# PaddlePALM

PaddlePALM (PArallel Learning from Multi-tasks) is a flexible, general and easy-to-use NLP large-scale pretraining and multi-task learning friendly framework. PALM is a high level framework aiming at **fastly** develop **high-performance** NLP models. With PALM, 8 steps to achieve a typical NLP task for supervised learning or pretraining. 6 steps to achieve multi-task learning for prepared tasks. Zero steps to adapt your code to large-scale training/inference (with multiple GPUs and multiple computation nodes).

PaddlePALM also provides state-of-the-art general purpose architectures (BERT,ERNIE,RoBERTa,...) as build-in model backbones. We have decoupled the model backbone, dataset reader and task output layers, so that you can easily replace any of the component to other candidates with quite minor changes of your code. In addition, PaddlePALM support customized development of any component, e.g, backbone, task head, reader and optimizer, which gives high flexibility for developers to adapt to complicated NLP scenes. 

然后给出一些成功案例和一些公开数据集的各个backbone的实验结果（BERT、ERNIE、RoBERTa）和一些成功的多任务学习示例。


## Package Overview

| **paddlepalm** | an open source NLP pretraining and multitask learning framework, built on paddlepaddle. |
| **paddlepalm.reader** | a collection of elastic task-specific dataset readers. |
| **paddlepalm.backbone** | a collection of classic NLP representation models, e.g., BERT, ERNIE, RoBERTa. |
| **paddlepalm.head** | a collection of task-specific output layers. |
| **paddlepalm.lr_sched** | a collection of learning rate schedualers. |
| **paddlepalm.optimizer** | a collection of optimizers. |
| **paddlepalm.downloader** | a download module for pretrained models with configure and vocab files. |
| **paddlepalm.Trainer** | the core unit to start a single task training/predicting session. A trainer is to build computation graph, manage training and evaluation process, achieve model/checkpoint saving and pretrain_model/checkpoint loading.|
| **paddlepalm.MultiHeadTrainer** | the core unit to start a multi-task training/predicting session. A MultiHeadTrainer is built based on several Trainers. Beyond the inheritance of Trainer, it additionally achieves model backbone reuse across tasks, trainer sampling for multi-task learning, and multi-head inference for effective evaluation and prediction. |


## Installation

PaddlePALM support both python2 and python3, linux and windows, CPU and GPU. The preferred way to install PaddlePALM is via `pip`. Just run following commands in your shell environment.

```bash
pip install paddlepalm
```

### Installing via source

```shell
git clone https://github.com/PaddlePaddle/PALM.git
cd PALM && python setup.py install
```

### Library Dependencies
- Python >= 2.7
- cuda >= 9.0
- cudnn >= 7.0
- PaddlePaddle >= 1.6.3 (请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装)


### Downloading pretrain models
We incorporate many pretrained models to initialize model backbone parameters. Training big NLP model, e.g., 12-layer transformers, with pretrained models is practically much more effective than that with randomly initialized parameters. To see all the available pretrained models and download, run following code in python interpreter (input command `python` in shell):

```python
>>> from paddlepalm import downloader
>>> downloader.ls('pretrain')
Available pretrain items:
  => roberta-cn-base
  => roberta-cn-large
  => bert-cn-base
  => bert-cn-large
  => bert-en-uncased-base
  => bert-en-uncased-large
  => bert-en-cased-base
  => bert-en-cased-large
  => ernie-en-uncased-base
  => ernie-en-uncased-large
  ...

>>> downloader.download('pretrain', 'bert-en-uncased-base', './pretrain_models')
...
```


## Usage

8 steps to start a typical NLP training task.

1. use `paddlepalm.reader` to create a *reader* for dataset loading and input features generation, then call `reader.load_data` method to load your training data.
2. use `paddlepalm.backbone` to create a model *backbone* to extract text features (e.g., contextual word embedding, sentence embedding).
3. register your *reader* with your *backbone* through `reader.register_with` method. After this step, your reader is able to yield input features used by backbone.
4. use `paddlepalm.head` to create a task output *head*. This head can provide task loss for training and predicting results for model inference.
5. create a task *trainer* with `paddlepalm.Trainer`, then build forward graph with backbone and task head (created in step 2 and 4) through `trainer.build_forward`.
6. use `paddlepalm.optimizer` (and `paddlepalm.lr_sched` if is necessary) to create a *optimizer*, then build backward through `trainer.build_backward`.
7. fit prepared reader and data (achieved in step 1) to trainer with `trainer.fit_reader` method.
8. load pretrain model with `trainer.load_pretrain`, or load checkpoint with `trainer.load_ckpt` or nothing to do for training from scratch, then do training with `trainer.train`.

More implementation details see following demos: [Sentiment Classification](), [Quora Question Pairs matching](), [Tagging](), [SQuAD machine Reading Comprehension]().

To save models/checkpoints during training, just call `trainer.set_saver` method. More implementation details see [this]().

To do predict/evaluation after a training stage, just create another three reader, backbone and head instance with `phase='predict'` (repeat step 1~4 above). Then do predicting with `predict` method in trainer (no need to create another trainer). More implementation details see [this]().

To run with multi-task learning mode:

1. repeatedly create components (i.e., reader, backbone and head) for each task followed with step 1~5 above. 
2. create empty trainers (each trainer is corresponded to one task) and pass them to create a `MultiHeadTrainer`. 
3. build multi-task forward graph with `multi_head_trainer.build_forward` method.
4. use `paddlepalm.optimizer` (and `paddlepalm.lr_sched` if is necessary) to create a *optimizer*, then build backward through `multi_head_trainer.build_backward`.
5. fit all prepared readers and data to multi_head_trainer with `multi_head_trainer.fit_readers` method.
6. randomly initialize model parameters with `multi_head_trainer.random_init_params` (and `multi_head_trainer.load_pretrain` if needed), then do training with `multi_head_trainer.train`.

The save/load and predict operations of a multi_head_trainer is the same as a trainer.

More implementation details of running multi-task learning with multi_head_trainer can be found [here]().


## License

This tutorial is contributed by [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and licensed under the [Apache-2.0 license](https://github.com/PaddlePaddle/models/blob/develop/LICENSE).

## 许可证书

此向导由[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)贡献，受[Apache-2.0 license](https://github.com/PaddlePaddle/models/blob/develop/LICENSE)许可认证。