U d&(@sddlmZmZmZmZddlZddlmZmZddlm Z ddlm Z e rtddl m Z eeeeeeefZneZ eeeZddd d d gZeejd Zeejd ZeejdZde eee e ddd ZeejdZeejdZdS))OptionalTupleListUnionN) _add_docstr_sparse)Tensor) TYPE_CHECKING)_dtypeaddmmmmsumsoftmax log_softmaxa% sparse.addmm(mat, mat1, mat2, *, beta=1., alpha=1.) -> Tensor This function does exact same thing as :func:`torch.addmm` in the forward, except that it supports backward for sparse COO matrix :attr:`mat1`. When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`. When inputs are COO tensors, this function also supports backward for both inputs. Supports both CSR and COO storage formats. .. note:: This function doesn't support computing derivaties with respect to CSR matrices. Args: mat (Tensor): a dense matrix to be added mat1 (Tensor): a sparse matrix to be multiplied mat2 (Tensor): a dense matrix to be multiplied beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) a Performs a matrix multiplication of the sparse matrix :attr:`mat1` and the (sparse or strided) matrix :attr:`mat2`. Similar to :func:`torch.mm`, if :attr:`mat1` is a :math:`(n \times m)` tensor, :attr:`mat2` is a :math:`(m \times p)` tensor, out will be a :math:`(n \times p)` tensor. When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`. When inputs are COO tensors, this function also supports backward for both inputs. Supports both CSR and COO storage formats. .. note:: This function doesn't support computing derivaties with respect to CSR matrices. Args: mat1 (Tensor): the first sparse matrix to be multiplied mat2 (Tensor): the second matrix to be multiplied, which could be sparse or dense Shape: The format of the output tensor of this function follows: - sparse x sparse -> sparse - sparse x dense -> dense Example:: >>> a = torch.randn(2, 3).to_sparse().requires_grad_(True) >>> a tensor(indices=tensor([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]), values=tensor([ 1.5901, 0.0183, -0.6146, 1.8061, -0.0112, 0.6302]), size=(2, 3), nnz=6, layout=torch.sparse_coo, requires_grad=True) >>> b = torch.randn(3, 2, requires_grad=True) >>> b tensor([[-0.6479, 0.7874], [-1.2056, 0.5641], [-1.1716, -0.9923]], requires_grad=True) >>> y = torch.sparse.mm(a, b) >>> y tensor([[-0.3323, 1.8723], [-1.8951, 0.7904]], grad_fn=) >>> y.sum().backward() >>> a.grad tensor(indices=tensor([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]), values=tensor([ 0.1394, -0.6415, -2.1639, 0.1394, -0.6415, -2.1639]), size=(2, 3), nnz=6, layout=torch.sparse_coo) a. sparse.sampled_addmm(input, mat1, mat2, *, beta=1., alpha=1., out=None) -> Tensor Performs a matrix multiplication of the dense matrices :attr:`mat1` and :attr:`mat2` at the locations specified by the sparsity pattern of :attr:`input`. The matrix :attr:`input` is added to the final result. Mathematically this performs the following operation: .. math:: \text{out} = \alpha\ (\text{mat1} \mathbin{@} \text{mat2})*\text{spy}(\text{input}) + \beta\ \text{input} where :math:`\text{spy}(\text{input})` is the sparsity pattern matrix of :attr:`input`, :attr:`alpha` and :attr:`beta` are the scaling factors. :math:`\text{spy}(\text{input})` has value 1 at the positions where :attr:`input` has non-zero values, and 0 elsewhere. .. note:: :attr:`input` must be a sparse CSR tensor. :attr:`mat1` and :attr:`mat2` must be dense tensors. This function is implemented only for tensors on CUDA devices. Args: input (Tensor): a sparse CSR matrix of shape `(m, n)` to be added and used to compute the sampled matrix multiplication mat1 (Tensor): a dense matrix of shape `(m, k)` to be multiplied mat2 (Tensor): a dense matrix of shape `(k, n)` to be multiplied Keyword args: beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`) out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`. Examples:: >>> input = torch.eye(3, device='cuda').to_sparse_csr() >>> mat1 = torch.randn(3, 5, device='cuda') >>> mat2 = torch.randn(5, 3, device='cuda') >>> torch.sparse.sampled_addmm(input, mat1, mat2) tensor(crow_indices=tensor([0, 1, 2, 3]), col_indices=tensor([0, 1, 2]), values=tensor([ 0.2847, -0.7805, -0.1900]), device='cuda:0', size=(3, 3), nnz=3, layout=torch.sparse_csr) >>> torch.sparse.sampled_addmm(input, mat1, mat2).to_dense() tensor([[ 0.2847, 0.0000, 0.0000], [ 0.0000, -0.7805, 0.0000], [ 0.0000, 0.0000, -0.1900]], device='cuda:0') >>> torch.sparse.sampled_addmm(input, mat1, mat2, beta=0.5, alpha=0.5) tensor(crow_indices=tensor([0, 1, 2, 3]), col_indices=tensor([0, 1, 2]), values=tensor([ 0.1423, -0.3903, -0.0950]), device='cuda:0', size=(3, 3), nnz=3, layout=torch.sparse_csr) )inputdimdtypereturncCsR|dkr(|dk rt||St|Sn&|dk r@tj|||dStj||dSdS)a4 Returns the sum of each row of the sparse tensor :attr:`input` in the given dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, reduce over all of them. When sum over all ``sparse_dim``, this method returns a dense tensor instead of a sparse tensor. All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output tensor having :attr:`dim` fewer dimensions than :attr:`input`. During backward, only gradients at ``nnz`` locations of :attr:`input` will propagate back. Note that the gradients of :attr:`input` is coalesced. Args: input (Tensor): the input sparse tensor dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce over all dims. dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. Default: dtype of :attr:`input`. Example:: >>> nnz = 3 >>> dims = [5, 5, 2, 3] >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) >>> V = torch.randn(nnz, dims[2], dims[3]) >>> size = torch.Size(dims) >>> S = torch.sparse_coo_tensor(I, V, size) >>> S tensor(indices=tensor([[2, 0, 3], [2, 4, 1]]), values=tensor([[[-0.6438, -1.6467, 1.4004], [ 0.3411, 0.0918, -0.2312]], [[ 0.5348, 0.0634, -2.0494], [-0.7125, -1.0646, 2.1844]], [[ 0.1276, 0.1874, -0.6334], [-1.9682, -0.5340, 0.7483]]]), size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) # when sum over only part of sparse_dims, return a sparse tensor >>> torch.sparse.sum(S, [1, 3]) tensor(indices=tensor([[0, 2, 3]]), values=tensor([[-1.4512, 0.4073], [-0.8901, 0.2017], [-0.3183, -1.7539]]), size=(5, 2), nnz=3, layout=torch.sparse_coo) # when sum over all sparse dim, return a dense tensor # with summed dims squeezed >>> torch.sparse.sum(S, [0, 1, 3]) tensor([-2.6596, -1.1450]) N)r)torchZ _sparse_sum)rrrr9/tmp/pip-unpacked-wheel-ua33x9lu/torch/sparse/__init__.pyr s8  a sparse.softmax(input, dim, *, dtype=None) -> Tensor Applies a softmax function. Softmax is defined as: :math:`\text{Softmax}(x_{i}) = \frac{exp(x_i)}{\sum_j exp(x_j)}` where :math:`i, j` run over sparse tensor indices and unspecified entries are ignores. This is equivalent to defining unspecified entries as negative infinity so that :math:`exp(x_k) = 0` when the entry with index :math:`k` has not specified. It is applied to all slices along `dim`, and will re-scale them so that the elements lie in the range `[0, 1]` and sum to 1. Args: input (Tensor): input dim (int): A dimension along which softmax will be computed. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None a sparse.log_softmax(input, dim, *, dtype=None) -> Tensor Applies a softmax function followed by logarithm. See :class:`~torch.sparse.softmax` for more details. Args: input (Tensor): input dim (int): A dimension along which softmax will be computed. dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor. If specified, the input tensor is casted to :attr:`dtype` before the operation is performed. This is useful for preventing data type overflows. Default: None )NN)typingrrrrrZtorch._Crrrr Z torch.typesr ZDTypeintZ DimOrDims__all__Z _sparse_addmmr Z _sparse_mmr Zsparse_sampled_addmmZ sampled_addmmr Z_sparse_softmaxrZ_sparse_log_softmaxrrrrrs4      2 5 D