XSlim

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XSlim is a Post-Training Quantization (PTQ) tool developed by SpacemiT. It integrates chip-optimized quantization strategies and provides a unified interface for ONNX model quantization via JSON configuration files.

• Features
• Installation
• Quick Start
• Documentation
• Samples
• Changelog
• Contributing
• License

Features

• INT8 / FP16 / Dynamic Quantization – multiple precision levels for different deployment scenarios
• JSON-driven configuration – simple, declarative quantization setup
• Python API & CLI – use as a library or from the command line
• Custom preprocessing – plug in your own preprocessing functions
• Expanded ONNX operator coverage – run Graphwise Analysis and quantization on models that use common arithmetic, activation, comparison, reduction, dropout, and opset-24 Pad patterns
• Automatic YOLO decode fusion – fuse supported YOLO decode subgraphs into a single spacemit_functions.YoloDecode node
• ONNX Function-aware export – preserve embedded FunctionProto definitions and emit required custom-domain imports automatically
• ONNX-based workflow – built on the ONNX ecosystem

Installation

python -m pip install xslim

Or install from source:

git clone https://github.com/spacemit-com/xslim.git
cd xslim
python -m pip install .

For local development, use an editable install:

python -m pip install -e .

Build metadata is defined in pyproject.toml, and the import package lives under the standard src/ layout. To build source and wheel distributions locally:

python -m pip install --upgrade build
python -m build

Quick Start

Python API

import xslim

# Using a JSON config file
xslim.quantize_onnx_model("config.json")

# Using a dict
config = {
    "model_parameters": {
        "onnx_model": "model.onnx",
        "working_dir": "./output"
    },
    "calibration_parameters": {
        "input_parameters": [{
            "mean_value": [123.675, 116.28, 103.53],
            "std_value": [58.395, 57.12, 57.375],
            "color_format": "rgb",
            "preprocess_file": "PT_IMAGENET",
            "data_list_path": "./calib_img_list.txt"
        }]
    }
}
xslim.quantize_onnx_model(config)

# You can also pass the model path and output path directly
xslim.quantize_onnx_model("config.json", "input.onnx", "output.onnx")

Command Line

# Installed CLI entry point
xslim --config config.json

# Module entry point also remains available
python -m xslim --config config.json

# Specify input and output model paths
xslim -c config.json -i input.onnx -o output.onnx

# Dynamic quantization (no config file needed)
xslim -i input.onnx -o output.onnx --dynq

# FP16 conversion (no config file needed)
xslim -i input.onnx -o output.onnx --fp16

# Convert the default ai.onnx opset to a target version
xslim -i input.onnx -o output.onnx --opset 20

# ONNX simplification only (no config file needed)
xslim -i input.onnx -o output.onnx

For config-free dynamic quantization and FP16 conversion, you can exclude operators with comma-separated names or types:

xslim -i input.onnx -o output.onnx --dynq --ignore_op_types Softmax,LayerNormalization
xslim -i input.onnx -o output.onnx --fp16 --ignore_op_names /model/head/MatMul

Static INT8 quantization expects a floating-point input model. If the model already contains QuantizeLinear or DequantizeLinear, XSlim stops with a clear error instead of quantizing an already-quantized graph again.

For supported YOLO exports, no extra switch is required: XSlim will try to fuse decode-heavy post-processing into spacemit_functions.YoloDecode during simplification and keep the corresponding ONNX FunctionProto in the exported model.

Documentation

• Configuration Reference – Full description of all JSON configuration options
• Examples – Step-by-step guides for INT8, FP16, dynamic quantization, custom preprocessing, and more
• Accuracy Tuning Guide – How to diagnose and improve quantization accuracy

Samples

See the samples directory for ready-to-run examples covering ResNet-18, MobileNet V3, BERT, and more. YOLO-specific usage notes are documented in the examples and accuracy-tuning guides.

Changelog

For a full list of published versions, see the Releases page.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the Apache License 2.0.