ttnn.log1p

ttnn.log1p(input_tensor: ttnn.Tensor, *, fast_and_approximate_mode: bool = False, memory_config: ttnn.MemoryConfig = None, output_tensor: ttnn.Tensor = None) → ttnn.Tensor

Applies log1p to input_tensor element-wise.

\[\mathrm{output\_tensor}_i = log1p(\mathrm{input\_tensor}_i)\]

Parameters:

input_tensor (ttnn.Tensor) – the input tensor. [Supported range: [-1, 1e7]]

Keyword Arguments:

fast_and_approximate_mode (bool, optional) – Use the fast and approximate mode. Defaults to False.
memory_config (ttnn.MemoryConfig, optional) – Memory configuration for the operation. Defaults to None.
output_tensor (ttnn.Tensor, optional) – preallocated output tensor. Defaults to None.

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16, BFLOAT8_B, FLOAT32	TILE	2, 3, 4

Example

# Create a tensor with specific values
tensor = ttnn.from_torch(
    torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16),
    dtype=ttnn.bfloat16,
    layout=ttnn.TILE_LAYOUT,
    device=device,
)

# Compute log(1 + x)
output = ttnn.log1p(tensor, fast_and_approximate_mode=True)
logger.info(f"Log(1 + x): {output}")