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<div class="section" id="torchscript-unsupported-pytorch-constructs">
<span id="jit-unsupported"></span><h1>TorchScript Unsupported Pytorch Constructs<a class="headerlink" href="#torchscript-unsupported-pytorch-constructs" title="Permalink to this headline">¶</a></h1>
<div class="section" id="torch-and-tensor-unsupported-attributes">
<h2>Torch and Tensor Unsupported Attributes<a class="headerlink" href="#torch-and-tensor-unsupported-attributes" title="Permalink to this headline">¶</a></h2>
<p>TorchScript supports most methods defined on <code class="docutils literal notranslate"><span class="pre">torch</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, but we do not have full coverage.
Here are specific known ops and categories of ops which have diverging behavior between
Python and TorchScript. If you encounter something else that is not supported please
file a GitHub issue. Deprecated ops are not listed below.</p>
<span class="target" id="module-torch.jit.unsupported_tensor_ops"></span><div class="section" id="unsupported-tensor-methods">
<h3>Unsupported Tensor Methods<a class="headerlink" href="#unsupported-tensor-methods" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="unsupported-tensor-properties">
<h3>Unsupported Tensor Properties<a class="headerlink" href="#unsupported-tensor-properties" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="functions-not-correctly-bound-on-torch">
<h3>Functions Not Correctly Bound on Torch<a class="headerlink" href="#functions-not-correctly-bound-on-torch" title="Permalink to this headline">¶</a></h3>
<p>The following functions will fail if used in TorchScript, either because they
are not bound on <cite>torch</cite> or because Python expects a different schema than
TorchScript.</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference internal" href="generated/torch.tensordot.html#torch.tensordot" title="torch.tensordot"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensordot()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.init.html#torch.nn.init.calculate_gain" title="torch.nn.init.calculate_gain"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.init.calculate_gain()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.init.html#torch.nn.init.eye_" title="torch.nn.init.eye_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.init.eye_()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.init.html#torch.nn.init.dirac_" title="torch.nn.init.dirac_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.init.dirac_()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.init.html#torch.nn.init.kaiming_normal_" title="torch.nn.init.kaiming_normal_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.init.kaiming_normal_()</span></code></a></p></li>
<li><p><a class="reference internal" href="nn.init.html#torch.nn.init.orthogonal_" title="torch.nn.init.orthogonal_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.init.orthogonal_()</span></code></a></p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.init.sparse()</span></code></p></li>
</ul>
</div></blockquote>
</div>
<div class="section" id="ops-with-divergent-schemas-between-torch-python">
<h3>Ops With Divergent Schemas Between Torch & Python<a class="headerlink" href="#ops-with-divergent-schemas-between-torch-python" title="Permalink to this headline">¶</a></h3>
<p>The following categories of ops have divergent schemas:</p>
<p>Functions which construct tensors from non-tensor inputs do not support the <cite>requires_grad</cite>
argument, except for <cite>torch.tensor</cite>. This covers the following ops:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference internal" href="generated/torch.norm.html#torch.norm" title="torch.norm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.norm()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.bartlett_window.html#torch.bartlett_window" title="torch.bartlett_window"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bartlett_window()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.blackman_window.html#torch.blackman_window" title="torch.blackman_window"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.blackman_window()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_like()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.empty_strided.html#torch.empty_strided" title="torch.empty_strided"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_strided()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.eye.html#torch.eye" title="torch.eye"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.eye()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.full.html#torch.full" title="torch.full"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.full()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.full_like.html#torch.full_like" title="torch.full_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.full_like()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.hamming_window.html#torch.hamming_window" title="torch.hamming_window"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.hamming_window()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.hann_window.html#torch.hann_window" title="torch.hann_window"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.hann_window()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linspace.html#torch.linspace" title="torch.linspace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linspace()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.logspace.html#torch.logspace" title="torch.logspace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.logspace()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.normal.html#torch.normal" title="torch.normal"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.normal()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.ones.html#torch.ones" title="torch.ones"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.ones()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.rand.html#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.rand_like.html#torch.rand_like" title="torch.rand_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand_like()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.randint_like.html#torch.randint_like" title="torch.randint_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randint_like()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.randn.html#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.randn_like.html#torch.randn_like" title="torch.randn_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn_like()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.randperm.html#torch.randperm" title="torch.randperm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randperm()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.tril_indices.html#torch.tril_indices" title="torch.tril_indices"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tril_indices()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.triu_indices.html#torch.triu_indices" title="torch.triu_indices"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.triu_indices()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.vander.html#torch.vander" title="torch.vander"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vander()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.zeros_like.html#torch.zeros_like" title="torch.zeros_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.zeros_like()</span></code></a></p></li>
</ul>
</div></blockquote>
<p>The following functions require <cite>dtype</cite>, <cite>layout</cite>, <cite>device</cite> as parameters in TorchScript,
but these parameters are optional in Python.</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference internal" href="generated/torch.randint.html#torch.randint" title="torch.randint"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randint()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.sparse_coo_tensor.html#torch.sparse_coo_tensor" title="torch.sparse_coo_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sparse_coo_tensor()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a></p></li>
</ul>
</div></blockquote>
</div>
</div>
<div class="section" id="pytorch-unsupported-modules-and-classes">
<h2>PyTorch Unsupported Modules and Classes<a class="headerlink" href="#pytorch-unsupported-modules-and-classes" title="Permalink to this headline">¶</a></h2>
<p>TorchScript cannot currently compile a number of other commonly used PyTorch
constructs. Below are listed the modules that TorchScript does not support, and
an incomplete list of PyTorch classes that are not supported. For unsupported modules
we suggest using <a class="reference internal" href="generated/torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.jit.trace()</span></code></a>.</p>
<blockquote>
<div><ul class="simple">
<li><p><p id="torch.nn.RNN"/><a class="reference internal" href="generated/torch.nn.RNN.html#torch.nn.RNN" title="torch.nn.RNN"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.RNN</span></code></a></p></li>
<li><p><p id="torch.nn.AdaptiveLogSoftmaxWithLoss"/><a class="reference internal" href="generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html#torch.nn.AdaptiveLogSoftmaxWithLoss" title="torch.nn.AdaptiveLogSoftmaxWithLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.AdaptiveLogSoftmaxWithLoss</span></code></a></p></li>
<li><p><a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a></p></li>
<li><p><p id="torch.autograd.enable_grad"/><a class="reference internal" href="generated/torch.autograd.enable_grad.html#torch.autograd.enable_grad" title="torch.autograd.enable_grad"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.enable_grad</span></code></a></p></li>
<li><p><p id="torch.Generator"/><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch.Generator"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Generator</span></code></a></p></li>
</ul>
</div></blockquote>
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