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  <div class="section" id="module-torch.__future__">
<span id="torch-future"></span><h1>torch.__future__<a class="headerlink" href="#module-torch.__future__" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="torch.__future__.set_overwrite_module_params_on_conversion">
<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/__future__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets whether to assign new tensors to the parameters instead of changing the
existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>.</p>
<p>When enabled, the following methods will assign new parameters to the module:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">module.{device}()</span></code> (e.g. <code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.cuda()</span></code>) for moving a module between devices</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">module.{dtype}()</span></code> (e.g. <code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.float()</span></code>) for converting a module to a different dtype</p></li>
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.to()</span></code></p></li>
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.to_empty()</span></code></p></li>
</ol>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>value</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to assign new tensors or not.</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.__future__.get_overwrite_module_params_on_conversion">
<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/__future__.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns whether to assign new tensors to the parameters instead of changing the
existing parameters in-place when converting an <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
<p>See <a class="reference internal" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="torch.__future__.set_overwrite_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_overwrite_module_params_on_conversion()</span></code></a> for more information.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.__future__.set_swap_module_params_on_conversion">
<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/__future__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> instead of setting <code class="docutils literal notranslate"><span class="pre">.data</span></code> to
change the existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> and instead
of <code class="docutils literal notranslate"><span class="pre">param.copy_(state_dict[key])</span></code> when loading a state dict into an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function takes precedence over <a class="reference internal" href="#torch.__future__.get_overwrite_module_params_on_conversion" title="torch.__future__.get_overwrite_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_overwrite_module_params_on_conversion()</span></code></a></p>
</div>
<p>When enabled, the following methods will swap the existing parameters in-place:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">module.{device}()</span></code> (e.g. <code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.cuda()</span></code>) for moving a module between devices</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">module.{dtype}()</span></code> (e.g. <code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.float()</span></code>) for converting a module to a different dtype</p></li>
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.to()</span></code></p></li>
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.to_empty()</span></code></p></li>
<li><p><code class="xref py py-meth docutils literal notranslate"><span class="pre">nn.Module.load_state_dict()</span></code></p></li>
</ol>
<p>The semantics for <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> when this is set are as follows:</p>
<ol class="arabic simple">
<li><p>For each parameter/buffer, its corresponding <code class="docutils literal notranslate"><span class="pre">state_dict['key']</span></code> is transformed via
<a class="reference internal" href="generated/torch.Tensor.module_load.html#torch.Tensor.module_load" title="torch.Tensor.module_load"><code class="xref py py-meth docutils literal notranslate"><span class="pre">module_load()</span></code></a> (i.e. <code class="docutils literal notranslate"><span class="pre">res</span> <span class="pre">=</span> <span class="pre">param.module_load(state_dict['key'])</span></code>)</p></li>
<li><p>If necessary, <code class="docutils literal notranslate"><span class="pre">res</span></code> will be wrapped in an <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code></p></li>
<li><p>The parameter/buffer in the module will be swapped via <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a>
with <code class="docutils literal notranslate"><span class="pre">res</span></code></p></li>
</ol>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>value</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – Whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> or not.</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch.__future__.get_swap_module_params_on_conversion">
<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/__future__.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> instead of setting .data to
change the existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
<p>See <a class="reference internal" href="#torch.__future__.set_swap_module_params_on_conversion" title="torch.__future__.set_swap_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_swap_module_params_on_conversion()</span></code></a> for more information.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>

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