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| <h1 class="relative group"><a id="exportando-modelos-para-onnx" 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="#exportando-modelos-para-onnx"><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>Exportando modelos para ONNX | |
| </span></h1> | |
| <p>Se você precisar implantar modelos 🤗 Transformers em ambientes de produção, recomendamos | |
| exporta-los para um formato serializado que pode ser carregado e executado em | |
| tempos de execução e hardware. Neste guia, mostraremos como exportar modelos 🤗 Transformers | |
| para <a href="http://onnx.ai" rel="nofollow">ONNX (Open Neural Network eXchange)</a>.</p> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Uma vez exportado, um modelo pode ser otimizado para inferência por meio de técnicas como | |
| quantização e poda. Se você estiver interessado em otimizar seus modelos para serem executados com | |
| máxima eficiência, confira a biblioteca <a href="https://github.com/huggingface/optimum" rel="nofollow">🤗 Optimum | |
| </a>.</p></div> | |
| <p>ONNX é um padrão aberto que define um conjunto comum de operadores e um formato de arquivo comum | |
| para representar modelos de aprendizado profundo em uma ampla variedade de estruturas, incluindo PyTorch e | |
| TensorFlow. Quando um modelo é exportado para o formato ONNX, esses operadores são usados para | |
| construir um grafo computacional (muitas vezes chamado de <em>representação intermediária</em>) que | |
| representa o fluxo de dados através da rede neural.</p> | |
| <p>Ao expor um grafo com operadores e tipos de dados padronizados, o ONNX facilita a | |
| alternar entre os frameworks. Por exemplo, um modelo treinado em PyTorch pode ser exportado para | |
| formato ONNX e depois importado no TensorFlow (e vice-versa).</p> | |
| <p>🤗 Transformers fornece um pacote <a href="main_classes/onnx"><code>transformers.onnx</code></a> que permite | |
| que você converta os checkpoints do modelo em um grafo ONNX aproveitando os objetos de configuração. | |
| Esses objetos de configuração vêm prontos para várias arquiteturas de modelo e são | |
| projetado para ser facilmente extensível a outras arquiteturas.</p> | |
| <p>As configurações prontas incluem as seguintes arquiteturas:</p> | |
| <ul><li>ALBERT</li> | |
| <li>BART</li> | |
| <li>BEiT</li> | |
| <li>BERT</li> | |
| <li>BigBird</li> | |
| <li>BigBird-Pegasus</li> | |
| <li>Blenderbot</li> | |
| <li>BlenderbotSmall</li> | |
| <li>BLOOM</li> | |
| <li>CamemBERT</li> | |
| <li>CLIP</li> | |
| <li>CodeGen</li> | |
| <li>Conditional DETR</li> | |
| <li>ConvBERT</li> | |
| <li>ConvNeXT</li> | |
| <li>ConvNeXTV2</li> | |
| <li>Data2VecText</li> | |
| <li>Data2VecVision</li> | |
| <li>DeBERTa</li> | |
| <li>DeBERTa-v2</li> | |
| <li>DeiT</li> | |
| <li>DETR</li> | |
| <li>DistilBERT</li> | |
| <li>ELECTRA</li> | |
| <li>ERNIE</li> | |
| <li>FlauBERT</li> | |
| <li>GPT Neo</li> | |
| <li>GPT-J</li> | |
| <li>GroupViT</li> | |
| <li>I-BERT</li> | |
| <li>LayoutLM</li> | |
| <li>LayoutLMv3</li> | |
| <li>LeViT</li> | |
| <li>Longformer</li> | |
| <li>LongT5</li> | |
| <li>M2M100</li> | |
| <li>Marian</li> | |
| <li>mBART</li> | |
| <li>MobileBERT</li> | |
| <li>MobileViT</li> | |
| <li>MT5</li> | |
| <li>OpenAI GPT-2</li> | |
| <li>OWL-ViT</li> | |
| <li>Perceiver</li> | |
| <li>PLBart</li> | |
| <li>ResNet</li> | |
| <li>RoBERTa</li> | |
| <li>RoFormer</li> | |
| <li>SegFormer</li> | |
| <li>SqueezeBERT</li> | |
| <li>Swin Transformer</li> | |
| <li>T5</li> | |
| <li>Table Transformer</li> | |
| <li>Vision Encoder decoder</li> | |
| <li>ViT</li> | |
| <li>XLM</li> | |
| <li>XLM-RoBERTa</li> | |
| <li>XLM-RoBERTa-XL</li> | |
| <li>YOLOS</li></ul> | |
| <p>Nas próximas duas seções, mostraremos como:</p> | |
| <ul><li>Exportar um modelo suportado usando o pacote <code>transformers.onnx</code>.</li> | |
| <li>Exportar um modelo personalizado para uma arquitetura sem suporte.</li></ul> | |
| <h2 class="relative group"><a id="exportando-um-modelo-para-onnx" 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="#exportando-um-modelo-para-onnx"><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>Exportando um modelo para ONNX | |
| </span></h2> | |
| <p>Para exportar um modelo 🤗 Transformers para o ONNX, primeiro você precisa instalar algumas | |
| dependências extras:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->pip install transformers[onnx]<!-- HTML_TAG_END --></pre></div> | |
| <p>O pacote <code>transformers.onnx</code> pode então ser usado como um módulo Python:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->python -m transformers.onnx --<span class="hljs-built_in">help</span> | |
| usage: Hugging Face Transformers ONNX exporter [-h] -m MODEL [--feature {causal-lm, ...}] [--opset OPSET] [--atol ATOL] output | |
| positional arguments: | |
| output Path indicating <span class="hljs-built_in">where</span> to store generated ONNX model. | |
| optional arguments: | |
| -h, --<span class="hljs-built_in">help</span> show this <span class="hljs-built_in">help</span> message and <span class="hljs-built_in">exit</span> | |
| -m MODEL, --model MODEL | |
| Model ID on huggingface.co or path on disk to load model from. | |
| --feature {causal-lm, ...} | |
| The <span class="hljs-built_in">type</span> of features to <span class="hljs-built_in">export</span> the model with. | |
| --opset OPSET ONNX opset version to <span class="hljs-built_in">export</span> the model with. | |
| --atol ATOL Absolute difference tolerance when validating the model.<!-- HTML_TAG_END --></pre></div> | |
| <p>A exportação de um checkpoint usando uma configuração pronta pode ser feita da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->python -m transformers.onnx --model=distilbert-base-uncased onnx/<!-- HTML_TAG_END --></pre></div> | |
| <p>Você deve ver os seguintes logs:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->Validating ONNX model... | |
| -[✓] ONNX model output names match reference model ({<span class="hljs-string">'last_hidden_state'</span>}) | |
| - Validating ONNX Model output <span class="hljs-string">"last_hidden_state"</span>: | |
| -[✓] (2, 8, 768) matches (2, 8, 768) | |
| -[✓] all values close (atol: 1e-05) | |
| All good, model saved at: onnx/model.onnx<!-- HTML_TAG_END --></pre></div> | |
| <p>Isso exporta um grafo ONNX do ponto de verificação definido pelo argumento <code>--model</code>. Nisso | |
| Por exemplo, é <code>distilbert-base-uncased</code>, mas pode ser qualquer checkpoint no Hugging | |
| Face Hub ou um armazenado localmente.</p> | |
| <p>O arquivo <code>model.onnx</code> resultante pode ser executado em um dos <a href="https://onnx.ai/supported-tools.html#deployModel" rel="nofollow">muitos | |
| aceleradores</a> que suportam o ONNX | |
| padrão. Por exemplo, podemos carregar e executar o modelo com <a href="https://onnxruntime.ai/" rel="nofollow">ONNX | |
| Tempo de execução</a> da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> onnxruntime <span class="hljs-keyword">import</span> InferenceSession | |
| <span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span>session = InferenceSession(<span class="hljs-string">"onnx/model.onnx"</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># ONNX Runtime expects NumPy arrays as input</span> | |
| <span class="hljs-meta">>>> </span>inputs = tokenizer(<span class="hljs-string">"Using DistilBERT with ONNX Runtime!"</span>, return_tensors=<span class="hljs-string">"np"</span>) | |
| <span class="hljs-meta">>>> </span>outputs = session.run(output_names=[<span class="hljs-string">"last_hidden_state"</span>], input_feed=<span class="hljs-built_in">dict</span>(inputs))<!-- HTML_TAG_END --></pre></div> | |
| <p>Os nomes de saída necessários (como <code>["last_hidden_state"]</code>) podem ser obtidos pegando uma | |
| configuração ONNX de cada modelo. Por exemplo, para DistilBERT temos:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.models.distilbert <span class="hljs-keyword">import</span> DistilBertConfig, DistilBertOnnxConfig | |
| <span class="hljs-meta">>>> </span>config = DistilBertConfig() | |
| <span class="hljs-meta">>>> </span>onnx_config = DistilBertOnnxConfig(config) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(<span class="hljs-built_in">list</span>(onnx_config.outputs.keys())) | |
| [<span class="hljs-string">"last_hidden_state"</span>]<!-- HTML_TAG_END --></pre></div> | |
| <p>O processo é idêntico para os checkpoints do TensorFlow no Hub. Por exemplo, podemos | |
| exportar um checkpoint TensorFlow puro do <a href="https://huggingface.co/keras-io" rel="nofollow">Keras | |
| </a> da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->python -m transformers.onnx --model=keras-io/transformers-qa onnx/<!-- HTML_TAG_END --></pre></div> | |
| <p>Para exportar um modelo armazenado localmente, você precisará ter os pesos e | |
| arquivos tokenizer armazenados em um diretório. Por exemplo, podemos carregar e salvar um checkpoint como:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># Load tokenizer and PyTorch weights form the Hub</span> | |
| <span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># Save to disk</span> | |
| <span class="hljs-meta">>>> </span>tokenizer.save_pretrained(<span class="hljs-string">"local-pt-checkpoint"</span>) | |
| <span class="hljs-meta">>>> </span>pt_model.save_pretrained(<span class="hljs-string">"local-pt-checkpoint"</span>)<!-- HTML_TAG_END --></pre></div> | |
| <p>Uma vez que o checkpoint é salvo, podemos exportá-lo para o ONNX apontando o <code>--model</code> | |
| argumento do pacote <code>transformers.onnx</code> para o diretório desejado:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->python -m transformers.onnx --model=local-pt-checkpoint onnx/<!-- HTML_TAG_END --></pre></div> | |
| <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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># Load tokenizer and TensorFlow weights from the Hub</span> | |
| <span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-comment"># Save to disk</span> | |
| <span class="hljs-meta">>>> </span>tokenizer.save_pretrained(<span class="hljs-string">"local-tf-checkpoint"</span>) | |
| <span class="hljs-meta">>>> </span>tf_model.save_pretrained(<span class="hljs-string">"local-tf-checkpoint"</span>)<!-- HTML_TAG_END --></pre></div> | |
| <p>Uma vez que o checkpoint é salvo, podemos exportá-lo para o ONNX apontando o <code>--model</code> | |
| argumento do pacote <code>transformers.onnx</code> para o diretório desejado:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->python -m transformers.onnx --model=local-tf-checkpoint onnx/<!-- HTML_TAG_END --></pre></div> | |
| <h2 class="relative group"><a id="selecionando-features-para-diferentes-tarefas-do-modelo" 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="#selecionando-features-para-diferentes-tarefas-do-modelo"><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>Selecionando features para diferentes tarefas do modelo | |
| </span></h2> | |
| <p>Cada configuração pronta vem com um conjunto de <em>features</em> que permitem exportar | |
| modelos para diferentes tipos de tarefas. Conforme mostrado na tabela abaixo, cada recurso é | |
| associado a uma <code>AutoClass</code> diferente:</p> | |
| <table><thead><tr><th>Feature</th> | |
| <th>Auto Class</th></tr></thead> | |
| <tbody><tr><td><code>causal-lm</code>, <code>causal-lm-with-past</code></td> | |
| <td><code>AutoModelForCausalLM</code></td></tr> | |
| <tr><td><code>default</code>, <code>default-with-past</code></td> | |
| <td><code>AutoModel</code></td></tr> | |
| <tr><td><code>masked-lm</code></td> | |
| <td><code>AutoModelForMaskedLM</code></td></tr> | |
| <tr><td><code>question-answering</code></td> | |
| <td><code>AutoModelForQuestionAnswering</code></td></tr> | |
| <tr><td><code>seq2seq-lm</code>, <code>seq2seq-lm-with-past</code></td> | |
| <td><code>AutoModelForSeq2SeqLM</code></td></tr> | |
| <tr><td><code>sequence-classification</code></td> | |
| <td><code>AutoModelForSequenceClassification</code></td></tr> | |
| <tr><td><code>token-classification</code></td> | |
| <td><code>AutoModelForTokenClassification</code></td></tr></tbody></table> | |
| <p>Para cada configuração, você pode encontrar a lista de recursos suportados por meio do | |
| <code>FeaturesManager</code>. Por exemplo, para DistilBERT temos:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.onnx.features <span class="hljs-keyword">import</span> FeaturesManager | |
| <span class="hljs-meta">>>> </span>distilbert_features = <span class="hljs-built_in">list</span>(FeaturesManager.get_supported_features_for_model_type(<span class="hljs-string">"distilbert"</span>).keys()) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(distilbert_features) | |
| [<span class="hljs-string">"default"</span>, <span class="hljs-string">"masked-lm"</span>, <span class="hljs-string">"causal-lm"</span>, <span class="hljs-string">"sequence-classification"</span>, <span class="hljs-string">"token-classification"</span>, <span class="hljs-string">"question-answering"</span>]<!-- HTML_TAG_END --></pre></div> | |
| <p>Você pode então passar um desses recursos para o argumento <code>--feature</code> no | |
| pacote <code>transformers.onnx</code>. Por exemplo, para exportar um modelo de classificação de texto, podemos | |
| escolher um modelo ajustado no Hub e executar:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->python -m transformers.onnx --model=distilbert-base-uncased-finetuned-sst-2-english \ | |
| --feature=sequence-classification onnx/<!-- HTML_TAG_END --></pre></div> | |
| <p>Isso exibe os seguintes logs:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->Validating ONNX model... | |
| -[✓] ONNX model output names match reference model ({<span class="hljs-string">'logits'</span>}) | |
| - Validating ONNX Model output <span class="hljs-string">"logits"</span>: | |
| -[✓] (2, 2) matches (2, 2) | |
| -[✓] all values close (atol: 1e-05) | |
| All good, model saved at: onnx/model.onnx<!-- HTML_TAG_END --></pre></div> | |
| <p>Observe que, neste caso, os nomes de saída do modelo ajustado são <code>logits</code> | |
| em vez do <code>last_hidden_state</code> que vimos com o checkpoint <code>distilbert-base-uncased</code> | |
| mais cedo. Isso é esperado, pois o modelo ajustado (fine-tuned) possui uma cabeça de classificação de sequência.</p> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Os recursos que têm um sufixo <code>with-pass</code> (como <code>causal-lm-with-pass</code>) correspondem a | |
| classes de modelo com estados ocultos pré-computados (chave e valores nos blocos de atenção) | |
| que pode ser usado para decodificação autorregressiva rápida.</p></div> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Para modelos do tipo <code>VisionEncoderDecoder</code>, as partes do codificador e do decodificador são | |
| exportados separadamente como dois arquivos ONNX chamados <code>encoder_model.onnx</code> e <code>decoder_model.onnx</code> respectivamente.</p></div> | |
| <h2 class="relative group"><a id="exportando-um-modelo-para-uma-arquitetura-sem-suporte" 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="#exportando-um-modelo-para-uma-arquitetura-sem-suporte"><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>Exportando um modelo para uma arquitetura sem suporte | |
| </span></h2> | |
| <p>Se você deseja exportar um modelo cuja arquitetura não é suportada nativamente pela | |
| biblioteca, há três etapas principais a seguir:</p> | |
| <ol><li>Implemente uma configuração ONNX personalizada.</li> | |
| <li>Exporte o modelo para o ONNX.</li> | |
| <li>Valide as saídas do PyTorch e dos modelos exportados.</li></ol> | |
| <p>Nesta seção, veremos como o DistilBERT foi implementado para mostrar o que está envolvido | |
| em cada passo.</p> | |
| <h3 class="relative group"><a id="implementando-uma-configurao-onnx-personalizada" 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="#implementando-uma-configurao-onnx-personalizada"><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>Implementando uma configuração ONNX personalizada | |
| </span></h3> | |
| <p>Vamos começar com o objeto de configuração ONNX. Fornecemos três classes abstratas que | |
| você deve herdar, dependendo do tipo de arquitetura de modelo que deseja exportar:</p> | |
| <ul><li>Modelos baseados em codificador herdam de <code>OnnxConfig</code></li> | |
| <li>Modelos baseados em decodificador herdam de <code>OnnxConfigWithPast</code></li> | |
| <li>Os modelos codificador-decodificador herdam de <code>OnnxSeq2SeqConfigWithPast</code></li></ul> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Uma boa maneira de implementar uma configuração ONNX personalizada é observar as | |
| implementação no arquivo <code>configuration_<model_name>.py</code> de uma arquitetura semelhante.</p></div> | |
| <p>Como o DistilBERT é um modelo baseado em codificador, sua configuração é herdada de | |
| <code>OnnxConfig</code>:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> Mapping, OrderedDict | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.onnx <span class="hljs-keyword">import</span> OnnxConfig | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DistilBertOnnxConfig</span>(<span class="hljs-title class_ inherited__">OnnxConfig</span>): | |
| <span class="hljs-meta">... </span> @<span class="hljs-built_in">property</span> | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">inputs</span>(<span class="hljs-params">self</span>) -> Mapping[<span class="hljs-built_in">str</span>, Mapping[<span class="hljs-built_in">int</span>, <span class="hljs-built_in">str</span>]]: | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> OrderedDict( | |
| <span class="hljs-meta">... </span> [ | |
| <span class="hljs-meta">... </span> (<span class="hljs-string">"input_ids"</span>, {<span class="hljs-number">0</span>: <span class="hljs-string">"batch"</span>, <span class="hljs-number">1</span>: <span class="hljs-string">"sequence"</span>}), | |
| <span class="hljs-meta">... </span> (<span class="hljs-string">"attention_mask"</span>, {<span class="hljs-number">0</span>: <span class="hljs-string">"batch"</span>, <span class="hljs-number">1</span>: <span class="hljs-string">"sequence"</span>}), | |
| <span class="hljs-meta">... </span> ] | |
| <span class="hljs-meta">... </span> )<!-- HTML_TAG_END --></pre></div> | |
| <p>Todo objeto de configuração deve implementar a propriedade <code>inputs</code> e retornar um mapeamento, | |
| onde cada chave corresponde a uma entrada esperada e cada valor indica o eixo | |
| dessa entrada. Para o DistilBERT, podemos ver que duas entradas são necessárias: <code>input_ids</code> e | |
| <code>attention_mask</code>. Essas entradas têm a mesma forma de <code>(batch_size, sequence_length)</code> | |
| é por isso que vemos os mesmos eixos usados na configuração.</p> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Notice that <code>inputs</code> property for <code>DistilBertOnnxConfig</code> returns an <code>OrderedDict</code>. This | |
| ensures that the inputs are matched with their relative position within the | |
| <code>PreTrainedModel.forward()</code> method when tracing the graph. We recommend using an | |
| <code>OrderedDict</code> for the <code>inputs</code> and <code>outputs</code> properties when implementing custom ONNX | |
| configurations.</p> | |
| <p>Observe que a propriedade <code>inputs</code> para <code>DistilBertOnnxConfig</code> retorna um <code>OrderedDict</code>. Este | |
| garante que as entradas sejam combinadas com sua posição relativa dentro do | |
| método <code>PreTrainedModel.forward()</code> ao traçar o grafo. Recomendamos o uso de um | |
| <code>OrderedDict</code> para as propriedades <code>inputs</code> e <code>outputs</code> ao implementar configurações personalizadas ONNX.</p></div> | |
| <p>Depois de implementar uma configuração ONNX, você pode instanciá-la fornecendo a | |
| configuração do modelo base da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig | |
| <span class="hljs-meta">>>> </span>config = AutoConfig.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span>onnx_config = DistilBertOnnxConfig(config)<!-- HTML_TAG_END --></pre></div> | |
| <p>O objeto resultante tem várias propriedades úteis. Por exemplo, você pode visualizar o conjunto de operadores ONNX | |
| que será usado durante a exportação:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(onnx_config.default_onnx_opset) | |
| <span class="hljs-number">11</span><!-- HTML_TAG_END --></pre></div> | |
| <p>Você também pode visualizar as saídas associadas ao modelo da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(onnx_config.outputs) | |
| OrderedDict([(<span class="hljs-string">"last_hidden_state"</span>, {<span class="hljs-number">0</span>: <span class="hljs-string">"batch"</span>, <span class="hljs-number">1</span>: <span class="hljs-string">"sequence"</span>})])<!-- HTML_TAG_END --></pre></div> | |
| <p>Observe que a propriedade outputs segue a mesma estrutura das entradas; ele retorna um | |
| <code>OrderedDict</code> de saídas nomeadas e suas formas. A estrutura de saída está ligada a | |
| escolha do recurso com o qual a configuração é inicializada. Por padrão, a configuração do ONNX | |
| é inicializada com o recurso <code>default</code> que corresponde à exportação de um | |
| modelo carregado com a classe <code>AutoModel</code>. Se você deseja exportar um modelo para outra tarefa, | |
| apenas forneça um recurso diferente para o argumento <code>task</code> quando você inicializar a configuração ONNX | |
| . Por exemplo, se quisermos exportar o DistilBERT com uma sequência | |
| de classificação, poderíamos usar:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig | |
| <span class="hljs-meta">>>> </span>config = AutoConfig.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>) | |
| <span class="hljs-meta">>>> </span>onnx_config_for_seq_clf = DistilBertOnnxConfig(config, task=<span class="hljs-string">"sequence-classification"</span>) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(onnx_config_for_seq_clf.outputs) | |
| OrderedDict([(<span class="hljs-string">'logits'</span>, {<span class="hljs-number">0</span>: <span class="hljs-string">'batch'</span>})])<!-- HTML_TAG_END --></pre></div> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Todas as propriedades e métodos básicos associados a <code>OnnxConfig</code> e | |
| as outras classes de configuração podem ser substituídas se necessário. Confira <code>BartOnnxConfig</code> | |
| para um exemplo avançado.</p></div> | |
| <h3 class="relative group"><a id="exportando-um-modelo" 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="#exportando-um-modelo"><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>Exportando um modelo | |
| </span></h3> | |
| <p>Depois de ter implementado a configuração do ONNX, o próximo passo é exportar o modelo. | |
| Aqui podemos usar a função <code>export()</code> fornecida pelo pacote <code>transformers.onnx</code>. | |
| Esta função espera a configuração do ONNX, juntamente com o modelo base e o tokenizer, | |
| e o caminho para salvar o arquivo exportado:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> pathlib <span class="hljs-keyword">import</span> Path | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.onnx <span class="hljs-keyword">import</span> export | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModel | |
| <span class="hljs-meta">>>> </span>onnx_path = Path(<span class="hljs-string">"model.onnx"</span>) | |
| <span class="hljs-meta">>>> </span>model_ckpt = <span class="hljs-string">"distilbert-base-uncased"</span> | |
| <span class="hljs-meta">>>> </span>base_model = AutoModel.from_pretrained(model_ckpt) | |
| <span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_ckpt) | |
| <span class="hljs-meta">>>> </span>onnx_inputs, onnx_outputs = export(tokenizer, base_model, onnx_config, onnx_config.default_onnx_opset, onnx_path)<!-- HTML_TAG_END --></pre></div> | |
| <p>Os <code>onnx_inputs</code> e <code>onnx_outputs</code> retornados pela função <code>export()</code> são listas de | |
| chaves definidas nas propriedades <code>inputs</code> e <code>outputs</code> da configuração. Uma vez que o | |
| modelo é exportado, você pode testar se o modelo está bem formado da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> onnx | |
| <span class="hljs-meta">>>> </span>onnx_model = onnx.load(<span class="hljs-string">"model.onnx"</span>) | |
| <span class="hljs-meta">>>> </span>onnx.checker.check_model(onnx_model)<!-- HTML_TAG_END --></pre></div> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se o seu modelo for maior que 2GB, você verá que muitos arquivos adicionais são criados | |
| durante a exportação. Isso é <em>esperado</em> porque o ONNX usa <a href="https://developers.google.com/protocol-buffers/" rel="nofollow">Protocol | |
| Buffers</a> para armazenar o modelo e estes | |
| têm um limite de tamanho de 2GB. Veja a <a href="https://github.com/onnx/onnx/blob/master/docs/ExternalData.md" rel="nofollow">ONNX | |
| documentação</a> para | |
| instruções sobre como carregar modelos com dados externos.</p></div> | |
| <h3 class="relative group"><a id="validando-a-sada-dos-modelos" 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="#validando-a-sada-dos-modelos"><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>Validando a saída dos modelos | |
| </span></h3> | |
| <p>A etapa final é validar se as saídas do modelo base e exportado concordam | |
| dentro de alguma tolerância absoluta. Aqui podemos usar a função <code>validate_model_outputs()</code> | |
| fornecida pelo pacote <code>transformers.onnx</code> da seguinte forma:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.onnx <span class="hljs-keyword">import</span> validate_model_outputs | |
| <span class="hljs-meta">>>> </span>validate_model_outputs( | |
| <span class="hljs-meta">... </span> onnx_config, tokenizer, base_model, onnx_path, onnx_outputs, onnx_config.atol_for_validation | |
| <span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div> | |
| <p>Esta função usa o método <code>generate_dummy_inputs()</code> para | |
| gerar entradas para o modelo base e o exportado, e a tolerância absoluta pode ser | |
| definida na configuração. Geralmente encontramos concordância numérica em 1e-6 a 1e-4 | |
| de alcance, embora qualquer coisa menor que 1e-3 provavelmente esteja OK.</p> | |
| <h2 class="relative group"><a id="contribuindo-com-uma-nova-configurao-para-transformers" 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="#contribuindo-com-uma-nova-configurao-para-transformers"><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>Contribuindo com uma nova configuração para 🤗 Transformers | |
| </span></h2> | |
| <p>Estamos procurando expandir o conjunto de configurações prontas e receber contribuições | |
| da comunidade! Se você gostaria de contribuir para a biblioteca, você | |
| precisará:</p> | |
| <ul><li>Implemente a configuração do ONNX no arquivo <code>configuration_<model_name>.py</code> correspondente | |
| Arquivo</li> | |
| <li>Incluir a arquitetura do modelo e recursos correspondentes em | |
| <code>~onnx.features.FeatureManager</code></li> | |
| <li>Adicione sua arquitetura de modelo aos testes em <code>test_onnx_v2.py</code></li></ul> | |
| <p>Confira como ficou a configuração do <a href="https://github.com/huggingface/transformers/pull/14868/files" rel="nofollow">IBERT | |
| </a> para obter uma | |
| idéia do que está envolvido.</p> | |
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