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<h1 class="relative group"><a id="optimization" 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="#optimization"><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>Optimization
	</span></h1>

<p>🤗 Optimum provides an <code>optimum.onnxruntime</code> package that enables you to apply graph optimization on many model hosted on the 🤗 hub using the <a href="https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers" rel="nofollow">ONNX Runtime</a> model optimization tool.</p>
<h2 class="relative group"><a id="creating-an-ortoptimizer" 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="#creating-an-ortoptimizer"><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>Creating an <code>ORTOptimizer</code></span></h2>

<p>The <code>ORTOptimizer</code> class is used to optimize your ONNX model. The class can be initialized using the <code>from_pretrained()</code> method, which supports different checkpoint formats.</p>
<ol><li>Using an already initialized <code>ORTModelForXXX</code> class.</li></ol>

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	Copied</div></button></div>
	<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, ORTModelForSequenceClassification

<span class="hljs-comment"># Loading ONNX Model from the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;optimum/distilbert-base-uncased-finetuned-sst-2-english&quot;</span>)

<span class="hljs-comment"># Create an optimizer from an ORTModelForXXX</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = ORTOptimizer.from_pretrained(model)<!-- HTML_TAG_END --></pre></div>
<ol start="2"><li>Using a local ONNX model from a directory.</li></ol>

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	Copied</div></button></div>
	<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-comment"># This assumes a model.onnx exists in path/to/model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = ORTOptimizer.from_pretrained(<span class="hljs-string">&quot;path/to/model&quot;</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="optimization-examples" 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="#optimization-examples"><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>Optimization examples
	</span></h2>

<p>Below you will find an easy end-to-end example on how to optimize <a href="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english" rel="nofollow">distilbert-base-uncased-finetuned-sst-2-english</a>.</p>

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	Copied</div></button></div>
	<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, ORTModelForSequenceClassification
<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>model_id = <span class="hljs-string">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>save_dir = <span class="hljs-string">&quot;/tmp/outputs&quot;</span>

<span class="hljs-comment"># Load a PyTorch model and export it to the ONNX format</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSequenceClassification.from_pretrained(model_id, from_transformers=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># Create the optimizer</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = ORTOptimizer.from_pretrained(model)

<span class="hljs-comment"># Define the optimization strategy by creating the appropriate configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimization_config = OptimizationConfig(
    optimization_level=<span class="hljs-number">2</span>,
    optimize_with_onnxruntime_only=<span class="hljs-literal">False</span>,
    optimize_for_gpu=<span class="hljs-literal">False</span>,
)

<span class="hljs-comment"># Optimize the model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config)<!-- HTML_TAG_END --></pre></div>
<p>Below you will find an easy end-to-end example on how to optimize a Seq2Seq model <a href="https://huggingface.co/sshleifer/distilbart-cnn-12-6" rel="nofollow">sshleifer/distilbart-cnn-12-6”</a>.</p>

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	Copied</div></button></div>
	<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, ORTModelForSeq2SeqLM
<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-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>model_id = <span class="hljs-string">&quot;sshleifer/distilbart-cnn-12-6&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>save_dir = <span class="hljs-string">&quot;/tmp/outputs&quot;</span>

<span class="hljs-comment"># Load a PyTorch model and export it to the ONNX format</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ORTModelForSeq2SeqLM.from_pretrained(model_id, from_transformers=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># Create the optimizer</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer = ORTOptimizer.from_pretrained(model)

<span class="hljs-comment"># Define the optimization strategy by creating the appropriate configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimization_config = OptimizationConfig(
    optimization_level=<span class="hljs-number">2</span>,
    optimize_with_onnxruntime_only=<span class="hljs-literal">False</span>,
    optimize_for_gpu=<span class="hljs-literal">False</span>,
)

<span class="hljs-comment"># Optimize the model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimizer.optimize(save_dir=save_dir, optimization_config=optimization_config)

<span class="hljs-comment"># Load the resulting optimized model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>optimized_model = ORTModelForSeq2SeqLM.from_pretrained(
    save_dir,
    encoder_file_name=<span class="hljs-string">&quot;encoder_model_optimized.onnx&quot;</span>,
    decoder_file_name=<span class="hljs-string">&quot;decoder_model_optimized.onnx&quot;</span>,
    decoder_file_with_past_name=<span class="hljs-string">&quot;decoder_with_past_model_optimized.onnx&quot;</span>,
)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(model_id)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokens = tokenizer(<span class="hljs-string">&quot;This is a sample input&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = optimized_model.generate(**tokens)<!-- HTML_TAG_END --></pre></div>


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