be None, “trunc”, “floor”. Defaults to None.

  • are_required_outputs (List[bool], optional) – List of required outputs. Defaults to [True, True].

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

  • input_grad (ttnn.Tensor, optional) – Preallocated output tensor for gradient of input_tensor. Defaults to None.

  • other_grad (ttnn.Tensor, optional) – Preallocated output tensor for gradient of other_tensor. Defaults to None.

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

Returns:

List of ttnn.Tensor – the output tensor.

Supports broadcasting.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16, BFLOAT8_B

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.div_bw(grad_tensor, tensor1, tensor2, round_mode = None)

>>> grad_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor = ttnn.from_torch(torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> scalar = 2
>>> output = ttnn.div_bw(grad_tensor, tensor, scalar, round_mode = None)