# mypy: allow-untyped-defsfromtypingimportList,Optional,Tuple,Unionimporttorchimporttorch.nn.functionalasFfromtorchimportSymInt,Tensorfromtorch._Cimport_add_docstr,_nested# type: ignore[attr-defined]fromtorch.typesimport_deviceasDevice,_dtypeasDType__all__=["to_padded_tensor","as_nested_tensor","nested_tensor","nested_tensor_from_jagged","narrow","masked_select",]# Allowlist these for weights_only load of NJTfrom._internal.nested_tensorimportNestedTensoras_NestedTensor,_rebuild_njttorch.serialization.add_safe_globals([_NestedTensor,_rebuild_njt])
[docs]defas_nested_tensor(ts:Union[Tensor,List[Tensor],Tuple[Tensor,...]],dtype:Optional[DType]=None,device:Optional[Device]=None,layout=None)->Tensor:r""" Constructs a nested tensor preserving autograd history from a tensor or a list / tuple of tensors. If a nested tensor is passed, it will be returned directly unless the device / dtype / layout differ. Note that converting device / dtype will result in a copy, while converting layout is not currently supported by this function. If a non-nested tensor is passed, it is treated as a batch of constituents of consistent size. A copy will be incurred if the passed device / dtype differ from those of the input OR if the input is non-contiguous. Otherwise, the input's storage will be used directly. If a tensor list is provided, tensors in the list are always copied during construction of the nested tensor. Args: ts (Tensor or List[Tensor] or Tuple[Tensor]): a tensor to treat as a nested tensor OR a list / tuple 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 layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor. Only strided and jagged layouts are supported. Default: if None, the strided layout. 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.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.]) >>> c = torch.randn(3, 5, requires_grad=True) >>> nt2 = torch.nested.as_nested_tensor(c) """is_tensor_list=isinstance(ts,(list,tuple))andall(isinstance(t,Tensor)fortints)ifnotisinstance(ts,Tensor)andnotis_tensor_list:raiseTypeError("as_nested_tensor(): Expected first argument to be a tensor or a list / tuple of tensors ")# convert tuple -> list if neededifis_tensor_listandnotisinstance(ts,list):ts=list(ts)ifisinstance(ts,Tensor)andts.dim()<2:raiseRuntimeError("as_nested_tensor(): Expected tensor argument to have dim() > 1")ifisinstance(ts,Tensor)andts.is_nested:iflayout==ts.layout:# return input directly or input copied to device / dtypereturnts.to(device=device,dtype=dtype)else:# TODO: Just use nt.to(layout=layout) when it exists.raiseRuntimeError("as_nested_tensor(): Converting between nested tensor layouts is not supported")iflayoutisNone:layout=torch.stridediflayout==torch.strided:ifisinstance(ts,Tensor):# contiguous() might be necessary to get flattened view.# we could probably be more precise about when to do this as an optimizationbuffer=ts.contiguous().view(-1).to(device=device,dtype=dtype)nested_sizes=torch.tensor([t.shapefortints])returntorch._nested_view_from_buffer(buffer,nested_sizes,*torch._nested_compute_contiguous_strides_offsets(nested_sizes))else:assertisinstance(ts,list)returntorch._nested_tensor_from_tensor_list(ts,dtype,None,device,None)eliflayout==torch.jagged:ifisinstance(ts,Tensor):ifdeviceisNone:device=ts.device# contiguous() might be necessary to get flattened view.# we could probably be more precise about when to do this as an optimizationvalues=ts.contiguous().flatten(0,1).to(device=device,dtype=dtype)batch_size=ts.shape[0]seq_len=ts.shape[1]offsets=torch.arange(0,batch_size*seq_len+1,seq_len,device=device,dtype=torch.int64)fromtorch.nested._internal.nested_tensorimportnested_view_from_values_offsetsreturnnested_view_from_values_offsets(values,offsets,min_seqlen=seq_len,max_seqlen=seq_len)else:fromtorch.nested._internal.nested_tensorimportjagged_from_listassertisinstance(ts,list)nt,_=jagged_from_list(ts,offsets=None,device=device,dtype=dtype)returnntelse:raiseRuntimeError(f"Specified layout is unsupported for nested tensors: {layout}")
# Note: This not only adds doc strings for the nested ops, but# also connects the torch.nested Python namespace to the torch._C._nested builtins.to_padded_tensor=_add_docstr(_nested.nested_to_padded_tensor,r"""to_padded_tensor(input, padding, output_size=None, out=None) -> TensorReturns 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.""",)
[docs]defnested_tensor(tensor_list,*,dtype=None,layout=None,device=None,requires_grad=False,pin_memory=False)->Tensor:r"""Constructs a nested tensor with no autograd history (also known as a "leaf tensor", see:ref:`Autograd mechanics <autograd-mechanics>`) from :attr:`tensor_list` a list of tensors.Args: tensor_list (List[array_like]): a list of tensors, or anything that can be passed to torch.tensor, where each element of the list has the same dimensionality.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. layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor. Only strided and jagged layouts are supported. Default: if None, the strided layout. 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 """iflayoutisNone:layout=torch.stridediflayout==torch.strided:return_nested.nested_tensor(tensor_list,dtype=dtype,device=device,requires_grad=requires_grad,pin_memory=pin_memory)eliflayout==torch.jagged:# Need to wrap lists of scalars as tensorslist_of_tensors=[tifisinstance(t,Tensor)elsetorch.as_tensor(t)fortintensor_list]fromtorch.nested._internal.nested_tensorimportjagged_from_listwithtorch.no_grad():nt,_=jagged_from_list(list_of_tensors,offsets=None,device=device,dtype=dtype)nt.requires_grad_(requires_grad)ifpin_memory:nt=nt.pin_memory()# type: ignore[assignment]returnntelse:raiseRuntimeError(f"Specified layout is unsupported for nested tensors: {layout}")
defnarrow(tensor:Tensor,dim:int,start:Union[int,Tensor],length:Union[int,Tensor],layout=torch.strided)->Tensor:r"""Constructs a nested tensor (which might be a view) from :attr:`tensor`, a strided tensor. This followssimilar semantics to torch.Tensor.narrow, where in the :attr:`dim`-th dimension the new nested tensorshows only the elements in the interval `[start, start+length)`. As nested representationsallow for a different `start` and `length` at each 'row' of that dimension, :attr:`start` and :attr:`length`can also be tensors of shape `tensor.shape[0]`.There's some differences depending on the layout you use for the nested tensor. If using strided layout,torch.narrow will do a copy of the narrowed data into a contiguous NT with strided layout, whilejagged layout narrow() will create a non-contiguous view of your original strided tensor. This particularrepresentation is really useful for representing kv-caches in Transformer models, as specializedSDPA kernels can deal with format easily, resulting in performance improvements.Args: tensor (:class:`torch.Tensor`): a strided tensor, which will be used as the underlying data for the nested tensor if using the jagged layout or will be copied for the strided layout. dim (int): the dimension where narrow will be applied. Only `dim=1` is supported for the jagged layout, while strided supports all dim start (Union[int, :class:`torch.Tensor`]): starting element for the narrow operation length (Union[int, :class:`torch.Tensor`]): number of elements taken during the narrow opKeyword arguments: layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor. Only strided and jagged layouts are supported. Default: if None, the strided layout.Example:: >>> starts = torch.tensor([0, 1, 2, 3, 4], dtype=torch.int64) >>> lengths = torch.tensor([3, 2, 2, 1, 5], dtype=torch.int64) >>> narrow_base = torch.randn(5, 10, 20) >>> nt_narrowed = torch.nested.narrow(narrow_base, 1, starts, lengths, layout=torch.jagged) >>> nt_narrowed.is_contiguous() False """ifnotisinstance(start,(int,SymInt,Tensor)):raiseRuntimeError("start must be an integer or a tensor")ifnotisinstance(length,(int,SymInt,Tensor)):raiseRuntimeError("length must be an integer or a tensor")iflayout==torch.strided:ifisinstance(start,Tensor)orisinstance(length,Tensor):raiseRuntimeError("start and length must be integers for the strided layout NT impl")# TODO: switch to as_nested_tensor(tensor) when it is availablent=as_nested_tensor(torch.unbind(tensor),layout=torch.strided).narrow(dim,start,length)eliflayout==torch.jagged:ifdim!=1:raiseRuntimeError("jagged layout only supports dim=1")fromtorch.nested._internal.nested_tensorimportjagged_from_tensor_and_lengthsifisinstance(start,(int,SymInt)):start=torch.tensor([start],device=tensor.device,dtype=torch.int64)ifisinstance(length,(int,SymInt)):length=torch.tensor([length],device=tensor.device,dtype=torch.int64)nt,_,_=jagged_from_tensor_and_lengths(tensor,start,length)else:raiseRuntimeError(f"Specified layout is unsupported for nested narrow: {layout}")returnntdefnested_tensor_from_jagged(values:Tensor,offsets:Optional[Tensor]=None,lengths:Optional[Tensor]=None,jagged_dim:Optional[int]=None,min_seqlen:Optional[int]=None,max_seqlen:Optional[int]=None,)->Tensor:r"""Constructs a jagged layout nested tensor from the given jagged components. The jagged layoutconsists of a required values buffer with the jagged dimension packed into a single dimension.The offsets / lengths metadata determines how this dimension is split into batch elementsand are expected to be allocated on the same device as the values buffer.Expected metadata formats: * offsets: Indices within the packed dimension splitting it into heterogeneously-sized batch elements. Example: [0, 2, 3, 6] indicates that a packed jagged dim of size 6 should be conceptually split into batch elements of length [2, 1, 3]. Note that both the beginning and ending offsets are required for kernel convenience (i.e. shape batch_size + 1). * lengths: Lengths of the individual batch elements; shape == batch_size. Example: [2, 1, 3] indicates that a packed jagged dim of size 6 should be conceptually split into batch elements of length [2, 1, 3].Note that it can be useful to provide both offsets and lengths. This describes a nested tensorwith "holes", where the offsets indicate the start position of each batch item and the lengthspecifies the total number of elements (see example below).The returned jagged layout nested tensor will be a view of the input values tensor.Args: values (:class:`torch.Tensor`): The underlying buffer in the shape of (sum_B(*), D_1, ..., D_N). The jagged dimension is packed into a single dimension, with the offsets / lengths metadata used to distinguish batch elements. offsets (optional :class:`torch.Tensor`): Offsets into the jagged dimension of shape B + 1. lengths (optional :class:`torch.Tensor`): Lengths of the batch elements of shape B. jagged_dim (optional int): Indicates which dimension in values is the packed jagged dimension. If None, this is set to dim=1 (i.e. the dimension immediately following the batch dimension). Default: None min_seqlen (optional int): If set, uses the specified value as the cached minimum sequence length for the returned nested tensor. This can be a useful alternative to computing this value on-demand, possibly avoiding a GPU -> CPU sync. Default: None max_seqlen (optional int): If set, uses the specified value as the cached maximum sequence length for the returned nested tensor. This can be a useful alternative to computing this value on-demand, possibly avoiding a GPU -> CPU sync. Default: NoneExample:: >>> values = torch.randn(12, 5) >>> offsets = torch.tensor([0, 3, 5, 6, 10, 12]) >>> nt = nested_tensor_from_jagged(values, offsets) >>> # 3D shape with the middle dimension jagged >>> nt.shape torch.Size([5, j2, 5]) >>> # Length of each item in the batch: >>> offsets.diff() tensor([3, 2, 1, 4, 2]) >>> values = torch.randn(6, 5) >>> offsets = torch.tensor([0, 2, 3, 6]) >>> lengths = torch.tensor([1, 1, 2]) >>> # NT with holes >>> nt = nested_tensor_from_jagged(values, offsets, lengths) >>> a, b, c = nt.unbind() >>> # Batch item 1 consists of indices [0, 1) >>> torch.equal(a, values[0:1, :]) True >>> # Batch item 2 consists of indices [2, 3) >>> torch.equal(b, values[2:3, :]) True >>> # Batch item 3 consists of indices [3, 5) >>> torch.equal(c, values[3:5, :]) True """fromtorch.fx._symbolic_traceimportis_fx_tracingifis_fx_tracing():raiseRuntimeError("torch.nested.nested_tensor_from_jagged does not support tracing with fx.symbolic_trace. ""Use fx.wrap to wrap the function that calls nested_tensor_from_jagged.")ifoffsetsisNone:iflengthsisNone:raiseRuntimeError("nested_tensor_from_jagged(): At least one of offsets or lengths is required.")else:# TODO: Truly support offsets=None at some point?# For now, just convert lengths -> offsets for kernel convenienceoffsets=F.pad(lengths.cumsum(0),(1,0))lengths=Noneifjagged_dimisNone:jagged_dim=1fromtorch.nested._internal.nested_tensorimportnested_view_from_values_offsets_lengthsreturnnested_view_from_values_offsets_lengths(values,offsets,lengths,ragged_idx=jagged_dim,min_seqlen=min_seqlen,max_seqlen=max_seqlen)defmasked_select(tensor:Tensor,mask:Tensor)->Tensor:r""" Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is represented with the offsets, this is unlike :func:`masked_select` where the output is collapsed to a 1D tensor. Args: tensor (:class:`torch.Tensor`): a strided tensor from which the jagged layout nested tensor is constructed from. mask (:class:`torch.Tensor`): a strided mask tensor which is applied to the tensor input Example:: >>> tensor = torch.randn(3, 3) >>> mask = torch.tensor([[False, False, True], [True, False, True], [False, False, True]]) >>> nt = torch.nested.masked_select(tensor, mask) >>> nt.shape torch.Size([3, j4]) >>> # Length of each item in the batch: >>> nt.offsets().diff() tensor([1, 2, 1]) >>> tensor = torch.randn(6, 5) >>> mask = torch.tensor([False]) >>> nt = torch.nested.masked_select(tensor, mask) >>> nt.shape torch.Size([6, j5]) >>> # Length of each item in the batch: >>> nt.offsets().diff() tensor([0, 0, 0, 0, 0, 0]) """iftensor.layout!=torch.strided:raiseRuntimeError(f"torch.nested.masked_select requires a strided tensor, given {tensor.layout}")ifmask.layout!=torch.strided:raiseRuntimeError(f"torch.nested.masked_select requires a strided mask, given: {mask.layout}")res_values=tensor.masked_select(mask)expanded_mask=mask.expand(tensor.shape)res_lengths=expanded_mask.sum(dim=tensor.ndim-1).view(-1)fromtorch.nested._internal.nested_tensorimport(nested_view_from_values_offsets,)returnnested_view_from_values_offsets(values=res_values,offsets=F.pad(res_lengths.cumsum(dim=0),(1,0)),)
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