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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 Intel can be used to apply popular compression techniques such as quantization, pruning and knowledge distillation. </p>
<h2 class="relative group"><a id="posttraining-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="#posttraining-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>Post-training optimization
	</span></h2>

<p>Post-training compression techniques such as dynamic and static quantization can be easily applied on your model using our <a href="https://huggingface.co/docs/optimum/intel_optimization#optimum.intel.neural_compressor.IncQuantizer" rel="nofollow"><code>IncQuantizer</code></a>.
Note that quantization is currently only supported for CPUs (only CPU backends are available), so we will not be utilizing GPUs / CUDA in the following examples.
To apply dynamic quantization on a fine-tuned DistilBERT, we first need to create the corresponding configuration describing the quantization details as well as the quantizer object used to later apply quantization:</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForQuestionAnswering, AutoTokenizer, pipeline
<span class="hljs-keyword">from</span> evaluate <span class="hljs-keyword">import</span> evaluator
<span class="hljs-keyword">from</span> optimum.intel.neural_compressor <span class="hljs-keyword">import</span> IncQuantizationConfig, IncQuantizer

model_id = <span class="hljs-string">&quot;distilbert-base-cased-distilled-squad&quot;</span>
max_eval_samples = <span class="hljs-number">100</span>
model = AutoModelForQuestionAnswering.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
eval_dataset = load_dataset(<span class="hljs-string">&quot;squad&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>).select(<span class="hljs-built_in">range</span>(max_eval_samples))
<span class="hljs-built_in">eval</span> = evaluator(<span class="hljs-string">&quot;question-answering&quot;</span>)
qa_pipeline = pipeline(<span class="hljs-string">&quot;question-answering&quot;</span>, model=model, tokenizer=tokenizer)

<span class="hljs-keyword">def</span> <span class="hljs-title function_">eval_func</span>(<span class="hljs-params">model</span>):
    qa_pipeline.model = model
    metrics = <span class="hljs-built_in">eval</span>.compute(model_or_pipeline=qa_pipeline, data=eval_dataset, metric=<span class="hljs-string">&quot;squad&quot;</span>)
    <span class="hljs-keyword">return</span> metrics[<span class="hljs-string">&quot;f1&quot;</span>]

<span class="hljs-comment"># Load the quantization configuration detailing the quantization we wish to apply</span>
config_name = <span class="hljs-string">&quot;echarlaix/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic&quot;</span>
quantization_config = IncQuantizationConfig.from_pretrained(config_name)

<span class="hljs-comment"># Instantiate our IncQuantizer using the desired configuration and the evaluation function</span>
<span class="hljs-comment"># used for the INC accuracy-driven tuning strategy</span>
quantizer = IncQuantizer(quantization_config, eval_func=eval_func)<!-- HTML_TAG_END --></pre></div>
<p>In a second step, we create our optimizer which will take care of the optimization process.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.intel.neural_compressor <span class="hljs-keyword">import</span> IncOptimizer

<span class="hljs-comment"># To instantiate the optimizer, we need the model we wish to optimize and the</span>
<span class="hljs-comment"># quantizer defining the quantization process</span>
optimizer = IncOptimizer(model, quantizer=quantizer)

<span class="hljs-comment"># Apply dynamic quantization</span>
quantized_model = optimizer.fit()

<span class="hljs-comment"># Save the resulting model and its corresponding configuration in the given directory</span>
save_dir = <span class="hljs-string">&quot;./quantized_model&quot;</span>
optimizer.save_pretrained(save_dir)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="while-training-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="#while-training-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>While training optimization
	</span></h2>

<p>The <code>IncTrainer</code> class provides an API to train your model while combining different compression techniques such as knowledge distillation, pruning and quantization.
The <code>IncTrainer</code> is very similar to the 🤗 Transformers <a href="https://huggingface.co/docs/transformers/main/en/main_classes/trainer#trainer" rel="nofollow"><code>Trainer</code></a>, which can be replaced with minimal changes in your code.</p>

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	<pre><!-- HTML_TAG_START -->from transformers import TrainingArguments, default_data_collator
<span class="hljs-deletion">-from transformers import Trainer</span>
<span class="hljs-addition">+from optimum.intel.neural_compressor import IncTrainer</span>

# Initialize our IncTrainer
<span class="hljs-deletion">-trainer = Trainer(</span>
<span class="hljs-addition">+trainer = IncTrainer(</span>
    model=model,
    args=TrainingArguments(output_dir, num_train_epochs=3.0),
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    data_collator=default_data_collator,
)<!-- HTML_TAG_END --></pre></div>
<p>Here is an example on how to combine magnitude pruning with dynamic quantization while fine-tuning a DistilBERT
First, we create the corresponding configuration describing the quantization and the pruning processes we wish to apply on the model.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.intel.neural_compressor <span class="hljs-keyword">import</span> IncPruningConfig, IncQuantizationConfig

<span class="hljs-comment"># The targeted sparsity is set to 10%</span>
target_sparsity = <span class="hljs-number">0.1</span>
config_name = <span class="hljs-string">&quot;echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1&quot;</span>
<span class="hljs-comment"># Load the quantization configuration detailing the quantization we wish to apply</span>
quantization_config = IncQuantizationConfig.from_pretrained(config_name)
<span class="hljs-comment"># Load the pruning configuration detailing the pruning we wish to apply</span>
pruning_config = IncPruningConfig.from_pretrained(config_name)<!-- HTML_TAG_END --></pre></div>
<p>Finally we instantiate our optimizer which will take care of the optimization process. The training function <code>train_func</code> will characterize the training step (<a href="https://github.com/huggingface/optimum-intel/blob/main/examples/neural_compressor/question-answering/run_qa.py#L748-L766" rel="nofollow">see</a> for more examples).</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.intel.neural_compressor <span class="hljs-keyword">import</span> IncOptimizer, IncPruner, IncQuantizer

<span class="hljs-comment"># Instantiate our IncQuantizer using the desired configuration</span>
quantizer = IncQuantizer(quantization_config, eval_func=eval_func)
<span class="hljs-comment"># Instantiate our IncPruner using the desired configuration</span>
pruner = IncPruner(pruning_config, eval_func=eval_func, train_func=train_func)
optimizer = IncOptimizer(model, quantizer=quantizer, pruner=pruner)
<span class="hljs-comment"># Apply pruning and quantization </span>
optimized_model = optimizer.fit()

<span class="hljs-comment"># Save the resulting model and its corresponding configuration in the given directory</span>
optimizer.save_pretrained(save_dir)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="loading-a-quantized-model" 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="#loading-a-quantized-model"><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>Loading a quantized model
	</span></h2>

<p>To load a quantized model hosted locally or on the 🤗 hub, you must instantiate you model using our <a href="https://huggingface.co/docs/optimum/intel_optimization#optimum.intel.neural_compressor.quantization.IncQuantizedModel" rel="nofollow"><code>IncQuantizedModelForXxx</code></a> classes.</p>

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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.intel.neural_compressor <span class="hljs-keyword">import</span> IncQuantizedModelForSequenceClassification

model_id = <span class="hljs-string">&quot;Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic&quot;</span>
model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id)<!-- HTML_TAG_END --></pre></div>
<p>You can load many more quantized models hosted on the hub under the Intel organization <a href="https://huggingface.co/Intel" rel="nofollow"><code>here</code></a>.</p>
<h2 class="relative group"><a id="inference-with-transformers-pipeline" 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="#inference-with-transformers-pipeline"><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>Inference with Transformers pipeline
	</span></h2>

<p>The quantized model can then easily be used to run inference with the Transformers <a href="https://huggingface.co/docs/transformers/main/en/main_classes/pipelines" rel="nofollow">pipelines</a>.</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe_cls = pipeline(<span class="hljs-string">&quot;text-classification&quot;</span>, model=model, tokenizer=tokenizer)
text = <span class="hljs-string">&quot;He&#x27;s a dreadful magician.&quot;</span>
outputs = pipe_cls(text)

[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;NEGATIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9880216121673584</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Check out the <a href="https://github.com/huggingface/optimum-intel/tree/main/examples" rel="nofollow"><code>examples</code></a> directory for more sophisticated usage.</p>


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