You can export a PyTorch model to Neuron with 🤗 Optimum to run inference on AWS Inferntia 1 and Inferentia 2.
There is an export function for each generation of the Inferentia accelerator, export_neuron
for INF1 and export_neuronx on INF2, but you will be able to use directly the export function export, which will select the proper
exporting function according to the environment.
Besides, you can check if the exported model is valid via validate_model_outputs, which compares
the compiled model’s output on Neuron devices to the PyTorch model’s output on CPU.
Exporting a PyTorch model to a Neuron compiled model involves specifying:
Depending on the choice of model and task, we represent the data above with configuration classes. Each configuration class is associated with
a specific model architecture, and follows the naming convention ArchitectureNameNeuronConfig. For instance, the configuration which specifies the Neuron
export of BERT models is BertNeuronConfig.
Since many architectures share similar properties for their Neuron configuration, 🤗 Optimum adopts a 3-level class hierarchy:
BertNeuronConfig mentioned above. These are the ones actually used to export models.| Architecture | Task |
|---|---|
| ALBERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| BERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| CamemBERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| ConvBERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| DeBERTa (INF2 only) | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| DeBERTa-v2 (INF2 only) | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| DistilBERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| ELECTRA | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| FlauBERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| GPT2 | text-generation |
| MobileBERT | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| MPNet | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| RoBERTa | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| RoFormer | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| XLM | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| XLM-RoBERTa | feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification |
| Stable Diffusion | text-to-image |
More details for checking supported tasks here.
More architectures coming soon, stay tuned! 🚀