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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/extending.func.html">Extending torch.func with autograd.Function</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
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<div class="section" id="quantization-accuracy-debugging">
<h1>Quantization Accuracy Debugging<a class="headerlink" href="#quantization-accuracy-debugging" title="Permalink to this heading">¶</a></h1>
<p>This document provides high level strategies for improving quantization
accuracy. If a quantized model has error compared to the original model,
we can categorize the error into:</p>
<ol class="arabic simple">
<li><p><strong>data insensitive error</strong> - caused by intrinsic model quantization error,
large portion of input data has large error</p></li>
<li><p><strong>data sensitive error</strong> - caused by outlier input data, small
portion of input data has large error</p></li>
<li><p><strong>implementation error</strong> - quantized kernel is not matching reference implementation</p></li>
</ol>
<div class="section" id="data-insensitive-error">
<h2>Data insensitive error<a class="headerlink" href="#data-insensitive-error" title="Permalink to this heading">¶</a></h2>
<div class="section" id="general-tips">
<h3>General tips<a class="headerlink" href="#general-tips" title="Permalink to this heading">¶</a></h3>
<ol class="arabic simple">
<li><p>For PTQ, ensure that the data you are calibrating with is representative
of your dataset. For example, for a classification problem a general
guideline is to have multiple samples in every category, and the overall
number of samples should be at least 100. There is no penalty for
calibrating with more data other than calibration time.</p></li>
<li><p>If your model has Conv-BN or Linear-BN patterns, consider fusing them.
If you are using FX graph mode quantization, this is done automatically
by the workflow. If you are using Eager mode quantization, you can do
this manually with the <code class="docutils literal notranslate"><span class="pre">torch.ao.quantization.fuse_modules</span></code> API.</p></li>
<li><p>Increase the precision of dtype of the problematic ops. Usually, fp32
will have the highest accuracy, followed by fp16, followed by dynamically
quantized int8, followed by statically quantized int8.</p>
<ol class="arabic simple">
<li><p>Note: this is trading off performance for accuracy.</p></li>
<li><p>Note: availability of kernels per dtype per op can vary by backend.</p></li>
<li><p>Note: dtype conversions add an additional performance cost. For example,
<code class="docutils literal notranslate"><span class="pre">fp32_op</span> <span class="pre">-></span> <span class="pre">quant</span> <span class="pre">-></span> <span class="pre">int8_op</span> <span class="pre">-></span> <span class="pre">dequant</span> <span class="pre">-></span> <span class="pre">fp32_op</span> <span class="pre">-></span> <span class="pre">quant</span> <span class="pre">-></span> <span class="pre">int8_op</span> <span class="pre">-></span> <span class="pre">dequant</span></code>
will have a performance penalty compared to
<code class="docutils literal notranslate"><span class="pre">fp32_op</span> <span class="pre">-></span> <span class="pre">fp32_op</span> <span class="pre">-></span> <span class="pre">quant</span> <span class="pre">-></span> <span class="pre">int8_op</span> <span class="pre">-></span> <span class="pre">int8_op</span> <span class="pre">-></span> <span class="pre">dequant</span></code>
because of a higher number of required dtype conversions.</p></li>
</ol>
</li>
<li><p>If you are using PTQ, consider using QAT to recover some of the accuracy loss
from quantization.</p></li>
</ol>
</div>
<div class="section" id="int8-quantization-tips">
<h3>Int8 quantization tips<a class="headerlink" href="#int8-quantization-tips" title="Permalink to this heading">¶</a></h3>
<ol class="arabic simple">
<li><p>If you are using per-tensor weight quantization, consider using per-channel
weight quantization.</p></li>
<li><p>If you are doing inference on <cite>fbgemm</cite>, ensure that you set the <cite>reduce_range</cite>
argument to <cite>False</cite> if your CPU is Cooperlake or newer, and to <cite>True</cite> otherwise.</p></li>
<li><p>Audit the input activation distribution variation across different samples.
If this variation is high, the layer may be suitable for dynamic quantization
but not static quantization.</p></li>
</ol>
</div>
</div>
<div class="section" id="data-sensitive-error">
<h2>Data sensitive error<a class="headerlink" href="#data-sensitive-error" title="Permalink to this heading">¶</a></h2>
<p>If you are using static quantization and a small portion of your input data is
resulting in high quantization error, you can try:</p>
<ol class="arabic simple">
<li><p>Adjust your calibration dataset to make it more representative of your
inference dataset.</p></li>
<li><p>Manually inspect (using Numeric Suite) which layers have high quantization
error. For these layers, consider leaving them in floating point or adjusting
the observer settings to choose a better scale and zero_point.</p></li>
</ol>
</div>
<div class="section" id="implementation-error">
<h2>Implementation error<a class="headerlink" href="#implementation-error" title="Permalink to this heading">¶</a></h2>
<p>If you are using PyTorch quantization with your own backend
you may see differences between the reference implementation of an
operation (such as <code class="docutils literal notranslate"><span class="pre">dequant</span> <span class="pre">-></span> <span class="pre">op_fp32</span> <span class="pre">-></span> <span class="pre">quant</span></code>) and the quantized implementation
(such as <cite>op_int8</cite>) of the op on the target hardware. This could mean one of two things:</p>
<ol class="arabic simple">
<li><p>the differences (usually small) are expected due to specific behavior of
the target kernel on the target hardware compared to fp32/cpu. An example of this
is accumulating in an integer dtype. Unless the kernel guarantees bitwise
equivalency with the reference implementation, this is expected.</p></li>
<li><p>the kernel on the target hardware has an accuracy issue. In this case, reach
out to the kernel developer.</p></li>
</ol>
</div>
</div>
<div class="section" id="numerical-debugging-tooling-prototype">
<h1>Numerical Debugging Tooling (prototype)<a class="headerlink" href="#numerical-debugging-tooling-prototype" title="Permalink to this heading">¶</a></h1>
<div class="toctree-wrapper compound">
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Numerical debugging tooling is early prototype and subject to change.</p>
</div>
<ul class="simple">
<li><p><a class="reference internal" href="torch.ao.ns._numeric_suite.html#torch-ao-ns-numeric-suite"><span class="std std-ref">torch.ao.ns._numeric_suite</span></a>
Eager mode numeric suite</p></li>
<li><p><a class="reference internal" href="torch.ao.ns._numeric_suite_fx.html#torch-ao-ns-numeric-suite-fx"><span class="std std-ref">torch.ao.ns._numeric_suite_fx</span></a>
FX numeric suite</p></li>
</ul>
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