There are currently three ways to convert your Hugging Face Transformers models to ONNX. In this section, you will learn how to export distilbert-base-uncased-finetuned-sst-2-english for text-classification using all three methods going from the low-level torch API to the most user-friendly high-level API of optimum. Each method will do exactly the same

Export with torch.onnx (low-level)

torch.onnx enables you to convert model checkpoints to an ONNX graph by the export method. But you have to provide a lot of values like input_names, dynamic_axes, etc.

You’ll first need to install some dependencies:

pip install transformers torch

exporting our checkpoint with export 

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# load model and tokenizer
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dummy_model_input = tokenizer("This is a sample", return_tensors="pt")

# export
torch.onnx.export(
    model, 
    tuple(dummy_model_input.values()),
    f="torch-model.onnx",  
    input_names=['input_ids', 'attention_mask'], 
    output_names=['logits'], 
    dynamic_axes={'input_ids': {0: 'batch_size', 1: 'sequence'}, 
                  'attention_mask': {0: 'batch_size', 1: 'sequence'}, 
                  'logits': {0: 'batch_size', 1: 'sequence'}}, 
    do_constant_folding=True, 
    opset_version=13, 
)

Export with transformers.onnx (mid-level)

transformers.onnx enables you to convert model checkpoints to an ONNX graph by leveraging configuration objects. That way you don’t have to provide the complex configuration for dynamic_axes etc.

You’ll first need to install some dependencies:

pip install transformers[onnx] torch

Exporting our checkpoint with the transformers.onnx.

from pathlib import Path
import transformers
from transformers.onnx import FeaturesManager
from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification

# load model and tokenizer
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
feature = "sequence-classification"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# load config
model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(model, feature=feature)
onnx_config = model_onnx_config(model.config)

# export
onnx_inputs, onnx_outputs = transformers.onnx.export(
        preprocessor=tokenizer,
        model=model,
        config=onnx_config,
        opset=13,
        output=Path("trfs-model.onnx")
)

Export with Optimum (high-level)

Optimum Inference includes methods to convert vanilla Transformers models to ONNX using the ORTModelForXxx classes. To convert your Transformers model to ONNX you simply have to pass from_transformers=True to the from_pretrained() method and your model will be loaded and converted to ONNX leveraging the transformers.onnx package under the hood.

You’ll first need to install some dependencies:

pip install optimum[onnxruntime]

Exporting our checkpoint with ORTModelForSequenceClassification

from optimum.onnxruntime import ORTModelForSequenceClassification

model = ORTModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english",from_transformers=True)

The best part about the conversion with Optimum is that you can immediately use the model to run predictions or load it inside a pipeline.