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<h1 class="relative group"><a id="quickstart" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#quickstart"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Quickstart
	</span></h1>

<p>At its core, 🤗 Optimum uses <em>configuration objects</em> to define parameters for optimization on different accelerators. These objects are then used to instantiate dedicated <em>optimizers</em>, <em>quantizers</em>, and <em>pruners</em>. For example, here’s how you can apply dynamic quantization with ONNX Runtime:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTConfig, ORTQuantizer

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># The model we wish to quantize</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model_checkpoint = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># The type of quantization to apply</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>qconfig = AutoQuantizationConfig.arm64(is_static=<span class="hljs-literal">False</span>, per_channel=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>quantizer = ORTQuantizer.from_pretrained(model_checkpoint, feature=<span class="hljs-string">&quot;sequence-classification&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Quantize the model!</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>quantizer.fit(model_checkpoint, output_dir=<span class="hljs-string">&quot;.&quot;</span>, feature=<span class="hljs-string">&quot;sequence-classification&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>quantizer = ORTQuantizer(
<span class="hljs-meta">... </span>    onnx_model_path=<span class="hljs-string">&quot;model.onnx&quot;</span>,
<span class="hljs-meta">... </span>    onnx_quantized_model_output_path=<span class="hljs-string">&quot;model-quantized.onnx&quot;</span>,
<span class="hljs-meta">... </span>    quantization_config=qconfig,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>In this example, we’ve quantized a model from the Hugging Face Hub, but it could also be a path to a local model directory. The <code>feature</code> argument in the <code>from_pretrained()</code> method corresponds to the type of task that we wish to quantize the model for. The result from applying the <code>export()</code> method is a <code>model-quantized.onnx</code> file that can be used to run inference. Here’s an example of how to load an ONNX Runtime model and generate predictions with it:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> functools <span class="hljs-keyword">import</span> partial
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load quantized model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ort_model = ORTModel(<span class="hljs-string">&quot;model-quantized.onnx&quot;</span>, quantizer._onnx_config)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Create a dataset or load one from the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = Dataset.from_dict({<span class="hljs-string">&quot;sentence&quot;</span>: [<span class="hljs-string">&quot;I love burritos!&quot;</span>]})
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_fn</span>(<span class="hljs-params">ex, tokenizer</span>):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> tokenizer(ex[<span class="hljs-string">&quot;sentence&quot;</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Tokenize the inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenized_ds = ds.<span class="hljs-built_in">map</span>(partial(preprocess_fn, tokenizer=quantizer.tokenizer))
<span class="hljs-meta">&gt;&gt;&gt; </span>ort_outputs = ort_model.evaluation_loop(tokenized_ds)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Extract logits!</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ort_outputs.predictions<!-- HTML_TAG_END --></pre></div>
<p>Similarly, you can apply static quantization by simply setting <code>is_static</code> to <code>True</code> when instantiating the <code>QuantizationConfig</code> object:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span>qconfig = AutoQuantizationConfig.arm64(is_static=<span class="hljs-literal">True</span>, per_channel=<span class="hljs-literal">False</span>)<!-- HTML_TAG_END --></pre></div>
<p>Static quantization relies on feeding batches of data through the model to estimate the activation quantization parameters ahead of inference time. To support this, 🤗 Optimum allows you to provide a <em>calibration dataset</em>. The calibration dataset can be a simple <code>Dataset</code> object from the 🤗 Datasets library, or any dataset that’s hosted on the Hugging Face Hub. For this example, we’ll pick the <a href="https://huggingface.co/datasets/glue/viewer/sst2/test" rel="nofollow"><code>sst2</code></a> dataset that the model was originally trained on:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime.configuration <span class="hljs-keyword">import</span> AutoCalibrationConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Create the calibration dataset</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>calibration_dataset = quantizer.get_calibration_dataset(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;glue&quot;</span>,
<span class="hljs-meta">... </span>    dataset_config_name=<span class="hljs-string">&quot;sst2&quot;</span>,
<span class="hljs-meta">... </span>    preprocess_function=partial(preprocess_fn, tokenizer=quantizer.tokenizer),
<span class="hljs-meta">... </span>    num_samples=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>    dataset_split=<span class="hljs-string">&quot;train&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Create the calibration configuration containing the parameters related to calibration.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>calibration_config = AutoCalibrationConfig.minmax(calibration_dataset)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Perform the calibration step: computes the activations quantization ranges</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>ranges = quantizer.fit(
<span class="hljs-meta">... </span>    dataset=calibration_dataset,
<span class="hljs-meta">... </span>    calibration_config=calibration_config,
<span class="hljs-meta">... </span>    onnx_model_path=<span class="hljs-string">&quot;model.onnx&quot;</span>,
<span class="hljs-meta">... </span>    operators_to_quantize=qconfig.operators_to_quantize,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Quantize the same way we did for dynamic quantization!</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>quantizer.export(
<span class="hljs-meta">... </span>    onnx_model_path=<span class="hljs-string">&quot;model.onnx&quot;</span>,
<span class="hljs-meta">... </span>    onnx_quantized_model_output_path=<span class="hljs-string">&quot;model-quantized.onnx&quot;</span>,
<span class="hljs-meta">... </span>    calibration_tensors_range=ranges,
<span class="hljs-meta">... </span>    quantization_config=qconfig,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>As a final example, let’s take a look at applying <em>graph optimizations</em> techniques such as operator fusion and constant folding. As before, we load a configuration object, but this time by setting the optimization level instead of the quantization approach:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime.configuration <span class="hljs-keyword">import</span> OptimizationConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># optimization_config=99 enables all available graph optimisations</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimization_config = OptimizationConfig(optimization_level=<span class="hljs-number">99</span>)<!-- HTML_TAG_END --></pre></div>
<p>Next, we load an <em>optimizer</em> to apply these optimisations to our model:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.onnxruntime <span class="hljs-keyword">import</span> ORTOptimizer

<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = ORTOptimizer.from_pretrained(
<span class="hljs-meta">... </span>    model_checkpoint,
<span class="hljs-meta">... </span>    feature=<span class="hljs-string">&quot;sequence-classification&quot;</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Export the optimized model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer.export(
<span class="hljs-meta">... </span>    onnx_model_path=<span class="hljs-string">&quot;model.onnx&quot;</span>,
<span class="hljs-meta">... </span>    onnx_optimized_model_output_path=<span class="hljs-string">&quot;model-optimized.onnx&quot;</span>,
<span class="hljs-meta">... </span>    optimization_config=optimization_config,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>And that’s it - the model is now optimized and ready for inference! As you can see, the process is similar in each case:</p>
<ol><li>Define the optimization / quantization strategies via an <code>OptimizationConfig</code> / <code>QuantizationConfig</code> object</li>
<li>Instantiate a <code>ORTQuantizer</code> or <code>ORTOptimizer</code> class</li>
<li>Apply the <code>export()</code> method</li>
<li>Run inference</li></ol>
<p>Check out the <a href="https://github.com/huggingface/optimum/tree/main/examples" rel="nofollow"><code>examples</code></a> directory for more sophisticated usage.</p>
<p>Happy optimising 🤗!</p>


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