ttnn.digamma

ttnn.digamma(input_tensor: ttnn.Tensor, *, memory_config: ttnn.MemoryConfig = None, output_tensor: ttnn.Tensor = None, sub_core_grids: ttnn.CoreRangeSet = None) → ttnn.Tensor

Applies digamma to input_tensor element-wise.

\[Performs digamma function on :attr:`input_tensor`.\]
Parameters:

input_tensor (ttnn.Tensor) – the input tensor. [supported for values greater than 0].

Keyword Arguments:

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

  • output_tensor (ttnn.Tensor, optional) – preallocated output tensor. Defaults to None.

  • sub_core_grids (ttnn.CoreRangeSet, optional) – sub core grids for the operation. Defaults to None.

Returns:

ttnn.Tensor – the output tensor.

Note

Supported dtypes and layouts:

Dtypes

Layouts

BFLOAT16, FLOAT32

TILE, ROW_MAJOR

Example

# Create a tensor with specific values
tensor = ttnn.from_torch(
    torch.tensor([[2, 3], [4, 5]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device
)

# Compute the digamma function (logarithmic derivative of gamma)
output = ttnn.digamma(tensor)
logger.info(f"Digamma: {output}")