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yt0705 merged 2 commits into from
Nov 6, 2025
Merged

Nightly Process Removal & Old URL Change #138

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Remove the nightly installation method
yt0705 Nov 5, 2025
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URL change in README
yt0705 Nov 5, 2025
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27 changes: 11 additions & 16 deletions README.md
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Expand Up @@ -15,22 +15,22 @@ Users can set the quantizers and all the quantization information for each layer

Please note that the quantization wrapper and the quantizers are framework-specific.

<img src="https://github.com/sony/mct_quantizers/raw/main/quantization_infra.png" width="700">
<img src="https://github.com/SonySemiconductorSolutions/mct-quantization-layers/raw/main/quantization_infra.png" width="700">

## Quantizers:

The library provides the "Inferable Quantizer" interface for implementing new quantizers.
This interface is based on the [`BaseInferableQuantizer`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/base_inferable_quantizer.py) class, which allows the definition of quantizers used for emulating inference-time quantization.
This interface is based on the [`BaseInferableQuantizer`](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/blob/main/mct_quantizers/common/base_inferable_quantizer.py) class, which allows the definition of quantizers used for emulating inference-time quantization.

On top of `BaseInferableQuantizer` the library defines a set of framework-specific quantizers for both weights and activations:
1. [Keras Quantizers](https://github.com/sony/mct_quantizers/tree/main/mct_quantizers/keras/quantizers)
2. [Pytorch Quantizers](https://github.com/sony/mct_quantizers/tree/main/mct_quantizers/pytorch/quantizers)
1. [Keras Quantizers](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/tree/main/mct_quantizers/keras/quantizers)
2. [Pytorch Quantizers](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/tree/main/mct_quantizers/pytorch/quantizers)

### The mark_quantizer Decorator

The [`@mark_quantizer`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/base_inferable_quantizer.py) decorator is used to assign each quantizer with static properties that define its task compatibility. Each quantizer class should be decorated with this decorator, which defines the following properties:
- [`QuantizationTarget`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/base_inferable_quantizer.py): An Enum that indicates whether the quantizer is intended for weights or activations quantization.
- [`QuantizationMethod`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/quant_info.py): A list of quantization methods (Uniform, Symmetric, etc.).
The [`@mark_quantizer`](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/blob/main/mct_quantizers/common/base_inferable_quantizer.py) decorator is used to assign each quantizer with static properties that define its task compatibility. Each quantizer class should be decorated with this decorator, which defines the following properties:
- [`QuantizationTarget`](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/blob/main/mct_quantizers/common/base_inferable_quantizer.py): An Enum that indicates whether the quantizer is intended for weights or activations quantization.
- [`QuantizationMethod`](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/blob/main/mct_quantizers/common/quant_info.py): A list of quantization methods (Uniform, Symmetric, etc.).
- `identifier`: A unique identifier for the quantizer class. This is a helper property that allows the creation of advanced quantizers for specific tasks.

## Getting Started
Expand All @@ -45,16 +45,11 @@ To install the latest stable release of MCT Quantizer from PyPi, run the followi
pip install mct-quantizers
```

If you prefer to use the nightly package (unstable version), you can install it with the following command:
```
pip install mct-quantizers-nightly
```

#### From Source
To work with the MCT Quantizers source code, follow these steps:
```
git clone https://github.com/sony/mct_quantizers.git
cd mct_quantizers
git clone https://github.com/SonySemiconductorSolutions/mct-quantization-layers.git
cd mct-quantization-layers
python setup.py install
```

Expand All @@ -68,7 +63,7 @@ For use with Tensorflow, please install the following package:
For use with PyTorch, please install the following package:
[torch](https://pytorch.org/)

You can also use the [requirements](https://github.com/sony/mct_quantizers/blob/main/requirements.txt) file to set up your environment.
You can also use the [requirements](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/blob/main/requirements.txt) file to set up your environment.

## License
[Apache License 2.0](https://github.com/sony/mct_quantizers/blob/main/LICENSE.md).
[Apache License 2.0](https://github.com/SonySemiconductorSolutions/mct-quantization-layers/blob/main/LICENSE.md).