ttnn.asinh

ttnn.asinh(input_tensor: ttnn.Tensor, *, memory_config: ttnn.MemoryConfig = None, output_tensor: ttnn.Tensor = None) → ttnn.Tensor

Applies asinh to input_tensor element-wise.

\[\mathrm{{output\_tensor}}_i = \verb|asinh|(\mathrm{{input\_tensor}}_i)\]
Parameters:

input_tensor (ttnn.Tensor) – the input tensor.

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.

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 random values
tensor = ttnn.rand([2, 2], dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)

# Compute the hyperbolic arcsine
output = ttnn.asinh(tensor)
logger.info(f"Hyperbolic arcsine: {output}")