optimum 1.8.4

Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality.

Hugging Face Optimum

🤗 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.

Installation

🤗 Optimum can be installed using pip as follows:

python -m pip install optimum

If 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	python -m pip install optimum[onnxruntime]
Intel Neural Compressor	python -m pip install optimum[neural-compressor]
OpenVINO	python -m pip install optimum[openvino,nncf]
Habana Gaudi Processor (HPU)	python -m pip install optimum[habana]

To install from source:

python -m pip install git+https://github.com/huggingface/optimum.git

For the accelerator-specific features, append #egg=optimum[accelerator_type] to the above command:

python -m pip install git+https://github.com/huggingface/optimum.git#egg=optimum[onnxruntime]

Accelerated Inference

🤗 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

The export and optimizations can be done both programmatically and with a command line.

Features summary

Features	ONNX Runtime	Neural Compressor	OpenVINO	TensorFlow Lite
Graph optimization	:heavy_check_mark:	N/A	:heavy_check_mark:	N/A
Post-training dynamic quantization	:heavy_check_mark:	:heavy_check_mark:	N/A	:heavy_check_mark:
Post-training static quantization	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
Quantization Aware Training (QAT)	N/A	:heavy_check_mark:	:heavy_check_mark:	N/A
FP16 (half precision)	:heavy_check_mark:	N/A	:heavy_check_mark:	:heavy_check_mark:
Pruning	N/A	:heavy_check_mark:	:heavy_check_mark:	N/A
Knowledge Distillation	N/A	:heavy_check_mark:	:heavy_check_mark:	N/A

ONNX + ONNX Runtime

It is possible to export 🤗 Transformers 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_onnx

These 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.

Run the exported model using ONNX Runtime

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 AutoTokenizer
from optimum.onnxruntime import ORTModelForQuestionAnswering

model_name = "roberta_base_qa_onnx"
tokenizer = AutoTokenizer.from_pretrained(model_name)
ort_model = ORTModelForQuestionAnswering.from_pretrained(model_name)

question = "What's Optimum?"
text = "Optimum is an awesome library everyone should use!"
inputs = tokenizer(question, text, return_tensors="pt") 

# Run with ONNX Runtime.
outputs = ort_model(**inputs)

answer_start_index = outputs.start_logits.argmax()
answer_end_index = outputs.end_logits.argmax()

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
answer = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)

More details on how to run ONNX models with ORTModelForXXX classes here.

TensorFlow Lite

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

OpenVINO

This requires to install the Optimum OpenVINO extra by doing pip install optimum[openvino,nncf].

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(model_id, export=True)
  model.save_pretrained("./distilbert")

  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.

Accelerated training

🤗 Optimum provides wrappers around the original 🤗 Transformers Trainer to enable training on powerful hardware easily. We support many providers:

• Habana's Gaudi processors
• ONNX Runtime (optimized for GPUs)

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.

ONNX Runtime

- 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,
+     feature="sequence-classification", # The model type to export to ONNX
      ...
  )

  # Use ONNX Runtime for training!
  trainer.train()

You can find more examples in the documentation and in the examples.