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<section id="module-torch.testing">
<span id="torch-testing"></span><h1>torch.testing<a class="headerlink" href="#module-torch.testing" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="torch.testing.assert_close">
<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_close</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_subclasses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_close" title="Permalink to this definition">¶</a></dt>
<dd><p>Asserts that <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are close.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are strided, non-quantized, real-valued, and finite, they are considered close if</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><mo stretchy="false">∣</mo><mtext>actual</mtext><mo>−</mo><mtext>expected</mtext><mo stretchy="false">∣</mo><mo>≤</mo><mtext mathvariant="monospace">atol</mtext><mo>+</mo><mtext mathvariant="monospace">rtol</mtext><mo>⋅</mo><mo stretchy="false">∣</mo><mtext>expected</mtext><mo stretchy="false">∣</mo></mrow><annotation encoding="application/x-tex">\lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">∣</span><span class="mord text"><span class="mord">actual</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">expected</span></span><span class="mclose">∣</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord texttt">atol</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6111em;"></span><span class="mord text"><span class="mord texttt">rtol</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">⋅</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">∣</span><span class="mord text"><span class="mord">expected</span></span><span class="mclose">∣</span></span></span></span></span></div><p>Non-finite values (<code class="docutils literal notranslate"><span class="pre">-inf</span></code> and <code class="docutils literal notranslate"><span class="pre">inf</span></code>) are only considered close if and only if they are equal. <code class="docutils literal notranslate"><span class="pre">NaN</span></code>’s are
only considered equal to each other if <code class="docutils literal notranslate"><span class="pre">equal_nan</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
<p>In addition, they are only considered close if they have the same
- <a class="reference internal" href="generated/torch.Tensor.device.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code></a> (if <code class="docutils literal notranslate"><span class="pre">check_device</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>),
- <code class="docutils literal notranslate"><span class="pre">dtype</span></code> (if <code class="docutils literal notranslate"><span class="pre">check_dtype</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>),
- <code class="docutils literal notranslate"><span class="pre">layout</span></code> (if <code class="docutils literal notranslate"><span class="pre">check_layout</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>), and
- stride (if <code class="docutils literal notranslate"><span class="pre">check_stride</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>).
If either <code class="docutils literal notranslate"><span class="pre">actual</span></code> or <code class="docutils literal notranslate"><span class="pre">expected</span></code> is a meta tensor, only the attribute checks will be performed.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
checked individually. Indices, namely <code class="docutils literal notranslate"><span class="pre">indices</span></code> for COO, <code class="docutils literal notranslate"><span class="pre">crow_indices</span></code> and <code class="docutils literal notranslate"><span class="pre">col_indices</span></code> for CSR and BSR,
or <code class="docutils literal notranslate"><span class="pre">ccol_indices</span></code> and <code class="docutils literal notranslate"><span class="pre">row_indices</span></code> for CSC and BSC layouts, respectively,
are always checked for equality whereas the values are checked for closeness according to the definition above.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are quantized, they are considered close if they have the same
<a class="reference internal" href="generated/torch.Tensor.qscheme.html#torch.Tensor.qscheme" title="torch.Tensor.qscheme"><code class="xref py py-meth docutils literal notranslate"><span class="pre">qscheme()</span></code></a> and the result of <a class="reference internal" href="generated/torch.Tensor.dequantize.html#torch.Tensor.dequantize" title="torch.Tensor.dequantize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dequantize()</span></code></a> is close according to the
definition above.</p>
<p><code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> can be <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a>’s or any tensor-or-scalar-likes from which
<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>’s can be constructed with <a class="reference internal" href="generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_tensor()</span></code></a>. Except for Python scalars the input types
have to be directly related. In addition, <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> can be <a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code></a>’s
or <a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Mapping</span></code></a>’s in which case they are considered close if their structure matches and all
their elements are considered close according to the above definition.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Python scalars are an exception to the type relation requirement, because their <code class="xref py py-func docutils literal notranslate"><span class="pre">type()</span></code>, i.e.
<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, and <a class="reference external" href="https://docs.python.org/3/library/functions.html#complex" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">complex</span></code></a>, is equivalent to the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> of a tensor-like. Thus,
Python scalars of different types can be checked, but require <code class="docutils literal notranslate"><span class="pre">check_dtype=False</span></code>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>actual</strong> (<em>Any</em>) – Actual input.</p></li>
<li><p><strong>expected</strong> (<em>Any</em>) – Expected input.</p></li>
<li><p><strong>allow_subclasses</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> (default) and except for Python scalars, inputs of directly related types
are allowed. Otherwise type equality is required.</p></li>
<li><p><strong>rtol</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>]</em>) – Relative tolerance. If specified <code class="docutils literal notranslate"><span class="pre">atol</span></code> must also be specified. If omitted, default
values based on the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> are selected with the below table.</p></li>
<li><p><strong>atol</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>]</em>) – Absolute tolerance. If specified <code class="docutils literal notranslate"><span class="pre">rtol</span></code> must also be specified. If omitted, default
values based on the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> are selected with the below table.</p></li>
<li><p><strong>equal_nan</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, two <code class="docutils literal notranslate"><span class="pre">NaN</span></code> values will be considered equal.</p></li>
<li><p><strong>check_device</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> (default), asserts that corresponding tensors are on the same
<a class="reference internal" href="generated/torch.Tensor.device.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code></a>. If this check is disabled, tensors on different
<a class="reference internal" href="generated/torch.Tensor.device.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code></a>’s are moved to the CPU before being compared.</p></li>
<li><p><strong>check_dtype</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> (default), asserts that corresponding tensors have the same <code class="docutils literal notranslate"><span class="pre">dtype</span></code>. If this
check is disabled, tensors with different <code class="docutils literal notranslate"><span class="pre">dtype</span></code>’s are promoted to a common <code class="docutils literal notranslate"><span class="pre">dtype</span></code> (according to
<a class="reference internal" href="generated/torch.promote_types.html#torch.promote_types" title="torch.promote_types"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.promote_types()</span></code></a>) before being compared.</p></li>
<li><p><strong>check_layout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> (default), asserts that corresponding tensors have the same <code class="docutils literal notranslate"><span class="pre">layout</span></code>. If this
check is disabled, tensors with different <code class="docutils literal notranslate"><span class="pre">layout</span></code>’s are converted to strided tensors before being
compared.</p></li>
<li><p><strong>check_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> and corresponding tensors are strided, asserts that they have the same stride.</p></li>
<li><p><strong>msg</strong> (<em>Optional</em><em>[</em><em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><em>Callable</em><em>[</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>]</em><em>]</em><em>]</em>) – Optional error message to use in case a failure occurs during
the comparison. Can also passed as callable in which case it will be called with the generated message and
should return the new message.</p></li>
</ul>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><ul class="simple">
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.10)"><strong>ValueError</strong></a> – If no <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> can be constructed from an input.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.10)"><strong>ValueError</strong></a> – If only <code class="docutils literal notranslate"><span class="pre">rtol</span></code> or <code class="docutils literal notranslate"><span class="pre">atol</span></code> is specified.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.10)"><strong>NotImplementedError</strong></a> – If a tensor is a meta tensor. This is a temporary restriction and will be relaxed in the
future.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If corresponding inputs are not Python scalars and are not directly related.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">allow_subclasses</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>, but corresponding inputs are not Python scalars and have
different types.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If the inputs are <a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequence</span></code></a>’s, but their length does not match.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If the inputs are <a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Mapping</span></code></a>’s, but their set of keys do not match.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If corresponding tensors do not have the same <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">check_layout</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, but corresponding tensors do not have the same
<code class="xref py py-attr docutils literal notranslate"><span class="pre">layout</span></code>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If only one of corresponding tensors is quantized.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If corresponding tensors are quantized, but have different <a class="reference internal" href="generated/torch.Tensor.qscheme.html#torch.Tensor.qscheme" title="torch.Tensor.qscheme"><code class="xref py py-meth docutils literal notranslate"><span class="pre">qscheme()</span></code></a>’s.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">check_device</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, but corresponding tensors are not on the same
<a class="reference internal" href="generated/torch.Tensor.device.html#torch.Tensor.device" title="torch.Tensor.device"><code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code></a>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">check_dtype</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, but corresponding tensors do not have the same <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">check_stride</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, but corresponding strided tensors do not have the same stride.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AssertionError" title="(in Python v3.10)"><strong>AssertionError</strong></a> – If the values of corresponding tensors are not close according to the definition above.</p></li>
</ul>
</dd>
</dl>
<p>The following table displays the default <code class="docutils literal notranslate"><span class="pre">rtol</span></code> and <code class="docutils literal notranslate"><span class="pre">atol</span></code> for different <code class="docutils literal notranslate"><span class="pre">dtype</span></code>’s. In case of mismatching
<code class="docutils literal notranslate"><span class="pre">dtype</span></code>’s, the maximum of both tolerances is used.</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p><code class="docutils literal notranslate"><span class="pre">dtype</span></code></p></th>
<th class="head"><p><code class="docutils literal notranslate"><span class="pre">rtol</span></code></p></th>
<th class="head"><p><code class="docutils literal notranslate"><span class="pre">atol</span></code></p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">float16</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-3</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">bfloat16</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.6e-2</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">float32</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">float64</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-7</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-7</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">complex32</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-3</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">complex64</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">complex128</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-7</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-7</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">quint8</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">quint2x4</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">quint4x2</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">qint8</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">qint32</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1.3e-6</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">1e-5</span></code></p></td>
</tr>
<tr class="row-even"><td><p>other</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">0.0</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">0.0</span></code></p></td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><a class="reference internal" href="#torch.testing.assert_close" title="torch.testing.assert_close"><code class="xref py py-func docutils literal notranslate"><span class="pre">assert_close()</span></code></a> is highly configurable with strict default settings. Users are encouraged
to <a class="reference external" href="https://docs.python.org/3/library/functools.html#functools.partial" title="(in Python v3.10)"><code class="xref py py-func docutils literal notranslate"><span class="pre">partial()</span></code></a> it to fit their use case. For example, if an equality check is needed, one might
define an <code class="docutils literal notranslate"><span class="pre">assert_equal</span></code> that uses zero tolrances for every <code class="docutils literal notranslate"><span class="pre">dtype</span></code> by default:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">functools</span>
<span class="gp">>>> </span><span class="n">assert_equal</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">assert_equal</span><span class="p">(</span><span class="mf">1e-9</span><span class="p">,</span> <span class="mf">1e-10</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">AssertionError</span>: <span class="n">Scalars are not equal!</span>
<span class="go">Absolute difference: 9.000000000000001e-10</span>
<span class="go">Relative difference: 9.0</span>
</pre></div>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># tensor to tensor comparison</span>
<span class="gp">>>> </span><span class="n">expected</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">1e0</span><span class="p">,</span> <span class="mf">1e-1</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">expected</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># scalar to scalar comparison</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">math</span>
<span class="gp">>>> </span><span class="n">expected</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># numpy array to numpy array comparison</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">expected</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1e0</span><span class="p">,</span> <span class="mf">1e-1</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">expected</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># sequence to sequence comparison</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="c1"># The types of the sequences do not have to match. They only have to have the same</span>
<span class="gp">>>> </span><span class="c1"># length and their elements have to match.</span>
<span class="gp">>>> </span><span class="n">expected</span> <span class="o">=</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="mf">1.0</span><span class="p">]),</span> <span class="mf">2.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)]</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">expected</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># mapping to mapping comparison</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">foo</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">1.0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bar</span> <span class="o">=</span> <span class="mf">2.0</span>
<span class="gp">>>> </span><span class="n">baz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># The types and a possible ordering of mappings do not have to match. They only</span>
<span class="gp">>>> </span><span class="c1"># have to have the same set of keys and their elements have to match.</span>
<span class="gp">>>> </span><span class="n">expected</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([(</span><span class="s2">"foo"</span><span class="p">,</span> <span class="n">foo</span><span class="p">),</span> <span class="p">(</span><span class="s2">"bar"</span><span class="p">,</span> <span class="n">bar</span><span class="p">),</span> <span class="p">(</span><span class="s2">"baz"</span><span class="p">,</span> <span class="n">baz</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"baz"</span><span class="p">:</span> <span class="n">baz</span><span class="p">,</span> <span class="s2">"bar"</span><span class="p">:</span> <span class="n">bar</span><span class="p">,</span> <span class="s2">"foo"</span><span class="p">:</span> <span class="n">foo</span><span class="p">}</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">expected</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">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="n">expected</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="gp">>>> </span><span class="c1"># By default, directly related instances can be compared</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">actual</span><span class="p">),</span> <span class="n">expected</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># This check can be made more strict with allow_subclasses=False</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">actual</span><span class="p">),</span> <span class="n">expected</span><span class="p">,</span> <span class="n">allow_subclasses</span><span class="o">=</span><span class="kc">False</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">TypeError</span>: <span class="n">No comparison pair was able to handle inputs of type</span>
<span class="go"><class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.</span>
<span class="gp">>>> </span><span class="c1"># If the inputs are not directly related, they are never considered close</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">expected</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">TypeError</span>: <span class="n">No comparison pair was able to handle inputs of type <class 'numpy.ndarray'></span>
<span class="go">and <class 'torch.Tensor'>.</span>
<span class="gp">>>> </span><span class="c1"># Exceptions to these rules are Python scalars. They can be checked regardless of</span>
<span class="gp">>>> </span><span class="c1"># their type if check_dtype=False.</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">check_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># NaN != NaN by default.</span>
<span class="gp">>>> </span><span class="n">expected</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="nb">float</span><span class="p">(</span><span class="s2">"Nan"</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">actual</span> <span class="o">=</span> <span class="n">expected</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">AssertionError</span>: <span class="n">Scalars are not close!</span>
<span class="go">Absolute difference: nan (up to 1e-05 allowed)</span>
<span class="go">Relative difference: nan (up to 1.3e-06 allowed)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">expected</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">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">actual</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">1.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="c1"># The default error message can be overwritten.</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s2">"Argh, the tensors are not close!"</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">AssertionError</span>: <span class="n">Argh, the tensors are not close!</span>
<span class="gp">>>> </span><span class="c1"># If msg is a callable, it can be used to augment the generated message with</span>
<span class="gp">>>> </span><span class="c1"># extra information</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_close</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">actual</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="k">lambda</span> <span class="n">msg</span><span class="p">:</span> <span class="sa">f</span><span class="s2">"Header</span><span class="se">\n\n</span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="se">\n\n</span><span class="s2">Footer"</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">AssertionError</span>: <span class="n">Header</span>
<span class="go">Tensor-likes are not close!</span>
<span class="go">Mismatched elements: 2 / 3 (66.7%)</span>
<span class="go">Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)</span>
<span class="go">Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)</span>
<span class="go">Footer</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.testing.make_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">make_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noncontiguous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_creation.html#make_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.make_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a tensor with the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code>, and <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>, and filled with
values uniformly drawn from <code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high)</span></code>.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code> are specified and are outside the range of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>’s representable
finite values then they are clamped to the lowest or highest representable finite value, respectively.
If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then the following table describes the default values for <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code>,
which depend on <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p><code class="docutils literal notranslate"><span class="pre">dtype</span></code></p></th>
<th class="head"><p><code class="docutils literal notranslate"><span class="pre">low</span></code></p></th>
<th class="head"><p><code class="docutils literal notranslate"><span class="pre">high</span></code></p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>boolean type</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">2</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>unsigned integral type</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">0</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">10</span></code></p></td>
</tr>
<tr class="row-even"><td><p>signed integral types</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">-9</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">10</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>floating types</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">-9</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">9</span></code></p></td>
</tr>
<tr class="row-even"><td><p>complex types</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">-9</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">9</span></code></p></td>
</tr>
</tbody>
</table>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape</strong> (<em>Tuple</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>...</em><em>]</em>) – Single integer or a sequence of integers defining the shape of the output tensor.</p></li>
<li><p><strong>dtype</strong> (<a class="reference internal" href="tensor_attributes.html#torch.dtype" title="torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>) – The data type of the returned tensor.</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference internal" href="tensor_attributes.html#torch.device" title="torch.device"><em>torch.device</em></a><em>]</em>) – The device of the returned tensor.</p></li>
<li><p><strong>low</strong> (<em>Optional</em><em>[</em><em>Number</em><em>]</em>) – Sets the lower limit (inclusive) of the given range. If a number is provided it is
clamped to the least representable finite value of the given dtype. When <code class="docutils literal notranslate"><span class="pre">None</span></code> (default),
this value is determined based on the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> (see the table above). Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
<li><p><strong>high</strong> (<em>Optional</em><em>[</em><em>Number</em><em>]</em>) – Sets the upper limit (exclusive) of the given range. If a number is provided it is
clamped to the greatest representable finite value of the given dtype. When <code class="docutils literal notranslate"><span class="pre">None</span></code> (default) this value
is determined based on the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> (see the table above). Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
<li><p><strong>requires_grad</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>]</em>) – If autograd should record operations on the returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>noncontiguous</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>]</em>) – If <cite>True</cite>, the returned tensor will be noncontiguous. This argument is
ignored if the constructed tensor has fewer than two elements.</p></li>
<li><p><strong>exclude_zero</strong> (<em>Optional</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>]</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code> then zeros are replaced with the dtype’s small positive value
depending on the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>. For bool and integer types zero is replaced with one. For floating
point types it is replaced with the dtype’s smallest positive normal number (the “tiny” value of the
<code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>’s <code class="xref py py-func docutils literal notranslate"><span class="pre">finfo()</span></code> object), and for complex types it is replaced with a complex number
whose real and imaginary parts are both the smallest positive normal number representable by the complex
type. Default <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Raises<span class="colon">:</span></dt>
<dd class="field-even"><ul class="simple">
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.10)"><strong>ValueError</strong></a> – if <code class="docutils literal notranslate"><span class="pre">requires_grad=True</span></code> is passed for integral <cite>dtype</cite></p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.10)"><strong>ValueError</strong></a> – If <code class="docutils literal notranslate"><span class="pre">low</span> <span class="pre">></span> <span class="pre">high</span></code>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.10)"><strong>ValueError</strong></a> – If either <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code> is <code class="docutils literal notranslate"><span class="pre">nan</span></code>.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#TypeError" title="(in Python v3.10)"><strong>TypeError</strong></a> – If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> isn’t supported by this function.</p></li>
</ul>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.testing</span> <span class="kn">import</span> <span class="n">make_tensor</span>
<span class="gp">>>> </span><span class="c1"># Creates a float tensor with values in [-1, 1)</span>
<span class="gp">>>> </span><span class="n">make_tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,),</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cpu'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">tensor([ 0.1205, 0.2282, -0.6380])</span>
<span class="gp">>>> </span><span class="c1"># Creates a bool tensor on CUDA</span>
<span class="gp">>>> </span><span class="n">make_tensor</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">)</span>
<span class="go">tensor([[False, False],</span>
<span class="go"> [False, True]], device='cuda:0')</span>
</pre></div>
</div>
</dd></dl>
</section>
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