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<div class="section" id="quantization">
<span id="quantization-doc"></span><h1>Quantization<a class="headerlink" href="#quantization" title="Permalink to this headline">¶</a></h1>
<div class="section" id="introduction-to-quantization">
<h2>Introduction to Quantization<a class="headerlink" href="#introduction-to-quantization" title="Permalink to this headline">¶</a></h2>
<p>Quantization refers to techniques for performing computations and storing
tensors at lower bitwidths than floating point precision. A quantized model
executes some or all of the operations on tensors with integers rather than
floating point values. This allows for a more compact model representation and
the use of high performance vectorized operations on many hardware platforms.
PyTorch supports INT8 quantization compared to typical FP32 models allowing for
a 4x reduction in the model size and a 4x reduction in memory bandwidth
requirements. Hardware support for INT8 computations is typically 2 to 4
times faster compared to FP32 compute. Quantization is primarily a technique to
speed up inference and only the forward pass is supported for quantized
operators.</p>
<p>PyTorch supports multiple approaches to quantizing a deep learning model. In
most cases the model is trained in FP32 and then the model is converted to
INT8. In addition, PyTorch also supports quantization aware training, which
models quantization errors in both the forward and backward passes using
fake-quantization modules. Note that the entire computation is carried out in
floating point. At the end of quantization aware training, PyTorch provides
conversion functions to convert the trained model into lower precision.</p>
<p>At lower level, PyTorch provides a way to represent quantized tensors and
perform operations with them. They can be used to directly construct models
that perform all or part of the computation in lower precision. Higher-level
APIs are provided that incorporate typical workflows of converting FP32 model
to lower precision with minimal accuracy loss.</p>
<p>Today, PyTorch supports the following backends for running quantized operators efficiently:</p>
<ul class="simple">
<li><p>x86 CPUs with AVX2 support or higher (without AVX2 some operations have
inefficient implementations)</p></li>
<li><p>ARM CPUs (typically found in mobile/embedded devices)</p></li>
</ul>
<p>The corresponding implementation is chosen automatically based on the PyTorch build mode.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>PyTorch 1.3 doesn’t provide quantized operator implementations on CUDA yet -
this is direction of future work. Move the model to CPU in order to test the
quantized functionality.</p>
<p>Quantization-aware training (through <a class="reference internal" href="#torch.quantization.FakeQuantize" title="torch.quantization.FakeQuantize"><code class="xref py py-class docutils literal notranslate"><span class="pre">FakeQuantize</span></code></a>)
supports both CPU and CUDA.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When preparing a quantized model, it is necessary to ensure that qconfig
and the engine used for quantized computations match the backend on which
the model will be executed. Quantization currently supports two backends:
fbgemm (for use on x86, <a class="reference external" href="https://github.com/pytorch/FBGEMM">https://github.com/pytorch/FBGEMM</a>) and qnnpack
(for use on the ARM QNNPACK library <a class="reference external" href="https://github.com/pytorch/QNNPACK">https://github.com/pytorch/QNNPACK</a>).
For example, if you are interested in quantizing a model to run on ARM, it
is recommended to set the qconfig by calling:</p>
<p><code class="docutils literal notranslate"><span class="pre">qconfig</span> <span class="pre">=</span> <span class="pre">torch.quantization.get_default_qconfig('qnnpack')</span></code></p>
<p>for post training quantization and</p>
<p><code class="docutils literal notranslate"><span class="pre">qconfig</span> <span class="pre">=</span> <span class="pre">torch.quantization.get_default_qat_qconfig('qnnpack')</span></code></p>
<p>for quantization aware training.</p>
<p>In addition, the torch.backends.quantized.engine parameter should be set to
match the backend. For using qnnpack for inference, the backend is set to
qnnpack as follows</p>
<p><code class="docutils literal notranslate"><span class="pre">torch.backends.quantized.engine</span> <span class="pre">=</span> <span class="pre">'qnnpack'</span></code></p>
</div>
</div>
<div class="section" id="quantized-tensors">
<h2>Quantized Tensors<a class="headerlink" href="#quantized-tensors" title="Permalink to this headline">¶</a></h2>
<p>PyTorch supports both per tensor and per channel asymmetric linear
quantization. Per tensor means that all the values within the tensor are
scaled the same way. Per channel means that for each dimension, typically
the channel dimension of a tensor, the values
in the tensor are scaled and offset by a different value (effectively
the scale and offset become vectors). This allows for lesser error in converting tensors
to quantized values.</p>
<p>The mapping is performed by converting the floating point tensors using</p>
<a class="reference internal image-reference" href="_images/math-quantizer-equation.png"><img alt="_images/math-quantizer-equation.png" src="_images/math-quantizer-equation.png" style="width: 40%;" /></a>
<p>Note that, we ensure that zero in floating point is represented with no error
after quantization, thereby ensuring that operations like padding do not cause
additional quantization error.</p>
<p>In order to do quantization in PyTorch, we need to be able to represent
quantized data in Tensors. A Quantized Tensor allows for storing
quantized data (represented as int8/uint8/int32) along with quantization
parameters like scale and zero_point. Quantized Tensors allow for many
useful operations making quantized arithmetic easy, in addition to
allowing for serialization of data in a quantized format.</p>
</div>
<div class="section" id="operation-coverage">
<h2>Operation coverage<a class="headerlink" href="#operation-coverage" title="Permalink to this headline">¶</a></h2>
<p>Quantized Tensors support a limited subset of data manipulation methods of the
regular full-precision tensor. (see list below)</p>
<p>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>Current quantized operation list is sufficient to cover typical CNN and RNN
models:</p>
<div class="section" id="quantized-torch-tensor-operations">
<h3>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></h3>
<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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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="tensors.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">
<h3><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></h3>
<p>Basic activations are supported.</p>
<ul class="simple">
<li><p><a class="reference internal" href="nn.functional.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="nn.functional.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="nn.functional.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="nn.functional.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="nn.functional.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="nn.functional.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="nn.functional.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="nn.functional.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="nn.functional.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">
<h3><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></h3>
<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><code class="docutils literal notranslate"><span class="pre">torch.nn.intrinsic</span></code> — 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.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.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.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.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><a class="reference internal" href="#torch.nn.intrinsic.LinearReLU" title="torch.nn.intrinsic.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><code class="docutils literal notranslate"><span class="pre">torch.nn.intrinsic.qat</span></code> — versions of layers for quantization-aware training:
* <a class="reference internal" href="#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
* <a class="reference internal" href="#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
* <a class="reference internal" href="#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
* <a class="reference internal" href="#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>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.nn.intrinsic.quantized</span></code> — quantized version of fused layers for
inference (no BatchNorm variants as it’s usually folded into convolution for
inference):
* <a class="reference internal" href="#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
* <a class="reference internal" href="#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
* <a class="reference internal" href="#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>
</div>
<div class="section" id="torch-nn-qat">
<h3><code class="docutils literal notranslate"><span class="pre">torch.nn.qat</span></code><a class="headerlink" href="#torch-nn-qat" title="Permalink to this headline">¶</a></h3>
<p>Layers for the quantization-aware training</p>
<ul class="simple">
<li><p><a class="reference internal" href="#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.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>
</ul>
</div>
<div class="section" id="torch-quantization">
<h3><code class="docutils literal notranslate"><span class="pre">torch.quantization</span></code><a class="headerlink" href="#torch-quantization" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><p>Functions for quantization:</p>
<ul>
<li><p><a class="reference internal" href="#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.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.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.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.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.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.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.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.quantize" title="torch.quantization.quantize"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantize()</span></code></a> — Converts a float module to quantized version</p></li>
<li><p><a class="reference internal" href="#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> — Converts a float module to
dynamically quantized version</p></li>
<li><p><a class="reference internal" href="#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> — Converts a float module to
quantized version used in quantization aware training</p></li>
<li><p><a class="reference internal" href="#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>
</ul>
</li>
<li><p><a class="reference internal" href="#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.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.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>
<li><p><a class="reference internal" href="#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>
<li><p>Default Observers. The rest of observers are available from
<code class="docutils literal notranslate"><span class="pre">torch.quantization.observer</span></code>:
* <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>
* <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>
* <code class="xref py py-class docutils literal notranslate"><span class="pre">Observer</span></code> — Abstract base class for observers</p></li>
<li><dl class="simple">
<dt>Quantization configurations</dt><dd><ul>
<li><p><a class="reference internal" href="#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.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.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.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.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.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>
</ul>
<p>Observers for computing the quantization parameters</p>
<ul class="simple">
<li><p><a class="reference internal" href="#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.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.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.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.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>
<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.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.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>
</ul>
</div>
<div class="section" id="torch-nn-quantized">
<h3><code class="docutils literal notranslate"><span class="pre">torch.nn.quantized</span></code><a class="headerlink" href="#torch-nn-quantized" title="Permalink to this headline">¶</a></h3>
<p>Quantized version of standard NN layers.</p>
<ul class="simple">
<li><p><a class="reference internal" href="#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.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.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.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.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.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.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.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.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.ReLU" title="torch.nn.quantized.ReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReLU</span></code></a> — Rectified linear unit</p></li>
<li><p><a class="reference internal" href="#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>
</ul>
</div>
<div class="section" id="torch-nn-quantized-dynamic">
<h3><code class="docutils literal notranslate"><span class="pre">torch.nn.quantized.dynamic</span></code><a class="headerlink" href="#torch-nn-quantized-dynamic" title="Permalink to this headline">¶</a></h3>
<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.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.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>
</ul>
</div>
<div class="section" id="torch-nn-quantized-functional">
<h3><code class="docutils literal notranslate"><span class="pre">torch.nn.quantized.functional</span></code><a class="headerlink" href="#torch-nn-quantized-functional" title="Permalink to this headline">¶</a></h3>
<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.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.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><a class="reference internal" href="#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.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.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.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.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><a class="reference internal" href="#torch.nn.quantized.functional.relu" title="torch.nn.quantized.functional.relu"><code class="xref py py-func docutils literal notranslate"><span class="pre">relu()</span></code></a> — Rectified linear unit</p></li>
<li><p><a class="reference internal" href="#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.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.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> — Bilenear
upsampler. Will be deprecated in favor of</p></li>
<li><p><a class="reference internal" href="#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.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.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">
<h3>Quantized dtypes and quantization schemes<a class="headerlink" href="#quantized-dtypes-and-quantization-schemes" title="Permalink to this headline">¶</a></h3>
<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 tensor, 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>
</div>
<div class="section" id="quantization-workflows">
<h2>Quantization Workflows<a class="headerlink" href="#quantization-workflows" title="Permalink to this headline">¶</a></h2>
<p>PyTorch provides three approaches to quantize models.</p>
<ol class="arabic">
<li><p>Post Training Dynamic Quantization: This is the simplest to apply form of
quantization where the weights are quantized ahead of time but the
activations are dynamically quantized during inference. This is used
for situations where the model execution time is dominated by loading
weights from memory rather than computing the matrix multiplications.
This is true for for LSTM and Transformer type models with small
batch size. Applying dynamic quantization to a whole model can be
done with a single call to <a class="reference internal" href="#torch.quantization.quantize_dynamic" title="torch.quantization.quantize_dynamic"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantization.quantize_dynamic()</span></code></a>.
See the <a class="reference external" href="https://pytorch.org/tutorials/#quantization-experimental">quantization tutorials</a></p></li>
<li><p>Post Training Static Quantization: This is the most commonly used form of
quantization where the weights are quantized ahead of time and the
scale factor and bias for the activation tensors is pre-computed
based on observing the behavior of the model during a calibration
process. Post Training Quantization is typically when both memory bandwidth
and compute savings are important with CNNs being a typical use case.
The general process for doing post training quantization is:</p>
<ol class="arabic simple">
<li><p>Prepare the model:</p>
<ol class="loweralpha simple">
<li><p>Specify where the activations are quantized and dequantized explicitly
by adding QuantStub and DeQuantStub modules.</p></li>
<li><p>Ensure that modules are not reused.</p></li>
<li><p>Convert any operations that require requantization into modules</p></li>
</ol>
</li>
<li><p>Fuse operations like conv + relu or conv+batchnorm + relu together to
improve both model accuracy and performance.</p></li>
<li><p>Specify the configuration of the quantization methods ‘97 such as
selecting symmetric or asymmetric quantization and MinMax or
L2Norm calibration techniques.</p></li>
<li><p>Use the <a class="reference internal" href="#torch.quantization.prepare" title="torch.quantization.prepare"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantization.prepare()</span></code></a> to insert modules
that will observe activation tensors during calibration</p></li>
<li><p>Calibrate the model by running inference against a calibration
dataset</p></li>
<li><p>Finally, convert the model itself with the
torch.quantization.convert() method. This does several things: it
quantizes the weights, computes and stores the scale and bias
value to be used each activation tensor, and replaces key
operators quantized implementations.</p></li>
</ol>
<p>See the <a class="reference external" href="https://pytorch.org/tutorials/#quantization-experimental">quantization tutorials</a></p>
</li>
<li><p>Quantization Aware Training: In the rare cases where post training
quantization does not provide adequate accuracy training can be done
with simulated quantization using the
<a class="reference internal" href="#torch.quantization.FakeQuantize" title="torch.quantization.FakeQuantize"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.quantization.FakeQuantize</span></code></a>. Computations will take place in
FP32 but with values clamped and rounded to simulate the effects of INT8
quantization. The sequence of steps is very similar.</p>
<ol class="arabic simple">
<li><p>Steps (1) and (2) are identical.</p></li>
</ol>
<ol class="arabic simple" start="3">
<li><p>Specify the configuration of the fake quantization methods ‘97 such as
selecting symmetric or asymmetric quantization and MinMax or Moving Average
or L2Norm calibration techniques.</p></li>
<li><p>Use the <a class="reference internal" href="#torch.quantization.prepare_qat" title="torch.quantization.prepare_qat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantization.prepare_qat()</span></code></a> to insert modules
that will simulate quantization during training.</p></li>
<li><p>Train or fine tune the model.</p></li>
<li><p>Identical to step (6) for post training quantization</p></li>
</ol>
<p>See the <a class="reference external" href="https://pytorch.org/tutorials/#quantization-experimental">quantization tutorials</a></p>
</li>
</ol>
<p>While default implementations of observers to select the scale factor and bias
based on observed tensor data are provided, developers can provide their own
quantization functions. Quantization can be applied selectively to different
parts of the model or configured differently for different parts of the model.</p>
<p>We also provide support for per channel quantization for <strong>conv2d()</strong>,
<strong>conv3d()</strong> and <strong>linear()</strong></p>
<p>Quantization workflows work by adding (e.g. adding observers as
<code class="docutils literal notranslate"><span class="pre">.observer</span></code> submodule) or replacing (e.g. converting <code class="docutils literal notranslate"><span class="pre">nn.Conv2d</span></code> to
<code class="docutils literal notranslate"><span class="pre">nn.quantized.Conv2d</span></code>) submodules in the model’s module hierarchy. It
means that the model stays a regular <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>-based instance throughout the
process and thus can work with the rest of PyTorch APIs.</p>
</div>
<div class="section" id="model-preparation-for-quantization">
<h2>Model Preparation for Quantization<a class="headerlink" href="#model-preparation-for-quantization" title="Permalink to this headline">¶</a></h2>
<p>It is necessary to currently make some modifications to the model definition
prior to quantization. This is because currently quantization works on a module
by module basis. Specifically, for all quantization techniques, the user needs to:</p>
<ol class="arabic simple">
<li><p>Convert any operations that require output requantization (and thus have
additional parameters) from functionals to module form.</p></li>
<li><p>Specify which parts of the model need to be quantized either by assigning
<code class="docutils literal notranslate"><span class="pre">`.qconfig</span></code> attributes on submodules or by specifying <code class="docutils literal notranslate"><span class="pre">qconfig_dict</span></code></p></li>
</ol>
<p>For static quantization techniques which quantize activations, the user needs
to do the following in addition:</p>
<ol class="arabic simple">
<li><p>Specify where activations are quantized and de-quantized. This is done using
<a class="reference internal" href="#torch.quantization.QuantStub" title="torch.quantization.QuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantStub</span></code></a> and
<a class="reference internal" href="#torch.quantization.DeQuantStub" title="torch.quantization.DeQuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeQuantStub</span></code></a> modules.</p></li>
<li><p>Use <a class="reference internal" href="#torch.nn.quantized.FloatFunctional" title="torch.nn.quantized.FloatFunctional"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.quantized.FloatFunctional</span></code></a> to wrap tensor operations
that require special handling for quantization into modules. Examples
are operations like <code class="docutils literal notranslate"><span class="pre">add</span></code> and <code class="docutils literal notranslate"><span class="pre">cat</span></code> which require special handling to
determine output quantization parameters.</p></li>
<li><p>Fuse modules: combine operations/modules into a single module to obtain
higher accuracy and performance. This is done using the
<a class="reference internal" href="#torch.quantization.fuse_modules" title="torch.quantization.fuse_modules"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.quantization.fuse_modules()</span></code></a> API, which takes in lists of modules
to be fused. We currently support the following fusions:
[Conv, Relu], [Conv, BatchNorm], [Conv, BatchNorm, Relu], [Linear, Relu]</p></li>
</ol>
</div>
<div class="section" id="id1">
<h2>torch.quantization<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<span class="target" id="module-torch.quantization"></span><p>This module implements the functions you call
directly to convert your model from FP32 to quantized form. For
example the <a class="reference internal" href="#torch.quantization.prepare" title="torch.quantization.prepare"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare()</span></code></a> is used in post training
quantization to prepares your model for the calibration step and
<a class="reference internal" href="#torch.quantization.convert" title="torch.quantization.convert"><code class="xref py py-func docutils literal notranslate"><span class="pre">convert()</span></code></a> actually converts the weights to int8 and
replaces the operations with their quantized counterparts. There are
other helper functions for things like quantizing the input to your
model and performing critical fusions like conv+relu.</p>
<div class="section" id="top-level-quantization-apis">
<h3>Top-level quantization APIs<a class="headerlink" href="#top-level-quantization-apis" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.quantization.quantize">
<code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">quantize</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">run_fn</em>, <em class="sig-param">run_args</em>, <em class="sig-param">mapping=None</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/quantization/quantize.html#quantize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.quantize" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts a float model to quantized model.</p>
<p>First it will prepare the model for calibration or training, then it calls
<cite>run_fn</cite> which will run the calibration step or training step,
after that we will call <cite>convert</cite> which will convert the model to a
quantized model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – input model</p></li>
<li><p><strong>run_fn</strong> – a function for evaluating the prepared model, can be a
function that simply runs the prepared model or a training loop</p></li>
<li><p><strong>run_args</strong> – positional arguments for <cite>run_fn</cite></p></li>
<li><p><strong>inplace</strong> – carry out model transformations in-place, the original module is mutated</p></li>
<li><p><strong>mapping</strong> – correspondence between original module types and quantized counterparts</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Quantized model.</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.quantization.quantize_dynamic">
<code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">quantize_dynamic</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">qconfig_spec=None</em>, <em class="sig-param">dtype=torch.qint8</em>, <em class="sig-param">mapping=None</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/quantization/quantize.html#quantize_dynamic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.quantize_dynamic" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts a float model to dynamic (i.e. weights-only) quantized model.</p>
<p>Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.</p>
<p>For simplest usage provide <cite>dtype</cite> argument that can be float16 or qint8. Weight-only quantization
by default is performed for layers with large weights size - i.e. Linear and RNN variants.</p>
<p>Fine grained control is possible with <cite>qconfig</cite> and <cite>mapping</cite> that act similarly to <cite>quantize()</cite>.
If <cite>qconfig</cite> is provided, the <cite>dtype</cite> argument is ignored.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> – input model</p></li>
<li><p><strong>qconfig_spec</strong> – <p>Either:</p>
<ul>
<li><p>A dictionary that maps from name or type of submodule to quantization
configuration, qconfig applies to all submodules of a given
module unless qconfig for the submodules are specified (when the
submodule already has qconfig attribute). Entries in the dictionary
need to be QConfigDynamic instances.</p></li>
<li><p>A set of types and/or submodule names to apply dynamic quantization to,
in which case the <cite>dtype</cite> argument is used to specifiy the bit-width</p></li>
</ul>
</p></li>
<li><p><strong>inplace</strong> – carry out model transformations in-place, the original module is mutated</p></li>
<li><p><strong>mapping</strong> – maps type of a submodule to a type of corresponding dynamically quantized version
with which the submodule needs to be replaced</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.quantization.quantize_qat">
<code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">quantize_qat</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">run_fn</em>, <em class="sig-param">run_args</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/quantization/quantize.html#quantize_qat"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.quantize_qat" title="Permalink to this definition">¶</a></dt>
<dd><p>Do quantization aware training and output a quantized model</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – input model</p></li>
<li><p><strong>run_fn</strong> – a function for evaluating the prepared model, can be a
function that simply runs the prepared model or a training
loop</p></li>
<li><p><strong>run_args</strong> – positional arguments for <cite>run_fn</cite></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Quantized model.</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.quantization.prepare">
<code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">prepare</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">inplace=False</em>, <em class="sig-param">white_list={<class 'torch.nn.quantized.modules.functional_modules.FloatFunctional'></em>, <em class="sig-param"><class 'torch.nn.modules.activation.ReLU'></em>, <em class="sig-param"><class 'torch.nn.qat.modules.activations.Hardswish'></em>, <em class="sig-param"><class 'torch.nn.modules.activation.ReLU6'></em>, <em class="sig-param"><class 'torch.nn.modules.rnn.LSTM'></em>, <em class="sig-param"><class 'torch.nn.qat.modules.linear.Linear'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.ConvReLU3d'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d'></em>, <em class="sig-param"><class 'torch.nn.qat.modules.conv.Conv2d'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.BNReLU3d'></em>, <em class="sig-param"><class 'torch.nn.modules.batchnorm.BatchNorm3d'></em>, <em class="sig-param"><class 'torch.nn.modules.normalization.LayerNorm'></em>, <em class="sig-param"><class 'torch.nn.modules.activation.Hardswish'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.BNReLU2d'></em>, <em class="sig-param"><class 'torch.quantization.stubs.QuantStub'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.ConvReLU2d'></em>, <em class="sig-param"><class 'torch.nn.modules.conv.Conv3d'></em>, <em class="sig-param"><class 'torch.nn.modules.batchnorm.BatchNorm2d'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d'></em>, <em class="sig-param"><class 'torch.nn.modules.conv.Conv2d'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.qat.modules.linear_relu.LinearReLU'></em>, <em class="sig-param"><class 'torch.nn.modules.conv.Conv1d'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.ConvBnReLU2d'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.LinearReLU'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.modules.fused.ConvBn2d'></em>, <em class="sig-param"><class 'torch.nn.modules.linear.Linear'></em>, <em class="sig-param"><class 'torch.nn.modules.container.Sequential'></em>, <em class="sig-param"><class 'torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d'>}</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/quantization/quantize.html#prepare"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.prepare" title="Permalink to this definition">¶</a></dt>
<dd><p>Prepares a copy of the model for quantization calibration or quantization-aware training.</p>
<p>Quantization configuration should be assigned preemptively
to individual submodules in <cite>.qconfig</cite> attribute.</p>
<p>The model will be attached with observer or fake quant modules, and qconfig
will be propagated.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – input model to be modified in-place</p></li>
<li><p><strong>inplace</strong> – carry out model transformations in-place, the original module is mutated</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.quantization.prepare_qat">
<code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">prepare_qat</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">mapping=None</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/quantization/quantize.html#prepare_qat"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.prepare_qat" title="Permalink to this definition">¶</a></dt>
<dd><p>Prepares a copy of the model for quantization calibration or
quantization-aware training and convers it to quantized version.</p>
<p>Quantization configuration should be assigned preemptively
to individual submodules in <cite>.qconfig</cite> attribute.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model</strong> – input model to be modified in-place</p></li>
<li><p><strong>mapping</strong> – dictionary that maps float modules to quantized modules to be
replaced.</p></li>
<li><p><strong>inplace</strong> – carry out model transformations in-place, the original module
is mutated</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.quantization.convert">
<code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">convert</code><span class="sig-paren">(</span><em class="sig-param">module</em>, <em class="sig-param">mapping=None</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/quantization/quantize.html#convert"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.convert" title="Permalink to this definition">¶</a></dt>
<dd><p>Converts the float module with observers (where we can get quantization
parameters) to a quantized module.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> – calibrated module with observers</p></li>
<li><p><strong>mapping</strong> – a dictionary that maps from float module type to quantized
module type, can be overwrritten to allow swapping user defined
Modules</p></li>
<li><p><strong>inplace</strong> – carry out model transformations in-place, the original module
is mutated</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torch.quantization.QConfig">
<em class="property">class </em><code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">QConfig</code><a class="reference internal" href="_modules/torch/quantization/qconfig.html#QConfig"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.QConfig" title="Permalink to this definition">¶</a></dt>
<dd><p>Describes how to quantize a layer or a part of the network by providing
settings (observer classes) for activations and weights respectively.</p>
<p>Note that QConfig needs to contain observer <strong>classes</strong> (like MinMaxObserver) or a callable that returns
instances on invocation, not the concrete observer instances themselves.
Quantization preparation function will instantiate observers multiple times for each of the layers.</p>
<p>Observer classes have usually reasonable default arguments, but they can be overwritten with <cite>with_args</cite>
method (that behaves like functools.partial):</p>
<blockquote>
<div><p>my_qconfig = QConfig(activation=MinMaxObserver.with_args(dtype=torch.qint8),
weight=default_observer.with_args(dtype=torch.qint8))</p>
</div></blockquote>
</dd></dl>
<dl class="class">
<dt id="torch.quantization.QConfigDynamic">
<em class="property">class </em><code class="sig-prename descclassname">torch.quantization.</code><code class="sig-name descname">QConfigDynamic</code><a class="reference internal" href="_modules/torch/quantization/qconfig.html#QConfigDynamic"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quantization.QConfigDynamic" title="Permalink to this definition">¶</a></dt>