Quantization

🤗 Optimum provides an optimum.onnxruntime package that enables you to apply quantization on many model hosted on the 🤗 hub using the ONNX Runtime quantization tool.

Creating an ORTQuantizer

The ORTQuantizer class is used to quantize your ONNX model. The class can be initialized using the from_pretrained() method, which supports different checkpoint formats.

  1. Using an already initialized ORTModelForXXX class.
>>> from optimum.onnxruntime import ORTQuantizer, ORTModelForSequenceClassification

# Loading ONNX Model from the Hub
>>> ort_model = ORTModelForSequenceClassification.from_pretrained("optimum/distilbert-base-uncased-finetuned-sst-2-english")

# Create a quantizer from a ORTModelForXXX
>>> quantizer = ORTQuantizer.from_pretrained(ort_model)

# Configuration
>>> ...

# Quantize the model
>>> quantizer.quantize(...)
  1. Using a local ONNX model from a directory.
>>> from optimum.onnxruntime import ORTQuantizer

# This assumes a model.onnx exists in path/to/model
>>> quantizer = ORTQuantizer.from_pretrained("path/to/model")

# Configuration
>>> ...

# Quantize the model
>>> quantizer.quantize(...)

Dynamic Quantization example

The ORTQuantizer class can be used to dynamically quantize your ONNX model. Below you will find an easy end-to-end example on how to dynamically quantize distilbert-base-uncased-finetuned-sst-2-english.

>>> from optimum.onnxruntime import ORTQuantizer, ORTModelForSequenceClassification
>>> from optimum.onnxruntime.configuration import AutoQuantizationConfig

>>> model_id = "distilbert-base-uncased-finetuned-sst-2-english"
# Load PyTorch model and convert to ONNX
>>> onnx_model = ORTModelForSequenceClassification.from_pretrained(model_id, from_transformers=True)

# Create quantizer
>>> quantizer = ORTQuantizer.from_pretrained(onnx_model)

# Define the quantization strategy by creating the appropriate configuration 
>>> dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False)

# Quantize the model
>>> model_quantized_path = quantizer.quantize(
    save_dir="path/to/output/model",
    quantization_config=dqconfig,
)

Static Quantization example

The ORTQuantizer class can be used to statically quantize your ONNX model. Below you will find an easy end-to-end example on how to statically quantize distilbert-base-uncased-finetuned-sst-2-english.

>>> from functools import partial
>>> from transformers import AutoTokenizer
>>> from optimum.onnxruntime import ORTQuantizer, ORTModelForSequenceClassification
>>> from optimum.onnxruntime.configuration import AutoQuantizationConfig, AutoCalibrationConfig

>>> model_id = "distilbert-base-uncased-finetuned-sst-2-english"

# Load PyTorch model and convert to ONNX and create Quantizer and setup config
>>> onnx_model = ORTModelForSequenceClassification.from_pretrained(model_id, from_transformers=True)
>>> tokenizer = AutoTokenizer.from_pretrained(model_id)
>>> quantizer = ORTQuantizer.from_pretrained(onnx_model)
>>> qconfig = AutoQuantizationConfig.arm64(is_static=True, per_channel=False)

# Create the calibration dataset
>>> def preprocess_fn(ex, tokenizer):
    return tokenizer(ex["sentence"])

>>> calibration_dataset = quantizer.get_calibration_dataset(
    "glue",
    dataset_config_name="sst2",
    preprocess_function=partial(preprocess_fn, tokenizer=tokenizer),
    num_samples=50,
    dataset_split="train",
)
# Create the calibration configuration containing the parameters related to calibration.
>>> calibration_config = AutoCalibrationConfig.minmax(calibration_dataset)

# Perform the calibration step: computes the activations quantization ranges
>>> ranges = quantizer.fit(
    dataset=calibration_dataset,
    calibration_config=calibration_config,
    operators_to_quantize=qconfig.operators_to_quantize,
)

# Apply static quantization on the model
>>> model_quantized_path = quantizer.quantize(
    save_dir="path/to/output/model",
    calibration_tensors_range=ranges,
    quantization_config=qconfig,
)

Quantize Seq2Seq models

The ORTQuantizer currently doesn’t support multi-file models, like ORTModelForSeq2SeqLM. If you want to quantize a Seq2Seq model, you have to quantize each model’s component individually using the ORTQuantizer class. Currently, only dynamic quantization is supported for Seq2Seq model.

  1. Load seq2seq model as ORTModelForSeq2SeqLM.
>>> from optimum.onnxruntime import ORTQuantizer, ORTModelForSeq2SeqLM
>>> from optimum.onnxruntime.configuration import AutoQuantizationConfig

# load Seq2Seq model and set model file directory
>>> model_id = "optimum/t5-small"
>>> onnx_model = ORTModelForSeq2SeqLM.from_pretrained(model_id)
>>> model_dir = onnx_model.model_save_dir
  1. Define Quantizer for encoder, decoder and decoder with past keys
# Create encoder quantizer
>>> encoder_quantizer = ORTQuantizer.from_pretrained(model_dir, file_name="encoder_model.onnx")

# Create decoder quantizer
>>> decoder_quantizer = ORTQuantizer.from_pretrained(model_dir, file_name="decoder_model.onnx")

# Create decoder with past key values quantizer
>>> decoder_wp_quantizer = ORTQuantizer.from_pretrained(model_dir, file_name="decoder_with_past_model.onnx")

# Create Quantizer list
>>> quantizer = [encoder_quantizer, decoder_quantizer, decoder_wp_quantizer]
  1. Quantize all models
# Define the quantization strategy by creating the appropriate configuration 
>>> dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False)

# Quantize the model
>>> [q.quantize(save_dir=".",quantization_config=dqconfig) for q in quantizer]