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Export 🤗 Transformers Models

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Export 🤗 Transformers Models

If you need to deploy 🤗 Transformers models in production environments, we recommend exporting them to a serialized format that can be loaded and executed on specialized runtimes and hardware. In this guide, we’ll show you how to export 🤗 Transformers models in two widely used formats: ONNX and TorchScript.

Once exported, a model can optimized for inference via techniques such as quantization and pruning. If you are interested in optimizing your models to run with maximum efficiency, check out the 🤗 Optimum library.

ONNX

The ONNX (Open Neural Network eXchange) project 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).

🤗 Transformers provides a transformers.onnx package that enables you to convert model checkpoints to an ONNX graph 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.

Ready-made configurations include the following architectures:

  • ALBERT
  • BART
  • BEiT
  • BERT
  • BigBird
  • BigBird-Pegasus
  • Blenderbot
  • BlenderbotSmall
  • BLOOM
  • CamemBERT
  • CodeGen
  • ConvBERT
  • ConvNeXT
  • Data2VecText
  • Data2VecVision
  • DeBERTa
  • DeBERTa-v2
  • DeiT
  • DETR
  • DistilBERT
  • ELECTRA
  • FlauBERT
  • GPT Neo
  • GPT-J
  • I-BERT
  • LayoutLM
  • LayoutLMv3
  • LongT5
  • M2M100
  • Marian
  • mBART
  • MobileBERT
  • MobileViT
  • OpenAI GPT-2
  • Perceiver
  • PLBart
  • ResNet
  • RoBERTa
  • RoFormer
  • SqueezeBERT
  • T5
  • ViT
  • XLM
  • XLM-RoBERTa
  • XLM-RoBERTa-XL
  • YOLOS

In the next two sections, we’ll show you how to:

  • Export a supported model using the transformers.onnx package.
  • Export a custom model for an unsupported architecture.

Exporting a model to ONNX

To export a 🤗 Transformers model to ONNX, you’ll first need to install some extra dependencies:

pip install transformers[onnx]

The transformers.onnx package can then be used as a Python module:

python -m transformers.onnx --help

usage: Hugging Face Transformers ONNX exporter [-h] -m MODEL [--feature {causal-lm, ...}] [--opset OPSET] [--atol ATOL] output

positional arguments:
  output                Path indicating where to store generated ONNX model.

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        Model ID on huggingface.co or path on disk to load model from.
  --feature {causal-lm, ...}
                        The type of features to export the model with.
  --opset OPSET         ONNX opset version to export the model with.
  --atol ATOL           Absolute difference tolerence when validating the model.

Exporting a checkpoint using a ready-made configuration can be done as follows:

python -m transformers.onnx --model=distilbert-base-uncased onnx/

which should show the following logs:

Validating ONNX model...
        -[✓] ONNX model output names match reference model ({'last_hidden_state'})
        - Validating ONNX Model output "last_hidden_state":
                -[✓] (2, 8, 768) matches (2, 8, 768)
                -[✓] all values close (atol: 1e-05)
All good, model saved at: onnx/model.onnx

This exports an ONNX graph of the checkpoint defined by the --model argument. In this example it is distilbert-base-uncased, but it can be any checkpoint on the Hugging Face Hub or one that’s stored locally.

The resulting model.onnx file can then be run on one of the many accelerators that support the ONNX standard. For example, we can 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 (i.e. ["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, we can export a pure TensorFlow checkpoint from the Keras organization as follows:

python -m transformers.onnx --model=keras-io/transformers-qa onnx/

To export a model that’s stored locally, you’ll need to have the model’s weights and tokenizer files stored in a directory. For example, we can load and save a checkpoint as follows:

Pytorch
Hide Pytorch content
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification

>>> # Load tokenizer and PyTorch weights form the Hub
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> pt_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> # Save to disk
>>> tokenizer.save_pretrained("local-pt-checkpoint")
>>> pt_model.save_pretrained("local-pt-checkpoint")

Once the checkpoint is saved, we can 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/
TensorFlow
Hide TensorFlow content
>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

>>> # Load tokenizer and TensorFlow weights from the Hub
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
>>> # Save to disk
>>> tokenizer.save_pretrained("local-tf-checkpoint")
>>> tf_model.save_pretrained("local-tf-checkpoint")

Once the checkpoint is saved, we can export it to ONNX by pointing the --model argument of the transformers.onnx package to the desired directory:

python -m transformers.onnx --model=local-tf-checkpoint onnx/

Selecting features for different model topologies

Each ready-made configuration comes with a set of features that enable you to export models for different types of topologies or tasks. As shown in the table below, each feature is associated with a different auto class:

Feature Auto Class
causal-lm, causal-lm-with-past AutoModelForCausalLM
default, default-with-past AutoModel
masked-lm AutoModelForMaskedLM
question-answering AutoModelForQuestionAnswering
seq2seq-lm, seq2seq-lm-with-past AutoModelForSeq2SeqLM
sequence-classification AutoModelForSequenceClassification
token-classification AutoModelForTokenClassification

For each configuration, you can find the list of supported features via the FeaturesManager. For example, for DistilBERT we have:

>>> from transformers.onnx.features import FeaturesManager

>>> distilbert_features = list(FeaturesManager.get_supported_features_for_model_type("distilbert").keys())
>>> print(distilbert_features)
["default", "masked-lm", "causal-lm", "sequence-classification", "token-classification", "question-answering"]

You can then pass one of these features to the --feature argument in the transformers.onnx package. For example, to export a text-classification model we can pick a fine-tuned model from the Hub and run:

python -m transformers.onnx --model=distilbert-base-uncased-finetuned-sst-2-english \
                            --feature=sequence-classification onnx/

which will display the following logs:

Validating ONNX model...
        -[✓] ONNX model output names match reference model ({'logits'})
        - Validating ONNX Model output "logits":
                -[✓] (2, 2) matches (2, 2)
                -[✓] all values close (atol: 1e-05)
All good, model saved at: onnx/model.onnx

Notice that in this case, the output names from the fine-tuned model are logits instead of the last_hidden_state we saw with the distilbert-base-uncased checkpoint earlier. This is expected since the fine-tuned model has a sequence classification head.

The features that have a with-past suffix (e.g. causal-lm-with-past) correspond to model topologies with precomputed hidden states (key and values in the attention blocks) that can be used for fast autoregressive decoding.

Exporting a model for an unsupported architecture

If you wish to export a model whose architecture is not natively supported by the library, there are three main steps to follow:

  1. Implement a custom ONNX configuration.
  2. Export the model to ONNX.
  3. Validate the outputs of the PyTorch and exported models.

In this section, we’ll look at how DistilBERT was implemented to show what’s involved with each step.

Implementing a custom ONNX configuration

Let’s start with the ONNX configuration object. We provide three abstract classes that you should inherit from, depending on the type of model architecture you wish to export:

A good way to implement a custom ONNX configuration is to look at the existing implementation in the configuration_<model_name>.py file of a similar architecture.

Since DistilBERT is an encoder-based model, its configuration inherits from OnnxConfig:

>>> from typing import Mapping, OrderedDict
>>> from transformers.onnx import OnnxConfig


>>> class DistilBertOnnxConfig(OnnxConfig):
...     @property
...     def inputs(self) -> Mapping[str, Mapping[int, str]]:
...         return OrderedDict(
...             [
...                 ("input_ids", {0: "batch", 1: "sequence"}),
...                 ("attention_mask", {0: "batch", 1: "sequence"}),
...             ]
...         )

Every configuration object must implement the inputs property and return a mapping, where each key corresponds to an expected input, and each value indicates the axis of that input. For DistilBERT, we can see that two inputs are required: input_ids and attention_mask. These inputs have the same shape of (batch_size, sequence_length) which is why we see the same axes used in the configuration.

Notice that inputs property for DistilBertOnnxConfig returns an OrderedDict. This ensures that the inputs are matched with their relative position within the PreTrainedModel.forward() method when tracing the graph. We recommend using an OrderedDict for the inputs and outputs properties when implementing custom ONNX configurations.

Once you have implemented an ONNX configuration, you can instantiate it by providing the base model’s configuration as follows:

>>> from transformers import AutoConfig

>>> config = AutoConfig.from_pretrained("distilbert-base-uncased")
>>> onnx_config = DistilBertOnnxConfig(config)

The resulting object has several useful properties. For example you can view the ONNX operator set that will be used during the export:

>>> print(onnx_config.default_onnx_opset)
11

You can also view the outputs associated with the model as follows:

>>> print(onnx_config.outputs)
OrderedDict([("last_hidden_state", {0: "batch", 1: "sequence"})])

Notice that the outputs property follows the same structure as the inputs; it returns an OrderedDict of named outputs and their shapes. The output structure is linked to the choice of feature that the configuration is initialised with. By default, the ONNX configuration is initialized with the default feature that corresponds to exporting a model loaded with the AutoModel class. If you want to export a different model topology, just provide a different feature to the task argument when you initialize the ONNX configuration. For example, if we wished to export DistilBERT with a sequence classification head, we could use:

>>> from transformers import AutoConfig

>>> config = AutoConfig.from_pretrained("distilbert-base-uncased")
>>> onnx_config_for_seq_clf = DistilBertOnnxConfig(config, task="sequence-classification")
>>> print(onnx_config_for_seq_clf.outputs)
OrderedDict([('logits', {0: 'batch'})])

All of the base properties and methods associated with OnnxConfig and the other configuration classes can be overriden if needed. Check out BartOnnxConfig for an advanced example.

Exporting the model

Once you have implemented the ONNX configuration, the next step is to export the model. Here we can use the export() function provided by the transformers.onnx package. This function expects the ONNX configuration, along with the base model and tokenizer, and the path to save the exported file:

>>> from pathlib import Path
>>> from transformers.onnx import export
>>> from transformers import AutoTokenizer, AutoModel

>>> onnx_path = Path("model.onnx")
>>> model_ckpt = "distilbert-base-uncased"
>>> base_model = AutoModel.from_pretrained(model_ckpt)
>>> tokenizer = AutoTokenizer.from_pretrained(model_ckpt)

>>> onnx_inputs, onnx_outputs = export(tokenizer, base_model, onnx_config, onnx_config.default_onnx_opset, onnx_path)

The onnx_inputs and onnx_outputs returned by the export() function are lists of the keys defined in the inputs and outputs properties of the configuration. Once the model is exported, you can test that the model is well formed as follows:

>>> import onnx

>>> onnx_model = onnx.load("model.onnx")
>>> onnx.checker.check_model(onnx_model)

If your model is larger than 2GB, you will see that many additional files are created during the export. This is expected because ONNX uses Protocol Buffers to store the model and these have a size limit of 2GB. See the ONNX documentation for instructions on how to load models with external data.

Validating the model outputs

The final step is to validate that the outputs from the base and exported model agree within some absolute tolerance. Here we can use the validate_model_outputs() function provided by the transformers.onnx package as follows:

>>> from transformers.onnx import validate_model_outputs

>>> validate_model_outputs(
...     onnx_config, tokenizer, base_model, onnx_path, onnx_outputs, onnx_config.atol_for_validation
... )

This function uses the OnnxConfig.generate_dummy_inputs() method to generate inputs for the base and exported model, and the absolute tolerance can be defined in the configuration. We generally find numerical agreement in the 1e-6 to 1e-4 range, although anything smaller than 1e-3 is likely to be OK.

Contributing a new configuration to 🤗 Transformers

We are looking to expand the set of ready-made configurations and welcome contributions from the community! If you would like to contribute your addition to the library, you will need to:

  • Implement the ONNX configuration in the corresponding configuration_<model_name>.py file
  • Include the model architecture and corresponding features in FeatureManager
  • Add your model architecture to the tests in test_onnx_v2.py

Check out how the configuration for IBERT was contributed to get an idea of what’s involved.

TorchScript

This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases, with more code examples, a more flexible implementation, and benchmarks comparing python-based codes with compiled TorchScript.

According to Pytorch’s documentation: “TorchScript is a way to create serializable and optimizable models from PyTorch code”. Pytorch’s two modules JIT and TRACE allow the developer to export their model to be re-used in other programs, such as efficiency-oriented C++ programs.

We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can be reused in a different environment than a Pytorch-based python program. Here we explain how to export and use our models using TorchScript.

Exporting a model requires two things:

  • a forward pass with dummy inputs.
  • model instantiation with the torchscript flag.

These necessities imply several things developers should be careful about. These are detailed below.

TorchScript flag and tied weights

This flag is necessary because most of the language models in this repository have tied weights between their Embedding layer and their Decoding layer. TorchScript does not allow the export of models that have tied weights, therefore it is necessary to untie and clone the weights beforehand.

This implies that models instantiated with the torchscript flag have their Embedding layer and Decoding layer separate, which means that they should not be trained down the line. Training would de-synchronize the two layers, leading to unexpected results.

This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models can be safely exported without the torchscript flag.

Dummy inputs and standard lengths

The dummy inputs are used to do a model forward pass. While the inputs’ values are propagating through the layers, Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used to create the “trace” of the model.

The trace is created relatively to the inputs’ dimensions. It is therefore constrained by the dimensions of the dummy input, and will not work for any other sequence length or batch size. When trying with a different size, an error such as:

The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2

will be raised. It is therefore recommended to trace the model with a dummy input size at least as large as the largest input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model will have been traced with a large input size however, the dimensions of the different matrix will be large as well, resulting in more calculations.

It is recommended to be careful of the total number of operations done on each input and to follow performance closely when exporting varying sequence-length models.

Using TorchScript in Python

Below is an example, showing how to save, load models as well as how to use the trace for inference.

Saving a model

This snippet shows how to use TorchScript to export a BertModel. Here the BertModel is instantiated according to a BertConfig class and then saved to disk under the filename traced_bert.pt

from transformers import BertModel, BertTokenizer, BertConfig
import torch

enc = BertTokenizer.from_pretrained("bert-base-uncased")

# Tokenizing input text
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = enc.tokenize(text)

# Masking one of the input tokens
masked_index = 8
tokenized_text[masked_index] = "[MASK]"
indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]

# Creating a dummy input
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
dummy_input = [tokens_tensor, segments_tensors]

# Initializing the model with the torchscript flag
# Flag set to True even though it is not necessary as this model does not have an LM Head.
config = BertConfig(
    vocab_size_or_config_json_file=32000,
    hidden_size=768,
    num_hidden_layers=12,
    num_attention_heads=12,
    intermediate_size=3072,
    torchscript=True,
)

# Instantiating the model
model = BertModel(config)

# The model needs to be in evaluation mode
model.eval()

# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)

# Creating the trace
traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
torch.jit.save(traced_model, "traced_bert.pt")

Loading a model

This snippet shows how to load the BertModel that was previously saved to disk under the name traced_bert.pt. We are re-using the previously initialised dummy_input.

loaded_model = torch.jit.load("traced_bert.pt")
loaded_model.eval()

all_encoder_layers, pooled_output = loaded_model(*dummy_input)

Using a traced model for inference

Using the traced model for inference is as simple as using its __call__ dunder method:

traced_model(tokens_tensor, segments_tensors)

Deploying HuggingFace TorchScript models on AWS using the Neuron SDK

AWS introduced the Amazon EC2 Inf1 instance family for low cost, high performance machine learning inference in the cloud. The Inf1 instances are powered by the AWS Inferentia chip, a custom-built hardware accelerator, specializing in deep learning inferencing workloads. AWS Neuron is the SDK for Inferentia that supports tracing and optimizing transformers models for deployment on Inf1. The Neuron SDK provides:

  1. Easy-to-use API with one line of code change to trace and optimize a TorchScript model for inference in the cloud.
  2. Out of the box performance optimizations for improved cost-performance
  3. Support for HuggingFace transformers models built with either PyTorch or TensorFlow.

Implications

Transformers Models based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, or its variants such as distilBERT and roBERTa will run best on Inf1 for non-generative tasks such as Extractive Question Answering, Sequence Classification, Token Classification. Alternatively, text generation tasks can be adapted to run on Inf1, according to this AWS Neuron MarianMT tutorial. More information about models that can be converted out of the box on Inferentia can be found in the Model Architecture Fit section of the Neuron documentation.

Dependencies

Using AWS Neuron to convert models requires the following dependencies and environment:

Converting a Model for AWS Neuron

Using the same script as in Using TorchScript in Python to trace a “BertModel”, you import torch.neuron framework extension to access the components of the Neuron SDK through a Python API.

from transformers import BertModel, BertTokenizer, BertConfig
import torch
import torch.neuron

And only modify the tracing line of code

from:

torch.jit.trace(model, [tokens_tensor, segments_tensors])

to:

torch.neuron.trace(model, [token_tensor, segments_tensors])

This change enables Neuron SDK to trace the model and optimize it to run in Inf1 instances.

To learn more about AWS Neuron SDK features, tools, example tutorials and latest updates, please see the AWS NeuronSDK documentation.