Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into a serialized format that can be loaded and executed on specialized runtimes and hardware.
🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or TensorFlow to serialized formats
such as ONNX and TFLite through its
exporters module. 🤗 Optimum also provides a set of performance optimization tools to train
and run models on targeted hardware with maximum efficiency.
This guide demonstrates how you can export 🤗 Transformers models to ONNX with 🤗 Optimum, for the guide on exporting models to TFLite, please refer to the Export to TFLite page.
ONNX (Open Neural Network eXchange) is an open standard that defines a common set of operators and a common file format to represent deep learning models in a wide variety of frameworks, including PyTorch and TensorFlow. When a model is exported to the ONNX format, these operators are used to construct a computational graph (often called an intermediate representation) which represents the flow of data through the neural network.
By exposing a graph with standardized operators and data types, ONNX makes it easy to switch between frameworks. For example, a model trained in PyTorch can be exported to ONNX format and then imported in TensorFlow (and vice versa).
Once exported to ONNX format, a model can be:
- optimized for inference via techniques such as graph optimization and quantization.
- run with ONNX Runtime via
ORTModelForXXXclasses, which follow the same
AutoModelAPI as the one you are used to in 🤗 Transformers.
- run with optimized inference pipelines, which has the same API as the pipeline() function in 🤗 Transformers.
🤗 Optimum provides support for the ONNX export by leveraging configuration objects. These configuration objects come ready-made for a number of model architectures, and are designed to be easily extendable to other architectures.
For the list of ready-made configurations, please refer to 🤗 Optimum documentation.
There are two ways to export a 🤗 Transformers model to ONNX, here we show both:
- export with 🤗 Optimum via CLI.
- export with 🤗 Optimum with
To export a 🤗 Transformers model to ONNX, first install an extra dependency:
pip install optimum[exporters]
To check out all available arguments, refer to the 🤗 Optimum docs, or view help in command line:
optimum-cli export onnx --help
To export a model’s checkpoint from the 🤗 Hub, for example,
distilbert-base-uncased-distilled-squad, run the following command:
optimum-cli export onnx --model distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
You should see the logs indicating progress and showing where the resulting
model.onnx is saved, like this:
Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx... -[✓] ONNX model output names match reference model (start_logits, end_logits) - Validating ONNX Model output "start_logits": -[✓] (2, 16) matches (2, 16) -[✓] all values close (atol: 0.0001) - Validating ONNX Model output "end_logits": -[✓] (2, 16) matches (2, 16) -[✓] all values close (atol: 0.0001) The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_onnx
The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you
saved both the model’s weights and tokenizer files in the same directory (
local_path). When using CLI, pass the
local_path to the
model argument instead of the checkpoint name on 🤗 Hub and provide the
You can review the list of supported tasks in the 🤗 Optimum documentation.
task argument is not provided, it will default to the model architecture without any task specific head.
optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
from transformers import AutoTokenizer from optimum.onnxruntime import ORTModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("distilbert_base_uncased_squad_onnx") model = ORTModelForQuestionAnswering.from_pretrained("distilbert_base_uncased_squad_onnx") inputs = tokenizer("What am I using?", "Using DistilBERT with ONNX Runtime!", return_tensors="pt") outputs = model(**inputs)
The process is identical for TensorFlow checkpoints on the Hub. For instance, here’s how you would export a pure TensorFlow checkpoint from the Keras organization:
optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
Alternative to CLI, you can export a 🤗 Transformers model to ONNX programmatically like so:
from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import AutoTokenizer model_checkpoint = "distilbert_base_uncased_squad" save_directory = "onnx/" # Load a model from transformers and export it to ONNX ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) # Save the onnx model and tokenizer ort_model.save_pretrained(save_directory) tokenizer.save_pretrained(save_directory)
If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
and if it is not, contribute to 🤗 Optimum
tranformers.onnx is no longer maintained, please export models with 🤗 Optimum as described above. This section will be removed in the future versions.
To export a 🤗 Transformers model to ONNX with
tranformers.onnx, install extra dependencies:
pip install transformers[onnx]
transformers.onnx package as a Python module to export a checkpoint using a ready-made configuration:
python -m transformers.onnx --model=distilbert-base-uncased onnx/
This exports an ONNX graph of the checkpoint defined by the
--model argument. Pass any checkpoint on the 🤗 Hub or one that’s stored locally.
model.onnx file can then be run on one of the many accelerators that support the ONNX standard. For example,
load and run the model with ONNX Runtime as follows:
from transformers import AutoTokenizer from onnxruntime import InferenceSession tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") session = InferenceSession("onnx/model.onnx") # ONNX Runtime expects NumPy arrays as input inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np") outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
The required output names (like
["last_hidden_state"]) can be obtained by taking a look at the ONNX configuration of
each model. For example, for DistilBERT we have:
from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig config = DistilBertConfig() onnx_config = DistilBertOnnxConfig(config) print(list(onnx_config.outputs.keys())) ["last_hidden_state"]
The process is identical for TensorFlow checkpoints on the Hub. For example, export a pure TensorFlow checkpoint like so:
python -m transformers.onnx --model=keras-io/transformers-qa onnx/
To export a model that’s stored locally, save the model’s weights and tokenizer files in the same directory (e.g.
then export it to ONNX by pointing the
--model argument of the
transformers.onnx package to the desired directory:
python -m transformers.onnx --model=local-pt-checkpoint onnx/