ttnn.repeat_interleave

ttnn.repeat_interleave = Operation(python_fully_qualified_name='ttnn.repeat_interleave', function=<ttnn._ttnn.operations.data_movement.repeat_interleave_t object>, preprocess_golden_function_inputs=<function default_preprocess_golden_function_inputs>, golden_function=<function _golden_function>, postprocess_golden_function_outputs=<function default_postprocess_golden_function_outputs>, is_cpp_operation=True, is_experimental=False)

Repeats elements of a tensor in the given dim.

Parameters:

  • input_tensor (ttnn.Tensor) – the input tensor.

  • repeats (number) – he number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.

  • dim (number) – the dimension to expand with the repetitions.

Keyword Arguments:

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

Returns:

ttnn.Tensor – the output tensor.

Example:

torch_input_tensor =

torch_result = torch.repeat_interleave(torch_input_tensor, repeats, dim=dim)

input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)

output = ttnn.repeat_interleave(input_tensor, repeats, dim=dim) >>> a = ttnn.from_torch(torch.rand(1, 1, 32, 32, dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device) >>> b = ttnn.repeat_interleave(a, 2, dim=0) >>> print(a.shape, b.shape) ttnn.Shape([1, 1, 32, 32]) ttnn.Shape([2, 1, 32, 32])