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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">
<span id="quantization-doc"></span><h1>Quantization<a class="headerlink" href="#quantization" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Quantization is in beta and subject to change.</p>
</div>
<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.html#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="quantization-api-summary">
<h2>Quantization API Summary<a class="headerlink" href="#quantization-api-summary" title="Permalink to this headline">¶</a></h2>
<p>There are three types of quantization supported in PyTorch:</p>
<ol class="arabic simple">
<li><p>dynamic quantization (weights quantized with activations read/stored in
floating point and quantized for compute.)</p></li>
<li><p>static quantization (weights quantized, activations quantized, calibration
required post training)</p></li>
<li><p>quantization aware training (weights quantized, activations quantized,
quantization numerics modeled during training)</p></li>
</ol>
<p>Please see our <a class="reference external" href="https://pytorch.org/blog/introduction-to-quantization-on-pytorch/">Introduction to Quantization on Pytorch</a> blog post
for a more comprehensive overview of the tradeoffs between these quantization
types.</p>
<div class="section" id="dynamic-quantization">
<h3>Dynamic Quantization<a class="headerlink" href="#dynamic-quantization" title="Permalink to this headline">¶</a></h3>
<p>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.</p>
<p>Diagram:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># original model</span>
<span class="c1"># all tensors and computations are in floating point</span>
<span class="n">previous_layer_fp32</span> <span class="o">--</span> <span class="n">linear_fp32</span> <span class="o">--</span> <span class="n">activation_fp32</span> <span class="o">--</span> <span class="n">next_layer_fp32</span>
<span class="o">/</span>
<span class="n">linear_weight_fp32</span>
<span class="c1"># dynamically quantized model</span>
<span class="c1"># linear and conv weights are in int8</span>
<span class="n">previous_layer_fp32</span> <span class="o">--</span> <span class="n">linear_int8_w_fp32_inp</span> <span class="o">--</span> <span class="n">activation_fp32</span> <span class="o">--</span> <span class="n">next_layer_fp32</span>
<span class="o">/</span>
<span class="n">linear_weight_int8</span>
</pre></div>
</div>
<p>API example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># define a floating point model</span>
<span class="k">class</span> <span class="nc">M</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="c1"># create a model instance</span>
<span class="n">model_fp32</span> <span class="o">=</span> <span class="n">M</span><span class="p">()</span>
<span class="c1"># create a quantized model instance</span>
<span class="n">model_int8</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">quantize_dynamic</span><span class="p">(</span>
<span class="n">model_fp32</span><span class="p">,</span> <span class="c1"># the original model</span>
<span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">},</span> <span class="c1"># a set of layers to dynamically quantize</span>
<span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span><span class="p">)</span> <span class="c1"># the target dtype for quantized weights</span>
<span class="c1"># run the model</span>
<span class="n">input_fp32</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">model_int8</span><span class="p">(</span><span class="n">input_fp32</span><span class="p">)</span>
</pre></div>
</div>
<p>To learn more about dynamic quantization please see our <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html">dynamic quantization tutorial</a>.</p>
</div>
<div class="section" id="static-quantization">
<h3>Static Quantization<a class="headerlink" href="#static-quantization" title="Permalink to this headline">¶</a></h3>
<p>Static quantization quantizes the weights and activations of the model. It
fuses activations into preceding layers where possible. It requires
calibration with a representative dataset to determine optimal quantization
parameters for activations. Post Training Quantization is typically used when
both memory bandwidth and compute savings are important with CNNs being a
typical use case. Static quantization is also known as Post Training
Quantization or PTQ.</p>
<p>Diagram:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># original model</span>
<span class="c1"># all tensors and computations are in floating point</span>
<span class="n">previous_layer_fp32</span> <span class="o">--</span> <span class="n">linear_fp32</span> <span class="o">--</span> <span class="n">activation_fp32</span> <span class="o">--</span> <span class="n">next_layer_fp32</span>
<span class="o">/</span>
<span class="n">linear_weight_fp32</span>
<span class="c1"># statically quantized model</span>
<span class="c1"># weights and activations are in int8</span>
<span class="n">previous_layer_int8</span> <span class="o">--</span> <span class="n">linear_with_activation_int8</span> <span class="o">--</span> <span class="n">next_layer_int8</span>
<span class="o">/</span>
<span class="n">linear_weight_int8</span>
</pre></div>
</div>
<p>API Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># define a floating point model where some layers could be statically quantized</span>
<span class="k">class</span> <span class="nc">M</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># QuantStub converts tensors from floating point to quantized</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quant</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">QuantStub</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
<span class="c1"># DeQuantStub converts tensors from quantized to floating point</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dequant</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">DeQuantStub</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># manually specify where tensors will be converted from floating</span>
<span class="c1"># point to quantized in the quantized model</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># manually specify where tensors will be converted from quantized</span>
<span class="c1"># to floating point in the quantized model</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dequant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="c1"># create a model instance</span>
<span class="n">model_fp32</span> <span class="o">=</span> <span class="n">M</span><span class="p">()</span>
<span class="c1"># model must be set to eval mode for static quantization logic to work</span>
<span class="n">model_fp32</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># attach a global qconfig, which contains information about what kind</span>
<span class="c1"># of observers to attach. Use 'fbgemm' for server inference and</span>
<span class="c1"># 'qnnpack' for mobile inference. Other quantization configurations such</span>
<span class="c1"># as selecting symmetric or assymetric quantization and MinMax or L2Norm</span>
<span class="c1"># calibration techniques can be specified here.</span>
<span class="n">model_fp32</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">get_default_qconfig</span><span class="p">(</span><span class="s1">'fbgemm'</span><span class="p">)</span>
<span class="c1"># Fuse the activations to preceding layers, where applicable.</span>
<span class="c1"># This needs to be done manually depending on the model architecture.</span>
<span class="c1"># Common fusions include `conv + relu` and `conv + batchnorm + relu`</span>
<span class="n">model_fp32_fused</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">fuse_modules</span><span class="p">(</span><span class="n">model_fp32</span><span class="p">,</span> <span class="p">[[</span><span class="s1">'conv'</span><span class="p">,</span> <span class="s1">'relu'</span><span class="p">]])</span>
<span class="c1"># Prepare the model for static quantization. This inserts observers in</span>
<span class="c1"># the model that will observe activation tensors during calibration.</span>
<span class="n">model_fp32_prepared</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">model_fp32_fused</span><span class="p">)</span>
<span class="c1"># calibrate the prepared model to determine quantization parameters for activations</span>
<span class="c1"># in a real world setting, the calibration would be done with a representative dataset</span>
<span class="n">input_fp32</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="n">model_fp32_prepared</span><span class="p">(</span><span class="n">input_fp32</span><span class="p">)</span>
<span class="c1"># Convert the observed model to a quantized model. This does several things:</span>
<span class="c1"># quantizes the weights, computes and stores the scale and bias value to be</span>
<span class="c1"># used with each activation tensor, and replaces key operators with quantized</span>
<span class="c1"># implementations.</span>
<span class="n">model_int8</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">model_fp32_prepared</span><span class="p">)</span>
<span class="c1"># run the model, relevant calculations will happen in int8</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">model_int8</span><span class="p">(</span><span class="n">input_fp32</span><span class="p">)</span>
</pre></div>
</div>
<p>To learn more about static quantization, please see the <a class="reference external" href="https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html">static quantization tutorial</a>.</p>
</div>
<div class="section" id="quantization-aware-training">
<h3>Quantization Aware Training<a class="headerlink" href="#quantization-aware-training" title="Permalink to this headline">¶</a></h3>
<p>Quantization Aware Training models the effects of quantization during training
allowing for higher accuracy compared to other quantization methods. During
training, all calculations are done in floating point, with fake_quant modules
modeling the effects of quantization by clamping and rounding to simulate the
effects of INT8. After model conversion, weights and
activations are quantized, and activations are fused into the preceding layer
where possible. It is commonly used with CNNs and yields a higher accuracy
compared to static quantization. Quantization Aware Training is also known as
QAT.</p>
<p>Diagram:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># original model</span>
<span class="c1"># all tensors and computations are in floating point</span>
<span class="n">previous_layer_fp32</span> <span class="o">--</span> <span class="n">linear_fp32</span> <span class="o">--</span> <span class="n">activation_fp32</span> <span class="o">--</span> <span class="n">next_layer_fp32</span>
<span class="o">/</span>
<span class="n">linear_weight_fp32</span>
<span class="c1"># model with fake_quants for modeling quantization numerics during training</span>
<span class="n">previous_layer_fp32</span> <span class="o">--</span> <span class="n">fq</span> <span class="o">--</span> <span class="n">linear_fp32</span> <span class="o">--</span> <span class="n">activation_fp32</span> <span class="o">--</span> <span class="n">fq</span> <span class="o">--</span> <span class="n">next_layer_fp32</span>
<span class="o">/</span>
<span class="n">linear_weight_fp32</span> <span class="o">--</span> <span class="n">fq</span>
<span class="c1"># quantized model</span>
<span class="c1"># weights and activations are in int8</span>
<span class="n">previous_layer_int8</span> <span class="o">--</span> <span class="n">linear_with_activation_int8</span> <span class="o">--</span> <span class="n">next_layer_int8</span>
<span class="o">/</span>
<span class="n">linear_weight_int8</span>
</pre></div>
</div>
<p>API Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="c1"># define a floating point model where some layers could benefit from QAT</span>
<span class="k">class</span> <span class="nc">M</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="c1"># QuantStub converts tensors from floating point to quantized</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quant</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">QuantStub</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
<span class="c1"># DeQuantStub converts tensors from quantized to floating point</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dequant</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">DeQuantStub</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dequant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="c1"># create a model instance</span>
<span class="n">model_fp32</span> <span class="o">=</span> <span class="n">M</span><span class="p">()</span>
<span class="c1"># model must be set to train mode for QAT logic to work</span>
<span class="n">model_fp32</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="c1"># attach a global qconfig, which contains information about what kind</span>
<span class="c1"># of observers to attach. Use 'fbgemm' for server inference and</span>
<span class="c1"># 'qnnpack' for mobile inference. Other quantization configurations such</span>
<span class="c1"># as selecting symmetric or assymetric quantization and MinMax or L2Norm</span>
<span class="c1"># calibration techniques can be specified here.</span>
<span class="n">model_fp32</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">get_default_qat_qconfig</span><span class="p">(</span><span class="s1">'fbgemm'</span><span class="p">)</span>
<span class="c1"># fuse the activations to preceding layers, where applicable</span>
<span class="c1"># this needs to be done manually depending on the model architecture</span>
<span class="n">model_fp32_fused</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">fuse_modules</span><span class="p">(</span><span class="n">model_fp32</span><span class="p">,</span>
<span class="p">[[</span><span class="s1">'conv'</span><span class="p">,</span> <span class="s1">'bn'</span><span class="p">,</span> <span class="s1">'relu'</span><span class="p">]])</span>
<span class="c1"># Prepare the model for QAT. This inserts observers and fake_quants in</span>
<span class="c1"># the model that will observe weight and activation tensors during calibration.</span>
<span class="n">model_fp32_prepared</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare_qat</span><span class="p">(</span><span class="n">model_fp32_fused</span><span class="p">)</span>
<span class="c1"># run the training loop (not shown)</span>
<span class="n">training_loop</span><span class="p">(</span><span class="n">model_fp32_prepared</span><span class="p">)</span>
<span class="c1"># Convert the observed model to a quantized model. This does several things:</span>
<span class="c1"># quantizes the weights, computes and stores the scale and bias value to be</span>
<span class="c1"># used with each activation tensor, fuses modules where appropriate,</span>
<span class="c1"># and replaces key operators with quantized implementations.</span>
<span class="n">model_fp32_prepared</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">model_int8</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">model_fp32_prepared</span><span class="p">)</span>
<span class="c1"># run the model, relevant calculations will happen in int8</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">model_int8</span><span class="p">(</span><span class="n">input_fp32</span><span class="p">)</span>
</pre></div>
</div>
<p>To learn more about quantization aware training, please see the <a class="reference external" href="https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html">QAT
tutorial</a>.</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="quantization-operation-coverage">
<h2>Quantization Operation coverage<a class="headerlink" href="#quantization-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. 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 <a class="reference internal" href="quantization-support.html"><span class="doc">list of supported operations</span></a> is sufficient to
cover typical CNN and RNN models</p>
<div class="toctree-wrapper compound">
</div>
</div>
<div class="section" id="quantization-customizations">
<h2>Quantization Customizations<a class="headerlink" href="#quantization-customizations" title="Permalink to this headline">¶</a></h2>
<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 (for example,
using <code class="docutils literal notranslate"><span class="pre">torch.nn.ReLU</span></code> instead of <code class="docutils literal notranslate"><span class="pre">torch.nn.functional.relu</span></code>).</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>.
For example, setting <code class="docutils literal notranslate"><span class="pre">model.conv1.qconfig</span> <span class="pre">=</span> <span class="pre">None</span></code> means that the
<code class="docutils literal notranslate"><span class="pre">model.conv</span></code> layer will not be quantized, and setting
<code class="docutils literal notranslate"><span class="pre">model.linear1.qconfig</span> <span class="pre">=</span> <span class="pre">custom_qconfig</span></code> means that the quantization
settings for <code class="docutils literal notranslate"><span class="pre">model.linear1</span></code> will be using <code class="docutils literal notranslate"><span class="pre">custom_qconfig</span></code> instead
of the global qconfig.</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.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
<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> modules.</p></li>
<li><p>Use <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">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.html#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="best-practices">
<h2>Best Practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
<ol class="arabic simple">
<li><p>Set the <code class="docutils literal notranslate"><span class="pre">reduce_range</span></code> argument on observers to <cite>True</cite> if you are using the
<code class="docutils literal notranslate"><span class="pre">fbgemm</span></code> backend. This argument prevents overflow on some int8 instructions
by reducing the range of quantized data type by 1 bit.</p></li>
</ol>
</div>
<div class="section" id="modules-that-provide-quantization-functions-and-classes">
<h2>Modules that provide quantization functions and classes<a class="headerlink" href="#modules-that-provide-quantization-functions-and-classes" title="Permalink to this headline">¶</a></h2>
<table class="docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="torch.quantization.html#torch-quantization"><span class="std std-ref">torch.quantization</span></a></p></td>
<td><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.html#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.html#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></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="torch.nn.intrinsic.html#torch-nn-intrinsic"><span class="std std-ref">torch.nn.intrinsic</span></a></p></td>
<td><p>This module implements the combined (fused) modules conv + relu which can
then be quantized.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="torch.nn.intrinsic.qat.html"><span class="doc">torch.nn.intrinsic.qat</span></a></p></td>
<td><p>This module implements the versions of those fused operations needed for
quantization aware training.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="torch.nn.intrinsic.quantized.html"><span class="doc">torch.nn.intrinsic.quantized</span></a></p></td>
<td><p>This module implements the quantized implementations of fused operations
like conv + relu.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="torch.nn.qat.html"><span class="doc">torch.nn.qat</span></a></p></td>
<td><p>This module implements versions of the key nn modules <strong>Conv2d()</strong> and
<strong>Linear()</strong> which run in FP32 but with rounding applied to simulate the
effect of INT8 quantization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="torch.nn.quantized.html"><span class="doc">torch.nn.quantized</span></a></p></td>
<td><p>This module implements the quantized versions of the nn layers such as
~`torch.nn.Conv2d` and <cite>torch.nn.ReLU</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="torch.nn.quantized.dynamic.html"><span class="doc">torch.nn.quantized.dynamic</span></a></p></td>
<td><p>Dynamically quantized <a class="reference internal" href="generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a>, <a class="reference internal" href="generated/torch.nn.LSTM.html#torch.nn.LSTM" title="torch.nn.LSTM"><code class="xref py py-class docutils literal notranslate"><span class="pre">LSTM</span></code></a>,
<a class="reference internal" href="generated/torch.nn.LSTMCell.html#torch.nn.LSTMCell" title="torch.nn.LSTMCell"><code class="xref py py-class docutils literal notranslate"><span class="pre">LSTMCell</span></code></a>, <a class="reference internal" href="generated/torch.nn.GRUCell.html#torch.nn.GRUCell" title="torch.nn.GRUCell"><code class="xref py py-class docutils literal notranslate"><span class="pre">GRUCell</span></code></a>, and
<a class="reference internal" href="generated/torch.nn.RNNCell.html#torch.nn.RNNCell" title="torch.nn.RNNCell"><code class="xref py py-class docutils literal notranslate"><span class="pre">RNNCell</span></code></a>.</p></td>
</tr>
</tbody>
</table>
</div>
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<ul>
<li><a class="reference internal" href="#">Quantization</a><ul>
<li><a class="reference internal" href="#introduction-to-quantization">Introduction to Quantization</a></li>
<li><a class="reference internal" href="#quantization-api-summary">Quantization API Summary</a><ul>
<li><a class="reference internal" href="#dynamic-quantization">Dynamic Quantization</a></li>
<li><a class="reference internal" href="#static-quantization">Static Quantization</a></li>
<li><a class="reference internal" href="#quantization-aware-training">Quantization Aware Training</a></li>
</ul>
</li>
<li><a class="reference internal" href="#quantized-tensors">Quantized Tensors</a></li>
<li><a class="reference internal" href="#quantization-operation-coverage">Quantization Operation coverage</a></li>
<li><a class="reference internal" href="#quantization-customizations">Quantization Customizations</a></li>
<li><a class="reference internal" href="#model-preparation-for-quantization">Model Preparation for Quantization</a></li>
<li><a class="reference internal" href="#best-practices">Best Practices</a></li>
<li><a class="reference internal" href="#modules-that-provide-quantization-functions-and-classes">Modules that provide quantization functions and classes</a></li>
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