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<div class="section" id="module-torch.ao.quantization">
<span id="quantization"></span><span id="quantization-doc"></span><h1>Quantization<a class="headerlink" href="#module-torch.ao.quantization" title="Permalink to this heading">¶</a></h1>
<span class="target" id="module-torch.ao.quantization.fx"></span><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 heading">¶</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 reduced precision rather than
full precision (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>
</div>
<div class="section" id="quantization-api-summary">
<h2>Quantization API Summary<a class="headerlink" href="#quantization-api-summary" title="Permalink to this heading">¶</a></h2>
<p>PyTorch provides three different modes of quantization: Eager Mode Quantization, FX Graph Mode Quantization (maintainence) and PyTorch 2 Export Quantization.</p>
<p>Eager Mode Quantization is a beta feature. User needs to do fusion and specify where quantization and dequantization happens manually, also it only supports modules and not functionals.</p>
<p>FX Graph Mode Quantization is an automated quantization workflow in PyTorch, and currently it’s a prototype feature, it is in maintainence mode since we have PyTorch 2 Export Quantization. It improves upon Eager Mode Quantization by adding support for functionals and automating the quantization process, although people might need to refactor the model to make the model compatible with FX Graph Mode Quantization (symbolically traceable with <code class="docutils literal notranslate"><span class="pre">torch.fx</span></code>). Note that FX Graph Mode Quantization is not expected to work on arbitrary models since the model might not be symbolically traceable, we will integrate it into domain libraries like torchvision and users will be able to quantize models similar to the ones in supported domain libraries with FX Graph Mode Quantization. For arbitrary models we’ll provide general guidelines, but to actually make it work, users might need to be familiar with <code class="docutils literal notranslate"><span class="pre">torch.fx</span></code>, especially on how to make a model symbolically traceable.</p>
<p>PyTorch 2 Export Quantization is the new full graph mode quantization workflow, released as prototype feature in PyTorch 2.1. With PyTorch 2, we are moving to a better solution for full program capture (torch.export) since it can capture a higher percentage (88.8% on 14K models) of models compared to torch.fx.symbolic_trace (72.7% on 14K models), the program capture solution used by FX Graph Mode Quantization. torch.export still has limitations around some python constructs and requires user involvement to support dynamism in the exported model, but overall it is an improvement over the previous program capture solution. PyTorch 2 Export Quantization is built for models captured by torch.export, with flexibility and productivity of both modeling users and backend developers in mind. The main features are
(1). Programmable API for configuring how a model is quantized that can scale to many more use cases
(2). Simplified UX for modeling users and backend developers since they only need to interact with a single object (Quantizer) for expressing user’s intention about how to quantize a model and what the backend support.
(3). Optional reference quantized model representation that can represent quantized computation with integer operations that maps closer to actual quantized computations that happens in hardware.</p>
<p>New users of quantization are encouraged to try out PyTorch 2 Export Quantization first, if it does not work well, user can try eager mode quantization.</p>
<p>The following table compares the differences between Eager Mode Quantization, FX Graph Mode Quantization and PyTorch 2 Export Quantization:</p>
<table class="docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td></td>
<td><p>Eager Mode
Quantization</p></td>
<td><p>FX Graph
Mode
Quantization</p></td>
<td><p>PyTorch 2 Export
Quantization</p></td>
</tr>
<tr class="row-even"><td><p>Release
Status</p></td>
<td><p>beta</p></td>
<td><p>prototype
(maintainence)</p></td>
<td><p>prototype</p></td>
</tr>
<tr class="row-odd"><td><p>Operator
Fusion</p></td>
<td><p>Manual</p></td>
<td><p>Automatic</p></td>
<td><p>Automatic</p></td>
</tr>
<tr class="row-even"><td><p>Quant/DeQuant
Placement</p></td>
<td><p>Manual</p></td>
<td><p>Automatic</p></td>
<td><p>Automatic</p></td>
</tr>
<tr class="row-odd"><td><p>Quantizing
Modules</p></td>
<td><p>Supported</p></td>
<td><p>Supported</p></td>
<td><p>Supported</p></td>
</tr>
<tr class="row-even"><td><p>Quantizing
Functionals/Torch
Ops</p></td>
<td><p>Manual</p></td>
<td><p>Automatic</p></td>
<td><p>Supported</p></td>
</tr>
<tr class="row-odd"><td><p>Support for
Customization</p></td>
<td><p>Limited Support</p></td>
<td><p>Fully
Supported</p></td>
<td><p>Fully Supported</p></td>
</tr>
<tr class="row-even"><td><p>Quantization Mode
Support</p></td>
<td><p>Post Training
Quantization:
Static, Dynamic,
Weight Only</p>
<p>Quantization Aware
Training:
Static</p>
</td>
<td><p>Post Training
Quantization:
Static, Dynamic,
Weight Only</p>
<p>Quantization Aware
Training:
Static</p>
</td>
<td><p>Defined by
Backend Specific
Quantizer</p></td>
</tr>
<tr class="row-odd"><td><p>Input/Output
Model Type</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>
(May need some
refactors to make
the model
compatible with FX
Graph Mode
Quantization)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code>
(captured by
<code class="docutils literal notranslate"><span class="pre">torch.export</span></code></p></td>
</tr>
</tbody>
</table>
<p>There are three types of quantization supported:</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>static 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>
<p>Operator coverage varies between dynamic and static quantization and is captured in the table below.</p>
<table class="docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td></td>
<td><p>Static
Quantization</p></td>
<td><p>Dynamic
Quantization</p></td>
</tr>
<tr class="row-even"><td><div class="line-block">
<div class="line">nn.Linear</div>
<div class="line">nn.Conv1d/2d/3d</div>
</div>
</td>
<td><div class="line-block">
<div class="line">Y</div>
<div class="line">Y</div>
</div>
</td>
<td><div class="line-block">
<div class="line">Y</div>
<div class="line">N</div>
</div>
</td>
</tr>
<tr class="row-odd"><td><div class="line-block">
<div class="line">nn.LSTM</div>
<div class="line"><br /></div>
<div class="line">nn.GRU</div>
</div>
</td>
<td><div class="line-block">
<div class="line">Y (through</div>
<div class="line">custom modules)</div>
<div class="line">N</div>
</div>
</td>
<td><div class="line-block">
<div class="line">Y</div>
<div class="line"><br /></div>
<div class="line">Y</div>
</div>
</td>
</tr>
<tr class="row-even"><td><div class="line-block">
<div class="line">nn.RNNCell</div>
<div class="line">nn.GRUCell</div>
<div class="line">nn.LSTMCell</div>
</div>
</td>
<td><div class="line-block">
<div class="line">N</div>
<div class="line">N</div>
<div class="line">N</div>
</div>
</td>
<td><div class="line-block">
<div class="line">Y</div>
<div class="line">Y</div>
<div class="line">Y</div>
</div>
</td>
</tr>
<tr class="row-odd"><td><p>nn.EmbeddingBag</p></td>
<td><p>Y (activations
are in fp32)</p></td>
<td><p>Y</p></td>
</tr>
<tr class="row-even"><td><p>nn.Embedding</p></td>
<td><p>Y</p></td>
<td><p>Y</p></td>
</tr>
<tr class="row-odd"><td><p>nn.MultiheadAttention</p></td>
<td><p>Y (through
custom modules)</p></td>
<td><p>Not supported</p></td>
</tr>
<tr class="row-even"><td><p>Activations</p></td>
<td><p>Broadly supported</p></td>
<td><p>Un-changed,
computations
stay in fp32</p></td>
</tr>
</tbody>
</table>
<div class="section" id="eager-mode-quantization">
<h3>Eager Mode Quantization<a class="headerlink" href="#eager-mode-quantization" title="Permalink to this heading">¶</a></h3>
<p>For a general introduction to the quantization flow, including different types of quantization, please take a look at <a class="reference internal" href="#general-quantization-flow">General Quantization Flow</a>.</p>
<div class="section" id="post-training-dynamic-quantization">
<h4>Post Training Dynamic Quantization<a class="headerlink" href="#post-training-dynamic-quantization" title="Permalink to this heading">¶</a></h4>
<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 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 LSTM 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>PTDQ 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="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">ao</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="post-training-static-quantization">
<h4>Post Training Static Quantization<a class="headerlink" href="#post-training-static-quantization" title="Permalink to this heading">¶</a></h4>
<p>Post Training Static Quantization (PTQ static) 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 Static Quantization is typically used when
both memory bandwidth and compute savings are important with CNNs being a
typical use case.</p>
<p>We may need to modify the model before applying post training static quantization. Please see <a class="reference internal" href="#model-preparation-for-eager-mode-static-quantization">Model Preparation for Eager Mode Static Quantization</a>.</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>PTSQ 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="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">ao</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">ao</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 'x86' for server inference and 'qnnpack'</span>
<span class="c1"># for mobile inference. Other quantization configurations such as selecting</span>
<span class="c1"># symmetric or asymmetric quantization and MinMax or L2Norm calibration techniques</span>
<span class="c1"># can be specified here.</span>
<span class="c1"># Note: the old 'fbgemm' is still available but 'x86' is the recommended default</span>
<span class="c1"># for server inference.</span>
<span class="c1"># model_fp32.qconfig = torch.ao.quantization.get_default_qconfig('fbgemm')</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">ao</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">'x86'</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">ao</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">ao</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">ao</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-for-static-quantization">
<h4>Quantization Aware Training for Static Quantization<a class="headerlink" href="#quantization-aware-training-for-static-quantization" title="Permalink to this heading">¶</a></h4>
<p>Quantization Aware Training (QAT) models the effects of quantization during training
allowing for higher accuracy compared to other quantization methods. We can do QAT for static, dynamic or weight only quantization. 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.</p>
<p>We may need to modify the model before applying post training static quantization. Please see <a class="reference internal" href="#model-preparation-for-eager-mode-static-quantization">Model Preparation for Eager Mode Static Quantization</a>.</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>QAT 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="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">ao</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">ao</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 eval for fusion 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 'x86' for server inference and 'qnnpack'</span>
<span class="c1"># for mobile inference. Other quantization configurations such as selecting</span>
<span class="c1"># symmetric or asymmetric quantization and MinMax or L2Norm calibration techniques</span>
<span class="c1"># can be specified here.</span>
<span class="c1"># Note: the old 'fbgemm' is still available but 'x86' is the recommended default</span>
<span class="c1"># for server inference.</span>
<span class="c1"># model_fp32.qconfig = torch.ao.quantization.get_default_qconfig('fbgemm')</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">ao</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">'x86'</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">ao</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 needs to be set to train for QAT logic to work</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">ao</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="o">.</span><span class="n">train</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">ao</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 class="section" id="model-preparation-for-eager-mode-static-quantization">
<h4>Model Preparation for Eager Mode Static Quantization<a class="headerlink" href="#model-preparation-for-eager-mode-static-quantization" title="Permalink to this heading">¶</a></h4>
<p>It is necessary to currently make some modifications to the model definition
prior to Eager mode 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_mapping</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="generated/torch.ao.quantization.QuantStub.html#torch.ao.quantization.QuantStub" title="torch.ao.quantization.QuantStub"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantStub</span></code></a> and
<a class="reference internal" href="generated/torch.ao.quantization.DeQuantStub.html#torch.ao.quantization.DeQuantStub" title="torch.ao.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="generated/torch.ao.nn.quantized.FloatFunctional.html#torch.ao.nn.quantized.FloatFunctional" title="torch.ao.nn.quantized.FloatFunctional"><code class="xref py py-class docutils literal notranslate"><span class="pre">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="generated/torch.ao.quantization.fuse_modules.fuse_modules.html#torch.ao.quantization.fuse_modules.fuse_modules" title="torch.ao.quantization.fuse_modules.fuse_modules"><code class="xref py py-func docutils literal notranslate"><span class="pre">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>
<div class="section" id="prototype-maintaince-mode-fx-graph-mode-quantization">
<h3>(Prototype - maintaince mode) FX Graph Mode Quantization<a class="headerlink" href="#prototype-maintaince-mode-fx-graph-mode-quantization" title="Permalink to this heading">¶</a></h3>
<p>There are multiple quantization types in post training quantization (weight only, dynamic and static) and the configuration is done through <cite>qconfig_mapping</cite> (an argument of the <cite>prepare_fx</cite> function).</p>
<p>FXPTQ 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="kn">from</span> <span class="nn">torch.ao.quantization</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">get_default_qconfig_mapping</span><span class="p">,</span>
<span class="n">get_default_qat_qconfig_mapping</span><span class="p">,</span>
<span class="n">QConfigMapping</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">import</span> <span class="nn">torch.ao.quantization.quantize_fx</span> <span class="k">as</span> <span class="nn">quantize_fx</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="n">model_fp</span> <span class="o">=</span> <span class="n">UserModel</span><span class="p">()</span>
<span class="c1">#</span>
<span class="c1"># post training dynamic/weight_only quantization</span>
<span class="c1">#</span>
<span class="c1"># we need to deepcopy if we still want to keep model_fp unchanged after quantization since quantization apis change the input model</span>
<span class="n">model_to_quantize</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model_fp</span><span class="p">)</span>
<span class="n">model_to_quantize</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">qconfig_mapping</span> <span class="o">=</span> <span class="n">QConfigMapping</span><span class="p">()</span><span class="o">.</span><span class="n">set_global</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ao</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">default_dynamic_qconfig</span><span class="p">)</span>
<span class="c1"># a tuple of one or more example inputs are needed to trace the model</span>
<span class="n">example_inputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">input_fp32</span><span class="p">)</span>
<span class="c1"># prepare</span>
<span class="n">model_prepared</span> <span class="o">=</span> <span class="n">quantize_fx</span><span class="o">.</span><span class="n">prepare_fx</span><span class="p">(</span><span class="n">model_to_quantize</span><span class="p">,</span> <span class="n">qconfig_mapping</span><span class="p">,</span> <span class="n">example_inputs</span><span class="p">)</span>
<span class="c1"># no calibration needed when we only have dynamic/weight_only quantization</span>
<span class="c1"># quantize</span>
<span class="n">model_quantized</span> <span class="o">=</span> <span class="n">quantize_fx</span><span class="o">.</span><span class="n">convert_fx</span><span class="p">(</span><span class="n">model_prepared</span><span class="p">)</span>
<span class="c1">#</span>
<span class="c1"># post training static quantization</span>
<span class="c1">#</span>
<span class="n">model_to_quantize</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model_fp</span><span class="p">)</span>