ttnn.slice

ttnn.slice(input_tensor, slice_start, slice_end, slice_step) → ttnn.Tensor

Returns a sliced tensor. If the input tensor is on host, the slice will be performed on host, and if its on device it will be performed on device.

Parameters:

• input_tensor – Input Tensor.

• slice_start – Start indices of input tensor. Values along each dim must be < input_tensor_shape[i].

• slice_end – End indices of input tensor. Values along each dim must be < input_tensor_shape[i].

• slice_step – (Optional[List[int[tensor rank]]) Step size for each dim. Default is None, which works out be 1 for each dimension.

Keyword Arguments:

• tensor (memory_config Memory Config of the output) –

• queue_id (uint8, optional) –

Returns:

ttnn.Tensor – the output tensor.

Example

>>> tensor = ttnn.slice(ttnn.from_torch(torch.zeros((1, 1, 64, 32), dtype=torch.bfloat16), device=device), [0, 0, 0, 0], [1, 1, 64, 16], [1, 1, 2, 1])
>>> print(tensor.shape)
[1, 1, 32, 16]
>>> input = ttnn.from_torch(torch.zeros((1, 1, 64, 32), dtype=torch.bfloat16), device=device)
>>> output = ttnn.slice(input, [0, 0, 0, 0], [1, 1, 32, 32])
>>> print(output.shape)
[1, 1, 32, 32]