As a submodule of PaddlePaddle framework, PaddleSlim is an open-source library for deep model compression and architecture search. PaddleSlim supports current popular deep compression techniques such as pruning, quantization, and knowledge distillation. Further, it also automates the search of hyperparameters and the design of lightweight deep architectures. In the future, we will develop more practically useful compression techniques for industrial-level applications and transfer these techniques to models in NLP.
## Outline
- Key Features
- Architecture of PaddleSlim
- Methods
- Experimental Results
## Key Features
The main key features of PaddleSlim are:
### Simple APIs
- It provides simple APIs for building and deploying lightweight and energy-efficient deep models on different platforms. Experimental hyperparameters can be set up by a simple configuration file.
- It requires just a little coding work for a model compression.
### Outstanding Performance
- For MobileNetV1 with limited redundancy, channel-based pruning can ensure lossless compression.
- Knowledge distillation can promote the performance of baseline models with a clear margin.
- Quantization after knowledge distillation can reduce model size and increase accuracy of models.
### Flexible APIs
- We automate the pruning process.
- Pruning strategy can be applied onto various deep architectures.
- We can distill multiple kinds of knowledge from teacher models to student models and self-defined losses for the corresponding knowledge distillation are supported.
- We support the deployment of multiple compression strategies.
## Architecture of PaddleSlim
To make the usage of PaddleSlim clearer and easier, we briefly introduce the background of how to implement the library.
The architecture of PaddleSlim is demonstrated in **Figure 1**. The high-level APIs often depend on several low-level APIs. We can see, knowledge distillation, quantization, and pruning are indirectly based on the Paddle framework. Currently, as a part of PaddlePaddle, user can use PaddleSlim for model compression and search after kindly download and install Paddle framework.
As shown in **Figure 1**, the top-level module, marked as yellow, is the API exposed to users. When we deploy compression methods in Python, we only need to construct an instance of Compressor.
We encapsulate each compression and search method to a compression strategy class. When we train the deep model to be compressed, the strategy class will be instantiated by using the configuration information registered by users, as shown in **Figure 2**. The logic of training process is encapsulated in our compression method. The jobs that users should do by themself is to define the structure of deep models, to prepare the training data, and to choose optimization strategy. This would surely help users save much effort.
Note: abs_max refers to dynamic strategy; moving_average_abs_max refers to static strategy; channel_wise_abs_max refers channel-wise quantization for weights in convolutional layers.
