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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="torch-div">
<h1>torch.div<a class="headerlink" href="#torch-div" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.div">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">div</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">other</em>, <em class="sig-param">*</em>, <em class="sig-param">rounding_mode=None</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.div" title="Permalink to this definition">¶</a></dt>
<dd><p>Divides each element of the input <code class="docutils literal notranslate"><span class="pre">input</span></code> by the corresponding element of
<code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>.</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mtext>out</mtext><mi>i</mi></msub><mo>=</mo><mfrac><msub><mtext>input</mtext><mi>i</mi></msub><msub><mtext>other</mtext><mi>i</mi></msub></mfrac></mrow><annotation encoding="application/x-tex">\text{out}_i = \frac{\text{input}_i}{\text{other}_i}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.76508em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord text"><span class="mord">out</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:2.18086em;vertical-align:-0.8360000000000001em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.3448600000000002em;"><span style="top:-2.3139999999999996em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord text"><span class="mord">other</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.6769999999999996em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord text"><span class="mord">input</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.21752399999999997em;"><span style="top:-2.4558600000000004em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.24414em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.8360000000000001em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span>
</div><div class="admonition note">
<p class="admonition-title">Note</p>
<p>By default, this performs a “true” division like Python 3.
See the <code class="xref py py-attr docutils literal notranslate"><span class="pre">rounding_mode</span></code> argument for floor division.</p>
</div>
<p>Supports <a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcasting to a common shape</span></a>,
<a class="reference internal" href="../tensor_attributes.html#type-promotion-doc"><span class="std std-ref">type promotion</span></a>, and integer, float, and complex inputs.
Always promotes integer types to the default scalar type.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the dividend</p></li>
<li><p><strong>other</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><em>Number</em>) – the divisor</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>rounding_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em>, </em><em>optional</em>) – <p>Type of rounding applied to the result:</p>
<ul>
<li><p>None - default behavior. Performs no rounding and, if both <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and
<code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> are integer types, promotes the inputs to the default scalar type.
Equivalent to true division in Python (the <code class="docutils literal notranslate"><span class="pre">/</span></code> operator) and NumPy’s <code class="docutils literal notranslate"><span class="pre">np.true_divide</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"trunc"</span></code> - rounds the results of the division towards zero.
Equivalent to C-style integer division.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"floor"</span></code> - rounds the results of the division down.
Equivalent to floor division in Python (the <code class="docutils literal notranslate"><span class="pre">//</span></code> operator) and NumPy’s <code class="docutils literal notranslate"><span class="pre">np.floor_divide</span></code>.</p></li>
</ul>
</p></li>
<li><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span> <span class="mf">0.3810</span><span class="p">,</span> <span class="mf">1.2774</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2972</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.3719</span><span class="p">,</span> <span class="mf">0.4637</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go">tensor([ 0.7620, 2.5548, -0.5944, -0.7438, 0.9274])</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</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="mf">0.3711</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.9353</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.4605</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2917</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span> <span class="mf">0.1815</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0111</span><span class="p">,</span> <span class="mf">0.9805</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.5923</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span> <span class="mf">0.1062</span><span class="p">,</span> <span class="mf">1.4581</span><span class="p">,</span> <span class="mf">0.7759</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.2344</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="o">-</span><span class="mf">0.1830</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0313</span><span class="p">,</span> <span class="mf">1.1908</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.4757</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span> <span class="mf">0.8032</span><span class="p">,</span> <span class="mf">0.2930</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.8113</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2308</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="go">tensor([[-0.4620, -6.6051, 0.5676, 1.2639],</span>
<span class="go"> [ 0.2260, -3.4509, -1.2086, 6.8990],</span>
<span class="go"> [ 0.1322, 4.9764, -0.9564, 5.3484],</span>
<span class="go"> [-0.2278, -0.1068, -1.4678, 6.3938]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rounding_mode</span><span class="o">=</span><span class="s1">'trunc'</span><span class="p">)</span>
<span class="go">tensor([[-0., -6., 0., 1.],</span>
<span class="go"> [ 0., -3., -1., 6.],</span>
<span class="go"> [ 0., 4., -0., 5.],</span>
<span class="go"> [-0., -0., -1., 6.]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rounding_mode</span><span class="o">=</span><span class="s1">'floor'</span><span class="p">)</span>
<span class="go">tensor([[-1., -7., 0., 1.],</span>
<span class="go"> [ 0., -4., -2., 6.],</span>
<span class="go"> [ 0., 4., -1., 5.],</span>
<span class="go"> [-1., -1., -2., 6.]])</span>
</pre></div>
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