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<h1>Source code for torch.autograd.function</h1><div class="highlight"><pre>
<span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">functools</span>
<span class="kn">import</span> <span class="nn">inspect</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</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">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">deprecated</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch._C</span> <span class="k">as</span> <span class="nn">_C</span>
<span class="kn">import</span> <span class="nn">torch._functorch</span> <span class="k">as</span> <span class="nn">_functorch</span>
<span class="kn">import</span> <span class="nn">torch.utils.hooks</span> <span class="k">as</span> <span class="nn">hooks</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_functions</span>
<span class="kn">from</span> <span class="nn">torch._functorch.autograd_function</span> <span class="kn">import</span> <span class="n">custom_function_call</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"FunctionCtx"</span><span class="p">,</span>
<span class="s2">"BackwardCFunction"</span><span class="p">,</span>
<span class="s2">"FunctionMeta"</span><span class="p">,</span>
<span class="s2">"Function"</span><span class="p">,</span>
<span class="s2">"once_differentiable"</span><span class="p">,</span>
<span class="s2">"InplaceFunction"</span><span class="p">,</span>
<span class="s2">"NestedIOFunction"</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1"># Unique id provider for each class inheriting from Function</span>
<span class="c1"># This is incremented in FunctionMeta during class definition</span>
<span class="n">AUTOGRAD_FUNCTION_COUNTER</span> <span class="o">=</span> <span class="n">itertools</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="c1"># Formerly known as: _ContextMethodMixin</span>
<span class="k">class</span> <span class="nc">FunctionCtx</span><span class="p">:</span>
<div class="viewcode-block" id="FunctionCtx.save_for_backward"><a class="viewcode-back" href="../../../generated/torch.autograd.function.FunctionCtx.save_for_backward.html#torch.autograd.FunctionCtx.save_for_backward">[docs]</a> <span class="k">def</span> <span class="nf">save_for_backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</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="w"> </span><span class="sa">r</span><span class="sd">"""Save given tensors for a future call to :func:`~Function.backward`.</span>
<span class="sd"> ``save_for_backward`` should be called at most once, in either the</span>
<span class="sd"> :func:`setup_context` or :func:`forward` methods, and only with tensors.</span>
<span class="sd"> All tensors intended to be used in the backward pass should be saved</span>
<span class="sd"> with ``save_for_backward`` (as opposed to directly on ``ctx``) to prevent</span>
<span class="sd"> incorrect gradients and memory leaks, and enable the application of saved</span>
<span class="sd"> tensor hooks. See :class:`torch.autograd.graph.saved_tensors_hooks`.</span>
<span class="sd"> Note that if intermediary tensors, tensors that are neither inputs</span>
<span class="sd"> nor outputs of :func:`forward`, are saved for backward, your custom Function</span>
<span class="sd"> may not support double backward.</span>
<span class="sd"> Custom Functions that do not support double backward should decorate their</span>
<span class="sd"> :func:`backward` method with ``@once_differentiable`` so that performing</span>
<span class="sd"> double backward raises an error. If you'd like to support double backward,</span>
<span class="sd"> you can either recompute intermediaries based on the inputs during backward</span>
<span class="sd"> or return the intermediaries as the outputs of the custom Function. See the</span>
<span class="sd"> `double backward tutorial <https://pytorch.org/tutorials/intermediate/custom_function_double_backward_tutorial.html>`_</span>
<span class="sd"> for more details.</span>
<span class="sd"> In :func:`backward`, saved tensors can be accessed through the :attr:`saved_tensors`</span>
<span class="sd"> attribute. Before returning them to the user, a check is made to ensure</span>
<span class="sd"> they weren't used in any in-place operation that modified their content.</span>
<span class="sd"> Arguments can also be ``None``. This is a no-op.</span>
<span class="sd"> See :ref:`extending-autograd` for more details on how to use this method.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)</span>
<span class="sd"> >>> class Func(Function):</span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):</span>
<span class="sd"> >>> w = x * z</span>
<span class="sd"> >>> out = x * y + y * z + w * y</span>
<span class="sd"> >>> ctx.save_for_backward(x, y, w, out)</span>
<span class="sd"> >>> ctx.z = z # z is not a tensor</span>
<span class="sd"> >>> return out</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> @once_differentiable</span>
<span class="sd"> >>> def backward(ctx, grad_out):</span>
<span class="sd"> >>> x, y, w, out = ctx.saved_tensors</span>
<span class="sd"> >>> z = ctx.z</span>
<span class="sd"> >>> gx = grad_out * (y + y * z)</span>
<span class="sd"> >>> gy = grad_out * (x + z + w)</span>
<span class="sd"> >>> gz = None</span>
<span class="sd"> >>> return gx, gy, gz</span>
<span class="sd"> >>></span>
<span class="sd"> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double)</span>
<span class="sd"> >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double)</span>
<span class="sd"> >>> c = 4</span>
<span class="sd"> >>> d = Func.apply(a, b, c)</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">to_save</span> <span class="o">=</span> <span class="n">tensors</span></div>
<span class="k">def</span> <span class="nf">save_for_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</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="w"> </span><span class="sa">r</span><span class="sd">"""Save given tensors for a future call to :func:`~Function.jvp`.</span>
<span class="sd"> ``save_for_forward`` should be called at most once, in either the</span>
<span class="sd"> :func:`setup_context` or :func:`forward` methods, and all arguments</span>
<span class="sd"> should be tensors.</span>
<span class="sd"> In :func:`jvp`, saved objects can be accessed through the :attr:`saved_tensors`</span>
<span class="sd"> attribute.</span>
<span class="sd"> Arguments can also be ``None``. This is a no-op.</span>
<span class="sd"> See :ref:`extending-autograd` for more details on how to use this method.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP</span>
<span class="sd"> >>> class Func(torch.autograd.Function):</span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):</span>
<span class="sd"> >>> ctx.save_for_backward(x, y)</span>
<span class="sd"> >>> ctx.save_for_forward(x, y)</span>
<span class="sd"> >>> ctx.z = z</span>
<span class="sd"> >>> return x * y * z</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def jvp(ctx, x_t, y_t, _):</span>
<span class="sd"> >>> x, y = ctx.saved_tensors</span>
<span class="sd"> >>> z = ctx.z</span>
<span class="sd"> >>> return z * (y * x_t + x * y_t)</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def vjp(ctx, grad_out):</span>
<span class="sd"> >>> x, y = ctx.saved_tensors</span>
<span class="sd"> >>> z = ctx.z</span>
<span class="sd"> >>> return z * grad_out * y, z * grad_out * x, None</span>
<span class="sd"> >>></span>
<span class="sd"> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double)</span>
<span class="sd"> >>> t = torch.tensor(1., dtype=torch.double)</span>
<span class="sd"> >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double)</span>
<span class="sd"> >>> c = 4</span>
<span class="sd"> >>></span>
<span class="sd"> >>> with fwAD.dual_level():</span>
<span class="sd"> >>> a_dual = fwAD.make_dual(a, t)</span>
<span class="sd"> >>> d = Func.apply(a_dual, b, c)</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</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">or</span> <span class="n">tensor</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="p">(</span>
<span class="s2">"save_for_forward expects all arguments to be tensors; you should "</span>
<span class="s2">"save non-tensors as attributes on ctx."</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">saved_for_forward</span> <span class="o">=</span> <span class="n">tensors</span>
<div class="viewcode-block" id="FunctionCtx.mark_dirty"><a class="viewcode-back" href="../../../generated/torch.autograd.function.FunctionCtx.mark_dirty.html#torch.autograd.FunctionCtx.mark_dirty">[docs]</a> <span class="k">def</span> <span class="nf">mark_dirty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</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="w"> </span><span class="sa">r</span><span class="sd">"""Mark given tensors as modified in an in-place operation.</span>
<span class="sd"> This should be called at most once, in either the :func:`setup_context`</span>
<span class="sd"> or :func:`forward` methods, and all arguments should be inputs.</span>
<span class="sd"> Every tensor that's been modified in-place in a call to :func:`forward`</span>
<span class="sd"> should be given to this function, to ensure correctness of our checks.</span>
<span class="sd"> It doesn't matter whether the function is called before or after</span>
<span class="sd"> modification.</span>
<span class="sd"> Examples::</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)</span>
<span class="sd"> >>> class Inplace(Function):</span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def forward(ctx, x):</span>
<span class="sd"> >>> x_npy = x.numpy() # x_npy shares storage with x</span>
<span class="sd"> >>> x_npy += 1</span>
<span class="sd"> >>> ctx.mark_dirty(x)</span>
<span class="sd"> >>> return x</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> @once_differentiable</span>
<span class="sd"> >>> def backward(ctx, grad_output):</span>
<span class="sd"> >>> return grad_output</span>
<span class="sd"> >>></span>
<span class="sd"> >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone()</span>
<span class="sd"> >>> b = a * a</span>
<span class="sd"> >>> Inplace.apply(a) # This would lead to wrong gradients!</span>
<span class="sd"> >>> # but the engine would not know unless we mark_dirty</span>
<span class="sd"> >>> # xdoctest: +SKIP</span>
<span class="sd"> >>> b.backward() # RuntimeError: one of the variables needed for gradient</span>
<span class="sd"> >>> # computation has been modified by an inplace operation</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dirty_tensors</span> <span class="o">=</span> <span class="n">args</span></div>
<span class="nd">@deprecated</span><span class="p">(</span>
<span class="s2">"`mark_shared_storage` is deprecated. "</span>
<span class="s2">"Tensors with shared storages are automatically tracked. "</span>
<span class="s2">"Note that calls to `set_()` are not tracked"</span><span class="p">,</span>
<span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">mark_shared_storage</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">pairs</span><span class="p">):</span>
<span class="k">pass</span>
<div class="viewcode-block" id="FunctionCtx.mark_non_differentiable"><a class="viewcode-back" href="../../../generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html#torch.autograd.FunctionCtx.mark_non_differentiable">[docs]</a> <span class="k">def</span> <span class="nf">mark_non_differentiable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</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="w"> </span><span class="sa">r</span><span class="sd">"""Mark outputs as non-differentiable.</span>
<span class="sd"> This should be called at most once, in either the :func:`setup_context`</span>
<span class="sd"> or :func:`forward` methods, and all arguments should be tensor outputs.</span>
<span class="sd"> This will mark outputs as not requiring gradients, increasing the</span>
<span class="sd"> efficiency of backward computation. You still need to accept a gradient</span>
<span class="sd"> for each output in :meth:`~Function.backward`, but it's always going to</span>
<span class="sd"> be a zero tensor with the same shape as the shape of a corresponding</span>
<span class="sd"> output.</span>
<span class="sd"> This is used e.g. for indices returned from a sort. See example::</span>
<span class="sd"> >>> class Func(Function):</span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def forward(ctx, x):</span>
<span class="sd"> >>> sorted, idx = x.sort()</span>
<span class="sd"> >>> ctx.mark_non_differentiable(idx)</span>
<span class="sd"> >>> ctx.save_for_backward(x, idx)</span>
<span class="sd"> >>> return sorted, idx</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> @once_differentiable</span>
<span class="sd"> >>> def backward(ctx, g1, g2): # still need to accept g2</span>
<span class="sd"> >>> x, idx = ctx.saved_tensors</span>
<span class="sd"> >>> grad_input = torch.zeros_like(x)</span>
<span class="sd"> >>> grad_input.index_add_(0, idx, g1)</span>
<span class="sd"> >>> return grad_input</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">non_differentiable</span> <span class="o">=</span> <span class="n">args</span></div>
<div class="viewcode-block" id="FunctionCtx.set_materialize_grads"><a class="viewcode-back" href="../../../generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html#torch.autograd.FunctionCtx.set_materialize_grads">[docs]</a> <span class="k">def</span> <span class="nf">set_materialize_grads</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Set whether to materialize grad tensors. Default is ``True``.</span>
<span class="sd"> This should be called only from either the :func:`setup_context` or</span>
<span class="sd"> :func:`forward` methods.</span>
<span class="sd"> If ``True``, undefined grad tensors will be expanded to tensors full of zeros</span>
<span class="sd"> prior to calling the :func:`backward` and :func:`jvp` methods.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)</span>
<span class="sd"> >>> class SimpleFunc(Function):</span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def forward(ctx, x):</span>
<span class="sd"> >>> return x.clone(), x.clone()</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> @once_differentiable</span>
<span class="sd"> >>> def backward(ctx, g1, g2):</span>
<span class="sd"> >>> return g1 + g2 # No check for None necessary</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # We modify SimpleFunc to handle non-materialized grad outputs</span>
<span class="sd"> >>> class Func(Function):</span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> def forward(ctx, x):</span>
<span class="sd"> >>> ctx.set_materialize_grads(False)</span>
<span class="sd"> >>> ctx.save_for_backward(x)</span>
<span class="sd"> >>> return x.clone(), x.clone()</span>
<span class="sd"> >>></span>
<span class="sd"> >>> @staticmethod</span>
<span class="sd"> >>> @once_differentiable</span>
<span class="sd"> >>> def backward(ctx, g1, g2):</span>
<span class="sd"> >>> x, = ctx.saved_tensors</span>
<span class="sd"> >>> grad_input = torch.zeros_like(x)</span>
<span class="sd"> >>> if g1 is not None: # We must check for None now</span>
<span class="sd"> >>> grad_input += g1</span>
<span class="sd"> >>> if g2 is not None:</span>
<span class="sd"> >>> grad_input += g2</span>
<span class="sd"> >>> return grad_input</span>
<span class="sd"> >>></span>
<span class="sd"> >>> a = torch.tensor(1., requires_grad=True)</span>
<span class="sd"> >>> b, _ = Func.apply(a) # induces g2 to be undefined</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">materialize_grads</span> <span class="o">=</span> <span class="n">value</span></div>
<span class="c1"># DO NOT USE: This is only defined to be able to load old serialized models</span>
<span class="n">_ContextMethodMixin</span> <span class="o">=</span> <span class="n">FunctionCtx</span>
<span class="k">class</span> <span class="nc">_HookMixin</span><span class="p">:</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">_register_hook</span><span class="p">(</span><span class="n">backward_hooks</span><span class="p">,</span> <span class="n">hook</span><span class="p">):</span>
<span class="k">if</span> <span class="n">backward_hooks</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">backward_hooks</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
<span class="n">handle</span> <span class="o">=</span> <span class="n">hooks</span><span class="o">.</span><span class="n">RemovableHandle</span><span class="p">(</span><span class="n">backward_hooks</span><span class="p">)</span>
<span class="n">backward_hooks</span><span class="p">[</span><span class="n">handle</span><span class="o">.</span><span class="n">id</span><span class="p">]</span> <span class="o">=</span> <span class="n">hook</span>
<span class="k">return</span> <span class="n">backward_hooks</span><span class="p">,</span> <span class="n">handle</span>
<div class="viewcode-block" id="BackwardCFunction"><a class="viewcode-back" href="../../../generated/torch.autograd.function.BackwardCFunction.html#torch.autograd.BackwardCFunction">[docs]</a><span class="k">class</span> <span class="nc">BackwardCFunction</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">_FunctionBase</span><span class="p">,</span> <span class="n">FunctionCtx</span><span class="p">,</span> <span class="n">_HookMixin</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> This class is used for internal autograd work. Do not use.</span>
<span class="sd"> """</span>
<div class="viewcode-block" id="BackwardCFunction.apply"><a class="viewcode-back" href="../../../generated/torch.autograd.function.BackwardCFunction.html#torch.autograd.BackwardCFunction.apply">[docs]</a> <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Apply method used when executing this Node during the backward</span>
<span class="sd"> """</span>
<span class="c1"># _forward_cls is defined by derived class</span>
<span class="c1"># The user should define either backward or vjp but never both.</span>
<span class="n">backward_fn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_cls</span><span class="o">.</span><span class="n">backward</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="n">vjp_fn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_cls</span><span class="o">.</span><span class="n">vjp</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">if</span> <span class="n">backward_fn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Function</span><span class="o">.</span><span class="n">backward</span> <span class="ow">and</span> <span class="n">vjp_fn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Function</span><span class="o">.</span><span class="n">vjp</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"Implementing both 'backward' and 'vjp' for a custom "</span>
<span class="s2">"Function is not allowed. You should only implement one "</span>
<span class="s2">"of them."</span>
<span class="p">)</span>
<span class="n">user_fn</span> <span class="o">=</span> <span class="n">vjp_fn</span> <span class="k">if</span> <span class="n">vjp_fn</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Function</span><span class="o">.</span><span class="n">vjp</span> <span class="k">else</span> <span class="n">backward_fn</span>
<span class="k">return</span> <span class="n">user_fn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span></div>
<div class="viewcode-block" id="BackwardCFunction.apply_jvp"><a class="viewcode-back" href="../../../generated/torch.autograd.function.BackwardCFunction.html#torch.autograd.BackwardCFunction.apply_jvp">[docs]</a> <span class="k">def</span> <span class="nf">apply_jvp</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Apply method used when executing forward mode AD during the forward</span>
<span class="sd"> """</span>
<span class="c1"># _forward_cls is defined by derived class</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_cls</span><span class="o">.</span><span class="n">jvp</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span></div>
<span class="k">def</span> <span class="nf">_compiled_autograd_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_cls</span><span class="o">.</span><span class="n">_compiled_autograd_key</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span></div>
<span class="k">class</span> <span class="nc">FunctionMeta</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Function metaclass.</span>
<span class="sd"> This metaclass sets up the following properties:</span>
<span class="sd"> _backward_cls: The Function class corresponding to the differentiated</span>
<span class="sd"> version of this function (which is generated on the fly by this</span>
<span class="sd"> metaclass).</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">attrs</span><span class="p">):</span>
<span class="n">backward_fn</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
<span class="n">name</span> <span class="o">+</span> <span class="s2">"Backward"</span><span class="p">,</span> <span class="p">(</span><span class="n">BackwardCFunction</span><span class="p">,),</span> <span class="p">{</span><span class="s2">"_forward_cls"</span><span class="p">:</span> <span class="bp">cls</span><span class="p">}</span>
<span class="p">)</span>
<span class="n">backward_fn</span><span class="o">.</span><span class="n">_autograd_function_id</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">AUTOGRAD_FUNCTION_COUNTER</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="n">backward_fn</span><span class="o">.</span><span class="n">_compiled_autograd_should_lift</span> <span class="o">=</span> <span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="s2">"_compiled_autograd_should_lift"</span><span class="p">,</span> <span class="kc">True</span>
<span class="p">)</span>
<span class="bp">cls</span><span class="o">.</span><span class="n">_backward_cls</span> <span class="o">=</span> <span class="n">backward_fn</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">attrs</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_SingleLevelFunction</span><span class="p">(</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_FunctionBase</span><span class="p">,</span> <span class="n">FunctionCtx</span><span class="p">,</span> <span class="n">_HookMixin</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">FunctionMeta</span>
<span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Define the forward of the custom autograd Function.</span>
<span class="sd"> This function is to be overridden by all subclasses.</span>
<span class="sd"> There are two ways to define forward:</span>
<span class="sd"> Usage 1 (Combined forward and ctx)::</span>
<span class="sd"> @staticmethod</span>
<span class="sd"> def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:</span>
<span class="sd"> pass</span>
<span class="sd"> - It must accept a context ctx as the first argument, followed by any</span>
<span class="sd"> number of arguments (tensors or other types).</span>
<span class="sd"> - See :ref:`combining-forward-context` for more details</span>
<span class="sd"> Usage 2 (Separate forward and ctx)::</span>
<span class="sd"> @staticmethod</span>
<span class="sd"> def forward(*args: Any, **kwargs: Any) -> Any:</span>
<span class="sd"> pass</span>
<span class="sd"> @staticmethod</span>
<span class="sd"> def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:</span>
<span class="sd"> pass</span>
<span class="sd"> - The forward no longer accepts a ctx argument.</span>
<span class="sd"> - Instead, you must also override the :meth:`torch.autograd.Function.setup_context`</span>
<span class="sd"> staticmethod to handle setting up the ``ctx`` object.</span>
<span class="sd"> ``output`` is the output of the forward, ``inputs`` are a Tuple of inputs</span>
<span class="sd"> to the forward.</span>
<span class="sd"> - See :ref:`extending-autograd` for more details</span>
<span class="sd"> The context can be used to store arbitrary data that can be then</span>
<span class="sd"> retrieved during the backward pass. Tensors should not be stored</span>
<span class="sd"> directly on `ctx` (though this is not currently enforced for</span>
<span class="sd"> backward compatibility). Instead, tensors should be saved either with</span>
<span class="sd"> :func:`ctx.save_for_backward` if they are intended to be used in</span>
<span class="sd"> ``backward`` (equivalently, ``vjp``) or :func:`ctx.save_for_forward`</span>
<span class="sd"> if they are intended to be used for in ``jvp``.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
<span class="s2">"You must implement the forward function for custom autograd.Function."</span>
<span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">setup_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">output</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""There are two ways to define the forward pass of an autograd.Function.</span>
<span class="sd"> Either:</span>
<span class="sd"> 1. Override forward with the signature ``forward(ctx, *args, **kwargs)``.</span>
<span class="sd"> ``setup_context`` is not overridden. Setting up the ctx for backward</span>
<span class="sd"> happens inside the ``forward``.</span>
<span class="sd"> 2. Override forward with the signature ``forward(*args, **kwargs)`` and</span>
<span class="sd"> override ``setup_context``. Setting up the ctx for backward happens</span>
<span class="sd"> inside ``setup_context`` (as opposed to inside the ``forward``)</span>
<span class="sd"> See :meth:`torch.autograd.Function.forward` and :ref:`extending-autograd` for more details.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"setup_context is not implemented."</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">*</span><span class="n">grad_outputs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="n">Any</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Define a formula for differentiating the operation with backward mode automatic differentiation.</span>
<span class="sd"> This function is to be overridden by all subclasses.</span>
<span class="sd"> (Defining this function is equivalent to defining the ``vjp`` function.)</span>
<span class="sd"> It must accept a context :attr:`ctx` as the first argument, followed by</span>
<span class="sd"> as many outputs as the :func:`forward` returned (None will be passed in</span>
<span class="sd"> for non tensor outputs of the forward function),</span>
<span class="sd"> and it should return as many tensors, as there were inputs to</span>
<span class="sd"> :func:`forward`. Each argument is the gradient w.r.t the given output,</span>
<span class="sd"> and each returned value should be the gradient w.r.t. the</span>
<span class="sd"> corresponding input. If an input is not a Tensor or is a Tensor not</span>
<span class="sd"> requiring grads, you can just pass None as a gradient for that input.</span>