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<h1>Source code for torch.autograd.forward_ad</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">.grad_mode</span> <span class="kn">import</span> <span class="n">_DecoratorContextManager</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"UnpackedDualTensor"</span><span class="p">,</span>
<span class="s2">"enter_dual_level"</span><span class="p">,</span>
<span class="s2">"exit_dual_level"</span><span class="p">,</span>
<span class="s2">"make_dual"</span><span class="p">,</span>
<span class="s2">"unpack_dual"</span><span class="p">,</span>
<span class="s2">"dual_level"</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1"># Global variable used to make the python API simpler to use</span>
<span class="n">_current_level</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">def</span> <span class="nf">enter_dual_level</span><span class="p">():</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Enter a new forward grad level.</span>
<span class="sd"> This level can be used to make and unpack dual Tensors to compute</span>
<span class="sd"> forward gradients.</span>
<span class="sd"> This function also updates the current level that is used by default</span>
<span class="sd"> by the other functions in this API.</span>
<span class="sd"> """</span>
<span class="k">global</span> <span class="n">_current_level</span>
<span class="n">new_level</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_enter_dual_level</span><span class="p">()</span>
<span class="k">if</span> <span class="n">new_level</span> <span class="o">!=</span> <span class="n">_current_level</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">"Entering a new forward AD level but the current level "</span>
<span class="s2">"is not valid. Make sure you did not modified it directly."</span>
<span class="p">)</span>
<span class="n">_current_level</span> <span class="o">=</span> <span class="n">new_level</span>
<span class="k">return</span> <span class="n">new_level</span>
<span class="k">def</span> <span class="nf">exit_dual_level</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Exit a forward grad level.</span>
<span class="sd"> This function deletes all the gradients associated with this</span>
<span class="sd"> level. Only deleting the latest entered level is allowed.</span>
<span class="sd"> This function also updates the current level that is used by default</span>
<span class="sd"> by the other functions in this API.</span>
<span class="sd"> """</span>
<span class="k">global</span> <span class="n">_current_level</span>
<span class="k">if</span> <span class="n">level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">level</span> <span class="o">=</span> <span class="n">_current_level</span>
<span class="k">if</span> <span class="n">level</span> <span class="o">!=</span> <span class="n">_current_level</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"Trying to exit a forward AD level that was not the last one "</span>
<span class="s2">"that was created. This is not supported."</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">_exit_dual_level</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
<span class="n">_current_level</span> <span class="o">=</span> <span class="n">level</span> <span class="o">-</span> <span class="mi">1</span>
<div class="viewcode-block" id="make_dual"><a class="viewcode-back" href="../../../generated/torch.autograd.forward_ad.make_dual.html#torch.autograd.forward_ad.make_dual">[docs]</a><span class="k">def</span> <span class="nf">make_dual</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">tangent</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Associate a tensor value with its tangent to create a "dual tensor" for forward AD gradient computation.</span>
<span class="sd"> The result is a new tensor aliased to :attr:`tensor` with :attr:`tangent` embedded</span>
<span class="sd"> as an attribute as-is if it has the same storage layout or copied otherwise.</span>
<span class="sd"> The tangent attribute can be recovered with :func:`unpack_dual`.</span>
<span class="sd"> This function is backward differentiable.</span>
<span class="sd"> Given a function `f` whose jacobian is `J`, it allows one to compute the Jacobian-vector product (`jvp`)</span>
<span class="sd"> between `J` and a given vector `v` as follows.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP("Undefined variables")</span>
<span class="sd"> >>> with dual_level():</span>
<span class="sd"> ... inp = make_dual(x, v)</span>
<span class="sd"> ... out = f(inp)</span>
<span class="sd"> ... y, jvp = unpack_dual(out)</span>
<span class="sd"> Please see the `forward-mode AD tutorial <https://pytorch.org/tutorials/intermediate/forward_ad_usage.html>`__</span>
<span class="sd"> for detailed steps on how to use this API.</span>
<span class="sd"> """</span>
<span class="c1"># See NOTE: [forward-mode AD decompositions mechanism]</span>
<span class="c1">#</span>
<span class="c1"># Import from torch._decomp import decompositions_for_jvp to register</span>
<span class="c1"># decompositions for jvp to the jit registry</span>
<span class="c1">#</span>
<span class="c1"># FIXME: We specify that __debug__ must be True because</span>
<span class="c1"># if python is run with -OO or -O flags (i.e., __debug__ is False), we encounter the</span>
<span class="c1"># following error:</span>
<span class="c1">#</span>
<span class="c1"># Return value was annotated as having type Tuple[NoneType, NoneType] but is actually of</span>
<span class="c1"># type Tuple[Tensor, Tensor]:</span>
<span class="c1"># File ".../torch/_decomp/__init__.py", line 1585</span>
<span class="c1"># else:</span>
<span class="c1"># buffer = z</span>
<span class="c1"># return min - torch.log1p(z), buffer</span>
<span class="c1"># ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"PYTORCH_JIT"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span> <span class="o">==</span> <span class="s2">"1"</span> <span class="ow">and</span> <span class="n">__debug__</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">torch._decomp</span> <span class="kn">import</span> <span class="n">decompositions_for_jvp</span> <span class="c1"># noqa: F401</span>
<span class="k">if</span> <span class="n">level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">level</span> <span class="o">=</span> <span class="n">_current_level</span>
<span class="k">if</span> <span class="n">level</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">"Trying to create a dual Tensor for forward AD but no level "</span>
<span class="s2">"exists, make sure to enter_dual_level() first."</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">()</span> <span class="ow">or</span> <span class="n">tensor</span><span class="o">.</span><span class="n">is_complex</span><span class="p">()):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"Expected primal to be floating point or complex, but got: </span><span class="si">{</span><span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">tangent</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">()</span> <span class="ow">or</span> <span class="n">tangent</span><span class="o">.</span><span class="n">is_complex</span><span class="p">()):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"Expected tangent to be floating point or complex, but got: </span><span class="si">{</span><span class="n">tangent</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_VF</span><span class="o">.</span><span class="n">_make_dual</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">tangent</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span></div>
<span class="n">_UnpackedDualTensor</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s2">"_UnpackedDualTensor"</span><span class="p">,</span> <span class="p">[</span><span class="s2">"primal"</span><span class="p">,</span> <span class="s2">"tangent"</span><span class="p">])</span>
<span class="k">class</span> <span class="nc">UnpackedDualTensor</span><span class="p">(</span><span class="n">_UnpackedDualTensor</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Namedtuple returned by :func:`unpack_dual` containing the primal and tangent components of the dual tensor.</span>
<span class="sd"> See :func:`unpack_dual` for more details.</span>
<span class="sd"> """</span>
<span class="k">pass</span>
<div class="viewcode-block" id="unpack_dual"><a class="viewcode-back" href="../../../generated/torch.autograd.forward_ad.unpack_dual.html#torch.autograd.forward_ad.unpack_dual">[docs]</a><span class="k">def</span> <span class="nf">unpack_dual</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Unpack a "dual tensor" to get both its Tensor value and its forward AD gradient.</span>
<span class="sd"> The result is a namedtuple ``(primal, tangent)`` where ``primal`` is a view of</span>
<span class="sd"> :attr:`tensor`'s primal and ``tangent`` is :attr:`tensor`'s tangent as-is.</span>
<span class="sd"> Neither of these tensors can be dual tensor of level :attr:`level`.</span>
<span class="sd"> This function is backward differentiable.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP("Undefined variables")</span>
<span class="sd"> >>> with dual_level():</span>
<span class="sd"> ... inp = make_dual(x, x_t)</span>
<span class="sd"> ... out = f(inp)</span>
<span class="sd"> ... y, jvp = unpack_dual(out)</span>
<span class="sd"> ... jvp = unpack_dual(out).tangent</span>
<span class="sd"> Please see the `forward-mode AD tutorial <https://pytorch.org/tutorials/intermediate/forward_ad_usage.html>`__</span>
<span class="sd"> for detailed steps on how to use this API.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">level</span> <span class="o">=</span> <span class="n">_current_level</span>
<span class="k">if</span> <span class="n">level</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">UnpackedDualTensor</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">primal</span><span class="p">,</span> <span class="n">dual</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_VF</span><span class="o">.</span><span class="n">_unpack_dual</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">)</span>
<span class="k">return</span> <span class="n">UnpackedDualTensor</span><span class="p">(</span><span class="n">primal</span><span class="p">,</span> <span class="n">dual</span><span class="p">)</span></div>
<div class="viewcode-block" id="dual_level"><a class="viewcode-back" href="../../../generated/torch.autograd.forward_ad.dual_level.html#torch.autograd.forward_ad.dual_level">[docs]</a><span class="k">class</span> <span class="nc">dual_level</span><span class="p">(</span><span class="n">_DecoratorContextManager</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Context-manager for forward AD, where all forward AD computation must occur within the ``dual_level`` context.</span>
<span class="sd"> .. Note::</span>
<span class="sd"> The ``dual_level`` context appropriately enters and exit the dual level to</span>
<span class="sd"> controls the current forward AD level, which is used by default by the other</span>
<span class="sd"> functions in this API.</span>
<span class="sd"> We currently don't plan to support nested ``dual_level`` contexts, however, so</span>
<span class="sd"> only a single forward AD level is supported. To compute higher-order</span>
<span class="sd"> forward grads, one can use :func:`torch.func.jvp`.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP("Undefined variables")</span>
<span class="sd"> >>> x = torch.tensor([1])</span>
<span class="sd"> >>> x_t = torch.tensor([1])</span>
<span class="sd"> >>> with dual_level():</span>
<span class="sd"> ... inp = make_dual(x, x_t)</span>
<span class="sd"> ... # Do computations with inp</span>
<span class="sd"> ... out = your_fn(inp)</span>
<span class="sd"> ... _, grad = unpack_dual(out)</span>
<span class="sd"> >>> grad is None</span>
<span class="sd"> False</span>
<span class="sd"> >>> # After exiting the level, the grad is deleted</span>
<span class="sd"> >>> _, grad_after = unpack_dual(out)</span>
<span class="sd"> >>> grad is None</span>
<span class="sd"> True</span>
<span class="sd"> Please see the `forward-mode AD tutorial <https://pytorch.org/tutorials/intermediate/forward_ad_usage.html>`__</span>
<span class="sd"> for detailed steps on how to use this API.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">enter_dual_level</span><span class="p">()</span>
<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">traceback</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="n">exit_dual_level</span><span class="p">()</span></div>
<span class="c1"># Private helper functions</span>
<span class="n">_is_fwd_grad_enabled</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_is_fwd_grad_enabled</span>
<span class="c1"># Private helper function to enable or disable fwd grad.</span>
<span class="c1"># If you're a user and want to use this, please file an issue to discuss the use case.</span>
<span class="k">class</span> <span class="nc">_set_fwd_grad_enabled</span><span class="p">(</span><span class="n">_DecoratorContextManager</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mode</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="bp">self</span><span class="o">.</span><span class="n">prev</span> <span class="o">=</span> <span class="n">_is_fwd_grad_enabled</span><span class="p">()</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_fwd_grad_enabled</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">traceback</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_fwd_grad_enabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="p">)</span>
</pre></div>
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