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<div class="section" id="torch-testing">
<h1>torch.testing<a class="headerlink" href="#torch-testing" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This module is in a PROTOTYPE state. New functions are still being added, and the available functions may change in
future PyTorch releases. We are actively looking for feedback for UI/UX improvements or missing functionalities.</p>
</div>
<span class="target" id="module-torch.testing"></span><dl class="py function">
<dt id="torch.testing.assert_close">
<code class="sig-prename descclassname"><span class="pre">torch.testing.</span></code><code class="sig-name descname"><span class="pre">assert_close</span></code><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_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">check_is_coalesced</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">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/_asserts.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="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">expected</span></code> are close.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr 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:0.2222222222222222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222222222222222em;"></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:0.2777777777777778em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.6944400000000001em;vertical-align:-0.08333em;"></span><span class="mord text"><span class="mord texttt">atol</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.61111em;vertical-align:0em;"></span><span class="mord text"><span class="mord texttt">rtol</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">⋅</span><span class="mspace" style="margin-right:0.2222222222222222em;"></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>and 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="xref py py-attr docutils literal notranslate"><span class="pre">check_device</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>), same <code class="docutils literal notranslate"><span class="pre">dtype</span></code> (if
<code class="xref py py-attr docutils literal notranslate"><span class="pre">check_dtype</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>), and the same stride (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">check_stride</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>). 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="xref py py-attr docutils literal notranslate"><span class="pre">equal_nan</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">expected</span></code> are sparse (either having COO or CSR layout), their strided members are
checked individually. Indices, namely <code class="docutils literal notranslate"><span class="pre">indices</span></code> for COO or <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 layout,
are always checked for equality whereas the values are checked for closeness according to the definition above.
Sparse COO tensors are only considered close if both are either coalesced or uncoalesced (if
<code class="xref py py-attr docutils literal notranslate"><span class="pre">check_is_coalesced</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>).</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr 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="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr 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="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr 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="xref py py-attr docutils literal notranslate"><span class="pre">check_dtype</span></code> to be set to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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="xref py py-attr 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="xref py py-attr 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_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>check_is_coalesced</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 corresponding tensors are sparse COO, checks that both
<code class="xref py py-attr docutils literal notranslate"><span class="pre">actual</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">expected</span></code> are either coalesced or uncoalesced. If this check is disabled,
tensors are <a class="reference internal" href="generated/torch.Tensor.coalesce.html#torch.Tensor.coalesce" title="torch.Tensor.coalesce"><code class="xref py py-meth docutils literal notranslate"><span class="pre">coalesce()</span></code></a>’ed before being compared.</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 internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>Diagnostics</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><em>]</em>) – Optional error message to use if the
values of corresponding tensors mismatch. Can be passed as callable in which case it will be called with
the mismatching tensors and a namespace of diagnostics about the mismatches. See below for details.</p></li>
</ul>
</dd>
<dt class="field-even">Raises</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="xref py py-attr docutils literal notranslate"><span class="pre">rtol</span></code> or <code class="xref py py-attr 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#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="xref py py-attr 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 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 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="xref py py-attr 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="xref py py-attr 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="xref py py-attr 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 <code class="xref py py-attr docutils literal notranslate"><span class="pre">check_is_coalesced</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, but corresponding sparse COO tensors are not both
either coalesced or uncoalesced.</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 colwidths-auto 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>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>
<p>The namespace of diagnostics that will be passed to <code class="xref py py-attr docutils literal notranslate"><span class="pre">msg</span></code> if its a callable has the following attributes:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">number_of_elements</span></code> (int): Number of elements in each tensor being compared.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">total_mismatches</span></code> (int): Total number of mismatches.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">max_abs_diff</span></code> (Union[int, float]): Greatest absolute difference of the inputs.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">max_abs_diff_idx</span></code> (Union[int, Tuple[int, …]]): Index of greatest absolute difference.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">atol</span></code> (float): Allowed absolute tolerance.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">max_rel_diff</span></code> (Union[int, float]): Greatest relative difference of the inputs.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">max_rel_diff_idx</span></code> (Union[int, Tuple[int, …]]): Index of greatest relative difference.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">rtol</span></code> (float): Allowed relative tolerance.</p></li>
</ul>
<p>For <code class="docutils literal notranslate"><span class="pre">max_abs_diff</span></code> and <code class="docutils literal notranslate"><span class="pre">max_rel_diff</span></code> the type depends on the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> of the inputs.</p>
<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: 8.999999703829253e-10</span>
<span class="go">Relative difference: 8.999999583666371</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">AssertionError</span>: <span class="n">Except for Python scalars, type equality is required if</span>
<span class="go">allow_subclasses=False, but got <class 'torch.nn.parameter.Parameter'> and</span>
<span class="go"><class 'torch.Tensor'> instead.</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">AssertionError</span>: <span class="n">Except for Python scalars, input types need to be directly</span>
<span class="go">related, but got <class 'numpy.ndarray'> and <class 'torch.Tensor'> instead.</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 mismatch 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"># The error message can also created at runtime by passing a callable.</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">custom_msg</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">diagnostics</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">diagnostics</span><span class="o">.</span><span class="n">total_mismatches</span> <span class="o">/</span> <span class="n">diagnostics</span><span class="o">.</span><span class="n">number_of_elements</span>
<span class="gp">... </span> <span class="k">return</span> <span class="p">(</span>
<span class="gp">... </span> <span class="sa">f</span><span class="s2">"Argh, we found </span><span class="si">{</span><span class="n">diagnostics</span><span class="o">.</span><span class="n">total_mismatches</span><span class="si">}</span><span class="s2"> mismatches! "</span>
<span class="gp">... </span> <span class="sa">f</span><span class="s2">"That is </span><span class="si">{</span><span class="n">ratio</span><span class="si">:</span><span class="s2">.1%</span><span class="si">}</span><span class="s2">!"</span>
<span class="gp">... </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="n">msg</span><span class="o">=</span><span class="n">custom_msg</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, we found 2 mismatches! That is 66.7%!</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.testing.make_tensor">
<code class="sig-prename descclassname"><span class="pre">torch.testing.</span></code><code class="sig-name descname"><span class="pre">make_tensor</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</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">dtype</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">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 colwidths-auto 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</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>) – A sequence of integers defining the shape of the output 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.torch.device" title="torch.torch.device"><em>torch.device</em></a><em>]</em>) – The device of the returned tensor.</p></li>
<li><p><strong>dtype</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code>) – The data type 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</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">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>
</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>
</div>
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