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<h1>Source code for torch.nested</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</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">_nested</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="kn">from</span> <span class="nn">torch.types</span> <span class="kn">import</span> <span class="n">_device</span> <span class="k">as</span> <span class="n">Device</span><span class="p">,</span> <span class="n">_dtype</span> <span class="k">as</span> <span class="n">DType</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"to_padded_tensor"</span><span class="p">,</span>
<span class="s2">"as_nested_tensor"</span><span class="p">,</span>
<span class="s2">"nested_tensor"</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1"># Nested Tensor constructor functions</span>
<div class="viewcode-block" id="as_nested_tensor"><a class="viewcode-back" href="../../nested.html#torch.nested.as_nested_tensor">[docs]</a><span class="k">def</span> <span class="nf">as_nested_tensor</span><span class="p">(</span>
<span class="n">tensor_list</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</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="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Device</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Constructs a nested tensor preserving autograd history from :attr:`tensor_list` a list of tensors.</span>
<span class="sd"> .. note::</span>
<span class="sd"> Tensors within the list are always copied by this function due to current nested tensor semantics.</span>
<span class="sd"> Args:</span>
<span class="sd"> tensor_list (List[Tensor]): a list of tensors with the same ndim</span>
<span class="sd"> Keyword arguments:</span>
<span class="sd"> dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.</span>
<span class="sd"> Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.</span>
<span class="sd"> device (:class:`torch.device`, optional): the desired device of returned nested tensor.</span>
<span class="sd"> Default: if None, same :class:`torch.device` as leftmost tensor in the list</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> a = torch.arange(3, dtype=torch.float, requires_grad=True)</span>
<span class="sd"> >>> b = torch.arange(5, dtype=torch.float, requires_grad=True)</span>
<span class="sd"> >>> nt = torch.nested.as_nested_tensor([a, b])</span>
<span class="sd"> >>> nt.is_leaf</span>
<span class="sd"> False</span>
<span class="sd"> >>> fake_grad = torch.nested.nested_tensor([torch.ones_like(a), torch.zeros_like(b)])</span>
<span class="sd"> >>> nt.backward(fake_grad)</span>
<span class="sd"> >>> a.grad</span>
<span class="sd"> tensor([1., 1., 1.])</span>
<span class="sd"> >>> b.grad</span>
<span class="sd"> tensor([0., 0., 0., 0., 0.])</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor_list</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">any</span><span class="p">(</span>
<span class="p">[</span><span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensor_list</span><span class="p">]</span>
<span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="s2">"nested_tensor(): Expected first argument to be a list of tensors "</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_nested_tensor_from_tensor_list</span><span class="p">(</span><span class="n">tensor_list</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span></div>
<span class="c1"># Note: This not only adds doc strings for the nested ops, but</span>
<span class="c1"># also connects the torch.nested Python namespace to the torch._C._nested builtins.</span>
<span class="n">to_padded_tensor</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span>
<span class="n">_nested</span><span class="o">.</span><span class="n">nested_to_padded_tensor</span><span class="p">,</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd">to_padded_tensor(input, padding, output_size=None, out=None) -> Tensor</span>
<span class="sd">Returns a new (non-nested) Tensor by padding the :attr:`input` nested tensor.</span>
<span class="sd">The leading entries will be filled with the nested data,</span>
<span class="sd">while the trailing entries will be padded.</span>
<span class="sd">.. warning::</span>
<span class="sd"> :func:`to_padded_tensor` always copies the underlying data,</span>
<span class="sd"> since the nested and the non-nested tensors differ in memory layout.</span>
<span class="sd">Args:</span>
<span class="sd"> padding (float): The padding value for the trailing entries.</span>
<span class="sd">Keyword args:</span>
<span class="sd"> output_size (Tuple[int]): The size of the output tensor.</span>
<span class="sd"> If given, it must be large enough to contain all nested data;</span>
<span class="sd"> else, will infer by taking the max size of each nested sub-tensor along each dimension.</span>
<span class="sd"> out (Tensor, optional): the output tensor.</span>
<span class="sd">Example::</span>
<span class="sd"> >>> nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))])</span>
<span class="sd"> nested_tensor([</span>
<span class="sd"> tensor([[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276],</span>
<span class="sd"> [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995]]),</span>
<span class="sd"> tensor([[-1.8546, -0.7194, -0.2918, -0.1846],</span>
<span class="sd"> [ 0.2773, 0.8793, -0.5183, -0.6447],</span>
<span class="sd"> [ 1.8009, 1.8468, -0.9832, -1.5272]])</span>
<span class="sd"> ])</span>
<span class="sd"> >>> pt_infer = torch.nested.to_padded_tensor(nt, 0.0)</span>
<span class="sd"> tensor([[[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276],</span>
<span class="sd"> [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995],</span>
<span class="sd"> [ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],</span>
<span class="sd"> [[-1.8546, -0.7194, -0.2918, -0.1846, 0.0000],</span>
<span class="sd"> [ 0.2773, 0.8793, -0.5183, -0.6447, 0.0000],</span>
<span class="sd"> [ 1.8009, 1.8468, -0.9832, -1.5272, 0.0000]]])</span>
<span class="sd"> >>> pt_large = torch.nested.to_padded_tensor(nt, 1.0, (2, 4, 6))</span>
<span class="sd"> tensor([[[ 1.6862, -1.1282, 1.1031, 0.0464, -1.3276, 1.0000],</span>
<span class="sd"> [-1.9967, -1.0054, 1.8972, 0.9174, -1.4995, 1.0000],</span>
<span class="sd"> [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],</span>
<span class="sd"> [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]],</span>
<span class="sd"> [[-1.8546, -0.7194, -0.2918, -0.1846, 1.0000, 1.0000],</span>
<span class="sd"> [ 0.2773, 0.8793, -0.5183, -0.6447, 1.0000, 1.0000],</span>
<span class="sd"> [ 1.8009, 1.8468, -0.9832, -1.5272, 1.0000, 1.0000],</span>
<span class="sd"> [ 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]])</span>
<span class="sd"> >>> pt_small = torch.nested.to_padded_tensor(nt, 2.0, (2, 2, 2))</span>
<span class="sd"> RuntimeError: Value in output_size is less than NestedTensor padded size. Truncation is not supported.</span>
<span class="sd">"""</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">nested_tensor</span> <span class="o">=</span> <span class="n">_add_docstr</span><span class="p">(</span>
<span class="n">_nested</span><span class="o">.</span><span class="n">nested_tensor</span><span class="p">,</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd">nested_tensor(tensor_list, *, dtype=None, device=None, requires_grad=False, pin_memory=False) -> Tensor</span>
<span class="sd">Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see</span>
<span class="sd">:ref:`Autograd mechanics <autograd-mechanics>`) from :attr:`tensor_list` a list of tensors.</span>
<span class="sd">Args:</span>
<span class="sd"> tensor_list (List[array_like]): a list of tensors, or anything that can be passed to torch.tensor,</span>
<span class="sd"> where each element of the list has the same dimensionality.</span>
<span class="sd">Keyword arguments:</span>
<span class="sd"> dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.</span>
<span class="sd"> Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.</span>
<span class="sd"> device (:class:`torch.device`, optional): the desired device of returned nested tensor.</span>
<span class="sd"> Default: if None, same :class:`torch.device` as leftmost tensor in the list</span>
<span class="sd"> requires_grad (bool, optional): If autograd should record operations on the</span>
<span class="sd"> returned nested tensor. Default: ``False``.</span>
<span class="sd"> pin_memory (bool, optional): If set, returned nested tensor would be allocated in</span>
<span class="sd"> the pinned memory. Works only for CPU tensors. Default: ``False``.</span>
<span class="sd">Example::</span>
<span class="sd"> >>> a = torch.arange(3, dtype=torch.float, requires_grad=True)</span>
<span class="sd"> >>> b = torch.arange(5, dtype=torch.float, requires_grad=True)</span>
<span class="sd"> >>> nt = torch.nested.nested_tensor([a, b], requires_grad=True)</span>
<span class="sd"> >>> nt.is_leaf</span>
<span class="sd"> True</span>
<span class="sd"> """</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
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