ttnn.addcdiv

ttnn.addcdiv(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor, input_tensor_c: ttnn.Tensor or Number, *, value: float | None, memory_config: ttnn.MemoryConfig = None) → ttnn.Tensor

Computes Addcdiv on input_tensor_a, input_tensor_b and input_tensor_c and returns the tensor with the same layout as input_tensor_a

Parameters:

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

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

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

Keyword Arguments:

  • value (float, optional) – scalar value to be multiplied.

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

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes

Layouts

Ranks

BFLOAT16

TILE

2, 3, 4

bfloat8_b/bfloat4_b supports only on TILE_LAYOUT

Example

# Create three tensors and a value for the operation
value = 1.0
tensor1 = ttnn.from_torch(
    torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device
)
tensor2 = ttnn.from_torch(
    torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device
)
tensor3 = ttnn.from_torch(
    torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device
)

# Perform the addcdiv operation: tensor1 + value * (tensor2 / tensor3)
output = ttnn.addcdiv(tensor1, tensor2, tensor3, value=value)
logger.info(f"Addcdiv result: {output}")