🤗 Optimum is an extension of 🤗 Transformers and Diffusers, providing a set of optimization tools enabling maximum efficiency to train and run models on targeted hardware, while keeping things easy to use.
🤗 Optimum can be installed using pip as follows:
python -m pip install optimumIf you'd like to use the accelerator-specific features of 🤗 Optimum, you can install the required dependencies according to the table below:
| Accelerator | Installation |
|---|---|
| ONNX Runtime | pip install --upgrade --upgrade-strategy eager optimum[onnxruntime] |
| Intel Neural Compressor | pip install --upgrade --upgrade-strategy eager optimum[neural-compressor] |
| OpenVINO | pip install --upgrade --upgrade-strategy eager optimum[openvino] |
| NVIDIA TensorRT-LLM | docker run -it --gpus all --ipc host huggingface/optimum-nvidia |
| AMD Instinct GPUs and Ryzen AI NPU | pip install --upgrade --upgrade-strategy eager optimum[amd] |
| AWS Trainum & Inferentia | pip install --upgrade --upgrade-strategy eager optimum[neuronx] |
| Habana Gaudi Processor (HPU) | pip install --upgrade --upgrade-strategy eager optimum[habana] |
| FuriosaAI | pip install --upgrade --upgrade-strategy eager optimum[furiosa] |
The --upgrade --upgrade-strategy eager option is needed to ensure the different packages are upgraded to the latest possible version.
To install from source:
python -m pip install git+https://github.com/huggingface/optimum.gitFor the accelerator-specific features, append optimum[accelerator_type] to the above command:
python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git🤗 Optimum provides multiple tools to export and run optimized models on various ecosystems:
- ONNX / ONNX Runtime
- TensorFlow Lite
- OpenVINO
- Habana first-gen Gaudi / Gaudi2, more details here
- AWS Inferentia 2 / Inferentia 1, more details here
- NVIDIA TensorRT-LLM , more details here
The export and optimizations can be done both programmatically and with a command line.
| Features | ONNX Runtime | Neural Compressor | OpenVINO | TensorFlow Lite |
|---|---|---|---|---|
| Graph optimization | ✔️ | N/A | ✔️ | N/A |
| Post-training dynamic quantization | ✔️ | ✔️ | N/A | ✔️ |
| Post-training static quantization | ✔️ | ✔️ | ✔️ | ✔️ |
| Quantization Aware Training (QAT) | N/A | ✔️ | ✔️ | N/A |
| FP16 (half precision) | ✔️ | N/A | ✔️ | ✔️ |
| Pruning | N/A | ✔️ | ✔️ | N/A |
| Knowledge Distillation | N/A | ✔️ | ✔️ | N/A |
Before you begin, make sure you have all the necessary libraries installed :
pip install --upgrade --upgrade-strategy eager optimum[openvino]It is possible to export 🤗 Transformers and Diffusers models to the OpenVINO format easily:
optimum-cli export openvino --model distilbert-base-uncased-finetuned-sst-2-english distilbert_sst2_ovIf you add --weight-format int8, the weights will be quantized to int8, check out our documentation for more detail on weight only quantization. To apply quantization on both weights and activations, you can find more information here.
To load a model and run inference with OpenVINO Runtime, you can just replace your AutoModelForXxx class with the corresponding OVModelForXxx class. To load a PyTorch checkpoint and convert it to the OpenVINO format on-the-fly, you can set export=True when loading your model.
- from transformers import AutoModelForSequenceClassification
+ from optimum.intel import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_id)
- model = AutoModelForSequenceClassification.from_pretrained(model_id)
+ model = OVModelForSequenceClassification.from_pretrained("distilbert_sst2_ov")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
results = classifier("He's a dreadful magician.")You can find more examples in the documentation and in the examples.
Before you begin, make sure you have all the necessary libraries installed :
pip install --upgrade --upgrade-strategy eager optimum[neural-compressor]Dynamic quantization can be applied on your model:
optimum-cli inc quantize --model distilbert-base-cased-distilled-squad --output ./quantized_distilbertTo load a model quantized with Intel Neural Compressor, hosted locally or on the 🤗 hub, you can do as follows :
from optimum.intel import INCModelForSequenceClassification
model_id = "Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic"
model = INCModelForSequenceClassification.from_pretrained(model_id)You can find more examples in the documentation and in the examples.
Before you begin, make sure you have all the necessary libraries installed :
pip install optimum[exporters,onnxruntime]It is possible to export 🤗 Transformers and Diffusers models to the ONNX format and perform graph optimization as well as quantization easily:
optimum-cli export onnx -m deepset/roberta-base-squad2 --optimize O2 roberta_base_qa_onnx
The model can then be quantized using onnxruntime:
optimum-cli onnxruntime quantize \
--avx512 \
--onnx_model roberta_base_qa_onnx \
-o quantized_roberta_base_qa_onnxThese commands will export deepset/roberta-base-squad2 and perform O2 graph optimization on the exported model, and finally quantize it with the avx512 configuration.
For more information on the ONNX export, please check the documentation.
Once the model is exported to the ONNX format, we provide Python classes enabling you to run the exported ONNX model in a seemless manner using ONNX Runtime in the backend:
- from transformers import AutoModelForQuestionAnswering
+ from optimum.onnxruntime import ORTModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
model_id = "deepset/roberta-base-squad2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
- model = AutoModelForQuestionAnswering.from_pretrained(model_id)
+ model = ORTModelForQuestionAnswering.from_pretrained("roberta_base_qa_onnx")
qa_pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
question = "What's Optimum?"
context = "Optimum is an awesome library everyone should use!"
results = qa_pipe(question=question, context=context)More details on how to run ONNX models with ORTModelForXXX classes here.
Before you begin, make sure you have all the necessary libraries installed :
pip install optimum[exporters-tf]Just as for ONNX, it is possible to export models to TensorFlow Lite and quantize them:
optimum-cli export tflite \
-m deepset/roberta-base-squad2 \
--sequence_length 384 \
--quantize int8-dynamic roberta_tflite_model
🤗 Optimum provides wrappers around the original 🤗 Transformers Trainer to enable training on powerful hardware easily. We support many providers:
- Habana's Gaudi processors
- AWS Trainium instances, check here
- ONNX Runtime (optimized for GPUs)
Before you begin, make sure you have all the necessary libraries installed :
pip install --upgrade --upgrade-strategy eager optimum[habana]- from transformers import Trainer, TrainingArguments
+ from optimum.habana import GaudiTrainer, GaudiTrainingArguments
# Download a pretrained model from the Hub
model = AutoModelForXxx.from_pretrained("bert-base-uncased")
# Define the training arguments
- training_args = TrainingArguments(
+ training_args = GaudiTrainingArguments(
output_dir="path/to/save/folder/",
+ use_habana=True,
+ use_lazy_mode=True,
+ gaudi_config_name="Habana/bert-base-uncased",
...
)
# Initialize the trainer
- trainer = Trainer(
+ trainer = GaudiTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
...
)
# Use Habana Gaudi processor for training!
trainer.train()You can find more examples in the documentation and in the examples.
- from transformers import Trainer, TrainingArguments
+ from optimum.onnxruntime import ORTTrainer, ORTTrainingArguments
# Download a pretrained model from the Hub
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
# Define the training arguments
- training_args = TrainingArguments(
+ training_args = ORTTrainingArguments(
output_dir="path/to/save/folder/",
optim="adamw_ort_fused",
...
)
# Create a ONNX Runtime Trainer
- trainer = Trainer(
+ trainer = ORTTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
...
)
# Use ONNX Runtime for training!
trainer.train()You can find more examples in the documentation and in the examples.
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