ttnn.repeat_bw

ttnn.repeat_bw = FastOperation(python_fully_qualified_name='ttnn.repeat_bw', function=<ttnn._ttnn.operations.unary_backward.repeat_bw_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)

Performs backward operations for repeat on input_tensor, with given grad_tensor using given shape.

Args:

grad_tensor (ttnn.Tensor): the input gradient tensor. input_tensor (ttnn.Tensor): the input tensor. shape (List[int]): Shape of tensor.

Keyword args:

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

Returns:

List of ttnn.Tensor: the output tensor.

Note:

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16	TILE	4

Example:

>>> grad_tensor = ttnn.from_torch(torch.rand([1, 1, 32, 32], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> input = ttnn.from_torch(torch.rand([2, 1, 32, 32], dtype=torch.bfloat16, requires_grad=True), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.repeat_bw(grad_tensor, input, shape)