ttnn.clamp

ttnn.clamp(input_tensor: ttnn.Tensor, *, min: ttnn.Tensor or number = None, max: ttnn.Tensor or number = None, memory_config: ttnn.MemoryConfig = None, output_tensor: ttnn.Tensor = None) → ttnn.Tensor

Applies clamp to input_tensor element-wise.

Parameters:

input_tensor (ttnn.Tensor) – the input tensor.

Keyword Arguments:

  • min (ttnn.Tensor or number) – Minimum value. Defaults to None.

  • max (ttnn.Tensor or number) – Maximum value. Defaults to None.

  • 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, INT32, FLOAT32

TILE

2, 3, 4

INT32 is supported only for Tensor-scalar-scalar version.

Example

>>> input_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3,4]], dtype=torch.bfloat16), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
>>> min_tensor = ttnn.from_torch(torch.tensor([[0, 2], [0,4]], dtype=torch.bfloat16), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
>>> max_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3,4]], dtype=torch.bfloat16), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.clamp(input_tensor, min_tensor, max_tensor)

>>> input_tensor = ttnn.from_torch(torch.tensor([[1, 2], [3,4]], dtype=torch.bfloat16), dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.clamp(input_tensor, min = 2, max = 9)