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<li class="toctree-l1"><a class="reference internal" href="../../notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../notes/autograd.html">Autograd mechanics</a></li>
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<h1>Source code for torch.autograd</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">``torch.autograd`` provides classes and functions implementing automatic</span>
<span class="sd">differentiation of arbitrary scalar valued functions. It requires minimal</span>
<span class="sd">changes to the existing code - you only need to declare :class:`Tensor` s</span>
<span class="sd">for which gradients should be computed with the ``requires_grad=True`` keyword.</span>
<span class="sd">As of now, we only support autograd for floating point :class:`Tensor` types (</span>
<span class="sd">half, float, double and bfloat16) and complex :class:`Tensor` types (cfloat, cdouble).</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">torch.types</span> <span class="kn">import</span> <span class="n">_TensorOrTensors</span><span class="p">,</span> <span class="n">_size</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">cast</span>
<span class="kn">from</span> <span class="nn">.variable</span> <span class="kn">import</span> <span class="n">Variable</span>
<span class="kn">from</span> <span class="nn">.function</span> <span class="kn">import</span> <span class="n">Function</span><span class="p">,</span> <span class="n">NestedIOFunction</span>
<span class="kn">from</span> <span class="nn">.gradcheck</span> <span class="kn">import</span> <span class="n">gradcheck</span><span class="p">,</span> <span class="n">gradgradcheck</span>
<span class="kn">from</span> <span class="nn">.grad_mode</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">no_grad</span><span class="p">,</span> <span class="n">enable_grad</span><span class="p">,</span> <span class="n">set_grad_enabled</span><span class="p">,</span> <span class="n">inference_mode</span><span class="p">,</span> <span class="n">set_multithreading_enabled</span><span class="p">,</span> <span class="n">_force_original_view_tracking</span><span class="p">,</span>
<span class="n">_unsafe_preserve_version_counter</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">.anomaly_mode</span> <span class="kn">import</span> <span class="n">detect_anomaly</span><span class="p">,</span> <span class="n">set_detect_anomaly</span>
<span class="kn">from</span> <span class="nn">..overrides</span> <span class="kn">import</span> <span class="n">has_torch_function</span><span class="p">,</span> <span class="n">handle_torch_function</span><span class="p">,</span> <span class="n">is_tensor_like</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">functional</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">forward_ad</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">graph</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">_vmap_internals</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Variable'</span><span class="p">,</span> <span class="s1">'Function'</span><span class="p">,</span> <span class="s1">'backward'</span><span class="p">,</span> <span class="s1">'grad_mode'</span><span class="p">]</span>
<span class="n">_OptionalTensor</span> <span class="o">=</span> <span class="n">Optional</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="n">_ShapeorNestedShape</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span><span class="n">_size</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">_size</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="k">def</span> <span class="nf">_calculate_shape</span><span class="p">(</span><span class="n">output</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="n">grad</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="n">is_grads_batched</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">_ShapeorNestedShape</span><span class="p">,</span> <span class="n">_ShapeorNestedShape</span><span class="p">]:</span>
<span class="c1"># is_same_size ensures that both tensors are either nested or non nested</span>
<span class="k">if</span> <span class="n">output</span><span class="o">.</span><span class="n">is_nested</span><span class="p">:</span>
<span class="k">if</span> <span class="n">is_grads_batched</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Batched grads are not supported with Nested Tensor."</span><span class="p">)</span>
<span class="n">out_shape</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">_nested_tensor_size</span><span class="p">()</span>
<span class="n">grad_shape</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">_nested_tensor_size</span><span class="p">()</span>
<span class="k">return</span> <span class="n">out_shape</span><span class="p">,</span> <span class="n">grad_shape</span>
<span class="n">reg_out_shape</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">shape</span>
<span class="n">reg_grad_shape</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">shape</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_grads_batched</span> <span class="k">else</span> <span class="n">grad</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">return</span> <span class="n">reg_out_shape</span><span class="p">,</span> <span class="n">reg_grad_shape</span>
<span class="k">def</span> <span class="nf">_make_grads</span><span class="p">(</span><span class="n">outputs</span><span class="p">:</span> <span class="n">Sequence</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="n">grads</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">_OptionalTensor</span><span class="p">],</span>
<span class="n">is_grads_batched</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">_OptionalTensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
<span class="n">new_grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">_OptionalTensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">out</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad</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="n">first_grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_grads_batched</span> <span class="k">else</span> <span class="n">grad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_same_size</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">first_grad</span><span class="p">):</span>
<span class="n">out_shape</span><span class="p">,</span> <span class="n">grad_shape</span> <span class="o">=</span> <span class="n">_calculate_shape</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">first_grad</span><span class="p">,</span> <span class="n">is_grads_batched</span><span class="p">)</span>
<span class="k">if</span> <span class="n">is_grads_batched</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"If `is_grads_batched=True`, we interpret the first "</span>
<span class="s2">"dimension of each grad_output as the batch dimension. "</span>
<span class="s2">"The sizes of the remaining dimensions are expected to match "</span>
<span class="s2">"the shape of corresponding output, but a mismatch "</span>
<span class="s2">"was detected: grad_output["</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grads</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">grad</span><span class="p">))</span> <span class="o">+</span> <span class="s2">"] has a shape of "</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grad_shape</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" and output["</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">out</span><span class="p">))</span> <span class="o">+</span> <span class="s2">"] has a shape of "</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">out_shape</span><span class="p">)</span> <span class="o">+</span> <span class="s2">". "</span>
<span class="s2">"If you only want some tensors in `grad_output` to be considered "</span>
<span class="s2">"batched, consider using vmap."</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Mismatch in shape: grad_output["</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grads</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">grad</span><span class="p">))</span> <span class="o">+</span> <span class="s2">"] has a shape of "</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grad_shape</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" and output["</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">out</span><span class="p">))</span> <span class="o">+</span> <span class="s2">"] has a shape of "</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">out_shape</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"."</span><span class="p">)</span>
<span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">is_complex</span> <span class="o">!=</span> <span class="n">grad</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">is_complex</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"For complex Tensors, both grad_output and output"</span>
<span class="s2">" are required to have the same dtype."</span>
<span class="s2">" Mismatch in dtype: grad_output["</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grads</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">grad</span><span class="p">))</span> <span class="o">+</span> <span class="s2">"] has a dtype of "</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">grad</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" and output["</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">out</span><span class="p">))</span> <span class="o">+</span> <span class="s2">"] has a dtype of "</span>
<span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">+</span> <span class="s2">"."</span><span class="p">)</span>
<span class="n">new_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
<span class="k">if</span> <span class="n">out</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"grad can be implicitly created only for scalar outputs"</span><span class="p">)</span>
<span class="n">new_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">preserve_format</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">new_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"gradients can be either Tensors or None, but got "</span> <span class="o">+</span>
<span class="nb">type</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">new_grads</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_tensor_or_tensors_to_tuple</span><span class="p">(</span><span class="n">tensors</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</span><span class="p">],</span> <span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="n">_OptionalTensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">)</span> <span class="o">*</span> <span class="n">length</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</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="k">return</span> <span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="p">)</span>
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>
<div class="viewcode-block" id="backward"><a class="viewcode-back" href="../../generated/torch.autograd.backward.html#torch.autograd.backward">[docs]</a><span class="k">def</span> <span class="nf">backward</span><span class="p">(</span>
<span class="n">tensors</span><span class="p">:</span> <span class="n">_TensorOrTensors</span><span class="p">,</span>
<span class="n">grad_tensors</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">retain_graph</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="p">,</span>
<span class="n">create_graph</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">grad_variables</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</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="kc">None</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Computes the sum of gradients of given tensors with respect to graph</span>
<span class="sd"> leaves.</span>
<span class="sd"> The graph is differentiated using the chain rule. If any of ``tensors``</span>
<span class="sd"> are non-scalar (i.e. their data has more than one element) and require</span>
<span class="sd"> gradient, then the Jacobian-vector product would be computed, in this</span>
<span class="sd"> case the function additionally requires specifying ``grad_tensors``.</span>
<span class="sd"> It should be a sequence of matching length, that contains the "vector"</span>
<span class="sd"> in the Jacobian-vector product, usually the gradient of the differentiated</span>
<span class="sd"> function w.r.t. corresponding tensors (``None`` is an acceptable value for</span>
<span class="sd"> all tensors that don't need gradient tensors).</span>
<span class="sd"> This function accumulates gradients in the leaves - you might need to zero</span>
<span class="sd"> ``.grad`` attributes or set them to ``None`` before calling it.</span>
<span class="sd"> See :ref:`Default gradient layouts<default-grad-layouts>`</span>
<span class="sd"> for details on the memory layout of accumulated gradients.</span>
<span class="sd"> .. note::</span>
<span class="sd"> Using this method with ``create_graph=True`` will create a reference cycle</span>
<span class="sd"> between the parameter and its gradient which can cause a memory leak.</span>
<span class="sd"> We recommend using ``autograd.grad`` when creating the graph to avoid this.</span>
<span class="sd"> If you have to use this function, make sure to reset the ``.grad`` fields of your</span>
<span class="sd"> parameters to ``None`` after use to break the cycle and avoid the leak.</span>
<span class="sd"> .. note::</span>
<span class="sd"> If you run any forward ops, create ``grad_tensors``, and/or call ``backward``</span>
<span class="sd"> in a user-specified CUDA stream context, see</span>
<span class="sd"> :ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.</span>
<span class="sd"> .. note::</span>
<span class="sd"> When ``inputs`` are provided and a given input is not a leaf,</span>
<span class="sd"> the current implementation will call its grad_fn (even though it is not strictly needed to get this gradients).</span>
<span class="sd"> It is an implementation detail on which the user should not rely.</span>
<span class="sd"> See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details.</span>
<span class="sd"> Args:</span>
<span class="sd"> tensors (Sequence[Tensor] or Tensor): Tensors of which the derivative will be</span>
<span class="sd"> computed.</span>
<span class="sd"> grad_tensors (Sequence[Tensor or None] or Tensor, optional): The "vector" in</span>
<span class="sd"> the Jacobian-vector product, usually gradients w.r.t. each element of</span>
<span class="sd"> corresponding tensors. None values can be specified for scalar Tensors or</span>
<span class="sd"> ones that don't require grad. If a None value would be acceptable for all</span>
<span class="sd"> grad_tensors, then this argument is optional.</span>
<span class="sd"> retain_graph (bool, optional): If ``False``, the graph used to compute the grad</span>
<span class="sd"> will be freed. Note that in nearly all cases setting this option to ``True``</span>
<span class="sd"> is not needed and often can be worked around in a much more efficient</span>
<span class="sd"> way. Defaults to the value of ``create_graph``.</span>
<span class="sd"> create_graph (bool, optional): If ``True``, graph of the derivative will</span>
<span class="sd"> be constructed, allowing to compute higher order derivative products.</span>
<span class="sd"> Defaults to ``False``.</span>
<span class="sd"> inputs (Sequence[Tensor] or Tensor, optional): Inputs w.r.t. which the gradient</span>
<span class="sd"> be will accumulated into ``.grad``. All other Tensors will be ignored. If</span>
<span class="sd"> not provided, the gradient is accumulated into all the leaf Tensors that</span>
<span class="sd"> were used to compute the attr::tensors.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_are_functorch_transforms_active</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"backward() called inside a functorch transform. This is not "</span>
<span class="s2">"supported, please use functorch.grad or functorch.vjp instead "</span>
<span class="s2">"or call backward() outside of functorch transforms."</span><span class="p">)</span>
<span class="k">if</span> <span class="n">grad_variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"'grad_variables' is deprecated. Use 'grad_tensors' instead."</span><span class="p">)</span>
<span class="k">if</span> <span class="n">grad_tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">grad_tensors</span> <span class="o">=</span> <span class="n">grad_variables</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"'grad_tensors' and 'grad_variables' (deprecated) "</span>
<span class="s2">"arguments both passed to backward(). Please only "</span>
<span class="s2">"use 'grad_tensors'."</span><span class="p">)</span>
<span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"'inputs' argument to backward() cannot be empty."</span><span class="p">)</span>
<span class="n">tensors</span> <span class="o">=</span> <span class="p">(</span><span class="n">tensors</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</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="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</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="k">else</span> \
<span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
<span class="n">grad_tensors_</span> <span class="o">=</span> <span class="n">_tensor_or_tensors_to_tuple</span><span class="p">(</span><span class="n">grad_tensors</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">))</span>
<span class="n">grad_tensors_</span> <span class="o">=</span> <span class="n">_make_grads</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">grad_tensors_</span><span class="p">,</span> <span class="n">is_grads_batched</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">retain_graph</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">retain_graph</span> <span class="o">=</span> <span class="n">create_graph</span>
<span class="c1"># The reason we repeat same the comment below is that</span>
<span class="c1"># some Python versions print out the first line of a multi-line function</span>
<span class="c1"># calls in the traceback and some print out the last line</span>
<span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span> <span class="c1"># Calls into the C++ engine to run the backward pass</span>
<span class="n">tensors</span><span class="p">,</span> <span class="n">grad_tensors_</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span>
<span class="n">allow_unreachable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">accumulate_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># Calls into the C++ engine to run the backward pass</span></div>
<div class="viewcode-block" id="grad"><a class="viewcode-back" href="../../generated/torch.autograd.grad.html#torch.autograd.grad">[docs]</a><span class="k">def</span> <span class="nf">grad</span><span class="p">(</span>
<span class="n">outputs</span><span class="p">:</span> <span class="n">_TensorOrTensors</span><span class="p">,</span>
<span class="n">inputs</span><span class="p">:</span> <span class="n">_TensorOrTensors</span><span class="p">,</span>
<span class="n">grad_outputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_TensorOrTensors</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">retain_graph</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="p">,</span>
<span class="n">create_graph</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">only_inputs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
<span class="n">allow_unused</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">is_grads_batched</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</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="o">...</span><span class="p">]:</span>
<span class="sa">r</span><span class="sd">"""Computes and returns the sum of gradients of outputs with respect to</span>
<span class="sd"> the inputs.</span>
<span class="sd"> ``grad_outputs`` should be a sequence of length matching ``output``</span>
<span class="sd"> containing the "vector" in vector-Jacobian product, usually the pre-computed</span>
<span class="sd"> gradients w.r.t. each of the outputs. If an output doesn't require_grad,</span>
<span class="sd"> then the gradient can be ``None``).</span>
<span class="sd"> .. note::</span>
<span class="sd"> If you run any forward ops, create ``grad_outputs``, and/or call ``grad``</span>
<span class="sd"> in a user-specified CUDA stream context, see</span>
<span class="sd"> :ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.</span>
<span class="sd"> .. note::</span>
<span class="sd"> ``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).</span>
<span class="sd"> To accumulate gradient for other parts of the graph, please use</span>
<span class="sd"> ``torch.autograd.backward``.</span>
<span class="sd"> Args:</span>
<span class="sd"> outputs (sequence of Tensor): outputs of the differentiated function.</span>
<span class="sd"> inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be</span>
<span class="sd"> returned (and not accumulated into ``.grad``).</span>
<span class="sd"> grad_outputs (sequence of Tensor): The "vector" in the vector-Jacobian product.</span>
<span class="sd"> Usually gradients w.r.t. each output. None values can be specified for scalar</span>
<span class="sd"> Tensors or ones that don't require grad. If a None value would be acceptable</span>
<span class="sd"> for all grad_tensors, then this argument is optional. Default: None.</span>
<span class="sd"> retain_graph (bool, optional): If ``False``, the graph used to compute the grad</span>
<span class="sd"> will be freed. Note that in nearly all cases setting this option to ``True``</span>
<span class="sd"> is not needed and often can be worked around in a much more efficient</span>
<span class="sd"> way. Defaults to the value of ``create_graph``.</span>
<span class="sd"> create_graph (bool, optional): If ``True``, graph of the derivative will</span>
<span class="sd"> be constructed, allowing to compute higher order derivative products.</span>
<span class="sd"> Default: ``False``.</span>
<span class="sd"> allow_unused (bool, optional): If ``False``, specifying inputs that were not</span>
<span class="sd"> used when computing outputs (and therefore their grad is always zero)</span>
<span class="sd"> is an error. Defaults to ``False``.</span>
<span class="sd"> is_grads_batched (bool, optional): If ``True``, the first dimension of each</span>
<span class="sd"> tensor in ``grad_outputs`` will be interpreted as the batch dimension.</span>
<span class="sd"> Instead of computing a single vector-Jacobian product, we compute a</span>
<span class="sd"> batch of vector-Jacobian products for each "vector" in the batch.</span>
<span class="sd"> We use the vmap prototype feature as the backend to vectorize calls</span>
<span class="sd"> to the autograd engine so that this computation can be performed in a</span>
<span class="sd"> single call. This should lead to performance improvements when compared</span>
<span class="sd"> to manually looping and performing backward multiple times. Note that</span>
<span class="sd"> due to this feature being experimental, there may be performance</span>
<span class="sd"> cliffs. Please use ``torch._C._debug_only_display_vmap_fallback_warnings(True)``</span>
<span class="sd"> to show any performance warnings and file an issue on github if warnings exist</span>
<span class="sd"> for your use case. Defaults to ``False``.</span>
<span class="sd"> """</span>
<span class="n">t_outputs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Tuple</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="o">...</span><span class="p">],</span> <span class="p">(</span><span class="n">outputs</span><span class="p">,)</span> <span class="k">if</span> <span class="n">is_tensor_like</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">outputs</span><span class="p">))</span>
<span class="n">t_inputs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Tuple</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="o">...</span><span class="p">],</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,)</span> <span class="k">if</span> <span class="n">is_tensor_like</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">))</span>
<span class="n">overridable_args</span> <span class="o">=</span> <span class="n">t_outputs</span> <span class="o">+</span> <span class="n">t_inputs</span>
<span class="k">if</span> <span class="n">has_torch_function</span><span class="p">(</span><span class="n">overridable_args</span><span class="p">):</span>
<span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span>
<span class="n">grad</span><span class="p">,</span>
<span class="n">overridable_args</span><span class="p">,</span>
<span class="n">t_outputs</span><span class="p">,</span>
<span class="n">t_inputs</span><span class="p">,</span>
<span class="n">grad_outputs</span><span class="o">=</span><span class="n">grad_outputs</span><span class="p">,</span>
<span class="n">retain_graph</span><span class="o">=</span><span class="n">retain_graph</span><span class="p">,</span>
<span class="n">create_graph</span><span class="o">=</span><span class="n">create_graph</span><span class="p">,</span>
<span class="n">only_inputs</span><span class="o">=</span><span class="n">only_inputs</span><span class="p">,</span>
<span class="n">allow_unused</span><span class="o">=</span><span class="n">allow_unused</span><span class="p">,</span>
<span class="n">is_grads_batched</span><span class="o">=</span><span class="n">is_grads_batched</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">only_inputs</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"only_inputs argument is deprecated and is ignored now "</span>
<span class="s2">"(defaults to True). To accumulate gradient for other "</span>
<span class="s2">"parts of the graph, please use torch.autograd.backward."</span><span class="p">)</span>
<span class="n">grad_outputs_</span> <span class="o">=</span> <span class="n">_tensor_or_tensors_to_tuple</span><span class="p">(</span><span class="n">grad_outputs</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">t_outputs</span><span class="p">))</span>
<span class="n">grad_outputs_</span> <span class="o">=</span> <span class="n">_make_grads</span><span class="p">(</span><span class="n">t_outputs</span><span class="p">,</span> <span class="n">grad_outputs_</span><span class="p">,</span> <span class="n">is_grads_batched</span><span class="o">=</span><span class="n">is_grads_batched</span><span class="p">)</span>
<span class="k">if</span> <span class="n">retain_graph</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">retain_graph</span> <span class="o">=</span> <span class="n">create_graph</span>
<span class="c1"># The reason we repeat same the comment several times below is because</span>
<span class="c1"># some Python versions print out the first line of multi-line function</span>
<span class="c1"># calls in the traceback and some print out the last line</span>
<span class="k">if</span> <span class="n">is_grads_batched</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">vjp</span><span class="p">(</span><span class="n">gO</span><span class="p">):</span>
<span class="k">return</span> <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span> <span class="c1"># Calls into the C++ engine to run the backward pass</span>
<span class="n">t_outputs</span><span class="p">,</span> <span class="n">gO</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">t_inputs</span><span class="p">,</span>
<span class="n">allow_unused</span><span class="p">,</span> <span class="n">accumulate_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># Calls into the C++ engine to run the backward pass</span>
<span class="k">return</span> <span class="n">_vmap_internals</span><span class="o">.</span><span class="n">_vmap</span><span class="p">(</span><span class="n">vjp</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">allow_none_pass_through</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">grad_outputs_</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span> <span class="c1"># Calls into the C++ engine to run the backward pass</span>
<span class="n">t_outputs</span><span class="p">,</span> <span class="n">grad_outputs_</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">t_inputs</span><span class="p">,</span>
<span class="n">allow_unused</span><span class="p">,</span> <span class="n">accumulate_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># Calls into the C++ engine to run the backward pass</span></div>
<span class="c1"># This function applies in case of gradient checkpointing for memory</span>
<span class="c1"># optimization. Currently, gradient checkpointing is supported only if the</span>
<span class="c1"># execution engine is invoked through torch.autograd.backward() and its</span>
<span class="c1"># inputs argument is not passed. It is not supported for torch.autograd.grad().</span>
<span class="c1"># This is because if inputs are specified, the gradient won't be calculated for</span>
<span class="c1"># anything else e.g. model parameters like weights, bias etc.</span>
<span class="c1">#</span>
<span class="c1"># This function returns whether the checkpointing is valid i.e. torch.autograd.backward</span>
<span class="c1"># or not i.e. torch.autograd.grad. The implementation works by maintaining a thread</span>
<span class="c1"># local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask</span>
<span class="c1"># in the stack and before a NodeTask is executed in evaluate_function, it</span>
<span class="c1"># checks for whether reentrant backwards is imperative or not.</span>
<span class="c1"># See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context</span>
<span class="k">def</span> <span class="nf">_is_checkpoint_valid</span><span class="p">():</span>
<span class="k">return</span> <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">is_checkpoint_valid</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">variable</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">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead"</span><span class="p">)</span>
<span class="c1"># Monkey patching variable.Variable to fix FX codegen. FX generates a call by roughly doing</span>
<span class="c1"># f"{fn.__module__}.{fn.__name__}(...). This yields torch.autograd.variable.Variable(...) in the</span>
<span class="c1"># output of an FX graph. Unfortunately the module name torch.autograd.variable is shadowed by the</span>
<span class="c1"># deprecated function - variable(...).</span>
<span class="n">variable</span><span class="o">.</span><span class="n">Variable</span> <span class="o">=</span> <span class="n">Variable</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_autograd_init</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"autograd initialization failed"</span><span class="p">)</span>
<span class="c1"># Import all native method/classes</span>
<span class="kn">from</span> <span class="nn">torch._C._autograd</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">_add_metadata_json</span><span class="p">,</span>
<span class="n">_disable_profiler</span><span class="p">,</span>
<span class="n">_disable_profiler_legacy</span><span class="p">,</span>
<span class="n">_enable_profiler</span><span class="p">,</span>
<span class="n">_enable_profiler_legacy</span><span class="p">,</span>
<span class="n">_enable_record_function</span><span class="p">,</span>
<span class="n">_kineto_step</span><span class="p">,</span>
<span class="n">_KinetoEvent</span><span class="p">,</span>
<span class="n">_pop_saved_tensors_default_hooks</span><span class="p">,</span>
<span class="n">_prepare_profiler</span><span class="p">,</span>
<span class="n">_profiler_enabled</span><span class="p">,</span>
<span class="n">_ProfilerResult</span><span class="p">,</span>
<span class="n">_push_saved_tensors_default_hooks</span><span class="p">,</span>
<span class="n">_record_function_with_args_enter</span><span class="p">,</span>
<span class="n">_record_function_with_args_exit</span><span class="p">,</span>
<span class="n">_set_empty_test_observer</span><span class="p">,</span>
<span class="n">_supported_activities</span><span class="p">,</span>
<span class="n">DeviceType</span><span class="p">,</span>
<span class="n">kineto_available</span><span class="p">,</span>
<span class="n">ProfilerEvent</span><span class="p">,</span>
<span class="n">SavedTensor</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch._C._profiler</span> <span class="kn">import</span> <span class="n">ProfilerActivity</span><span class="p">,</span> <span class="n">ProfilerConfig</span><span class="p">,</span> <span class="n">ProfilerState</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">profiler</span>
<span class="k">def</span> <span class="nf">_register_py_tensor_class_for_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="bp">cls</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="nb">type</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"cls isn't a typeinfo object"</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">_register_py_class_for_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span>
</pre></div>
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