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<h1>Source code for torch.sparse</h1><div class="highlight"><pre>
<span></span><span class="c1"># The Tensor classes are added to this module by python_tensor.cpp</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Any</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_add_docstr</span><span class="p">,</span> <span class="n">_sparse</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="c1"># Semi structured sparsity support</span>
<span class="kn">from</span> <span class="nn">.semi_structured</span> <span class="kn">import</span> <span class="n">SparseSemiStructuredTensor</span><span class="p">,</span> <span class="n">to_sparse_semi_structured</span>
<span class="c1"># A workaround to support both TorchScript and MyPy:</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">TYPE_CHECKING</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">torch.types</span> <span class="kn">import</span> <span class="n">_dtype</span> <span class="k">as</span> <span class="n">DType</span>
<span class="n">DimOrDims</span> <span class="o">=</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]]</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># The JIT doesn't understand Union, nor torch.dtype here</span>
<span class="n">DType</span> <span class="o">=</span> <span class="nb">int</span>
<span class="n">DimOrDims</span> <span class="o">=</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'addmm'</span><span class="p">,</span>
<span class="s1">'check_sparse_tensor_invariants'</span><span class="p">,</span>
<span class="s1">'mm'</span><span class="p">,</span>
<span class="s1">'sum'</span><span class="p">,</span>
<span class="s1">'softmax'</span><span class="p">,</span>
<span class="s1">'log_softmax'</span><span class="p">,</span>
<span class="s1">'SparseSemiStructuredTensor'</span><span class="p">,</span>
<span class="s1">'to_sparse_semi_structured'</span><span class="p">,</span>
<span class="s1">'as_sparse_gradcheck'</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">addmm</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span><span class="n">_sparse</span><span class="o">.</span><span class="n">_sparse_addmm</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"""</span>
<span class="s2">sparse.addmm(mat, mat1, mat2, *, beta=1., alpha=1.) -> Tensor</span>
<span class="s2">This function does exact same thing as :func:`torch.addmm` in the forward,</span>
<span class="s2">except that it supports backward for sparse COO matrix :attr:`mat1`.</span>
<span class="s2">When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`.</span>
<span class="s2">When inputs are COO tensors, this function also supports backward for both inputs.</span>
<span class="s2">Supports both CSR and COO storage formats.</span>
<span class="s2">.. note::</span>
<span class="s2"> This function doesn't support computing derivaties with respect to CSR matrices.</span>
<span class="s2">Args:</span>
<span class="s2"> mat (Tensor): a dense matrix to be added</span>
<span class="s2"> mat1 (Tensor): a sparse matrix to be multiplied</span>
<span class="s2"> mat2 (Tensor): a dense matrix to be multiplied</span>
<span class="s2"> beta (Number, optional): multiplier for :attr:`mat` (:math:`\beta`)</span>
<span class="s2"> alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)</span>
<span class="s2">"""</span><span class="p">)</span>
<span class="n">mm</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span><span class="n">_sparse</span><span class="o">.</span><span class="n">_sparse_mm</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"""</span>
<span class="s2"> Performs a matrix multiplication of the sparse matrix :attr:`mat1`</span>
<span class="s2"> and the (sparse or strided) matrix :attr:`mat2`. Similar to :func:`torch.mm`, if :attr:`mat1` is a</span>
<span class="s2"> :math:`(n \times m)` tensor, :attr:`mat2` is a :math:`(m \times p)` tensor, out will be a</span>
<span class="s2"> :math:`(n \times p)` tensor.</span>
<span class="s2"> When :attr:`mat1` is a COO tensor it must have `sparse_dim = 2`.</span>
<span class="s2"> When inputs are COO tensors, this function also supports backward for both inputs.</span>
<span class="s2"> Supports both CSR and COO storage formats.</span>
<span class="s2">.. note::</span>
<span class="s2"> This function doesn't support computing derivaties with respect to CSR matrices.</span>
<span class="s2"> This function also additionally accepts an optional :attr:`reduce` argument that allows</span>
<span class="s2"> specification of an optional reduction operation, mathematically performs the following operation:</span>
<span class="s2">.. math::</span>
<span class="s2"> z_</span><span class="si">{ij}</span><span class="s2"> = \bigoplus_{k = 0}^{K - 1} x_</span><span class="si">{ik}</span><span class="s2"> y_</span><span class="si">{kj}</span>
<span class="s2">where :math:`\bigoplus` defines the reduce operator. :attr:`reduce` is implemented only for</span>
<span class="s2">CSR storage format on CPU device.</span>
<span class="s2">Args:</span>
<span class="s2"> mat1 (Tensor): the first sparse matrix to be multiplied</span>
<span class="s2"> mat2 (Tensor): the second matrix to be multiplied, which could be sparse or dense</span>
<span class="s2"> reduce (str, optional): the reduction operation to apply for non-unique indices</span>
<span class="s2"> (:obj:`"sum"`, :obj:`"mean"`, :obj:`"amax"`, :obj:`"amin"`). Default :obj:`"sum"`.</span>
<span class="s2">Shape:</span>
<span class="s2"> The format of the output tensor of this function follows:</span>
<span class="s2"> - sparse x sparse -> sparse</span>
<span class="s2"> - sparse x dense -> dense</span>
<span class="s2">Example::</span>
<span class="s2"> >>> a = torch.tensor([[1., 0, 2], [0, 3, 0]]).to_sparse().requires_grad_()</span>
<span class="s2"> >>> a</span>
<span class="s2"> tensor(indices=tensor([[0, 0, 1],</span>
<span class="s2"> [0, 2, 1]]),</span>
<span class="s2"> values=tensor([1., 2., 3.]),</span>
<span class="s2"> size=(2, 3), nnz=3, layout=torch.sparse_coo, requires_grad=True)</span>
<span class="s2"> >>> b = torch.tensor([[0, 1.], [2, 0], [0, 0]], requires_grad=True)</span>
<span class="s2"> >>> b</span>
<span class="s2"> tensor([[0., 1.],</span>
<span class="s2"> [2., 0.],</span>
<span class="s2"> [0., 0.]], requires_grad=True)</span>
<span class="s2"> >>> y = torch.sparse.mm(a, b)</span>
<span class="s2"> >>> y</span>
<span class="s2"> tensor([[0., 1.],</span>
<span class="s2"> [6., 0.]], grad_fn=<SparseAddmmBackward0>)</span>
<span class="s2"> >>> y.sum().backward()</span>
<span class="s2"> >>> a.grad</span>
<span class="s2"> tensor(indices=tensor([[0, 0, 1],</span>
<span class="s2"> [0, 2, 1]]),</span>
<span class="s2"> values=tensor([1., 0., 2.]),</span>
<span class="s2"> size=(2, 3), nnz=3, layout=torch.sparse_coo)</span>
<span class="s2"> >>> c = a.detach().to_sparse_csr()</span>
<span class="s2"> >>> c</span>
<span class="s2"> tensor(crow_indices=tensor([0, 2, 3]),</span>
<span class="s2"> col_indices=tensor([0, 2, 1]),</span>
<span class="s2"> values=tensor([1., 2., 3.]), size=(2, 3), nnz=3,</span>
<span class="s2"> layout=torch.sparse_csr)</span>
<span class="s2"> >>> y1 = torch.sparse.mm(c, b, 'sum')</span>
<span class="s2"> >>> y1</span>
<span class="s2"> tensor([[0., 1.],</span>
<span class="s2"> [6., 0.]], grad_fn=<SparseMmReduceImplBackward0>)</span>
<span class="s2"> >>> y2 = torch.sparse.mm(c, b, 'max')</span>
<span class="s2"> >>> y2</span>
<span class="s2"> tensor([[0., 1.],</span>
<span class="s2"> [6., 0.]], grad_fn=<SparseMmReduceImplBackward0>)</span>
<span class="s2">"""</span><span class="p">)</span>
<span class="n">sampled_addmm</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span><span class="n">_sparse</span><span class="o">.</span><span class="n">sparse_sampled_addmm</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"""</span>
<span class="s2">sparse.sampled_addmm(input, mat1, mat2, *, beta=1., alpha=1., out=None) -> Tensor</span>
<span class="s2">Performs a matrix multiplication of the dense matrices :attr:`mat1` and :attr:`mat2` at the locations</span>
<span class="s2">specified by the sparsity pattern of :attr:`input`. The matrix :attr:`input` is added to the final result.</span>
<span class="s2">Mathematically this performs the following operation:</span>
<span class="s2">.. math::</span>
<span class="s2"> \text</span><span class="si">{out}</span><span class="s2"> = \alpha\ (\text</span><span class="si">{mat1}</span><span class="s2"> \mathbin{@} \text</span><span class="si">{mat2}</span><span class="s2">)*\text</span><span class="si">{spy}</span><span class="s2">(\text</span><span class="si">{input}</span><span class="s2">) + \beta\ \text</span><span class="si">{input}</span>
<span class="s2">where :math:`\text</span><span class="si">{spy}</span><span class="s2">(\text</span><span class="si">{input}</span><span class="s2">)` is the sparsity pattern matrix of :attr:`input`, :attr:`alpha`</span>
<span class="s2">and :attr:`beta` are the scaling factors.</span>
<span class="s2">:math:`\text</span><span class="si">{spy}</span><span class="s2">(\text</span><span class="si">{input}</span><span class="s2">)` has value 1 at the positions where :attr:`input` has non-zero values, and 0 elsewhere.</span>
<span class="s2">.. note::</span>
<span class="s2"> :attr:`input` must be a sparse CSR tensor. :attr:`mat1` and :attr:`mat2` must be dense tensors.</span>
<span class="s2">Args:</span>
<span class="s2"> input (Tensor): a sparse CSR matrix of shape `(m, n)` to be added and used to compute</span>
<span class="s2"> the sampled matrix multiplication</span>
<span class="s2"> mat1 (Tensor): a dense matrix of shape `(m, k)` to be multiplied</span>
<span class="s2"> mat2 (Tensor): a dense matrix of shape `(k, n)` to be multiplied</span>
<span class="s2">Keyword args:</span>
<span class="s2"> beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`)</span>
<span class="s2"> alpha (Number, optional): multiplier for :math:`mat1 @ mat2` (:math:`\alpha`)</span>
<span class="s2"> out (Tensor, optional): output tensor. Ignored if `None`. Default: `None`.</span>
<span class="s2">Examples::</span>
<span class="s2"> >>> input = torch.eye(3, device='cuda').to_sparse_csr()</span>
<span class="s2"> >>> mat1 = torch.randn(3, 5, device='cuda')</span>
<span class="s2"> >>> mat2 = torch.randn(5, 3, device='cuda')</span>
<span class="s2"> >>> torch.sparse.sampled_addmm(input, mat1, mat2)</span>
<span class="s2"> tensor(crow_indices=tensor([0, 1, 2, 3]),</span>
<span class="s2"> col_indices=tensor([0, 1, 2]),</span>
<span class="s2"> values=tensor([ 0.2847, -0.7805, -0.1900]), device='cuda:0',</span>
<span class="s2"> size=(3, 3), nnz=3, layout=torch.sparse_csr)</span>
<span class="s2"> >>> torch.sparse.sampled_addmm(input, mat1, mat2).to_dense()</span>
<span class="s2"> tensor([[ 0.2847, 0.0000, 0.0000],</span>
<span class="s2"> [ 0.0000, -0.7805, 0.0000],</span>
<span class="s2"> [ 0.0000, 0.0000, -0.1900]], device='cuda:0')</span>
<span class="s2"> >>> torch.sparse.sampled_addmm(input, mat1, mat2, beta=0.5, alpha=0.5)</span>
<span class="s2"> tensor(crow_indices=tensor([0, 1, 2, 3]),</span>
<span class="s2"> col_indices=tensor([0, 1, 2]),</span>
<span class="s2"> values=tensor([ 0.1423, -0.3903, -0.0950]), device='cuda:0',</span>
<span class="s2"> size=(3, 3), nnz=3, layout=torch.sparse_csr)</span>
<span class="s2">"""</span><span class="p">)</span>
<div class="viewcode-block" id="sum"><a class="viewcode-back" href="../../generated/torch.sparse.sum.html#torch.sparse.sum">[docs]</a><span class="k">def</span> <span class="nf">sum</span><span class="p">(</span><span class="nb">input</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="n">DimOrDims</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DType</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return the sum of each row of the given sparse tensor.</span>
<span class="sd"> Returns the sum of each row of the sparse tensor :attr:`input` in the given</span>
<span class="sd"> dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,</span>
<span class="sd"> reduce over all of them. When sum over all ``sparse_dim``, this method</span>
<span class="sd"> returns a dense tensor instead of a sparse tensor.</span>
<span class="sd"> All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output</span>
<span class="sd"> tensor having :attr:`dim` fewer dimensions than :attr:`input`.</span>
<span class="sd"> During backward, only gradients at ``nnz`` locations of :attr:`input`</span>
<span class="sd"> will propagate back. Note that the gradients of :attr:`input` is coalesced.</span>
<span class="sd"> Args:</span>
<span class="sd"> input (Tensor): the input sparse tensor</span>
<span class="sd"> dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce</span>
<span class="sd"> over all dims.</span>
<span class="sd"> dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.</span>
<span class="sd"> Default: dtype of :attr:`input`.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> nnz = 3</span>
<span class="sd"> >>> dims = [5, 5, 2, 3]</span>
<span class="sd"> >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),</span>
<span class="sd"> torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)</span>
<span class="sd"> >>> V = torch.randn(nnz, dims[2], dims[3])</span>
<span class="sd"> >>> size = torch.Size(dims)</span>
<span class="sd"> >>> # xdoctest: +IGNORE_WANT("non-deterministic")</span>
<span class="sd"> >>> S = torch.sparse_coo_tensor(I, V, size)</span>
<span class="sd"> >>> S</span>
<span class="sd"> tensor(indices=tensor([[2, 0, 3],</span>
<span class="sd"> [2, 4, 1]]),</span>
<span class="sd"> values=tensor([[[-0.6438, -1.6467, 1.4004],</span>
<span class="sd"> [ 0.3411, 0.0918, -0.2312]],</span>
<span class="sd"> [[ 0.5348, 0.0634, -2.0494],</span>
<span class="sd"> [-0.7125, -1.0646, 2.1844]],</span>
<span class="sd"> [[ 0.1276, 0.1874, -0.6334],</span>
<span class="sd"> [-1.9682, -0.5340, 0.7483]]]),</span>
<span class="sd"> size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)</span>
<span class="sd"> # when sum over only part of sparse_dims, return a sparse tensor</span>
<span class="sd"> >>> torch.sparse.sum(S, [1, 3])</span>
<span class="sd"> tensor(indices=tensor([[0, 2, 3]]),</span>
<span class="sd"> values=tensor([[-1.4512, 0.4073],</span>
<span class="sd"> [-0.8901, 0.2017],</span>
<span class="sd"> [-0.3183, -1.7539]]),</span>
<span class="sd"> size=(5, 2), nnz=3, layout=torch.sparse_coo)</span>
<span class="sd"> # when sum over all sparse dim, return a dense tensor</span>
<span class="sd"> # with summed dims squeezed</span>
<span class="sd"> >>> torch.sparse.sum(S, [0, 1, 3])</span>
<span class="sd"> tensor([-2.6596, -1.1450])</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_sparse_sum</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_sparse_sum</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_sparse_sum</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_sparse_sum</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span></div>
<span class="n">softmax</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span><span class="n">_sparse</span><span class="o">.</span><span class="n">_sparse_softmax</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"""</span>
<span class="s2">sparse.softmax(input, dim, *, dtype=None) -> Tensor</span>
<span class="s2">Applies a softmax function.</span>
<span class="s2">Softmax is defined as:</span>
<span class="s2">:math:`\text</span><span class="si">{Softmax}</span><span class="s2">(x_</span><span class="si">{i}</span><span class="s2">) = \frac{exp(x_i)}{\sum_j exp(x_j)}`</span>
<span class="s2">where :math:`i, j` run over sparse tensor indices and unspecified</span>
<span class="s2">entries are ignores. This is equivalent to defining unspecified</span>
<span class="s2">entries as negative infinity so that :math:`exp(x_k) = 0` when the</span>
<span class="s2">entry with index :math:`k` has not specified.</span>
<span class="s2">It is applied to all slices along `dim`, and will re-scale them so</span>
<span class="s2">that the elements lie in the range `[0, 1]` and sum to 1.</span>
<span class="s2">Args:</span>
<span class="s2"> input (Tensor): input</span>
<span class="s2"> dim (int): A dimension along which softmax will be computed.</span>
<span class="s2"> dtype (:class:`torch.dtype`, optional): the desired data type</span>
<span class="s2"> of returned tensor. If specified, the input tensor is</span>
<span class="s2"> casted to :attr:`dtype` before the operation is</span>
<span class="s2"> performed. This is useful for preventing data type</span>
<span class="s2"> overflows. Default: None</span>
<span class="s2">"""</span><span class="p">)</span>
<span class="n">log_softmax</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span><span class="n">_sparse</span><span class="o">.</span><span class="n">_sparse_log_softmax</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"""</span>
<span class="s2">sparse.log_softmax(input, dim, *, dtype=None) -> Tensor</span>
<span class="s2">Applies a softmax function followed by logarithm.</span>
<span class="s2">See :class:`~torch.sparse.softmax` for more details.</span>
<span class="s2">Args:</span>
<span class="s2"> input (Tensor): input</span>
<span class="s2"> dim (int): A dimension along which softmax will be computed.</span>
<span class="s2"> dtype (:class:`torch.dtype`, optional): the desired data type</span>
<span class="s2"> of returned tensor. If specified, the input tensor is</span>
<span class="s2"> casted to :attr:`dtype` before the operation is</span>
<span class="s2"> performed. This is useful for preventing data type</span>
<span class="s2"> overflows. Default: None</span>
<span class="s2">"""</span><span class="p">)</span>
<span class="n">spdiags</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span>
<span class="n">_sparse</span><span class="o">.</span><span class="n">_spdiags</span><span class="p">,</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd">sparse.spdiags(diagonals, offsets, shape, layout=None) -> Tensor</span>
<span class="sd">Creates a sparse 2D tensor by placing the values from rows of</span>
<span class="sd">:attr:`diagonals` along specified diagonals of the output</span>
<span class="sd">The :attr:`offsets` tensor controls which diagonals are set.</span>
<span class="sd">- If :attr:`offsets[i]` = 0, it is the main diagonal</span>
<span class="sd">- If :attr:`offsets[i]` < 0, it is below the main diagonal</span>
<span class="sd">- If :attr:`offsets[i]` > 0, it is above the main diagonal</span>
<span class="sd">The number of rows in :attr:`diagonals` must match the length of :attr:`offsets`,</span>
<span class="sd">and an offset may not be repeated.</span>
<span class="sd">Args:</span>
<span class="sd"> diagonals (Tensor): Matrix storing diagonals row-wise</span>
<span class="sd"> offsets (Tensor): The diagonals to be set, stored as a vector</span>
<span class="sd"> shape (2-tuple of ints): The desired shape of the result</span>
<span class="sd">Keyword args:</span>
<span class="sd"> layout (:class:`torch.layout`, optional): The desired layout of the</span>
<span class="sd"> returned tensor. ``torch.sparse_coo``, ``torch.sparse_csc`` and ``torch.sparse_csr``</span>
<span class="sd"> are supported. Default: ``torch.sparse_coo``</span>
<span class="sd">Examples:</span>
<span class="sd">Set the main and first two lower diagonals of a matrix::</span>
<span class="sd"> >>> diags = torch.arange(9).reshape(3, 3)</span>
<span class="sd"> >>> diags</span>
<span class="sd"> tensor([[0, 1, 2],</span>
<span class="sd"> [3, 4, 5],</span>
<span class="sd"> [6, 7, 8]])</span>
<span class="sd"> >>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3))</span>
<span class="sd"> >>> s</span>
<span class="sd"> tensor(indices=tensor([[0, 1, 2, 1, 2, 2],</span>
<span class="sd"> [0, 1, 2, 0, 1, 0]]),</span>
<span class="sd"> values=tensor([0, 1, 2, 3, 4, 6]),</span>
<span class="sd"> size=(3, 3), nnz=6, layout=torch.sparse_coo)</span>
<span class="sd"> >>> s.to_dense()</span>
<span class="sd"> tensor([[0, 0, 0],</span>
<span class="sd"> [3, 1, 0],</span>
<span class="sd"> [6, 4, 2]])</span>
<span class="sd">Change the output layout::</span>
<span class="sd"> >>> diags = torch.arange(9).reshape(3, 3)</span>
<span class="sd"> >>> diags</span>
<span class="sd"> tensor([[0, 1, 2],[3, 4, 5], [6, 7, 8])</span>
<span class="sd"> >>> s = torch.sparse.spdiags(diags, torch.tensor([0, -1, -2]), (3, 3), layout=torch.sparse_csr)</span>
<span class="sd"> >>> s</span>
<span class="sd"> tensor(crow_indices=tensor([0, 1, 3, 6]),</span>
<span class="sd"> col_indices=tensor([0, 0, 1, 0, 1, 2]),</span>
<span class="sd"> values=tensor([0, 3, 1, 6, 4, 2]), size=(3, 3), nnz=6,</span>
<span class="sd"> layout=torch.sparse_csr)</span>
<span class="sd"> >>> s.to_dense()</span>
<span class="sd"> tensor([[0, 0, 0],</span>
<span class="sd"> [3, 1, 0],</span>
<span class="sd"> [6, 4, 2]])</span>
<span class="sd">Set partial diagonals of a large output::</span>
<span class="sd"> >>> diags = torch.tensor([[1, 2], [3, 4]])</span>
<span class="sd"> >>> offsets = torch.tensor([0, -1])</span>
<span class="sd"> >>> torch.sparse.spdiags(diags, offsets, (5, 5)).to_dense()</span>
<span class="sd"> tensor([[1, 0, 0, 0, 0],</span>
<span class="sd"> [3, 2, 0, 0, 0],</span>
<span class="sd"> [0, 4, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0, 0]])</span>
<span class="sd">.. note::</span>
<span class="sd"> When setting the values along a given diagonal the index into the diagonal</span>
<span class="sd"> and the index into the row of :attr:`diagonals` is taken as the</span>
<span class="sd"> column index in the output. This has the effect that when setting a diagonal</span>
<span class="sd"> with a positive offset `k` the first value along that diagonal will be</span>
<span class="sd"> the value in position `k` of the row of :attr:`diagonals`</span>
<span class="sd">Specifying a positive offset::</span>
<span class="sd"> >>> diags = torch.tensor([[1, 2, 3], [1, 2, 3], [1, 2, 3]])</span>
<span class="sd"> >>> torch.sparse.spdiags(diags, torch.tensor([0, 1, 2]), (5, 5)).to_dense()</span>
<span class="sd"> tensor([[1, 2, 3, 0, 0],</span>
<span class="sd"> [0, 2, 3, 0, 0],</span>
<span class="sd"> [0, 0, 3, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0, 0],</span>
<span class="sd"> [0, 0, 0, 0, 0]])</span>
<span class="sd">"""</span><span class="p">)</span>
<div class="viewcode-block" id="check_sparse_tensor_invariants"><a class="viewcode-back" href="../../generated/torch.sparse.check_sparse_tensor_invariants.html#torch.sparse.check_sparse_tensor_invariants">[docs]</a><span class="k">class</span> <span class="nc">check_sparse_tensor_invariants</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""A tool to control checking sparse tensor invariants.</span>
<span class="sd"> The following options exists to manage sparsr tensor invariants</span>
<span class="sd"> checking in sparse tensor construction:</span>
<span class="sd"> 1. Using a context manager:</span>
<span class="sd"> .. code:: python</span>
<span class="sd"> with torch.sparse.check_sparse_tensor_invariants():</span>
<span class="sd"> run_my_model()</span>
<span class="sd"> 2. Using a procedural approach:</span>
<span class="sd"> .. code:: python</span>
<span class="sd"> prev_checks_enabled = torch.sparse.check_sparse_tensor_invariants.is_enabled()</span>
<span class="sd"> torch.sparse.check_sparse_tensor_invariants.enable()</span>
<span class="sd"> run_my_model()</span>
<span class="sd"> if not prev_checks_enabled:</span>
<span class="sd"> torch.sparse.check_sparse_tensor_invariants.disable()</span>
<span class="sd"> 3. Using function decoration:</span>
<span class="sd"> .. code:: python</span>
<span class="sd"> @torch.sparse.check_sparse_tensor_invariants()</span>
<span class="sd"> def run_my_model():</span>
<span class="sd"> ...</span>
<span class="sd"> run_my_model()</span>
<span class="sd"> 4. Using ``check_invariants`` keyword argument in sparse tensor constructor call.</span>
<span class="sd"> For example:</span>
<span class="sd"> >>> torch.sparse_csr_tensor([0, 1, 3], [0, 1], [1, 2], check_invariants=True)</span>
<span class="sd"> Traceback (most recent call last):</span>
<span class="sd"> File "<stdin>", line 1, in <module></span>
<span class="sd"> RuntimeError: `crow_indices[..., -1] == nnz` is not satisfied.</span>
<span class="sd"> """</span>
<div class="viewcode-block" id="check_sparse_tensor_invariants.is_enabled"><a class="viewcode-back" href="../../generated/torch.sparse.check_sparse_tensor_invariants.html#torch.sparse.check_sparse_tensor_invariants.is_enabled">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">is_enabled</span><span class="p">():</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Return True if the sparse tensor invariants checking is enabled.</span>
<span class="sd"> .. note::</span>
<span class="sd"> Use :func:`torch.sparse.check_sparse_tensor_invariants.enable` or</span>
<span class="sd"> :func:`torch.sparse.check_sparse_tensor_invariants.disable` to</span>
<span class="sd"> manage the state of the sparse tensor invariants checks.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_check_sparse_tensor_invariants</span><span class="p">()</span></div>
<div class="viewcode-block" id="check_sparse_tensor_invariants.enable"><a class="viewcode-back" href="../../generated/torch.sparse.check_sparse_tensor_invariants.html#torch.sparse.check_sparse_tensor_invariants.enable">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">enable</span><span class="p">():</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Enable sparse tensor invariants checking in sparse tensor constructors.</span>
<span class="sd"> .. note::</span>
<span class="sd"> By default, the sparse tensor invariants checks are disabled. Use</span>
<span class="sd"> :func:`torch.sparse.check_sparse_tensor_invariants.is_enabled` to</span>
<span class="sd"> retrieve the current state of sparse tensor invariants checking.</span>
<span class="sd"> .. note::</span>
<span class="sd"> The sparse tensor invariants check flag is effective to all sparse</span>
<span class="sd"> tensor constructors, both in Python and ATen.</span>
<span class="sd"> The flag can be locally overridden by the ``check_invariants``</span>
<span class="sd"> optional argument of the sparse tensor constructor functions.</span>
<span class="sd"> """</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_check_sparse_tensor_invariants</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span></div>
<div class="viewcode-block" id="check_sparse_tensor_invariants.disable"><a class="viewcode-back" href="../../generated/torch.sparse.check_sparse_tensor_invariants.html#torch.sparse.check_sparse_tensor_invariants.disable">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">disable</span><span class="p">():</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Disable sparse tensor invariants checking in sparse tensor constructors.</span>
<span class="sd"> See :func:`torch.sparse.check_sparse_tensor_invariants.enable` for more information.</span>
<span class="sd"> """</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_check_sparse_tensor_invariants</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span></div>
<span class="c1"># context manager support</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">enable</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="n">enable</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_state</span> <span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">saved_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'This context manager instance is already activated.'</span>
<span class="s1">' Use a different context manager instance for context nesting.'</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_enabled</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_check_sparse_tensor_invariants</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">traceback</span><span class="p">):</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">saved_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_check_sparse_tensor_invariants</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">saved_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_state</span> <span class="o">=</span> <span class="kc">None</span>
<span class="c1"># decorator support</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mth</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">test_mth</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">):</span>
<span class="k">return</span> <span class="n">mth</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">test_mth</span></div>
<div class="viewcode-block" id="as_sparse_gradcheck"><a class="viewcode-back" href="../../generated/torch.sparse.as_sparse_gradcheck.html#torch.sparse.as_sparse_gradcheck">[docs]</a><span class="k">def</span> <span class="nf">as_sparse_gradcheck</span><span class="p">(</span><span class="n">gradcheck</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Decorate function, to extend gradcheck for sparse tensors.</span>
<span class="sd"> Decorator for torch.autograd.gradcheck or its functools.partial</span>
<span class="sd"> variants that extends the gradcheck function with support to input</span>
<span class="sd"> functions that operate on or/and return sparse tensors.</span>
<span class="sd"> The specified gradcheck function itself is guaranteed to operate</span>
<span class="sd"> on strided tensors only.</span>
<span class="sd"> For example:</span>
<span class="sd"> >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck)</span>
<span class="sd"> >>> x = torch.tensor([[0, 1], [2, 3]], dtype=torch.float64).to_sparse_coo().requires_grad_(True)</span>
<span class="sd"> >>> gradcheck(lambda x: x.to_sparse_csr(), x)</span>
<span class="sd"> True</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">gradcheck_with_sparse_support</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Create gradcheck with support for sparse tensors.</span>
<span class="sd"> Same as :func:`torch.autograd.gradcheck` but with sparse tensors inputs and outputs support.</span>
<span class="sd"> """</span>
<span class="n">masked</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'masked'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">sparse_layouts</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_csr</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_csc</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsr</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsc</span><span class="p">}</span>
<span class="n">sparse_compressed_layouts</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_csr</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_csc</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsr</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsc</span><span class="p">}</span>
<span class="n">sparse_block_layouts</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsr</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_bsc</span><span class="p">}</span>
<span class="n">STRIDED_REPRESENTATION</span> <span class="o">=</span> <span class="s1">'__STRIDED_REPRESENTATION__'</span>
<span class="k">def</span> <span class="nf">convert_to_strided_representation</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Convert differentiable non-strided tensors to a representation containing differentiable strided tensors."""</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">args</span><span class="p">,</span>
<span class="n">new_args</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">obj</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="n">obj</span><span class="o">.</span><span class="n">requires_grad</span> <span class="ow">and</span> <span class="n">obj</span><span class="o">.</span><span class="n">layout</span> <span class="ow">in</span> <span class="n">sparse_layouts</span><span class="p">:</span>