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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="quantization-operation-coverage">
<h1>Quantization Operation coverage<a class="headerlink" href="#quantization-operation-coverage" title="Permalink to this headline">¶</a></h1>
<p>Quantized Tensors support a limited subset of data manipulation methods of the
regular full-precision tensor. For NN operators included in PyTorch, we
restrict support to:</p>
<blockquote>
<div><ol class="arabic simple">
<li><p>8 bit weights (data_type = qint8)</p></li>
<li><p>8 bit activations (data_type = quint8)</p></li>
</ol>
</div></blockquote>
<p>Note that operator implementations currently only
support per channel quantization for weights of the <strong>conv</strong> and <strong>linear</strong>
operators. Furthermore the minimum and the maximum of the input data is
mapped linearly to the minimum and the maximum of the quantized data
type such that zero is represented with no quantization error.</p>
<p>Additional data types and quantization schemes can be implemented through
the <a class="reference external" href="https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html">custom operator mechanism</a>.</p>
<p>Many operations for quantized tensors are available under the same API as full
float version in <code class="docutils literal notranslate"><span class="pre">torch</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.nn</span></code>. Quantized version of NN modules that
perform re-quantization are available in <code class="docutils literal notranslate"><span class="pre">torch.nn.quantized</span></code>. Those
operations explicitly take output quantization parameters (scale and zero_point) in
the operation signature.</p>
<p>In addition, we also support fused versions corresponding to common fusion
patterns that impact quantization at: <cite>torch.nn.intrinsic.quantized</cite>.</p>
<p>For quantization aware training, we support modules prepared for quantization
aware training at <cite>torch.nn.qat</cite> and <cite>torch.nn.intrinsic.qat</cite></p>
<p>The following operation list is sufficient to cover typical CNN and RNN models</p>
<div class="section" id="quantized-torch-tensor-operations">
<h2>Quantized <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> operations<a class="headerlink" href="#quantized-torch-tensor-operations" title="Permalink to this headline">¶</a></h2>
<p>Operations that are available from the <code class="docutils literal notranslate"><span class="pre">torch</span></code> namespace or as methods on
Tensor for quantized tensors:</p>
<ul class="simple">
<li><p><a class="reference internal" href="generated/torch.quantize_per_tensor.html#torch.quantize_per_tensor" title="torch.quantize_per_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize_per_tensor()</span></code></a> - Convert float tensor to quantized tensor
with per-tensor scale and zero point</p></li>
<li><p><a class="reference internal" href="generated/torch.quantize_per_channel.html#torch.quantize_per_channel" title="torch.quantize_per_channel"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize_per_channel()</span></code></a> - Convert float tensor to quantized
tensor with per-channel scale and zero point</p></li>
<li><p>View-based operations like <a class="reference internal" href="generated/torch.Tensor.view.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-meth docutils literal notranslate"><span class="pre">view()</span></code></a>,
<a class="reference internal" href="generated/torch.Tensor.as_strided.html#torch.Tensor.as_strided" title="torch.Tensor.as_strided"><code class="xref py py-meth docutils literal notranslate"><span class="pre">as_strided()</span></code></a>, <a class="reference internal" href="generated/torch.Tensor.expand.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-meth docutils literal notranslate"><span class="pre">expand()</span></code></a>,
<a class="reference internal" href="generated/torch.Tensor.flatten.html#torch.Tensor.flatten" title="torch.Tensor.flatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">flatten()</span></code></a>, <a class="reference internal" href="generated/torch.Tensor.select.html#torch.Tensor.select" title="torch.Tensor.select"><code class="xref py py-meth docutils literal notranslate"><span class="pre">select()</span></code></a>, python-style
indexing, etc - work as on regular tensor (if quantization is not
per-channel)</p></li>
<li><dl class="simple">
<dt>Comparators</dt><dd><ul>
<li><p><a class="reference internal" href="generated/torch.Tensor.ne.html#torch.Tensor.ne" title="torch.Tensor.ne"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ne()</span></code></a> — Not equal</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.eq.html#torch.Tensor.eq" title="torch.Tensor.eq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">eq()</span></code></a> — Equal</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.ge.html#torch.Tensor.ge" title="torch.Tensor.ge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ge()</span></code></a> — Greater or equal</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.le.html#torch.Tensor.le" title="torch.Tensor.le"><code class="xref py py-meth docutils literal notranslate"><span class="pre">le()</span></code></a> — Less or equal</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.gt.html#torch.Tensor.gt" title="torch.Tensor.gt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">gt()</span></code></a> — Greater</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.lt.html#torch.Tensor.lt" title="torch.Tensor.lt"><code class="xref py py-meth docutils literal notranslate"><span class="pre">lt()</span></code></a> — Less</p></li>
</ul>
</dd>
</dl>
</li>
<li><p><a class="reference internal" href="generated/torch.Tensor.copy_.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> — Copies src to self in-place</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.clone.html#torch.Tensor.clone" title="torch.Tensor.clone"><code class="xref py py-meth docutils literal notranslate"><span class="pre">clone()</span></code></a> — Returns a deep copy of the passed-in tensor</p></li>
<li><p><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> — Convert quantized tensor to float tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.equal.html#torch.Tensor.equal" title="torch.Tensor.equal"><code class="xref py py-meth docutils literal notranslate"><span class="pre">equal()</span></code></a> — Compares two tensors, returns true if
quantization parameters and all integer elements are the same</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.int_repr.html#torch.Tensor.int_repr" title="torch.Tensor.int_repr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">int_repr()</span></code></a> — Prints the underlying integer representation
of the quantized tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.max.html#torch.Tensor.max" title="torch.Tensor.max"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max()</span></code></a> — Returns the maximum value of the tensor (reduction only)</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.mean.html#torch.Tensor.mean" title="torch.Tensor.mean"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mean()</span></code></a> — Mean function. Supported variants: reduction, dim, out</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.min.html#torch.Tensor.min" title="torch.Tensor.min"><code class="xref py py-meth docutils literal notranslate"><span class="pre">min()</span></code></a> — Returns the minimum value of the tensor (reduction only)</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.q_scale.html#torch.Tensor.q_scale" title="torch.Tensor.q_scale"><code class="xref py py-meth docutils literal notranslate"><span class="pre">q_scale()</span></code></a> — Returns the scale of the per-tensor quantized tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.q_zero_point.html#torch.Tensor.q_zero_point" title="torch.Tensor.q_zero_point"><code class="xref py py-meth docutils literal notranslate"><span class="pre">q_zero_point()</span></code></a> — Returns the zero_point of the per-tensor
quantized zero point</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.q_per_channel_scales.html#torch.Tensor.q_per_channel_scales" title="torch.Tensor.q_per_channel_scales"><code class="xref py py-meth docutils literal notranslate"><span class="pre">q_per_channel_scales()</span></code></a> — Returns the scales of the
per-channel quantized tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.q_per_channel_zero_points.html#torch.Tensor.q_per_channel_zero_points" title="torch.Tensor.q_per_channel_zero_points"><code class="xref py py-meth docutils literal notranslate"><span class="pre">q_per_channel_zero_points()</span></code></a> — Returns the zero points of
the per-channel quantized tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.q_per_channel_axis.html#torch.Tensor.q_per_channel_axis" title="torch.Tensor.q_per_channel_axis"><code class="xref py py-meth docutils literal notranslate"><span class="pre">q_per_channel_axis()</span></code></a> — Returns the channel axis of the
per-channel quantized tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.resize_.html#torch.Tensor.resize_" title="torch.Tensor.resize_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">resize_()</span></code></a> — In-place resize</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.sort.html#torch.Tensor.sort" title="torch.Tensor.sort"><code class="xref py py-meth docutils literal notranslate"><span class="pre">sort()</span></code></a> — Sorts the tensor</p></li>
<li><p><a class="reference internal" href="generated/torch.Tensor.topk.html#torch.Tensor.topk" title="torch.Tensor.topk"><code class="xref py py-meth docutils literal notranslate"><span class="pre">topk()</span></code></a> — Returns k largest values of a tensor</p></li>
</ul>
</div>
<div class="section" id="torch-nn-functional">
<h2><code class="docutils literal notranslate"><span class="pre">torch.nn.functional</span></code><a class="headerlink" href="#torch-nn-functional" title="Permalink to this headline">¶</a></h2>
<p>Basic activations are supported.</p>
<ul class="simple">
<li><p><a class="reference internal" href="generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><code class="xref py py-meth docutils literal notranslate"><span class="pre">relu()</span></code></a> — Rectified linear unit (copy)</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.relu_.html#torch.nn.functional.relu_" title="torch.nn.functional.relu_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">relu_()</span></code></a> — Rectified linear unit (inplace)</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.elu.html#torch.nn.functional.elu" title="torch.nn.functional.elu"><code class="xref py py-meth docutils literal notranslate"><span class="pre">elu()</span></code></a> - ELU</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.max_pool2d.html#torch.nn.functional.max_pool2d" title="torch.nn.functional.max_pool2d"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_pool2d()</span></code></a> - Maximum pooling</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.adaptive_avg_pool2d.html#torch.nn.functional.adaptive_avg_pool2d" title="torch.nn.functional.adaptive_avg_pool2d"><code class="xref py py-meth docutils literal notranslate"><span class="pre">adaptive_avg_pool2d()</span></code></a> - Adaptive average pooling</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.avg_pool2d.html#torch.nn.functional.avg_pool2d" title="torch.nn.functional.avg_pool2d"><code class="xref py py-meth docutils literal notranslate"><span class="pre">avg_pool2d()</span></code></a> - Average pooling</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.interpolate.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">interpolate()</span></code></a> - Interpolation</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.hardsigmoid.html#torch.nn.functional.hardsigmoid" title="torch.nn.functional.hardsigmoid"><code class="xref py py-meth docutils literal notranslate"><span class="pre">hardsigmoid()</span></code></a> - Hardsigmoid</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.hardswish.html#torch.nn.functional.hardswish" title="torch.nn.functional.hardswish"><code class="xref py py-meth docutils literal notranslate"><span class="pre">hardswish()</span></code></a> - Hardswish</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.hardtanh.html#torch.nn.functional.hardtanh" title="torch.nn.functional.hardtanh"><code class="xref py py-meth docutils literal notranslate"><span class="pre">hardtanh()</span></code></a> - Hardtanh</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.upsample.html#torch.nn.functional.upsample" title="torch.nn.functional.upsample"><code class="xref py py-meth docutils literal notranslate"><span class="pre">upsample()</span></code></a> - Upsampling</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.upsample_bilinear.html#torch.nn.functional.upsample_bilinear" title="torch.nn.functional.upsample_bilinear"><code class="xref py py-meth docutils literal notranslate"><span class="pre">upsample_bilinear()</span></code></a> - Bilinear Upsampling</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.functional.upsample_nearest.html#torch.nn.functional.upsample_nearest" title="torch.nn.functional.upsample_nearest"><code class="xref py py-meth docutils literal notranslate"><span class="pre">upsample_nearest()</span></code></a> - Upsampling Nearest</p></li>
</ul>
</div>
<div class="section" id="torch-nn-intrinsic">
<h2><code class="docutils literal notranslate"><span class="pre">torch.nn.intrinsic</span></code><a class="headerlink" href="#torch-nn-intrinsic" title="Permalink to this headline">¶</a></h2>
<p>Fused modules are provided for common patterns in CNNs. Combining several
operations together (like convolution and relu) allows for better quantization
accuracy</p>
<ul class="simple">
<li><p><cite>torch.nn.intrinsic</cite> — float versions of the modules, can be swapped with
quantized version 1 to 1:</p>
<ul>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvBn1d" title="torch.nn.intrinsic.ConvBn1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBn1d</span></code></a> — Conv1d + BatchNorm1d</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvBn2d" title="torch.nn.intrinsic.ConvBn2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBn2d</span></code></a> — Conv2d + BatchNorm</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvBn3d" title="torch.nn.intrinsic.ConvBn3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBn3d</span></code></a> — Conv3d + BatchNorm3d</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvBnReLU1d" title="torch.nn.intrinsic.ConvBnReLU1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBnReLU1d</span></code></a> — Conv1d + BatchNorm1d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvBnReLU2d" title="torch.nn.intrinsic.ConvBnReLU2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBnReLU2d</span></code></a> — Conv2d + BatchNorm + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvBnReLU3d" title="torch.nn.intrinsic.ConvBnReLU3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBnReLU3d</span></code></a> — Conv3d + BatchNorm3d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvReLU1d" title="torch.nn.intrinsic.ConvReLU1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU1d</span></code></a> — Conv1d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvReLU2d" title="torch.nn.intrinsic.ConvReLU2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU2d</span></code></a> — Conv2d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.html#torch.nn.intrinsic.ConvReLU3d" title="torch.nn.intrinsic.ConvReLU3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU3d</span></code></a> — Conv3d + ReLU</p></li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearReLU</span></code> — Linear + ReLU</p></li>
</ul>
</li>
<li><p><cite>torch.nn.intrinsic.qat</cite> — versions of layers for quantization-aware training:</p>
<ul>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.ConvBn2d" title="torch.nn.intrinsic.qat.ConvBn2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBn2d</span></code></a> — Conv2d + BatchNorm</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.ConvBn3d" title="torch.nn.intrinsic.qat.ConvBn3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBn3d</span></code></a> — Conv3d + BatchNorm3d</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.ConvBnReLU2d" title="torch.nn.intrinsic.qat.ConvBnReLU2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBnReLU2d</span></code></a> — Conv2d + BatchNorm + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.ConvBnReLU3d" title="torch.nn.intrinsic.qat.ConvBnReLU3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvBnReLU3d</span></code></a> — Conv3d + BatchNorm3d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.ConvReLU2d" title="torch.nn.intrinsic.qat.ConvReLU2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU2d</span></code></a> — Conv2d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.ConvReLU3d" title="torch.nn.intrinsic.qat.ConvReLU3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU3d</span></code></a> — Conv3d + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.qat.html#torch.nn.intrinsic.qat.LinearReLU" title="torch.nn.intrinsic.qat.LinearReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearReLU</span></code></a> — Linear + ReLU</p></li>
</ul>
</li>
<li><p><cite>torch.nn.intrinsic.quantized</cite> — quantized version of fused layers for
inference (no BatchNorm variants as it’s usually folded into convolution for
inference):</p>
<ul>
<li><p><a class="reference internal" href="torch.nn.intrinsic.quantized.html#torch.nn.intrinsic.quantized.LinearReLU" title="torch.nn.intrinsic.quantized.LinearReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearReLU</span></code></a> — Linear + ReLU</p></li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU1d</span></code> — 1D Convolution + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.quantized.html#torch.nn.intrinsic.quantized.ConvReLU2d" title="torch.nn.intrinsic.quantized.ConvReLU2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU2d</span></code></a> — 2D Convolution + ReLU</p></li>
<li><p><a class="reference internal" href="torch.nn.intrinsic.quantized.html#torch.nn.intrinsic.quantized.ConvReLU3d" title="torch.nn.intrinsic.quantized.ConvReLU3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvReLU3d</span></code></a> — 3D Convolution + ReLU</p></li>
</ul>
</li>
</ul>
</div>
<div class="section" id="torch-nn-qat">
<h2><cite>torch.nn.qat</cite><a class="headerlink" href="#torch-nn-qat" title="Permalink to this headline">¶</a></h2>
<p>Layers for the quantization-aware training</p>
<ul class="simple">
<li><p><a class="reference internal" href="torch.nn.qat.html#torch.nn.qat.Linear" title="torch.nn.qat.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a> — Linear (fully-connected) layer</p></li>
<li><p><a class="reference internal" href="torch.nn.qat.html#torch.nn.qat.Conv2d" title="torch.nn.qat.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> — 2D convolution</p></li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code> — 3D convolution</p></li>
</ul>
</div>
<div class="section" id="torch-quantization">
<h2><cite>torch.quantization</cite><a class="headerlink" href="#torch-quantization" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>Functions for eager mode quantization:</p>
<ul>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.add_observer_" title="torch.quantization.add_observer_"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_observer_()</span></code></a> — Adds observer for the leaf
modules (if quantization configuration is provided)</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.add_quant_dequant" title="torch.quantization.add_quant_dequant"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_quant_dequant()</span></code></a>— Wraps the leaf child module using <a class="reference internal" href="torch.quantization.html#torch.quantization.QuantWrapper" title="torch.quantization.QuantWrapper"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantWrapper</span></code></a></p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.convert" title="torch.quantization.convert"><code class="xref py py-func docutils literal notranslate"><span class="pre">convert()</span></code></a> — Converts float module with
observers into its quantized counterpart. Must have quantization
configuration</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.get_observer_dict" title="torch.quantization.get_observer_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_observer_dict()</span></code></a> — Traverses the module
children and collects all observers into a <code class="docutils literal notranslate"><span class="pre">dict</span></code></p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.prepare" title="torch.quantization.prepare"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare()</span></code></a> — Prepares a copy of a model for
quantization</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.prepare_qat" title="torch.quantization.prepare_qat"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_qat()</span></code></a> — Prepares a copy of a model for
quantization aware training</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.propagate_qconfig_" title="torch.quantization.propagate_qconfig_"><code class="xref py py-func docutils literal notranslate"><span class="pre">propagate_qconfig_()</span></code></a> — Propagates quantization
configurations through the module hierarchy and assign them to each leaf
module</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.quantize" title="torch.quantization.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize()</span></code></a> — Function for eager mode post training static quantization</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.quantize_dynamic" title="torch.quantization.quantize_dynamic"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize_dynamic()</span></code></a> — Function for eager mode post training dynamic quantization</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.quantize_qat" title="torch.quantization.quantize_qat"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize_qat()</span></code></a> — Function for eager mode quantization aware training function</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.swap_module" title="torch.quantization.swap_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_module()</span></code></a> — Swaps the module with its
quantized counterpart (if quantizable and if it has an observer)</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.default_eval_fn" title="torch.quantization.default_eval_fn"><code class="xref py py-func docutils literal notranslate"><span class="pre">default_eval_fn()</span></code></a> — Default evaluation function
used by the <a class="reference internal" href="torch.quantization.html#torch.quantization.quantize" title="torch.quantization.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantization.quantize()</span></code></a></p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.fuse_modules" title="torch.quantization.fuse_modules"><code class="xref py py-func docutils literal notranslate"><span class="pre">fuse_modules()</span></code></a></p></li>
</ul>
</li>
<li><p>Functions for FX graph mode quantization:
* <code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_fx()</span></code> - Function for preparing the model for post training quantization with FX graph mode quantization
* <code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_qat_fx()</span></code> - Function for preparing the model for quantization aware training with FX graph mode quantization
* <code class="xref py py-func docutils literal notranslate"><span class="pre">convert_fx()</span></code> - Function for converting a prepared model to a quantized model with FX graph mode quantization</p></li>
<li><dl class="simple">
<dt>Quantization configurations</dt><dd><ul>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.QConfig" title="torch.quantization.QConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">QConfig</span></code></a> — Quantization configuration class</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">default_qconfig</span></code> — Same as
<code class="docutils literal notranslate"><span class="pre">QConfig(activation=default_observer,</span> <span class="pre">weight=default_weight_observer)</span></code>
(See <code class="xref py py-class docutils literal notranslate"><span class="pre">QConfig</span></code>)</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">default_qat_qconfig</span></code> — Same as
<code class="docutils literal notranslate"><span class="pre">QConfig(activation=default_fake_quant,</span>
<span class="pre">weight=default_weight_fake_quant)</span></code> (See
<code class="xref py py-class docutils literal notranslate"><span class="pre">QConfig</span></code>)</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">default_dynamic_qconfig</span></code> — Same as
<code class="docutils literal notranslate"><span class="pre">QConfigDynamic(weight=default_weight_observer)</span></code> (See
<code class="xref py py-class docutils literal notranslate"><span class="pre">QConfigDynamic</span></code>)</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">float16_dynamic_qconfig</span></code> — Same as
<code class="docutils literal notranslate"><span class="pre">QConfigDynamic(weight=NoopObserver.with_args(dtype=torch.float16))</span></code>
(See <code class="xref py py-class docutils literal notranslate"><span class="pre">QConfigDynamic</span></code>)</p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>Stubs</dt><dd><ul>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.DeQuantStub" title="torch.quantization.DeQuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeQuantStub</span></code></a> - placeholder module for
dequantize() operation in float-valued models</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.QuantStub" title="torch.quantization.QuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantStub</span></code></a> - placeholder module for
quantize() operation in float-valued models</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.QuantWrapper" title="torch.quantization.QuantWrapper"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantWrapper</span></code></a> — wraps the module to be
quantized. Inserts the <a class="reference internal" href="torch.quantization.html#torch.quantization.QuantStub" title="torch.quantization.QuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantStub</span></code></a> and</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.DeQuantStub" title="torch.quantization.DeQuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeQuantStub</span></code></a></p></li>
</ul>
</dd>
</dl>
</li>
<li><p>Observers for computing the quantization parameters</p>
<ul>
<li><p>Default Observers. The rest of observers are available from
<code class="docutils literal notranslate"><span class="pre">torch.quantization.observer</span></code>:</p>
<ul>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">default_observer</span></code> — Same as <code class="docutils literal notranslate"><span class="pre">MinMaxObserver.with_args(reduce_range=True)</span></code></p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">default_weight_observer</span></code> — Same as <code class="docutils literal notranslate"><span class="pre">MinMaxObserver.with_args(dtype=torch.qint8,</span> <span class="pre">qscheme=torch.per_tensor_symmetric)</span></code></p></li>
</ul>
</li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Observer</span></code> — Abstract base class for observers</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.MinMaxObserver" title="torch.quantization.MinMaxObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">MinMaxObserver</span></code></a> — Derives the quantization
parameters from the running minimum and maximum of the observed tensor inputs
(per tensor variant)</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.MovingAverageMinMaxObserver" title="torch.quantization.MovingAverageMinMaxObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">MovingAverageMinMaxObserver</span></code></a> — Derives the
quantization parameters from the running averages of the minimums and
maximums of the observed tensor inputs (per tensor variant)</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.PerChannelMinMaxObserver" title="torch.quantization.PerChannelMinMaxObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">PerChannelMinMaxObserver</span></code></a> — Derives the
quantization parameters from the running minimum and maximum of the observed
tensor inputs (per channel variant)</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.MovingAveragePerChannelMinMaxObserver" title="torch.quantization.MovingAveragePerChannelMinMaxObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">MovingAveragePerChannelMinMaxObserver</span></code></a> — Derives
the quantization parameters from the running averages of the minimums and
maximums of the observed tensor inputs (per channel variant)</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.HistogramObserver" title="torch.quantization.HistogramObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistogramObserver</span></code></a> — Derives the quantization
parameters by creating a histogram of running minimums and maximums.</p></li>
</ul>
</li>
<li><dl class="simple">
<dt>Observers that do not compute the quantization parameters:</dt><dd><ul>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.RecordingObserver" title="torch.quantization.RecordingObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingObserver</span></code></a> — Records all incoming
tensors. Used for debugging only.</p></li>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.NoopObserver" title="torch.quantization.NoopObserver"><code class="xref py py-class docutils literal notranslate"><span class="pre">NoopObserver</span></code></a> — Pass-through observer. Used
for situation when there are no quantization parameters (i.e.
quantization to <code class="docutils literal notranslate"><span class="pre">float16</span></code>)</p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>FakeQuantize module</dt><dd><ul>
<li><p><a class="reference internal" href="torch.quantization.html#torch.quantization.FakeQuantize" title="torch.quantization.FakeQuantize"><code class="xref py py-class docutils literal notranslate"><span class="pre">FakeQuantize</span></code></a> — Module for simulating the
quantization/dequantization at training time</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
</div>
<div class="section" id="torch-nn-quantized">
<h2><cite>torch.nn.quantized</cite><a class="headerlink" href="#torch-nn-quantized" title="Permalink to this headline">¶</a></h2>
<p>Quantized version of standard NN layers.</p>
<ul class="simple">
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Quantize" title="torch.nn.quantized.Quantize"><code class="xref py py-class docutils literal notranslate"><span class="pre">Quantize</span></code></a> — Quantization layer, used to
automatically replace <a class="reference internal" href="torch.quantization.html#torch.quantization.QuantStub" title="torch.quantization.QuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantStub</span></code></a></p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.DeQuantize" title="torch.nn.quantized.DeQuantize"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeQuantize</span></code></a> — Dequantization layer, used to
replace <a class="reference internal" href="torch.quantization.html#torch.quantization.DeQuantStub" title="torch.quantization.DeQuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeQuantStub</span></code></a></p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.FloatFunctional" title="torch.nn.quantized.FloatFunctional"><code class="xref py py-class docutils literal notranslate"><span class="pre">FloatFunctional</span></code></a> — Wrapper class to make
stateless float operations stateful so that they can be replaced with
quantized versions</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.QFunctional" title="torch.nn.quantized.QFunctional"><code class="xref py py-class docutils literal notranslate"><span class="pre">QFunctional</span></code></a> — Wrapper class for quantized
versions of stateless operations like <code class="docutils literal notranslate"><span class="pre">torch.add</span></code></p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Conv1d" title="torch.nn.quantized.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> — 1D convolution</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Conv2d" title="torch.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> — 2D convolution</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Conv3d" title="torch.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> — 3D convolution</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Linear" title="torch.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a> — Linear (fully-connected) layer</p></li>
<li><p><a class="reference internal" href="generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a> — 2D max pooling</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.ReLU6" title="torch.nn.quantized.ReLU6"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReLU6</span></code></a> — Rectified linear unit with cut-off at
quantized representation of 6</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.ELU" title="torch.nn.quantized.ELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ELU</span></code></a> — ELU</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.Hardswish" title="torch.nn.quantized.Hardswish"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardswish</span></code></a> — Hardswish</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.BatchNorm2d" title="torch.nn.quantized.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a> — BatchNorm2d. <em>Note: this module is usually fused with Conv or Linear. Performance on ARM is not optimized</em>.</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.BatchNorm3d" title="torch.nn.quantized.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a> — BatchNorm3d. <em>Note: this module is usually fused with Conv or Linear. Performance on ARM is not optimized</em>.</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.LayerNorm" title="torch.nn.quantized.LayerNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">LayerNorm</span></code></a> — LayerNorm. <em>Note: performance on ARM is not optimized</em>.</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.GroupNorm" title="torch.nn.quantized.GroupNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupNorm</span></code></a> — GroupNorm. <em>Note: performance on ARM is not optimized</em>.</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.InstanceNorm1d" title="torch.nn.quantized.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a> — InstanceNorm1d. <em>Note: performance on ARM is not optimized</em>.</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.InstanceNorm2d" title="torch.nn.quantized.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a> — InstanceNorm2d. <em>Note: performance on ARM is not optimized</em>.</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.InstanceNorm3d" title="torch.nn.quantized.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a> — InstanceNorm3d. <em>Note: performance on ARM is not optimized</em>.</p></li>
</ul>
</div>
<div class="section" id="torch-nn-quantized-dynamic">
<h2><cite>torch.nn.quantized.dynamic</cite><a class="headerlink" href="#torch-nn-quantized-dynamic" title="Permalink to this headline">¶</a></h2>
<p>Layers used in dynamically quantized models (i.e. quantized only on weights)</p>
<ul class="simple">
<li><p><a class="reference internal" href="torch.nn.quantized.dynamic.html#torch.nn.quantized.dynamic.Linear" title="torch.nn.quantized.dynamic.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a> — Linear (fully-connected) layer</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.dynamic.html#torch.nn.quantized.dynamic.LSTM" title="torch.nn.quantized.dynamic.LSTM"><code class="xref py py-class docutils literal notranslate"><span class="pre">LSTM</span></code></a> — Long-Short Term Memory RNN module</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.dynamic.html#torch.nn.quantized.dynamic.LSTMCell" title="torch.nn.quantized.dynamic.LSTMCell"><code class="xref py py-class docutils literal notranslate"><span class="pre">LSTMCell</span></code></a> — LSTM Cell</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.dynamic.html#torch.nn.quantized.dynamic.GRUCell" title="torch.nn.quantized.dynamic.GRUCell"><code class="xref py py-class docutils literal notranslate"><span class="pre">GRUCell</span></code></a> — GRU Cell</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.dynamic.html#torch.nn.quantized.dynamic.RNNCell" title="torch.nn.quantized.dynamic.RNNCell"><code class="xref py py-class docutils literal notranslate"><span class="pre">RNNCell</span></code></a> — RNN Cell</p></li>
</ul>
</div>
<div class="section" id="torch-nn-quantized-functional">
<h2><cite>torch.nn.quantized.functional</cite><a class="headerlink" href="#torch-nn-quantized-functional" title="Permalink to this headline">¶</a></h2>
<p>Functional versions of quantized NN layers (many of them accept explicit
quantization output parameters)</p>
<ul class="simple">
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.adaptive_avg_pool2d" title="torch.nn.quantized.functional.adaptive_avg_pool2d"><code class="xref py py-func docutils literal notranslate"><span class="pre">adaptive_avg_pool2d()</span></code></a> — 2D adaptive average pooling</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.avg_pool2d" title="torch.nn.quantized.functional.avg_pool2d"><code class="xref py py-func docutils literal notranslate"><span class="pre">avg_pool2d()</span></code></a> — 2D average pooling</p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">avg_pool3d()</span></code> — 3D average pooling</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.conv1d" title="torch.nn.quantized.functional.conv1d"><code class="xref py py-func docutils literal notranslate"><span class="pre">conv1d()</span></code></a> — 1D convolution</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.conv2d" title="torch.nn.quantized.functional.conv2d"><code class="xref py py-func docutils literal notranslate"><span class="pre">conv2d()</span></code></a> — 2D convolution</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.conv3d" title="torch.nn.quantized.functional.conv3d"><code class="xref py py-func docutils literal notranslate"><span class="pre">conv3d()</span></code></a> — 3D convolution</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">interpolate()</span></code></a> — Down-/up- sampler</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.linear" title="torch.nn.quantized.functional.linear"><code class="xref py py-func docutils literal notranslate"><span class="pre">linear()</span></code></a> — Linear (fully-connected) op</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.max_pool2d" title="torch.nn.quantized.functional.max_pool2d"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_pool2d()</span></code></a> — 2D max pooling</p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">elu()</span></code> — ELU</p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">hardsigmoid()</span></code> — Hardsigmoid</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.hardswish" title="torch.nn.quantized.functional.hardswish"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardswish()</span></code></a> — Hardswish</p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">hardtanh()</span></code> — Hardtanh</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.upsample" title="torch.nn.quantized.functional.upsample"><code class="xref py py-func docutils literal notranslate"><span class="pre">upsample()</span></code></a> — Upsampler. Will be
deprecated in favor of <a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">interpolate()</span></code></a></p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.upsample_bilinear" title="torch.nn.quantized.functional.upsample_bilinear"><code class="xref py py-func docutils literal notranslate"><span class="pre">upsample_bilinear()</span></code></a> — Bilinear
upsampler. Will be deprecated in favor of</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">interpolate()</span></code></a></p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.upsample_nearest" title="torch.nn.quantized.functional.upsample_nearest"><code class="xref py py-func docutils literal notranslate"><span class="pre">upsample_nearest()</span></code></a> — Nearest neighbor
upsampler. Will be deprecated in favor of</p></li>
<li><p><a class="reference internal" href="torch.nn.quantized.html#torch.nn.quantized.functional.interpolate" title="torch.nn.quantized.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">interpolate()</span></code></a></p></li>
</ul>
</div>
<div class="section" id="quantized-dtypes-and-quantization-schemes">
<h2>Quantized dtypes and quantization schemes<a class="headerlink" href="#quantized-dtypes-and-quantization-schemes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.qscheme</span></code> — Type to describe the quantization scheme of a tensor.
Supported types:</p>
<ul>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.per_tensor_affine</span></code> — per tensor, asymmetric</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.per_channel_affine</span></code> — per channel, asymmetric</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.per_tensor_symmetric</span></code> — per tensor, symmetric</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.per_channel_symmetric</span></code> — per channel, symmetric</p></li>
</ul>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.dtype</span></code> — Type to describe the data. Supported types:</p>
<ul>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.quint8</span></code> — 8-bit unsigned integer</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.qint8</span></code> — 8-bit signed integer</p></li>
<li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">torch.qint32</span></code> — 32-bit signed integer</p></li>
</ul>
</li>
</ul>
</div>
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