quantization-rs 0.7.0

Neural network quantization toolkit for ONNX models

quantize-rs Python API

Python bindings for quantize-rs, a neural network quantization toolkit for ONNX models.

Installation

pip install quantization-rs

Build from source (requires Rust toolchain and maturin):

pip install maturin
maturin develop --release --features python

API reference

quantize(input_path, output_path, bits=8, per_channel=False)

Weight-based quantization. Loads the model, quantizes all weight tensors, and saves the result in ONNX QDQ format.

Parameters:

Name	Type	Default	Description
input_path	str	required	Path to input ONNX model
output_path	str	required	Path to save quantized model
bits	int	8	Bit width: 4 or 8
per_channel	bool	False	Use per-channel quantization (separate scale/zp per output channel)

Example:

import quantize_rs

quantize_rs.quantize("model.onnx", "model_int8.onnx", bits=8)
quantize_rs.quantize("model.onnx", "model_int4.onnx", bits=4, per_channel=True)

---

quantize_with_calibration(input_path, output_path, ...)

Activation-based calibration quantization. Runs inference on calibration samples to determine optimal quantization ranges per layer, then quantizes using those ranges.

Parameters:

Name	Type	Default	Description
input_path	str	required	Path to input ONNX model
output_path	str	required	Path to save quantized model
calibration_data	str or None	None	Path to .npy file (shape [N, ...]), or None for random samples
bits	int	8	Bit width: 4 or 8
per_channel	bool	False	Per-channel quantization
method	str	"minmax"	Calibration method (see below)
num_samples	int	100	Number of random samples when calibration_data is None
sample_shape	list[int] or None	None	Shape of random samples; auto-detected from model if None

Calibration methods:

Method	Description
"minmax"	Uses observed min/max from activations
"percentile"	Clips at 99.9th percentile to reduce outlier sensitivity
"entropy"	Selects range minimizing KL divergence between original and quantized distributions
"mse"	Selects range minimizing mean squared error

Example:

import quantize_rs

# With real calibration data
quantize_rs.quantize_with_calibration(
    "resnet18.onnx",
    "resnet18_int8.onnx",
    calibration_data="calibration_samples.npy",
    method="minmax"
)

# With random samples (auto-detects input shape from model)
quantize_rs.quantize_with_calibration(
    "resnet18.onnx",
    "resnet18_int8.onnx",
    num_samples=100,
    sample_shape=[3, 224, 224],
    method="percentile"
)

---

model_info(input_path)

Returns metadata about an ONNX model.

Parameters:

Name	Type	Default	Description
input_path	str	required	Path to ONNX model

Returns: ModelInfo object with the following fields:

Field	Type	Description
name	str	Graph name
version	int	Model version
num_nodes	int	Number of computation nodes
inputs	list[str]	Input tensor names
outputs	list[str]	Output tensor names

Example:

info = quantize_rs.model_info("model.onnx")
print(f"Name: {info.name}")
print(f"Nodes: {info.num_nodes}")
print(f"Inputs: {info.inputs}")
print(f"Outputs: {info.outputs}")

Preparing calibration data

For best results, use 50-200 representative samples from your validation or training set:

import numpy as np

# Collect preprocessed samples
samples = []
for img in validation_dataset[:100]:
    preprocessed = preprocess(img)  # your preprocessing pipeline
    samples.append(preprocessed)

# Save as .npy (shape: [num_samples, channels, height, width])
calibration_data = np.stack(samples)
np.save("calibration_samples.npy", calibration_data)

# Use during quantization
quantize_rs.quantize_with_calibration(
    "model.onnx",
    "model_int8.onnx",
    calibration_data="calibration_samples.npy",
    method="minmax"
)

If you do not have calibration data, the function generates random samples. This is adequate for testing but will produce less accurate quantization than real data.

ONNX Runtime integration

Quantized models use the standard DequantizeLinear operator and load directly in ONNX Runtime:

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("model_int8.onnx")
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: your_input})

Limitations

• ONNX format only. Export PyTorch/TensorFlow models to ONNX before quantizing.
• Requires ONNX opset >= 10 for per-tensor quantization, >= 13 for per-channel (automatically upgraded if needed).
• INT4 values are stored as INT8 bytes in the ONNX file (DequantizeLinear requires INT8 input in opsets < 21).

License

MIT