U <c@sddlmZmZddlZddlmZmZddlmZddlm Z ddlm Z ddd gZ ddd d d eeee ee eeeeed d d Zdeeee ee edddZeejdZdS))ListOptionalN) _add_docstr_nested)Tensor)_dtype)_deviceto_padded_tensoras_nested_tensor nested_tensorF)dtypedevice requires_grad pin_memory) tensor_listr r rrreturncCsVt|trtdd|Dr$tddd|D}t||d||}|rR|||S)u Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see :ref:`Autograd mechanics `) from :attr:`tensor_list` a list of tensors. Args: tensor_list (List[Tensor]): a list of tensors with the same ndim Keyword arguments: dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor. Default: if None, same :class:`torch.dtype` as leftmost tensor in the list. device (:class:`torch.device`, optional): the desired device of returned nested tensor. Default: if None, same :class:`torch.device` as leftmost tensor in the list requires_grad (bool, optional): If autograd should record operations on the returned nested tensor. Default: ``False``. pin_memory (bool, optional): If set, returned nested tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: ``False``. Example:: >>> a = torch.arange(3, dtype=torch.float, requires_grad=True) >>> b = torch.arange(5, dtype=torch.float, requires_grad=True) >>> nt = torch.nested.nested_tensor([a, b], requires_grad=True) >>> nt.is_leaf True cSsg|]}t| qStorchZ is_tensor.0trr9/tmp/pip-unpacked-wheel-gikjz4vx/torch/nested/__init__.py ,sz!nested_tensor..Anested_tensor(): Expected first argument to be a list of tensors cSsg|] }|qSr)detachrrrrr.sN) isinstancelistany TypeErrorr_nested_tensor_from_tensor_listZrequires_grad_)rr r rrZnew_datantrrrr s )rr r rcCs6t|trtdd|Dr$tdt||d|dS)a Constructs a nested tensor preserving autograd history from :attr:`tensor_list` a list of tensors. .. note:: Tensors within the list are always copied by this function due to current nested tensor semantics. Args: tensor_list (List[Tensor]): a list of tensors with the same ndim Keyword arguments: dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor. Default: if None, same :class:`torch.dtype` as leftmost tensor in the list. device (:class:`torch.device`, optional): the desired device of returned nested tensor. Default: if None, same :class:`torch.device` as leftmost tensor in the list Example:: >>> a = torch.arange(3, dtype=torch.float, requires_grad=True) >>> b = torch.arange(5, dtype=torch.float, requires_grad=True) >>> nt = torch.nested.as_nested_tensor([a, b]) >>> nt.is_leaf False >>> fake_grad = torch.nested_tensor([torch.ones_like(a), torch.zeros_like(b)]) >>> nt.backward(fake_grad) >>> a.grad tensor([1., 1., 1.]) >>> b.grad tensor([0., 0., 0., 0., 0.]) cSsg|]}t| qSrrrrrrrRsz$as_nested_tensor..rN)rrrrrr )rr r rrrr 4sa to_padded_tensor(input, padding, output_size=None, out=None) -> Tensor Returns a new (non-nested) Tensor by padding the :attr:`input` nested tensor. The leading entries will be filled with the nested data, while the trailing entries will be padded. .. warning:: :func:`to_padded_tensor` always copies the underlying data, since the nested and the non-nested tensors differ in memory layout. Args: padding (float): The padding value for the trailing entries. Keyword args: output_size (Tuple[int]): The size of the output tensor. If given, it must be large enough to contain all nested data; else, will infer by taking the max size of each nested sub-tensor along each dimension. out (Tensor, optional): the output tensor. Example:: >>> nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))]) nested_tensor([ tensor([[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276], [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995]]), tensor([[-1.8546, -0.7194, -0.2918, -0.1846], [ 0.2773, 0.8793, -0.5183, -0.6447], [ 1.8009, 1.8468, -0.9832, -1.5272]]) ]) >>> pt_infer = torch.nested.to_padded_tensor(nt, 0.0) tensor([[[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276], [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995], [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]], [[-1.8546, -0.7194, -0.2918, -0.1846, 0.0000], [ 0.2773, 0.8793, -0.5183, -0.6447, 0.0000], [ 1.8009, 1.8468, -0.9832, -1.5272, 0.0000]]]) >>> pt_large = torch.nested.to_padded_tensor(nt, 1.0, (2, 4, 6)) tensor([[[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276, 1.0000], [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]], [[-1.8546, -0.7194, -0.2918, -0.1846, 1.0000, 1.0000], [ 0.2773, 0.8793, -0.5183, -0.6447, 1.0000, 1.0000], [ 1.8009, 1.8468, -0.9832, -1.5272, 1.0000, 1.0000], [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]]) >>> pt_small = torch.nested.to_padded_tensor(nt, 2.0, (2, 2, 2)) RuntimeError: Value in output_size is less than NestedTensor padded size. Truncation is not supported. )NN)typingrrrZtorch._CrrrZ torch.typesrZDTyperZDevice__all__boolr r Znested_to_padded_tensorr rrrrs*    #"%